Year: 2014

No one can truly predict the future, but that doesn’t stop us from trying. It’s just too much fun. We asked CIOs, research firms and a few tech vendors to predict the IT and tech trends that will shape the new year. It’s by no means a scientific or definitive list. But it hopefully captures some of the big lessons we’ve learned this year, and the challenges and opportunities that lie ahead.

Sometimes, even the most mundane things can extend some profound perspectives.

There was this retailer who noted a simple habit of one of his regular morning shoppers while running through video footage: she would walk into the store, pick up a pint of milk, follow that up with bananas, and check out. Imagine her surprise when one such morning she found bottles of banana milkshake stacked innocently along her shopping path. The retailer in the story, UK-based Tesco, started on the data analytics journey several years ago and hasn’t looked back – it is famous for incorporating customer science into almost every aspect of business.

Globally, some QSRs (quick service restaurants) like McDonald’s employ quantitative video ethnography (trained cameras) on drive-through lanes to determine what items are to be displayed on its digital menu-board dotting the driveway. When the lines are longer, the menu features items that can be dished out speedily; during low traffic, the menu is promptly changed to reflect items that take longer to prepare.

In fact, McDonald’s use of analytics is pervasive – it uses point-of-sale data fed into a global data warehouse from the 34,000-plus outlets worldwide, in addition to data unique to each restaurant-such as demand, customer arrival patterns, in-store and drive-through configurations, product mix, staffing, layout, menu etc. As part of its simulation modelling, the QSR uses a variety of technologies, including eye tracking to study how customers move through a restaurant and video analytics to track time spent in the store and drive-through.

In the US, retail chain Macy’s adjusts pricing in near-real time for 73 million items on the basis of demand and inventory, using SAS Institute technology. Why is all this interesting? Because these companies aren’t the digital-born-and-bred variety. They don’t possess the advantages of, say, e-commerce giants, for whom data analytics is pretty much a hygiene factor. Increasingly, offline retailers – and brands whose core revenues aren’t from digital means – are trying to bring themselves up to speed on all things big data, to push their business goals and keep up with competition.

For many such companies, research has moved from data based on survey to data based on consumption. With greater access to transaction data, rather than asking customers what is working for them, conclusions are being drawn on a proactive basis. Disciplines like sales analytics/channel analytics have now become de rigueur. “This particularly helps in promotions and discounts; you can immediately tweak them on the basis of market feedback/trends,” says Saurabh Mittal, vice-president, client services, consumer packaged goods and retail, Fractal Analytics.

Big data in retail is doubling in India every six-eight months. “With single digit margins, big data is most important for retail,” says D Shivakumar, CEO, PepsiCo India. “With over 100 million active social media users today, location is slated to become very important, and hence a focus on smartphones is essential.”

Clearly, technology is a key enabler of a brand’s go-to-market strategy. Three companies in particular stand out when it comes to data readiness: Future Group, which is using its loyalty data for increasing consumption and launching apt packaging; PepsiCo, for better operational efficiency at the ground level, and Nivea, for its operations and product innovations.

At the operational level:
Every day, there’s a huge amount of data generated pertaining to sales, products, customer feedback etc. Based on the business model and data requirements, you have to figure out the best data mix. At Nivea, the sales team gets daily, weekly and monthly dashboards customised for each individual, depending on which territory the sales officer is in, so that he is not overloaded with data. He gets 10-12 critical data points/parameters relevant to him every 24 hours so that he can manage his work better. There is a hierarchy approach where his senior gets a view of a larger territory, and in turn, his senior gets a view of the complete zone and so on. Giving executives actionable data they can actually use is better than overwhelming everyone with a full sales report. “It isn’t just about the availability of data; it is about focus on the right data points,” explains Rakshit Hargave, MD, Nivea India.

Nivea uses certain customised ERP (enterprise resource planning) packages for conducting historical trending on the basis of inputs given by managers. Whether it is for demand or supply planning, dispatch or inventory planning, this approach helps in managing a larger system with multiple SKUs and locations. Elements like stock forecast accuracy have become much better compared to 8-10 years ago where manual systems of estimates would get collated, leaving in its trail a higher possibility of error.

“Operations is anyway a left-brain job: process-led and scientific,” says Sandip Tarkas, president, consumer strategy, Future Group. The company uses data to look at product adjacencies (a combination of products sold best as a package), product display and the depreciation rate of inventory. Carpets can either be stacked horizontally on the ground in a pile or dangled from hangers vertically, for instance. The Future Group uses algorithms to figure out at what rate they are picked up under each kind of display, thereby arriving at the right kind of display for every category.

Now consider PepsiCo, which has a four-fold objective for its sales automation system: standardise, simplify, automate and eliminate. The system enables frontline sales teams to streamline processes and manage time better. “We introduced hand-held devices in urban markets three years ago that capture market data that is integrated with our back-office operation software at the distributor point to manage inventories and billing,” says Sudipto Mozumdar, senior director, customer development, PepsiCo India. “This has helped us to move all the distributor claims from manual to 100 per cent automated.”

Let’s start with the millions of retailers that stock PepsiCo’s products. Through hand-held devices, the company tracks what each store is buying – and selling – over a period of time. With this knowledge, PepsiCo can tailor products to specific stores based on their needs and consumption. For example, if a big store caters to an affluent clientele in the vicinity, PepsiCo can target/tailor its new premium launch to the segment that frequents this store instead of launching the offering simultaneously across all stores. This sort of launch is sharper and targeted, and the company can garner quick feedback before going the whole hog.

PepsiCo is currently working on developing modules where it can help the salesman push particular SKUs that a store finds easy to move off the shelves. “So it will be like a ‘prompt’ that will remind the sales executive that the store that he is visiting sells ‘x’ products, and since he hasn’t sold ‘x’ to them for a while, he needs to push this product,” says Mozumdar. “In a sense, we will partner with retailers, because we don’t want to end up selling things to him which he doesn’t sell further on, thereby blocking his inventory.” From just using data to create targeted programmes, this will help the company get into proactive selling.

PepsiCo also equips its sales force with tablets that enable them to get role-based reports on their KPIs. That apart, PepsiCo aids distributors when it comes to their working capital. Software tracks how much he is selling, how much of credit is going into the market, which are the stores he caters to, etc. Now, as he has visibility on what his stocking norms are, the orders that get generated and driven to him can actually be tailored to replenish what he has sold. So, the distributor becomes more efficient.

PepsiCo funnels all this information into dashboards that gives the company a good sense of how it is doing on a particular initiative. The company’s advertising can be played on these tablets to showcase before the retailer the company’s upcoming products, making it a coaching tool. This way, the whole interaction moves a notch higher than the standard product brochure familiarisation process of the pre-digital world.

The product is the hero:
A conventional consumer company works in a different manner than digital-led companies. “From a personal care consumer company’s perspective, I would define certain technical variables in terms of numbers based on past records or what competition does,” says Hargave of Nivea, talking about the relationship between product development and big data.

Data also helps Nivea do a lot of claim testing. Says Hargave, “For example, if you are looking at certain levels of moisturisation or dark spot correction, there is a guidance data system that the product has to do ‘x’ correction in ‘y’ time. So numbers play an important role because unless you quantify things, you can’t develop products.”

The genre of whitening deodorants is a result of data analytics, says Nivea. While the segment is prevalent in the West, feedback from deodorant users in India on social media led Nivea to conceptualise and launch the whitening deo range in the country in 2012.

Data from loyalty marketing:
Even until a decade ago, retailers would fall back on focus groups for data because transactional data was never mapped the way it currently is. “Now we know which customer comes at what frequency, purchases what kind of items, her average ticket size, her family size based on what is purchased, and her geographical footprint – which outlets she prefers to shop at. All this information can be extracted from loyalty cards. “The customer drops a lot of cookies on you,” quips Tarkas of Future Group.

While its customer loyalty card Payback maps shopping occasions and profiles its customers accordingly, one of the group’s loyalty programmes goes beyond that. In 2010, Future Group launched its telecom brand T24 in partnership with Tata Teleservices to provide additional loyalty benefits to its customers. With T24 the company got into a customer’s life. “We know who she banks with and where she fills her car’s petrol… without violating privacy, of course,” says Tarkas. “We can get to know which customer is roaming a lot out of her city based on her mobile usage, and hence what sort of travel products should be targeted at her.”

Which brings us to distance mapping: how far a customer is willing to travel to reach, say, a Big Bazaar outlet. “A lot of our stores reflect a very high long-distance customer profile. We call these our feeder stores,” explains Tarkas. For example, the LIC Road store in Kolkata, close to both Howrah Station and Sealdah Station, is one such feeder store. Shoppers at the store are found to commute from nearby towns to Kolkata for work – when they share their permanent address for loyalty membership, it is often a long-distance one. These customers have larger ticket sizes, but their frequency of visits is lower. Many such customers aren’t quite sure if they can get a T24 recharge in their own town -which they can – so they end up recharging for Rs 3,000-5,000 at one go just in case they can’t come back for a while.

Market demand projections:
For any marketer, predicting demand or forecasting the size of a potential market is a tough task. But with ammunition like big data, PepsiCo considers itself market-ready. “Depending on why you want to enter a particular state in India, certain numbers (of stores) will be thrown at you from our data system,” says Mozumdar. There are models to predict how many stores a product needs to get into. For instance, the model will tell you if you get into 200,000 stores, you would end up selling to the tune of Rs ‘x’.

Future Group too is gung-ho about predictive analytics. For one, it does life-stage segmentation of customers to get a sense of her future buying patterns. Say, when a customer starts buying diapers, you get to know that there is an infant at home. There on, you can predict the child’s development and the kind of things the mother will buy along the way. “A lot of people buy apparels for their kids from hypermarkets because kids grow very fast, and parents may not wish to spend big money on top labels,” observes Tarkas.

Marketing mailers at Future Group are based on the RFM (reach, frequency and monetary value) model. The company evaluates the merchandise that is picked up and comes up with the ‘next logical offering’. “We have done this in a lot of categories and gained traction with several customers and often we get the ‘aha’ moment, where customers feel we understand them,” says Tarkas. This is similar to how online players like travel sites use cookies to track you while you are browsing other sites, to remind you of unfinished transactions. “In fact, there is more information available offline. The potential is vastly unexplored,” Tarkas sums up.

Companies need to start thinking about how analysts or business managers will be tasked with “actionizing” real-time data in ways that affect real-time decisions.

Srikanth Velamakanni, CEO and founder of Fractal Analytics, recently cited several key trends that he sees in companies’ big data initiatives. Among them are:

• The need to consolidate big data into a single repository;
• The rise of machine intelligence and data;
• An increase in firms using locational data;
• More collaboration between humans and machines; and
• Use of big data for new analytics experimentation.

Most of these initiatives are organized around immediately “actionizing” big data so it can act on situations “in the moment,” whether these actions are automated or human responses. For the same reason, we are seeing more organizations looking at storage investments and big data initiatives that use real-time data that can enable real-time decision making.

Real-time big data in retail

One of the most active industry sectors in “actionizing” data is retail, which has gone from watching web traffic trends, shopping cart abandonment rates, and customer buying preferences to new experimentation into customer behavior that now includes direct responses to customer browsing patterns that will hopefully produce more sales.

For instance, a consumer on an ecommerce site might show great interest in purchasing an item as demonstrated by the number of product pages on the website the consumer has reviewed, yet not go through with the purchase; or the consumer might go so far as to add the item to her shopping cart without completing the purchase. In these cases, some ecommerce retailers are now taking real-time steps to come back to consumers, perhaps with a downwardly revised price to see if that will incent the sale. The goal is real-time engagement with consumers in the decide-and-buy process to capture more sales.

Real-time big data in manufacturing and utility companies

In the manufacturing and utility sectors, companies are busy harnessing a plethora of big data being kicked out by sensors and other machine-based communications. They want to improve their process automation capabilities and to correspondingly reduce the labor intensiveness of operations.

Global companies believe that improvement in process automation driven by big data delivered over the internet will enable them to integrate their manufacturing workflows so a process in the US can trigger a subsequent flow of work processes in a factory in another part of the world, such as the Philippines. “For some time, we have been thinking that machines will entirely replace humans in these processes,” said Velamakanni, “but what companies are discovering is that machines can capably accomplish roughly 90 percent of the tasks — but they still require human management and judgment to address those parts of processes that present unique problems that machine automation can’t easily solve.”

The need for highly skilled employees to “actionize” data

In both of these use cases, the call will be for more highly skilled workers and managers who can collaborate with the automation in real time to make the decisions needed on the factory floor, or for modifying online marketing strategies to capture revenue. This is an area of big data “actionizing” that has not yet garnered much attention in company employee and management skills training and recruitment nor in college and university IT and business curricula.

The requirements for highly skilled employees who can “actionize” real-time big data consequently fall between the cracks of how many companies currently have their big data/analytics teams organized. Business analysts are assigned to these teams, and it is their primary job to define the correct queries of big data to make a business difference for the company. Their biggest job is on the front end of data harvesting, where they lay out approaches that help determine which data to capture and how to best exploit it.

In the future, these analysts, or perhaps more likely line of business managers, will be asked to “actionize” the results from this data that continuously pours out in real time so they affect real-time decisions that impact manufacturing, sales, energy management, and many other real-time concerns. This is the “next frontier” of business line management skills that companies need to be thinking about now.

Can companies do this?

Mark Sucrese, marketing technology director at Dell, discussed the company’s multi-year journey into a transformation of customer touch points that included strategy revisions to customer relationship management (CRM), technical support, sales channels, and email. The goal, according to Sucrese, was to “understand all vehicles” with which customers communicate with Dell and to move to real-time decisions on this data that could enhance customer relationships and generate more revenue. Sucrese reports a 30% increase in customer click rates on emails, a 40% increase in revenue per click, and an overall 19% improvement in sales. He cites the move to real-time decision making based on real-time data analytics as a major contributing factor.

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Fractal Analytics is one of the largest independent analytics companies in the world that are operating out of India and is behind Mu Sigma, which is currently in talks with investors to raise upwards of $200 million in fresh funding which could value the company more than Flipkart. Nevertheless, Fractal, which is backed by TA Associates, has made its own mark in the global markets. Aimia Inc, a Toronto Stock Exchange-listed loyalty management programme firm, is picking a minority stake in the firm for an undisclosed amount. The two firms have also inked a commercial partnership.

Techcircle.in spoke to Srikanth Velamakanni, co-founder and CEO of Fractal, and Vikas Choudhury, COO & CFO of Aimia India, to know more about the partnership, and Fractal’s future plans, competition and more.

Here are the edited excerpts:

Can you take us through the partnership with Aimia? How would the companies benefit from this?

Choudhury (Aimia): We are a provider of loyalty management solutions and currently employ more than 4,000 people in over 20 countries. We offer our clients, partners and members various solutions to launch and manage coalition loyalty programmes.

We own and operate loyalty programmes in countries including Canada, the UK and Italy. AImia also owns stakes in Air Miles Middle East; Mexico’s leading coalition loyalty programme Club Premier; Brazil’s Prismah Fidelidade, and China Rewards that enables members to earn and redeem a common currency.

We together are seeing good synergies and are looking to leverage the clients, expertise and the market access of each other across the globe.

Velamakanni (Fractal): Fractal has been in the market for quite some time. We have been the first player in the analytics space. When we started, no analytics solutions existed. We worked towards the creation of the industry.

Our key strengths include our understanding of the customer behaviorur and the ability to serve them better. Given the kind of reach and scope of Aimia as a loyalty programme company and its access to more than 300 million customers globally, the power of being able to leverage the data is quite unique. Therefore, it is making sense for us to form a strategic move through this partnership.

We want to go to the market jointly and increase analytics usage across the world. We also want to create joint IP (intellectual property).

How large is Aimia’s presence in India? What prompted you to ink a deal with Fractal? How much equity is Aimia picking?

Choudhury: We launched our operations in India four years ago, and are now one of the largest proprietary loyalty management companies in the world. In India, we are working with four large clients, including the Taj Group of Hotels, Tata Capital, Axis Bank and Standard Chartered bank.

We believe that making an equity investment will essentially align with the goals of both companies going forward. Our chief strategy officer Eric Monteiro is joining Fractal’s board.

We believe that the companies will have an unparalleled access to the talent of each other. In addition, Fractal will be able to get access to our onsite global presence, while we will get access to its expertise. Besides, the work culture of both the entities match, and we can leverage each other’s knowledge and clients.

Velamakanni: The most important dimension of this partnership is joint IP creation. It is hard to create joint IP without stronger relationship. So, it makes sense for us to get them as a strategic investor.

Aimia has loyalty business and access to customer data across the world. We are also expanding our presence to Panama, Mexico City, Geneva, China, Australia and Netherlands. Together we can conquer new markets, while leveraging each other’s clients in the existing markets.

We are not at liberty to disclose the transaction details.

How does Aimia address data privacy concerns in the markets it is operating?

Choudhury: We are using data with a lot of discretion. We never misuse customer data; we always ask consumers what they will allow us to do with their data. We analyse and action data, but we never sell data.

Although Mu Sigma was launched four years after Fractal came into existence, it became a much bigger firm. How stiff is the competition between both firms?

Velamakanni: We compete quite well with Mu Sigma. It is a fact that they have more reach in terms of the number of sales people across the world and sector focus. Moreover, Mu Sigma has raised hundreds of millions of dollar ahead of us, and we are four years behind them in terms of raising money.

However, from a competition standpoint our clients win rate is significantly higher. And from a positioning stand point, we pay 30-40 per cent higher salary than Mu Sigma does. In terms of talent, almost 60 per cent our employees are from the premium institutes such as IIMs and IITs.

From a size point of view, we are the second-largest analytics player. Indeed, we want to be the BMW of the analytics world—to be the most respected brand, not necessarily the largest.

In a direct face off with Mu Sigma, we win more often than them. We may win 75 per cent of the deals vis-à-vis Mu sigma (this is a very rough estimate)’

Are you foraying into more verticals?

Velamakanni: We are currently serving clients in financial services, consumer packaged goods (CPG), telecom, insurance and technology. We are now adding lifestyle and health sciences this year. We have just acquired our first life sciences client in the UK.

Our aim is to become a broad-based analytics player serving many industries. We will add these industries gradually.

How do you plan use the money that is coming from Aimia?

Velamakanni: We do have plenty of cash in our balance sheet right now. Being a profitable business and having raised funding from TA Associates, we will use the money for over overall growth. We also want to use some of these proceedings to strategically acquire startups in the space. We are looking for companies with IP or with a good clientele and team.

Are you in talks with any startups for acquisitions yet?

Velamakanni: Yes, we are in talks with over half-a-dozen companies, but we are not in a position to announce anything yet.

Are there Indian firms, too?

Velamakanni: There are all kinds of startups – from India, Asia and the US

Are you looking at raising more funds anytime soon?

Velamakanni: Not right now. In my view, fundraising in general is time consuming and distracting. We don’t want to be a fundraising company – rather we want to be running the company.

Can you share revenue figures?

Velamakanni: We cannot share the exact revenue details. That said, we grow about 50-60 per cent year on year, and are improving our profitability overall.

Mu Sigma is looking to hit IPO in two-three years. Are you nursing similar plans?

Velamakanni: Our current way of thinking is that we want to have the scale and visibility of a public company before hitting the public market. Our immediate goal is to build the company for the next two-three years. A decision to go public or not will be taken at a later stage, because being pubic also comes with its own baggage. We don’t want to take a hasty decision on it.

What is your view on the Indian Big Data market? What per cent of your revenues come from India?

Velamakanni: My understanding is that India has incredibly smart and analytics-oriented people, but it is somewhat low in terms of operational excellence and process maturity. My experience of working with Indian clients is not that good. There is excitement around Big Data, but when it comes to the real use of analytics and getting value out of that, I think India is still a distance away from the rest of the world. The potential is very huge in India, but is a very different market from a price and service point of view.

Our big sweet spots are the US, Europe and Australia.

(Edited by Joby Puthuparampil Johnson)

A few years ago, marketing maven Seth Godin wrote a book based on the simple, if counter-intuitive idea – winners do quit and quitters do win. According to Godin, it’s people who know when to quit a tough situation, and what dips to persist through who are the superstars. This is familiar territory for most entrepreneurs.

A minority stake could mean a major leg-up on the competition

The partnership expands Aimia’s analytics operations, giving it access to best-in-class analytics.

MONTREAL, Aug. 12, 2014 /CNW Telbec/

Aimia, a global leader in loyalty management, has formed a strategic long-term partnership with Fractal Analytics, a leading provider of advanced analytics. The exclusive commercial agreement will deepen Aimia’s analytics capabilities, extending its unparalleled customer insights to deliver a more comprehensive understanding of consumer behavior and improve marketing return-on-investment. It will also extend Aimia’s loyalty expertise to Fractal’s existing clients. As part of the partnership, Aimia is making a minority equity investment in Fractal.

The partnership expands Aimia’s analytics operations, giving it access to best-in-class analytics to enhance its current core capabilities and the opportunity to rapidly embed some of Fractal’s existing predictive analytics solutions such as Customer Genomics(R) into Aimia’s loyalty solutions. It will also allow more rapid development of new offerings leveraging rich customer data across its programs, products and regions.

“This strategic partnership gives Aimia dedicated access to specialized and scarce top talent as our global analytics business continues to grow, ” said Eric Monteiro, Chief Strategy and Analytics Officer, Aimia, who will join Fractal’s board of directors as part of the equity investment. “Fractal brings a mature and experienced team of sophisticated analytics professionals to meet the complex needs of our clients.”

“CMOs across industries increasingly recognize the importance of deeply understanding their customers and hyper-personalizing their experience to inspire their loyalty,” said Srikanth Velamakanni, Co-founder and Chief Executive Officer, Fractal Analytics. “Our partnership with Aimia gives companies access to the world’s best loyalty analytics expertise.”

Founded in 2000, Fractal provides advanced analytics to more than 50 Fortune 1,000 global companies. Fractal has 13 offices across the globe and expects to grow its global headcount from 700 to more than 1,000 by the end of 2014.

About Aimia

Aimia Inc. (“Aimia”) is a global leader in loyalty management. Employing more than 4,300 people in 20 countries worldwide, Aimia offers clients, partners and members proven expertise in launching and managing coalition loyalty programs, delivering proprietary loyalty services, creating value through loyalty analytics and driving innovation in the emerging digital, mobile and social communications spaces.

Aimia owns and operates Aeroplan, Canada’s premier coalition loyalty program, Nectar, the United Kingdom’s largest coalition loyalty program, Nectar Italia, and Smart Button, a leading provider of SaaS loyalty solutions. In addition, Aimia owns stakes in Air Miles Middle East, Travel Club, Spain’s leading coalition loyalty program, Club Premier, Mexico’s leading coalition loyalty program, China Rewards, the first coalition loyalty program in China that enables members to earn and redeem a common currency, Think Big, the owner and operator of BIG – AirAsia and Tune Group’s loyalty program, Brazil’s Prismah Fidelidade and i2c, a joint venture with Sainsbury’s offering insight and data analytics services in the UK to retailers and suppliers. Aimia also holds a minority position in Cardlytics, a US-based private company operating in card-linked marketing, Aimia is listed on the Toronto Stock Exchange (TSX: AIM). For more information, visit us at www.aimia.com.

About Fractal Analytics

Fortune 500 companies recognize analytics is a competitive advantage to understand customers and make better decisions. Fractal Analytics delivers insight, innovation and impact to them through predictive analytics and visual story-telling.

Fractal Analytics’ flagship Customer Genomics(R) solution helps marketers learn complex customer behavior at an individual level. Its proprietary pattern recognition and machine-learning algorithms learn from every transaction and customer interaction, including social media, to help marketers build a complete view of individual customers across attitudinal and behavioral dimensions. In June of 2013, global private equity firm TA Associates acquired a minority stake in the company for an investment of $25 million, and in May of 2013, information technology and research advisor Gartner named Fractal as one of the top five “Cool Vendors in Analytics.” Learn more at www.fractal.ai

SOURCE AIMIA

/CONTACT: Aimia Investor Contact Karen Keyes

+1 416 352 3728 Aimia Media Contact Megan Ratcliffe

+44 20 7152 4881 Fractal Analytics Contact Liam Collopy

Harden Communications Partners, LLC

+1 510 635 4150 Ext. 1009

[email protected]

Copyright CNW Group 2014 

MONTREAL, Aug. 12, 2014 /PRNewswire/

Aimia, a global leader in loyalty management, has formed a strategic long-term partnership with Fractal Analytics, a leading provider ofadvanced analytics. The exclusive commercial agreement will deepen Aimia’s analytics capabilities, extending its unparalleled customer insights to deliver a more comprehensive understanding of consumer behavior and improve marketing return-on-investment. It will also extend Aimia’s loyalty expertise to Fractal’s existing clients. As part of the partnership, Aimia is making a minority equity investment in Fractal.

The partnership expands Aimia’s analytics operations, giving it access to best-in-class analytics to enhance its current core capabilities and the opportunity to rapidly embed some of Fractal’s existing predictive analytics solutions such as Customer Genomicsœ into Aimia’s loyalty solutions. It will also allow more rapid development of new offerings leveraging rich customer data across its programs, products and regions.

“This strategic partnership gives Aimia dedicated access to specialized and scarce top talent as our global analytics business continues to grow,” said Eric Monteiro, Chief Strategy and Analytics Officer, Aimia, who will join Fractal’s board of directors as part of the equity investment. “Fractal brings a mature and experienced team of sophisticated analytics professionals to meet the complex needs of our clients.”

“CMOs across industries increasingly recognize the importance of deeply understanding their customers and hyper-personalizing their experience to inspire their loyalty,” said Srikanth Velamakanni, Co-founder and Chief Executive Officer, Fractal Analytics. “Our partnership with Aimia gives companies access to the world’s best loyalty analytics expertise.”

Founded in 2000, Fractal provides advanced analytics to more than 50 Fortune 1,000 global companies. Fractal has 13 offices across the globe and expects to grow its global headcount from 700 to more than 1,000 by the end of 2014.

About Aimia

Aimia Inc. (“Aimia”) is a global leader in loyalty management. Employing more than 4,300 people in 20 countries worldwide, Aimia offers clients, partners and members proven expertise in launching and managing coalition loyalty programs, delivering proprietary loyalty services, creating value through loyalty analytics and driving innovation in the emerging digital, mobile and social communications spaces.

Aimia owns and operates Aeroplan, Canada’s premier coalition loyalty program, Nectar, the United Kingdom’s largest coalition loyalty program, Nectar Italia, and Smart Button, a leading provider of SaaS loyalty solutions. In addition, Aimia owns stakes in Air Miles Middle East, Travel Club, Spain’s leading coalition loyalty program, Club Premier, Mexico’s leading coalition loyalty program, China Rewards, the first coalition loyalty program in China that enables members to earn and redeem a common currency, Think Big, the owner and operator of BIG – AirAsia and Tune Group’s loyalty program, Brazil’s Prismah Fidelidade and i2c, a joint venture with Sainsbury’s offering insight and data analytics services in the UK to retailers and suppliers. Aimia also holds a minority position in Cardlytics, a US-based private company operating in card-linked marketing, Aimia is listed on the Toronto Stock Exchange CA:AIM +3.93% . For more information, visit us at www.aimia.com .

About Fractal Analytics

Fortune 500 companies recognize analytics is a competitive advantage to understand customers and make better decisions. Fractal Analytics delivers insight, innovation and impact to them through predictive analytics and visual story-telling.

Fractal Analytics’ flagship Customer Genomicsœ solution helps marketers learn complex customer behavior at an individual level. Its proprietary pattern recognition and machine-learning algorithms learn from every transaction and customer interaction, including social media, to help marketers build a complete view of individual customers across attitudinal and behavioral dimensions. In June of 2013, global private equity firm TA Associates acquired a minority stake in the company for an investment of $25 million, and in May of 2013, information technology and research advisor Gartner named Fractal as one of the top five “Cool Vendors in Analytics.” Learn more at www.fractal.ai

SOURCE AIMIA

Copyright (C) 2014 PR Newswire. All rights reserved

Looking to strengthen its analytics capabilities, Aimia today announced it will invest in Fractal Analytics as part of a “long-term partnership.”

The Montreal, Quebec-based loyalty management company said it hopes to augment its 4,300-person workforce with the “best in class” analytics team from Fractal, a 14-year-old company best known for its flagship product, Customer Genomics. Fractal has about 700 employees and plans to add another 300 by year end.

“This strategic partnership gives Aimia dedicated access to specialized and  scarce top talent as our global analytics business continues to grow,” said Eric Monteiro, Aimia’s chief strategy and analytics officer. “Fractal brings a mature and experienced team of sophisticated analytics professionals to meet the complex needs of our clients.”

Ownership Stake

The companies said the investment will give Aimia, whose stock trades on the Toronto exchange, a minority stake in Fractal.  But they didn’t specify either the size of the investment or Aimia’s ownership share in Fractal. Monteiro will join Fractal’s board of directors. Executives of the two companies weren’t immediately available to provide additional details.

In mid-2013, privately held Fractal, based in San Mateo, Calif., announced a $25 million cash infusion from TA Associates, a private equity growth fund that has raised about $18 billion in investment capital.

In addition to the exchange of talent and money, both parties get some practical advantages from of the deal. Aimia said it will “rapidly embed” Fractal’s predictive analytics solutions into Aimia’s loyalty services. The agreement also provides Fractal the benefits of Aimia’s substantial experience in customer loyalty.

Aimia’s loyalty programs include Aeroplan in Canada, Nectar in the UK and Nectar Italia. It owns stakes in Air Miles Middle East, Travel Club in Spain, Club Premier in Mexico, Prismah Fidelidade and i2c in Brazil, and China Rewards, which was China’s first cash-based loyalty program. Aimia also holds a minority stake in Cardlyltics, which offers card-linked marketing services.

“CMOs across industries increasingly recognize the importance of deeply understanding their customers and hyper-personalizing their experience to inspire their loyalty,” Fractal CEO Srikanth Velamakanni said in a statement. “Our partnership with Aimia gives companies access to the world’s best loyalty analytics expertise.”

Velamakanni, pictured above, co-founded Fractal in Mumbai in 2000, moving its headquarters to New Jersey in 2005 and then Silicon Valley in 2010.

‘Huge’ Potential

In a CMSWire story that appeared in April, he explained how his company blends “a whole host of data” about individual preferences with real-time analytics to solve customer questions as they arise. He said a typical example might involve finding the best restaurant for lunch for an individual in downtown San Francisco based on the known tastes and budget of that visitor.

In an interview for that story, Velamakanni spoke of the challenge of linking individuals and their personal data across channels in real-time.

“This is a hot space from the standpoint of clients trying to solve these problems,” he said at the time. “The expectations are huge for what this space can deliver.”

Fractal Analytics allows employees unprecedented elbow room to decide their career paths.

Analytics vendor recognized for outstanding revenue growth

SAN MATEO, Calif., Jul 28, 2014 (BUSINESS WIRE) — Fractal Analytics ( www.FractalAnalytics.com ), a global provider of advanced analytics, was listed as one of the Fast 50 Asian American Businesses by the US Pan Asian American Chamber of Commerce Education Foundation (USPAACC) for the second consecutive year. The award recognizes the outstanding economic achievements of Asian American-owned businesses and their exemplary growth in the national economy.

“We are honored to receive this award. There is a vast amount of value waiting to be created for consumers and corporations through data and analytics,” said Pranay Agrawal, Co-Founder and Executive Vice President of Fractal Analytics. “Fractal Analytics will continue to strive to tap into this opportunity at an accelerated pace by delivering innovative solutions at great value.”

Winners were judged based on their revenue and growth rate over the course of three years, which were verified and ranked by Ernst & Young. Cumulatively, Fractal Analytics and the 49 other Asian American businesses recognized at the conference generated an average revenue of $3 billion and a growth rate of up to 300 percent.

About Fractal Analytics

Fortune 500 companies recognize analytics is a competitive advantage to understand customers and make better decisions. Fractal Analytics delivers insight, innovation and impact to them through predictive analytics and visual story-telling.

Fractal Analytics’ flagship Customer Genomics™ solution helps marketers learn complex customer behavior at an individual level. Its proprietary pattern recognition and machine-learning algorithms learn from every transaction and customer interaction, including social media, to help marketers build a complete view of individual customers across attitudinal and behavioral dimensions. In June of 2013, global private equity firm TA Associates acquired a minority stake in the company for an investment of $25 million, and in May of 2013, information technology and research advisor Gartner named Fractal as one of the a top five “Cool Vendors in Analytics.”

Learn more at www.FractalAnalytics.com .

About the US Pan Asian American Chamber of Commerce Education Foundation (USPAACC)

Founded in 1984 as a non-profit and non-partisan organization, the US Pan Asian American Chamber of Commerce Education Foundation (USPAACC) is headquartered in Washington, DC with Regional Chapters in CA, NY, TX, GA, IL, CT, MD-VA-DC National Capital Area. USPAACC is the single unified voice for equal opportunity for Asian American businesses. We promote and propel economic growth by opening doors to business, educational and professional opportunities for Asian Americans and their business partners in corporate America, government at the federal, state and local levels, and the small and minority business community. For 29 years, USPAACC has served and will continue to serve as the gateway to large corporate and government contracts, top-caliber Asian American and small and minority suppliers, key information about Asian Americans and business opportunities in the dynamic Asia-Pacific market.

SOURCE: Fractal Analytics

Fractal Analytics
Liam Collopy, 510-635-4150
Executive Vice President
[email protected]

Copyright Business Wire 2014

In today’s fast evolving corporate world, technology and business are two sides of the same coin. Technology is critical to enable businesses’ to enhance efficiency and cut costs. For this to happen, CIOs/CTOs/Technology heads have to transform from being ‘technical plumbers’ to ‘business enablers’.

To address this change, TechGig.com, an online technology community, started a series of boardroom discussions on ‘The Evolution of Role of CIO/CTO: Information and Technology to Innovation’.

The first edition of the series was recently held in Mumbai. Amit Das, MD & CEO 3i Infotech BPO and global head-BI & Analytics, 3i Infotech Ltd; Nataraj N, CIO, Hexaware Technologies; Prameet Savla, CTO, AccelyaKale; Sanjiv Patki, COO, Allied Digital; and Rasesh Shah, SVP-key accounts and operations, Fractal Analytics, attended the session.

Is talent discovery and management a challenge?

Initiating the discussion, Nataraj N of Hexaware Technologies said, “We have to re-skill and re-engineer for innovation. Organisations should focus on tapping relevant skills in new talent and coaching existing talent for innovation.”

Sharing a product development company’s perspective, Prameet Savla of AccelyaKale said that to stay on the top of their game, product development companies need to have talent that can think out-of-the-box, which leads to disruptive products in the market.

Rasesh Shah of Fractal Analytics pointed out that being in the business of solving problems, they look for talent that understands business, technology and statistics and is able to do client servicing, all together. “Our biggest challenge is to find all these skills in one person and train them in a particular technology, which is changing by the minute.

According to Amit Das, MD & CEO 3i Infotech BPO and global head-BI & Analytics, 3i Infotech Ltd, technology has become like an assembly line. “Most companies are hiring the business person; the technology person; the statistics person, separately. To manage these people, they hire a client-servicing person. The biggest challenge is that our system is not designed to nurture or mentor talent as they grow.”

“Universally, the kind of skills required by tech companies cannot be imparted at the college level. Cross skilling in terms of business, technology, statistics and client-servicing is most important for them as they grow in the organisation. However, this is not easy to achieve as this will lead to efficiency loss,” added Sanjiv Patki of Allied Digital.

Driving Innovation

Rasesh Shah: “It is the power of social recognition that motivates employees to innovate.”

Prameet Savla: “People who can and want to innovate seek social acceptance from their peers and leaders.”

Amit Shah: “To foster innovation, we host ‘Idea Bulls’ camp where we ask employees what the next big idea/solution is where the company should invest. We then recognise the best ideators in the group.”

Nataraj N: “We provide funding for great ideas and innovators in our company and involve them in productisation of their idea.”

Sanjiv Patki: “We encourage team innovation over individual innovation. There are people who are shy and are not good communicators, but have great ideas. To tap their talent, we have introduced the practice of brain mapping/writing.”

Experts agreed that the business environment is changing rapidly, acting as a catalyst for many start-ups. Indian companies, large, medium and start-ups, are getting to a point where they will be leaders in the marketplace and organisational innovation.

Key Takeaways

• Job Rotation, re-skilling and rewards & recognition motivate tech talent to innovate
• Creating the next level of tech talent is the biggest challenge
• Institutes should move beyond curriculum and focus on innovation
• School and college curriculums should allow cross-linkages; so an arts student can become an analyst or an engineer
• CIOs should focus on building a culture of knowledge sharing to drive innovation

Business World’s ‘After Hours’ section features book review on “Essentialism: The Disciplined Pursuit of Less”, by Srikanth Velamakanni

Read the book review

Author: Srikanth Velamakanni, co-founder & CEO, Fractal Analytics

Good salespeople know this and focus on fulfilling customer’s shopping mission by offering relevant recommendations. In the online world, analytics aims to substitute this

responsive salesperson with three important differences:

• We can leverage a “perfect” memory and “infinite” computing power to find what is relevant to the customer at the moment
• We can reconfigure the store layout and “show” customer only these relevant things
• We can leverage reviews and buying behaviour of friends/peers

With every action customers are conveying information about their attitudes, preferences, life-stage and socio-economic status. If you regularly buy a full basket of groceries, but never buy meat, odds are higher that you might be vegetarian. What if this kind of customer understanding can be developed algorithmically at internet scale covering millions of customers, billions of transactions and interactions? We can create rich customer understanding across several dimensions. Companies have developed solutions to understand customers, decode their “genome” and use it to make better recommendations.

Once you understand your shopper, you must also learn what she is trying to do. What search terms did she use? What is the time of the day, day of the week and her geo-location? When you search “red roses” instead of “cheap flowers”, Google results and Ads are very different. In the first instance, Google shows fewer ads than in the second instance where Google “knows” you are more likely to buy. Understanding customer context can dramatically improve the relevance of product recommendations.

Once we “know” the shopper and the context, we can hyper-personalise her experience of the store with relevant products and offers that meet her needs. This is a problem of plenty — analytical techniques discussed above can prune and prioritise these offers.

When you see a store having sections like “people who bought this also bought this” or “people who considered this product ultimately bought this”, the recommendation engine is at work to personalise the page. The higher the relevance of recommendations, the bigger is the size of the shopping basket.

If relevant information about our friends is presented, it can increase conversion and help shoppers take the leap of faith required before high involvement purchases. Advice from other users is usually seen as credible, unbiased information and improves customer conversion and size of the customer’s shopping basket.

Online stores frequently have limited time offers (for instance, additional 15 per cent off for three days only) and basket size-based offers (for instance, additional 10 per cent off if you spend more than Rs 5,000) to create time pressure and improve size of the customer’s shopping basket. Customers can get hooked to these tactics and stores might find it difficult to wean customers away from these expensive tactics.

Stores can experiment by understanding customer price elasticity and offering every customer a unique price at which they are willing to buy. So, a store can charge one customer a high price if she is price insensitive and offer another customer enough discount to make her buy. When such pricing is permitted by law, it can still lead to customer angst and loss of trust.

The National Association of Software and Services Companies (Nasscom) has plans to establish India as a ‘centre of excellence’ for big data and analytics.

The information technology and services body expects to see new early-stage firms to capture the emerging business potential in this space in the country.

To keep pace with it, Nasscom had last year constituted the Analytics Interest Group, which is responsible to chart the future roadmap for the segment.

Speaking to Business Standard, Srikanth Velamakanni, part of the group and CEO of Fractal Analytics, said Nasscom would help identify talents in the space and support them by subjecting to accelerator programmes and engaging them with angel investors.

Nasscom in a report, along with Blueocean Market Intelligence, titled ‘Institutionalisation of Analytics in India: Big Opportunity, Big Outcome,’ indicated the analytics market in India is slated to more than double to reach $2.3 billion by 2018 from about $1 billion last fiscal.

The report also said the domestic market, which was at $163 million, would also double in size and reach $375 million by 2018.

However, experts in the big data and analytics domain say data privacy, non-availability of structured data warehouses and a lack of proper knowledge among companies to adopt them would be the key concerns for the Indian market.

NASSCOM president R. Chandrashekhar releases ‘Accenture’s report big success with Big Data’ at the 2nd edition of Big Data and Analytics Summit 2014 in Hyderabad on Friday. He was flanked by Managing Directors Accenture Analytics Michael Svilar and Arnab Chakraborty is seen. Photo: Mohammed Yousuf

While analytics and big data companies were recording an annual growth of almost 40 per cent, the sector was witnessing an acute shortage of professionals, said industry experts at a one-day event organised by Nasscom here on Friday.

Foreseeing the need for such professionals, Nasscom had begun an Analytics Interest Group of six members to address the issue.

“Presently, there are only 10,000-15,000 analytics and data experts in the country, and there will be a shortage of two lakh data scientists in the country over the next few years,” CEO of Fractal and a member of the Analytics Interest Group, Srikanth Velamakanni, told the gathering at the Big Data and Analytics Summit 2014.

“We are working with the industry and the academia – engaging with the ISB, IIMs and top B-schools – on how to develop talent in this field. We are also conducting workshops and certificate courses for young graduates. This is a lucrative field as a data scientist or analytical expert can earn double the salary of an IT professional.”

Ashwin Mittal, president of Blue Ocean, a data analytics firm, said the demand for data professionals can be gauged from the fact that the number of big data providers in the country grew from 300 to almost 600 in the last two years. Blue Ocean jointly released a report with NASSCOM during the event pegged the Indian analytics markets at above $2 billion by 2018.

“We are conducting a workshop called ‘paatshaala’. As part of this, we train a large pool of engineers for a period of 4-8 weeks in analytics,” said Mr. Mittal.

Cyient’s assistant general manager (Analytics) Bhoopathi Rapolu said they were on the look-out for potential in the field by visiting B-Schools.

“There is lack of education on analytics in traditional educational institutions. Specialised training is important for these skills and some of the corporate companies are doing it. There is a lot of demand for data scientists since their services are required in many sectors,” Mr. Rapolu said.

The National Association of Software and Services Companies (Nasscom) has plans to establish India as a ‘centre of excellence’ for big data and analytics.

The information technology and services body expects to see new early-stage firms to capture the emerging business potential in this space in the country.

To keep pace with it, Nasscom had last year constituted the Analytics Interest Group, which is responsible to chart the future roadmap for the segment.

Speaking to Business Standard, Srikanth Velamakanni, part of the group and CEO of Fractal Analytics, said Nasscom would help identify talents in the space and support them by subjecting to accelerator programmes and engaging them with angel investors.

Nasscom in a report, along with Blueocean Market Intelligence, titled ‘Institutionalisation of Analytics in India: Big Opportunity, Big Outcome,’ indicated the analytics market in India is slated to more than double to reach $2.3 billion by 2018 from about $1 billion last fiscal.

The report also said the domestic market, which was at $163 million, would also double in size and reach $375 million by 2018.

However, experts in the big data and analytics domain say data privacy, non-availability of structured data warehouses and a lack of proper knowledge among companies to adopt them would be the key concerns for the Indian market.

Information technology and services body expects to see new early-stage firms to capture the emerging business potential in this space

The National Association of Software and Services Companies (Nasscom) has plans to establish India as a ‘centre of excellence’ for big data and analytics.

The information technology and services body expects to see new early-stage firms to capture the emerging business potential in this space in the country.

To keep pace with it, Nasscom had last year constituted the Analytics Interest Group, which is responsible to chart the future roadmap for the segment.

Speaking to Business Standard, Srikanth Velamakanni, part of the group and CEO of Fractal Analytics, said Nasscom would help identify talents in the space and support them by subjecting to accelerator programmes and engaging them with angel investors.

Nasscom in a report, along with Blueocean Market Intelligence, titled ‘Institutionalisation of Analytics in India: Big Opportunity, Big Outcome,’ indicated the analytics market in India is slated to more than double to reach $2.3 billion by 2018 from about $1 billion last fiscal.

The report also said the domestic market, which was at $163 million, would also double in size and reach $375 million by 2018.

However, experts in the big data and analytics domain say data privacy, non-availability of structured data warehouses and a lack of proper knowledge among companies to adopt them would be the key concerns for the Indian market.

Data analytics firm Fractal is setting up an office in Bangalore.

It is expected to invest $20 million over the next three years in its proposed Bangalore outfit.

Fractal already has offices in Mumbai and Gurgaon.

“We have made the commitment… selected the location at Brindavan Tech Village,” said Srikanth Velamakanni, Chief Executive Officer, Fractal Analytics Inc. The $20 million investment would go into hiring people and putting in place the infrastructure.

The California-headquartered firm hopes to attract more techie talent here considering Bangalore’s status as the tech hub of the country. “The world of analytics is influenced by technology a lot, lot more than we imagined 15 years ago. Expanding that techie part is something we wanted to do in Bangalore and this is a great place to attract that kind of talent,” Mr. Velamakanni said. By the end of this year, Fractal is looking to have offices in 13 countries. The Indian offices were crucial since they hosted 600 of its 700 employees globally. Bangalore office is expected to have 300 staffers, mostly new hires.

Fractal, which helps clients understand data and customers better, is looking to raise the global headcount to 1,000 this year.

Can organisations train their sales force to use predictive analytics to add to bottom line?

As the business landscape gets more disruptive, customers more fickle and product lifecycles short, there is tremendous pressure on the sales force to deliver more and soon. Sales executives are expected to grow revenue year on year with fewer sales resources. In this scenario, the tools to predict where the next opportunity lies and close deals faster, at a lower cost per sale, can give any sales professional the competitive edge they need.

Natwar Mall, SVP, Fractal Sciences, Fractal Analytics tells us how organisations can maximise this opportunity.

Are sales organisations maximising the opportunity to use analytics for better performance?

No, organisations are capturing more data about prospects, leads, etc, but are failing to capture the entire sales cycle data. If you want to win customers, you need to harness all of this data – from within the organisation and beyond – to make smarter predictions about their needs and behaviours.

There is a lot of data but very little insights. How can sales use data and analytics to improve performance?

There is a lot of data being produced by the analytics and IT teams, however they are not solving the problem. We believe that there should be complexity in creating analytics but there should be absolute simplicity in understanding it. Data teams need to understand the pain points of sales and provide the right inputs to better business performance.

The best example here is the FMCG industry, here the sales team are scouring cities, covering 30-40 outlets in a day, it is impossible for that individual to analyse customer behaviour, but if they were given inputs about an outlet that tells them what products sell there, who buys them, how are buying decisions made that would resolve a lot of the pain areas. It is incumbent upon data teams to simplify data and present solutions.

Should organisations train their salesforce to use data? If yes, how?

Yes, they should be trained. However, firstly, this will need a change in attitude. Sales is considered an art, organisations should train them to understand that sales is an art and a science. Secondly, sales teams have to be given the right tools, can we tell an insurance agent what a high potential lifetime customer looks like, that will increase his conversion rate.

While sales is about communication, a lot of it is also about how you actively research. A lot of data about prospects is available in the social space and in the proprietary data of the organisation like the CRM tools, it is important for them to assimilate this data and gauge how to grow their business.

Please share an example of how sales performance improved by using analytics.

An auto and home insurance organisation was struggling with the quality of customer acquisition, even though they were meeting their numbers. They looked at their customers in the past three years and evolved data around – the range of products they bought and the length of time they stayed with the company and came up with ‘a life time value model’ of a customer. Using analytics they were able to build profiles to make insurance agents understand good customers. That led to a 10-15% year on year growth in profits. They were able to get high grade customers. In sales, time is precious if that is spent on a less effective lead you are setting yourself up for failure.

• Sales professionals can use data analysis to focus their time on the opportunities most likely to close, enabling them to efficiently manage their pipeline and provide more consistent results.
• By studying patterns in the past, one can predict strong opportunities. As a result, sales leaders are able to deliver more confident and accurate forecasts.
• By mapping common buying preferences, the sales force can prescribe new pricing and selling strategies.

As the business landscape gets more disruptive, customers more fickle and product lifecycles short, there is tremendous pressure on the sales force to deliver more and soon. Sales executives are expected to grow revenue year on year with fewer sales resources. In this scenario, the tools to predict where the next opportunity lies and close deals faster, at a lower cost per sale, can give any sales professional the competitive edge they need.

Natwar Mall, SVP, Fractal Sciences, Fractal Analytics tells us how organisations can maximise this opportunity.

Are sales organisations maximising the opportunity to use analytics for better performance?

No, organisations are capturing more data about prospects, leads, etc, but are failing to capture the entire sales cycle data. If you want to win customers, you need to harness all of this data – from within the organisation and beyond – to make smarter predictions about their needs and behaviours.

There is a lot of data but very little insights. How can sales use data and analytics to improve performance?

There is a lot of data being produced by the analytics and IT teams, however they are not solving the problem. We believe that there should be complexity in creating analytics but there should be absolute simplicity in understanding it. Data teams need to understand the pain points of sales and provide the right inputs to better business performance.

The best example here is the FMCG industry, here the sales team are scouring cities, covering 30-40 outlets in a day, it is impossible for that individual to analyse customer behaviour, but if they were given inputs about an outlet that tells them what products sell there, who buys them, how are buying decisions made that would resolve a lot of the pain areas. It is incumbent upon data teams to simplify data and present solutions.

Should organisations train their salesforce to use data? If yes, how?

Yes, they should be trained. However, firstly, this will need a change in attitude. Sales is considered an art, organisations should train them to understand that sales is an art and a science. Secondly, sales teams have to be given the right tools, can we tell an insurance agent what a high potential lifetime customer looks like, that will increase his conversion rate.

While sales is about communication, a lot of it is also about how you actively research. A lot of data about prospects is available in the social space and in the proprietary data of the organisation like the CRM tools, it is important for them to assimilate this data and gauge how to grow their business.

Please share an example of how sales performance improved by using analytics.

An auto and home insurance organisation was struggling with the quality of customer acquisition, even though they were meeting their numbers. They looked at their customers in the past three years and evolved data around – the range of products they bought and the length of time they stayed with the company and came up with ‘a life time value model’ of a customer. Using analytics they were able to build profiles to make insurance agents understand good customers. That led to a 10-15% year on year growth in profits. They were able to get high grade customers. In sales, time is precious if that is spent on a less effective lead you are setting yourself up for failure.

• Sales professionals can use data analysis to focus their time on the opportunities most likely to close, enabling them to efficiently manage their pipeline and provide more consistent results.

• By studying patterns in the past, one can predict strong opportunities. As a result, sales leaders are able to deliver more confident and accurate forecasts.

• By mapping common buying preferences, the sales force can prescribe new pricing and selling strategies.

In analytics lingo, a customer “genome” is the person’s unique and individual purchasing “imprint.” Find out how “genomic” analytics is a way to build a profitable customer base.

When I was heading the marketing efforts of a financial institution years ago, we looked at how many customers were not actively using their credit cards. The dormancy level was nearly one third.

Dormancy matters when you start evaluating your customers in an effort to determine which are most profitable and which are dead weight. The income financial institutions derive when consumers use their credit cards comes in the form of interest on unpaid debt that is carried forward and that card users pay, but also in the form of Interchange income — a portion of each charge that the underlying card issuer (e.g., MasterCard, Visa, etc.) pays the financial institution when its customers patronize the card. Interest and Interchange income can turn into big money.

Today’s big data and analytics efforts bring welcome relief to banks, insurance companies, healthcare agencies, nonprofits, and other organizations that have habitually struggled with finding the most profitable customers and then selling to them. A new set of analytics reports can move these companies forward in connecting with their best customers.

A use case profile

Fractal Analytics talks about a banking user struggling in an industry where 40% of cardholders are inactive and 60% are unprofitable. The bank wanted to increase spending in its existing credit cardholder base, so it implemented a customer analytics framework that was targeted at improving first-hand understanding of these customers’ needs. “Viewing our customers through this framework allowed us to appreciate the value of each customer,” said the bank’s vice president of customer marketing. “More importantly, it enabled us to design segment-based strategies to increase customer lifetime value.”

Once the bank understood who its most profitable customers were, it developed a “genomic” understanding of how these customers spent their money and found that insurance and food expenses were among the leading “spend” categories. This enabled the bank to plan and target promotions built around these major spend areas. By doing so, the bank increased its value per customer while decreasing expenses on marketing campaigns, likely because the campaigns were better targeted.

Genomic marketing’s capabilities

Stories like this have encouraged companies to pursue genomic marketing that is propelled by big data analytics capable of profiling everything about a customer that an institution wants to know — from his age, occupation, and amount of annual spend to how he appropriates that “spend” to his lifestyle needs and preferences. In analytics lingo, we call this a customer “genome” (i.e., the customer’s unique and individual purchasing “imprint”).

Genomic marketing abounds in many industry verticals today. We see it most often on large commercial websites like Amazon, which analyzes your recent buying behaviors and proactively recommends books or movies you might enjoy (and purchase), based on your prior buying patterns.

The good news for small and midsize companies competing in these markets is that cloud-based web information can be collected and analyzed as big data from internet activity. With the help of connecting application programming interfaces (APIs), this unstructured internet data can even be integrated with your systems of record data so you can get a complete look at your customers. This levels the analytics playing field for smaller companies when they go up against behemoth enterprises.

Does this solve everything when it comes to building a profitable customer base?

Operationally, companies still have to effect the necessary cultural changes to make their employees sales savvy and operationally competent. In banking, where tellers have historically been operationally oriented, some institutions have been hiring new employees out of the retail industry, where sales skills come with the employees. Now with the addition of consumer genome analytics, selling to profitable customers, understanding their needs, and filling those needs has gotten even easier.

MUMBAI: The number of internet users in India has reached 205 million in 2013 with a 40% YOY growth. This ranks India at the 3rd position, marginally behind US with 207 million users and China which currently has 300 million internet users. Online shopping has become popular in India with a large number of shopping portals and 137 million urban & 68 million rural users.

With increase in mobile internet users it witnessed that more than 50% of urban internet users accessing the internet daily & the demography includes youth, working men, older men & non-working women. Urban centers like Mumbai, Bangalore, Delhi, Chennai and other metros have a huge internet user base. Recently India also breached the 100 million users benchmark of Facebook making it the country which is aggressively taking to social media to connect.

On the back of these amazing achievements of the country in this new age medium of connectivity which made the world flat, Confederation of Indian Industry (CII) organized the first edition of its conference called ‘digitize’ for businesses to explore new avenues in the changing times.

Speaking at the session of the first edition of CII’s ‘digitize’ conference Ninad Karpe, Managing Director & CEO, Aptech, articulated how the evolution of computers and internet has changed beliefs of the generations over the last few decades. The advent of digital media is surely going to change the accessibility barriers in education, healthcare and many other social sectors across the globe. He also added that the present historic moment which India is living on back of the biggest democratic elections sees a huge impact of social media and also highlights how governments apart from the industry sees it as a potential medium to reach the masses.

There are more things digital around us than we can imagine, with increasing connection of machine to machine, human to human and machine to human with this sort of digital integration, whatever our problems we can definitely look at a digital solution for it, said Arvind Sharma, president, Advertising Agencies Association of India and the chief guest at first edition of CII’s ‘digitize’ conference.

The conference also had sessions on big data: Deciphering information for effective decisions and golden age of mobile where eminent speakers from KPMG, Fractal Analytics, Genpact, Digital Quotient and vServ highlighted how in today’s time, companies are seeking more methodologies to synchronize and standardize there data. It also highlighted the growing use of analytics in the Indian businesses to become more customer centric.

Online shopping has become popular in India with a large number of shopping portalsand 137 million urban & 68 million rural users.

The next time you’re downtown, stop and look around you: people, stores, banks, transit, restaurants, stoplights — all of them constantly generating and consuming data. Now think back to the people, all of them with their own destinations, purpose, concerns, needs and schedules — more data.

Since 2000, when he co-founded Fractal Analytics in Mumbai, Srikanth Velamakanni has been looking closely at the data that defines our lives, our jobs, our towns, even ourselves. Fractal has helped scores of clients sort it all out to better serve each customer, analyzing client data along with its in-house data warehouse to provide near real-time solutions.

Fractal moved to New Jersey in 2005, then relocated again to San Mateo, Calif. in 2010 to be closer to Silicon Valley. It just opened an office in Rome, will soon expand to Switzerland and has already opened in Canada. Today it provides data analytics services to companies with revenues of $10 billion to $100 billion in sales, deriving 55 percent of its revenue from the retail/packaged goods sector, 40 percent in financial services/insurance and 5 percent from technology and telecom.

Fractal’s New Market

The company is about to branch into the area of life sciences and healthcare, perhaps the hottest sector in the US economy with the implementation of national healthcare underway. Just as data can help a retailer understand a customer’s needs better, Fractal believes it can help doctors to better understand the needs of their patients.

CMSWire had he chance to sit down with Velamakanni and Fractal CMO Careen Foster to discuss what companies to do to better meet the needs of their customers while leaving consumers in control of the data in their lives.

Murphy: When I think of predictive analytics, I think of it as part of a rainbow. These days, I think of customer expectation management as being most of that rainbow. How do you think about customer expectations in relation to analytics?

Velamakanni: When we talk about customer expectations, there are two aspects to it. Obviously, there’s a whole host of data that we have about customers, their transactions with the firm, their data transactions and other things. Using all that, we can have a 360-degree view of the customer — what they like, what they don’t like — we can get really, really specific. Are you a Nikon guy or a Canon guy? Do you like iPhone or Android? Microsoft of Google? Every single thing.  As a retailer, you can understand your customer in a similar way — hundreds of thousands of variables. So the first part is to understand customers truly by understanding transaction data.

The second part is about solving their problems in real time, depending on what they’re doing right now. So if I’m standing on the street and searching for a restaurant at 12:30 and I have a preference for Chinese cuisine and I tend to spend $100 per meal, now I know which of the 50 restaurants in this neighborhood works best for me. I can solve the customers problem in real time, using all the stuff I know about this customer. Once you understand customers and you can solve their problems, you can get the kind of loyalty that we all want from the customer.

There was a study published a year or so ago that said between Google, Facebook and Amazon, people are twice as likely to share their information with Amazon as with Google or Facebook because the relationship with Amazon is clear — they’re selling and we’re buying. It’s also clear they won’t misuse your information. They will use your information to make your experience better. It’s not entirely clear what Facebook and Google will do with your data. Sometimes you feel they will use the information for other purposes, or that you are the product, stuff like that. If you can crack these two pieces of the puzzle, I think you’re really meeting their expectations. I know you wrote the book Web Rules. In many ways, what you wrote in that book is what is happening now. It was very futuristic back then, but it’s all real now.

Murphy: I was going to ask you about customer expectations for privacy, but you got there first.  There are some generational differences, and perhaps some cultural and geographic differences, between people who do or don’t give their information online.  I think the reason I don’t like it when Facebook tries to push ads on my page is because their analytics aren’t as good, and the ads don’t match my interests.

Velamakanni: Customers expect to be in control. If you can be transparent with them, say “this is what I’m collecting” and “this is what I’ll delete.”  Google is doing a little of that. You can see all the data, and you can say “please don’t use this information or this information.”  Not everyone uses [those options], but they’re definitely giving the control to you. You can control what we share and what we don’t share. That’s what customers expect, and they want to understand that if I let you use this information, this is what I will get in return. I think most of the world is there.

With Facebook, I don’t have any trust in what is happening to my information. All the privacy controls are hidden in so many different places. They’re trying to make it better, but a year ago it was not possible to control how your information would be used.

Murphy: We are somewhere down the path towards real-time analytics, but we haven’t mastered the art yet.  I suspect it will be years before it’s perfected.  Someday, when I’m looking for a quiet place for us to talk near Union Square, I’ll be able to go to my phone and get that. But that takes a lot of information and to crunch all that data in real-time is something that is simply not possible today. How long do you think it will be before we get to that totally connected, IoT kind of world?

Velamakanni: The ultimate vision may be many years. My sense is the ultimate vision will be redefined. We don’t even know what’s possible now with billions of sensors and all these cameras. It is changing. Our expectations 10 years ago were very different from today, and I think we’re already close to meeting many of the expectations from 10 or 15 years ago. It’s a moving goal post and I really don’t know if there’s a date where we can say we will meet customer expectations for real-time analytics. But it’s getting really, really sophisticated as we speak. Look at maps, for example. What was our expectation 15 or 20 years ago for maps, and look at what’s happening today with real-time traffic reports.

This morning, I was supposed to be some place at 8 am, and I was driving with a friend who was using Google Maps with Waze included, giving real-time traffic information. But we ended up reaching the place later than if we had not used Google Maps for one reason: Google Maps doesn’t include car-pool information. It gives you the best route if you are not using the car-pool lane. Maybe somebody will write that app and it will be the next Waze for $1 billion. But you can see that expectations have changed. My expectation this morning was that it should have realized we were in the car-pool lane and given us better information. But that’s because I have already done so much that my expectation has changed.

Murphy: The thing about expectations is that if you don’t have any, then everything is fine. If you have expectations, and they’re not met, then the company is in trouble. You can tell so much about what your customer are thinking, but you don’t know the rest of it — and that hole is the hard part. If an analytics system knew we were looking for a restaurant to sit and talk, it could have given us a lot of suggestions, but most wouldn’t have met our expectations.

Velamakanni: Today there is no way to even ask this question. To say, “I want a quiet place” — systems don’t understand human language and intentions. Yes, there’s a long way to go.

Foster: We were just talking about this with one of the online websites for travel — the need to break out even more granular information in preferences. For example, I don’t just want a fitness center, I want very specific fitness center stuff. How do you tailor it so I can find the one hotel that is going to meet my needs?

Murphy: If I was searching for blue jean prices on my website, and I drove to the mall and got a text saying “20 percent off on all blue jeans” today, I might check it out.

Velamakanni: I think that one thing the customer expects, but doesn’t always get, is that the company will have all the information in all the channels. So if I search for something and you go to the store, your expectation is that all that information is reflected in how the store knows me.  If I see 20 percent off online, and I go to the store, and the 20 percent off is not there, it doesn’t make sense. The expectation is that my experience is consistent and the organization has my information at hand at all points in time. It’s such a basic expectation that all of us have, and it’s not fulfilled right now. My web experience is different and if I call the call center, and they have no idea what happened, I have to start all over again.

Murphy: Fractal is in a crowded space. There are a lot of companies with services like what you’re doing. How can you play in field? Is it getting too crowded? Will there be a shakeout? Are you looking for an acquisition?

Velamakanni: This is a hot space from the standpoint of clients trying to solve these problems. The expectations are huge for what this space can deliver. There are maybe 50 books on big data right now, and they all talk about what Google is doing, what Netflix is doing, or what somebody is doing. When you look at the real-world companies, there is a huge gap. Most of the companies we talk to are $10 billion to $100 billion in revenue. Most companies we talk to are in the middle of that range. Our goal is to move them from the lower left-hand quadrant to the upper right-hand quadrant. [Editor’s note: By comparison, Google had 2013 sales of $59.8 billion.]

Murphy: It’s probably hard to sell when you compete against big companies — big blue companies, for example. They can say “Here’s the solution, we can put it together.” How do you compete against that?

Velamakanni: We sound exactly like IBM would sound, except there’s another zero on their price tag. IBM can do everything that we can. Unfortunately for them, they have this software business, they have this consulting business, they have this hardware business. On paper, do they have the capability to do exactly what we’re saying? They do. Operationally, can they actually achieve it? It’s really hard for them unless you’re a Fortune 100 company and you’re willing to say, “I’ll write a $50 million check.”  Most of our companies aren’t ready to write a $50 million check. We can start with maybe $1 million and scale-up for $10 million, $50 million or whatever they want.

Murphy: You guys have the analytics part, but you don’t have a lot of the other parts. If you go into a company, they already have a CRM system, they already have a content management system, they already have a sales management system, they have a contact database and they have collaboration tools. So you come in and you talk about analytics. They must say “Well, that sounds great, but how do we hook all this stuff up?”  What’s your response to that?

Velamakanni: That’s actually a key advantage for us. We don’t have anything else to sell, no other fish to fry. We say, we can work with all the data that you have. Now that they have invested in all the technology in the world, and haven’t solved their problems, we can get this data, solve the problems as a result. And we deliver it all as a service to them — there’s no integration work. Whatever software is required, we do at our end. We will plug right back into their data warehouses, or campaign management systems or whatever else they have. We’ completely agnostic about their data infrastructure. All we want is to populate their data infrastructure with the data and the analytics that they want. We fit in quite well with what they have.

Murphy: Is everything you do cloud-based?

Velamakanni: Most of it is cloud-based. The data and analytics are running on our on-premises cloud. Only when clients are comfortable are we willing to put it in the public cloud. It’s very rare. So far, our clients haven’t really embraced that.

Companies finding fun-filled ways to engage staff at work

There’s plenty to keep Deepti Gupta occupied at work. An assistant manager of marketing at Makemytrip, Gupta clocks in 9-10 hours a day, a given in an online company, and just about gets enough time to squeeze in one lunch break. But, the monotony of mails, calls and meetings is often broken up by a beach day, a Balinese Ramayana, kung fu lessons, or Valentine’s Day celebrations.

On Valentine’s Day, Makemytrip put a florist and a chocolate stall in office to help eager Valentines make an impression. Gupta could convey her affection for her colleagues through heart-shaped messages, which could be named or anonymous. Karaoke contests kept the day live and buzzing. “One does not really walk up to people daily to let them know they’re valued.

This gave me a chance to express my love for those close to me,” she says. For Women’s Day, her male colleagues got mailers from HR on doing something special for women teammates, and Gupta was greeted with cupcakes and flowers. The need to have fun at work has stemmed from the need to engage a predominantly young workforce. “Since we have a young workforce, we ensure our activities are attuned to their needs,” said a Makemytrip spokesperson.

Activities at the company were also linked to the new product launches in exotic destinations like Balinese Ramayana for Bali, Kung Fu for China and a bellydancing performance for Jordan. Companies are trying to match up to the pace of the millenials joining them, and are looking for fun ways to energise them. While Deutsche Bank started Zumba classes to step up the tempo last month, Snapdeal launched Beat the Monday Blues three months ago. The company has an average age of 26, and it finds it essential to keep introducing methods to keep employees interested. “Beat the Monday Blues is designed

coming to work on Monday. We organise a snack each Monday which can be anything like a samosa Monday or a doughnut day,” says Anupama Beri, head of HR. To keep stress levels at a minimum, senior team leaders are provided drivers and team members are eligible for a small amount of cash in case they wish to buy something from the site.

In the current fiscal, Accenture set up fun committees consisting of HR members and business heads to plan and execute various activities at a team/project level. Specific budgets are assigned for these and the decisions on the type of activity are made at the team level based on the interests and profile of the teams concerned.

Activities include short trips, hikes and picnics, cafeteria games and competitions, a BPO with balance programme for BPO employees, women’s Olympics, which includes competing in sports such as basketball, shotput, 100-metre race, a speech and evaluation contest and in-house music bands. Fractal Analytics, which has close to 650 employees, has a ‘Break-free forum’, which is responsible for fun and entertainment in the organisation. Each location has a break-free forum, which consists of 10-12 employees in their mid 20s.

They serve as fun ambassadors, initiating different activities ranging from recurring monthly activities to ad hoc celebrations and festivities to annual events like family day, sports events, festive celebrations on holi and diwali. “For a group of analysts getting lost in the maze of numbers and algorithms, a gaming room serves as an absolute breather.

The room has all forms of indoor games including fussball, carom board, chess and Xbox. Once every two weeks, employees are also encouraged to drop everything work related, and meet to discuss anything besides work,” says Garima Sharma, director, HR. Besides Zumba, Deutsche has organised a stepathlon, heritage walks in Mumbai, and the Deutsche Premier League, a box cricket night tournament which included female colleagues.

“We decided to offer our staff avenues to pursue varied interests in the office premises itself which they are unable to do so on account of time and travel commitments,” says Makarand Khatavkar, MD and head of HR. SAP Labs has 25 interest groups such as XQUIZIT for quiz enthusiasts; SAP Roadies for runners and cyclists and SAP FC, the official football team of SAP Labs India.

One of the most popular interest groups at SAP is Literati, a corporate book club with over 2,500 members. Since its inception in 2009, it has hosted more than 100 authors, including Shashi Tharoor, Amish Tripathi, Chetan Bhagat, and William Dalrymple. At HCL, employees use the internal social networking platform MEME to run online interest clubs for photography, food, pets and bikes.

The need to engage youngsters led Dabur to install a pool table in their campus a few months ago. The company also tied up with Cafe Coffee Day to set up a Cafe Coffee Day Express outlet. “If it is about clearing the air with somebody after a difficult conversation, employees like heading here,” says V Krishnan, executive VP, HR, Dabur.

Only a few years ago, Google was a cool serach engine looking for a revenue model. In 2002, four years after launching ‘search’, the company launched a pay per click (PCC) model on its Adwords platform where marketers paid for click instead of impressions.

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Computer-plus-human effort always beats just human beings or just supercomputers — Srikanth Velamakanni | Co-Founder, Fractal Analytics Mu Sigma’s frenetic growth has helped it hog a lot of the limelight among firms based out of India that offer analytics services. But before Mu Sigma there was Fractal Analytics. IIM-Ahmedabad alumni Srikanth Velamakanni and Pranay Agrawal founded Fractal in 2000, four years before Mu Sigma. But the mistake they made, Velamakanni says, was in looking first at India as the market, and entering the US market as late as 2006. Mu Sigma, on the contrary, started off in Chicago, where founder Dhiraj Rajaram was then based.

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While companies are open to discuss how they attract, retain and groom talent, dealing with underperformers is a sticky conversation. ET speaks to human resource experts to understand five ways to deal with those who may not be performing to their best at work.

Communicate

“This ought to be the first step. The records show clear signs that someone is not making the cut, but nothing is obvious till we talk about it,” says Mark Driscoll, human capital leader, PwC India.

This is third and concluding part of the interview with Srikanth Velamakanni, Co-founder & CEO, Fractal Analytics.

In the first part, Srikanth shared how Fractal came into being, and how they overcame the initial challenges they faced. In the second part, we discussed how Fractal manages the career cycle of analysts at Fractal (hiring, training, engagement and attrition), and the challenges that occupied Srikanth’s mind.

In this part, we will discuss the key strategic bets Fractal foresees in the next few years, and how Srikanth sees the industry evolving. He has also given some career advice for people in this industry.

KJ: What are the strategic bets for Fractal in next few years in terms of tools, people and infrastructure?

SV: Following are the key bets for us:

• The biggest bet from a vertical perspective, is entering new sectors. We are currently focussed on consumer packaged goods and financial services. We are now foraying into analytics for technology and life-sciences as well.
• In terms of tools, we have placed some big bets on creating an internal environment where people can work in an error free environment. These tools are based on visual workflow and hence ensure quality by making sure errors get flagged in the process flow.
• Another big thing for us is the entire area around human-machine co-operation. This is an exciting space. For example, take data harmonization problem => Data comes from different sources in different formats and with different context. Machines can’t match these sources accurately and human led interventions are very time consuming. The solution is to create algorithms where machines take care of maximum matching and humans intervene only to deliver on last mile challenges.
• Another question we are constantly thinking about is “How to simplify front end for a complex back-end?” Our customers are business users and need not know all the complex algorithms running at the back end. For example Google has a simple interface to search which is enabled by very complex algorithms at the back end. We aim to achieve a similar outcome for our products / services.

KJ: What about Big-data?

SV: We are already processing things on distributed systems using some of the latest platforms like Hadoop and MongoDB. It is still a small team and we are handling data in magnitudes of terabytes. We are not processing petabytes of data yet. This is something we are building. Goal to have a system which can handle at least 1 Petabyte of data by end of this year.

KJ: If you were a fresher starting in analytics industry today, how would you shape up your career?

SV: My advice would be to take up a career track and be clear in how you would be successful in it. There is a lucrative future in all the four specializations we talked about. And like any other science, this industry will also move from generalists to those having a super-specialization.

So analysts should ensure that their career moves in a ‘T’ shape. They should have deep knowledge in at least one domain and have a broad perspective about the overall Analytics industry at the same time. At younger stage, people should be willing to move industries and learn quickly.

• If you want to be a data scientist, you have to be at the fore-front of Machine learning / AI
• People wanting to become Analytics Consultants should gain business knowledge of their domain. They can’t succeed until they understand the domain completely.
• BI experts should understand how all the tools work. While the tools may change over time, the knowledge about them will eventually make you a better professional.

This industry is set to boom for the next 40 – 50 years. It is something like joining the IT industry back in the early 80s. This is a dynamic space with lots of opportunities. Any big company in this space can go out of business very soon because of the fast pace of the industry. But as long as you have the right talent, you have a bright future.

KJ: Any advice for Analytics Vidhya’s Audience?

SV: For people who are trying to switch to Analytics as a career, my strong recommendation would be to develop an understanding of probability and see if they are enjoying it. They have to take pleasure in understanding probability, to be an analytics professional. My suggestion to them would be to look up AI and Machine learning courses on Coursera. They are really good courses. Then they should see if this is something which excites them.

For professionals already in the industry, I would suggest that they learn continuously to build their career in a T-shape, like I just described. The other thing they should think about is, ‘which is the right Organization for them to join?’ And where can they build the required skills? This choice should not be based on their location. They should look out for the right company, the right culture, where they can spend 10 – 15 years of their career. I have seen youngsters tend to be a bit restless and they switch many jobs for pay and designation. I think pay is not as important and if you have the right skills and are in right Organization. The pay will eventually catch up.

KJ: Thanks a ton Srikanth for the quality time you spent in discussing these questions and providing your perspective. I think it will go a long way to help audience of Analytics Vidhya.

For those who have missed the first 2 parts of this series, you can read them here:

• Part 1 describing how Fractal started, initial challenges and how they were overcome?
• Part 2 describing how Fractal manages hiring, training, engagement and attrition of analysts.

I think all the information / perspective provided by Srikanth is invaluable. There is tons of useful advice in these 3 pieces of interview. Over next few days, I’ll continue to reflect on what I have learnt by talking to Srikanth.

Last week, we released the first part of this interview. In that part, Srikanth had shared his experience with starting up Fractal, the challenges faced by the team and how they overcame the challenges. If you missed that part, you can read it here.

If you are someone like me (who wants to learn more about Analytics) and have read the first part, this part of the interview is for you. I gained immensely from this interview and I am excited about extending this experience with all of you.

In this part of interview, Srikanth shares their hiring, training, and managing strategy at Fractal; and how Fractal, expects their people to choose their own career, and manages employee attrition.

KJ: Fractal has produced some of the best Analytics professionals in Indian Analytics Industry. How do you hire people? What do you look at time of hiring? How do you train them once they are hired? How do you ensure that they contribute what they bring to the table?

SV: For Hiring:

These are the attributes we lookout for:

• Most important thing to look for is Passion for Analytics. (Remember, this was first thing out of the 3 things to look out for in life). Frankly, when you get into depth of analytics, things get difficult. You need to get into details while slicing and dicing data. If you don’t have passion for analytics, you may not succeed when you get there.
• Next, we look for strong understanding of probability, mathematics and statistics.
• We also look for structured thinking like any consulting company will look for. Can the candidate take an amorphous business question and then structure it to come out with various hypothesis and a business solution?

Apart from these skills, we also look at values. People need to work in very collaborative manner with teams across the globe. So, we look for integrity and commitment to work .

We have created an environment in Fractal where we put extreme trust in our people. We don’t monitor their inputs. We are always on the look out for people who fulfill this extreme trust and not violate it. We look for people who can sustain a very high trust culture. We believe that not having to micro-manage people creates an extremely happy and creative workplace.

In fact, while recruiting people in the middle-management, we ensure we do not hire micro-managers.

Training:

We have a pretty robust on-boarding programme for 5 weeks. It includes 3 weeks of class-room training, followed by a 2 week program which involves, working on a business problem in teams. All the concepts (like structured thinking, presentation skills etc.) and tools learnt over in first 3 weeks are put to use in these 2 weeks. In last week of this induction programme, fresh recruits get introduced to the sector they will be working for,  where they get specific knowledge of the domain.

After this 5 week programme, new recruits get to shadow one senior, or rather, more experienced person on a project. So, they are part of a team, but we do not charge our clients for this period. This helps new recruits learn while they are working with other people. This doesn’t always happen, if the project is fast paced, given the fact that hiring in this industry is very difficult. However, we always ensure that a person has the right skills and exposure to the domain before he ventures out on his own. The moment they are out of an induction programme, they get to interact with CXOs in the industry, so we prepare them for this as well, in our induction programme.

KJ: Analytics industry is crippled with high attrition. How do you manage this, especially when you are investing so much in the training?

We have to invest in people, without expecting anything in return. If people leave, they leave. However, we have experienced that when you trust people, they respond. And this thought process always works, at least it has worked for us. In 2012 (Calendar year) the attrition at Fractal Analytics was 14%, in 2013 it rose a bit, but we got the figure down again in 2014.

The reason for this is that we have created an environment where we trust our people. There is no monitoring, people can come in any time and they can go any time. Our employees have that freedom. In fact simple expense re-imbursement policy, dress policy, no private parking spaces, no cabins, are some of the policies which help us create this culture.

We have created a system where analysts can move out of their project on their own. If an analyst is not interested in a particular project, we help him find another role within Fractal. There is complete flexibility to choose your own career path.

There are 4 career tracks we offer at Fractal:

• Analytics Consulting
• Data Scientist
• Programme Manager
• Big data engineer

We believe that “You are the CEO of your career, and we respect the choices you will make.” We don’t micromanage our employees’ decisions. Through these policies, we endeavour to take all the noise surrounding an analyst, away, so that he can focus on the work at hand.

We want our people to focus on creating the best analytical solution for our clients, and in turn make Fractal a great place to work.  It is our endeavour, to make Fractal the most respected player in this industry.

KJ: What are the challenges occupying your mind currently?

SV: One big challenge for us has been getting good people in offices outside India. Through our policies, we have got access to a very good talent pool in India, but this is not fully true for the US market. We have a relatively small office in the US. This is something we plan to change over time.

The other challenge we face, is that a lot changing in this industry rapidly. Half-life of knowledge is about 3 years. Hence, we need to be aware of the latest tools and techniques. In order to address this, we have created Fractal Academy where people can get trained in various aspects of analytics. We have integrated platforms like eDX and Coursera and people get credit in Fractal Academy for completing relevant courses on these platforms.

We have also made changes to our career tracks in line with changes in industry. Till some time back, we used to hire everyone as analytics consultant. The person was supposed to do everything from meeting & consulting the client to building the models and managing the programme. In order to keep up with fast pace of the industry, we now have 4 different career tracks for people:

• Analytics consultants – Traditional client facing consultants who meet client, capture their requirement, design solutions and help implement the solutions.
• Data scientists – These people are not facing clients, They work on algorithms, software codes and machine learning (e.g. Recommender systems).
• Programme managers – BI programme managers create easy to use and visually appealing programmes so that information can be retrieved on click of a button.
• Big data engineers – People working on big data technologies and platforms e.g. Hadoop

Now we are hiring people in these different roles.

In the concluding part of this interview, we will share strategic bets for Fractal over next few years, Srikanth’s view about how Analytics industry will shape up and his advice to audience of Analytics Vidhya. Stay tuned with us and we will be out with the final part of the interview shortly.

The best part of running this blog has been, connecting with some of the best people in the Analytics Industry. I recently, got an opportunity to interview Srikanth Velamakanni, Co-founder and CEO of Fractal Analytics. The interaction was, undoubtedly, a great learning experience; something which I have tried to extend to all our readers, through this post.

Srikanth holds an MBA degree from Indian Institute of Management Ahmedabad and a bachelor’s degree in Electrical Engineering from Indian Institute of Technology, Delhi. Before co-founding Fractal in the year 2000, Srikanth was an Investment banker and had worked with ANZ and ICICI Bank, managing asset & mortgage based securities. Srikanth is passionate about learning and reading, especially about behavioral economics, positive psychology, and neuroscience. He loves listening to Audiobooks and is an analytics and technology evangelist.

In about an hour, which I spent talking to him, I developed deep respect for him as a professional and as a person. I hope that someday I would be able to make a similar impact in the Analytics Industry.

I plan to release the interview in three modules in the coming days. The structure for the same, would be as follows:

• Part 1 – Starting Fractal, initial challenges and how they were overcome.
• Part 2 – Building an Organization. Hiring, training, trusting your employees to build a world class Organization and dealing with attrition.
• Part 3 – Future bets for Fractal and evolution of Industry. His advice to people entering the industry.

Here is the first part of the interview:

KJ: You started Fractal back in the year 2000. At that time, data science and analytics were not as popular as they are today. You were one of the earliest start-ups in the industry. So, what was the idea when you started? How did it happen? How did you decide that this is the idea you would want to pursue full time in the future?

SV: Sure. When we started, we obviously didn’t have analytics in our mind. We (founders of Fractal) had quit our jobs and wanted to do something together. We actually did a little bit of search and thought that we should be in the IT space. However, after talking to people (senior veterans) in the industry, we realized that until and unless you are focussed (on a particular aspect), it is not a good idea to be in the IT space.

We were doing soul searching and looked at various things. After a while we realized that we are good at Financial Services and we are good at Maths. We understand probabilities & statistics and had worked on problems like hand-writing recognition and face recognition during college.

While talking to a few people in ICICI, we learn’t that they were facing challenges in identifying the credit worthiness of customers. This got us very interested, as we knew, credit risk could be identified with a lot of statistical tools. So it turned out, that we were actually the first ones to build a statistical scorecard (for identifying credit risk), in India. This score card, as mentioned earlier, was for ICICI Bank (to predict default on their personal loans). We were pretty excited because this could enable them to do real time risk scoring.

When we finished this project, we realized that this was something exciting.

To think of it, what do you need in life? You need three things:

• Pleasure – Whatever you are working on should be fun
• Meaning – It should add meaning and value to the world.
• Strength – It should be an area of strength

So it felt like this industry was meant for us.

What we did not know was how big this area was? Whether we could make money in this area or not? To be very honest, we were MBAs from IIM Ahmehdabad, but we did not do any major business planning in terms of how big this space was going to become.

The next thing we did, was to build a default prediction model for corporates using information regarding stock prices. We could simulate stock prices and see default ratios under various circumstances and could come out with pretty good models predicting when a company could default. In fact, we came out with models, which were better than Moody’s and S&P. Moody’s and S&P were both backward looking where as we were using predictive modelling to come out with default probability.

Another thing which was fascinating at that time, was the entire area of consumer behaviour. How do people change their behaviour? This was 2001 and it was recession time. We observed that consumer behaviour was changing dramatically in a few sectors. Specifically, when we started working with Hindustan Lever, their Chief Economist told us, “Here is all the panel data I have. Tell me what I am not looking at.” We looked at various aspects; we studied whether customers were changing products or brands they used to purchase before? And how were the purchasing patterns shifting?

Then we realized that this is it, this is the space we had to be in.

We did not have any role model. But we felt this was it was an exciting space to be in, and all the three elements (Pleasure, Meaning and Strength) looked to be in place, and we just kept going. We have never looked back since.

KJ: That almost brings me to the next question. This was a new area when you started. You were treading the path for the first time for yourself. When you start in that kind of scenario, there are challenges that you come across. The challenges could be, proving to customers that Analytics can or has added value. So, what type of challenges did you come across and how did you tackle them?

SV: Great question! Infact we did face a lot of significant challenges in our initial days. Right at the start while working with ICICI bank, we had to prove ourselves. We did not have any experience in building models. So we had to prove to them, that we know what we are talking about. Remember, this was about building a risk model. If you built a wrong risk model, it could cost the bank a fortune. It was not easy. ICICI bank asked us, “Why should we believe in you and not work with Fair Issac in the U.S.?” So we said that we will do some original research on the topic and come back to you with what we think about the space. We spent a couple of months writing a research paper on the subject. We took that to ICICI and told them that this is the state of the art research on the subject matter. They really liked what we had put together for them. This was the way we could earn their trust, so that they could share their data and we could build the first statistical scorecard.

Another instance was a conversation with Citibank. We met them in Mumbai and then in Chennai. They said that “We have massive team of Ph.Ds. sitting in New York, who know how to build all this stuff. You guys are 20 something and you have got no experience in the domain. So how can you beat what we are doing?”

So this time, we said “We understand that you might have a great team. We also believe in what we are doing. Why don’t we set up a challenge: You give us the data, we will not charge you anything. We will build the model and see if this adds value to what you are doing.” We have to give credit to them that they accepted the challenge and gave us all the data. We built a personal loan cross-sell model for Credit card customers and then also looked at the price sensitivity of the customers in the model. That was a new thing at that time. They had not done a lot of experimentation on various price points. So we had thin data to build the model.

Once we built this model, these models performed extremely well and all of us were surprised how accurate they turned out to be. At that time, they came back and said “You guys have clearly beaten what we were doing internally.” So that brought a lot of credibility to us. With clients like Citibank, ICICI bank and Hindustan Lever, we could now go and pitch confidently to other people about our services.

KJ: Interesting! This idea about pitching yourself against some of the best people in the industry sounds fascinating!

SV: In fact, we did a similar exercise with Capital One in the US. They challenged us whether we could predict when a customer would attrite (rather than just who would attrite)? We put together a survival analysis framework in terms of when would the customer attrite and what intervention could be made to retain a customer. This framework also provided answers like whether an intervention was effective or not and by how long would it increase the customer life-cycle. This framework was very fascinating and they loved what we had put together.

These are the kind of projects we have worked on. We have done a lot of stuff. We did not focus on making a lot of money. We focussed on doing a lot of cool stuff and focussed on building our credibility and expertise.

In next part of the interview, we will discuss Fractal’s hiring and training policy and how do they tackle employee attrition, which is one of the biggest challenges in the Indian Analytics industry today. For me personally, the key take-aways were the three elements which Srikanth focused on and some of the non-traditional pitches they made in the beginning of their journey. Stay tuned with us for more of this exciting and insightful conversation with Srikanth, which will be released in the next 2 parts.

The success of analytics story in India has a huge bearing on the leaders that drive it today. The emergence of analytics and big data posed its own set of opportunities/ challenges and these leaders are to a large extent instrumental in shaping the analytics industry that we witness today.

Analytics India Magazine’s annual ranking of the 10 Most Influential Analytics Leaders in India honors individuals in Analytics/ Big Data industry who are deemed by their peers and an expert panel to be the most influential individuals in India, in terms of Impact, Leadership, Entrepreneurship and Analytics evangelism.

Pankaj Kulshreshtha – Business Leader, Analytics & Research at Genpact

6 of the 10 leaders listed here have their roots in Genpact or a GE subsidiary. This goes on to display the amount of impact that the company has had on the analytics industry in India. Besides being among the early starters in Analytics, Genpact is also considered among the most successful, and have churned out the best analytics talent over the years. Genpact has about 5,000 people in its analytics practice, largely out of Bangalore.

Pankaj Kulshreshtha leads the Analytics and Research practice globally at Genpact. This practice is part of Genpact’s Smart Decision Services that enables clients across industries to make smarter decisions in sales and marketing, cost, and risk management using data and insights. Under his leadership, Genpact’s Analytics business has grown to one of the largest and most extensive practices in the industry.

Pankaj joined Genpact in 1998 and was one of the first people who launched offshore analytics teams in India. At Genpact, Pankaj helped various GE businesses set up analytics teams and in 2005 moved to GE Money UK, where he led risk management for various portfolios and was Chief Risk Officer for the loans business in his last role. He then rejoined Genpact in 2008.

Rohit Tandon – Vice President, Strategy WW Head of HP Global Analytics

As head of Global Analytics, Rohit Tandon is helping drive the Analytics ecosystem to support HP’s vision and priorities.

He is a veteran industry executive known for pioneering and guiding new businesses to success. In Rohit’s leadership HP Global Analytics grew from a small team to a large analytics organization. Today as part of HP’s Corporate Strategy team, Global Analytics is also chartered to lead HP’s Analytics delivery ecosystem, and bring together similar teams to drive innovations that support HP’s enterprise priorities. The organization has access to talent as well as cutting edge tools and techniques that help innovate & customize analytical solutions for sustained competitive advantage.

Prior to joining HP, Rohit was the Executive Vice President at IBM’s Global Processing Services unit where he was responsible for Strategy, IT, Quality and Transformation. During his tenure he was instrumental in setting up and leading IBM’s Knowledge Services business including Analytics where he also acquired a company for IBM.

Prior to IBM, Rohit drove the Analytics business at Genpact, leading a team of ~2500 analytics practitioners. Rohit is a Certified Master Black Belt in Six Sigma and a Certified Quality Leader in Six Sigma. He also holds a patent in his name for optimization algorithms designed by him.

Sameer Dhanrajani – Business Leader, Cognizant Analytics

Among the leading IT service providers in India, Cognizant Technology Solutions is considered the most aggressive in terms of Analytics. Cognizant Analytics resides within H3 (Horizon 3), as a part of Cognizant’s Emerging Business Accelerator (EBA) tower and is a pivotal component in the SMAC stack (Social, Mobility, Analytics and Cloud). Cognizant Analytics unit is regarded as a distinguished market leader and differentiator through incisive focus on topical actionable, applied and prescriptive analytics based solutions coupled with focused consulting approach, IP based non-linear platforms and deeply entrenched customer centric engagement model.

Sameer Dhanrajani is spearheading Cognizant Analytics practice. In his capacity, Sameer is leading end-to-end business spheres of Cognizant Analytics and is responsible for crafting differentiated strategies around analytics consulting, platforms and services coupled with creating best of breed GTM, business development, operational excellence solutioning exercises and delivering transformational analytics engagements.

Prior to Cognizant, Sameer was the Country Head for Fidelity National Financial and pioneered India’s first captive to be based on nonlinear growth model with platform based value propositions. He has also served as Vice President – Analytics at Genpact and Director – Alliance Consulting Inc. in his previous assignments.

As a recognized industry thought leader in outsourcing, analytics space; Sameer has been recipient of “Outstanding Leadership Award” at India Human Capital Summit and “Exemplary Leader Award” during the Asia Pacific HRM Congress Awards. Sameer is also core member of the NASSCOM Analytics Special Interest Group and is on industry advisory council of ISB (Indian School of Business) and has been quoted across multiple business media and news publications.

Srikanth Velamakanni – Co founder and Chief Executive Officer at Fractal Analytics

Fractal was among the earlier Indian start-ups in the area of Business Analytics, and also one the most successful boutique analytics firm too.

Srikanth leads Fractal’s global presence in business intelligence, consumer insights, predictive analytics, and optimization sciences, delivered through visual story-telling. Prior to co-founding Fractal, he consulted with major market leaders such as Visa, P&G, Citibank, HDFC Bank and SAP in predictive analytics for risk management and marketing effectiveness, as well as structured debt transactions and collateralized bond obligations at ANZ Investment Bank and ICICI.

Read Interview of Srikanth Velamakanni

Pankaj Rai – Director, Global Analytics at Dell

Pankaj Rai is the Director of Dell Global Analytics (DGA). Pankaj has been with Dell for ~8 years and has been with DGA for close to 5 years. Prior to this he was working with the India President’s office and managed all strategic and corporate planning related initiatives of Dell in India. In this role, he was responsible for helping Dell diversify and grow its footprint in India as also represent Dell outside in industry forums.

Prior to joining Dell, Pankaj was Head, Program Management Office at Standard Chartered Bank in their Singapore regional office wherein he helped grow their shared service centers in India and Malaysia.

In his professional career spanning 20 years, Pankaj started as a management consultant and then went on to work in the financial services industry in India wherein he worked with ICICI and GE Capital in a variety of roles covering sales, risk management, 6 sigma & operations.

Amit Khanna, Partner at KPMG

Amit joined KPMG India in Jan 2014 after 18 years of experience as a head of Data Analytics Practice for domestic Businesses. Prior to joining KPMG, he was a Partner and Managing Director with Accenture Management Consulting where he led the global consulting practice for Business Analytics, Customer insights, Sales and Marketing transformation. He started the Accenture Analytics consulting practice in India in 2008 serving both Indian & Global clients and grew the team size to 500+ resources in a span of 5 years. Amit has extensive experience in running analytics driven transformation/ programs for the clients. He was one of the founder members in building advance analytics capability for Accenture Management Consulting and was part of Accenture advance analytics global leadership team.

Amit has also done lot of work in areas of analytics capability development & analytics adaption in Organizations. He has worked extensively with various universities to develop Analytics & Data scientist curriculum. He personally own 2 analytics patents, he has also worked with large global clients to help them design their analytics organization & adapt a fact based culture.

Before Accenture, Amit was service delivery lead for customer and marketing analytics team in GECIS (now Genpact). He has helped developing GE analytics team for NA, Europe & APAC.

He has held senior positions in sales and marketing leadership in consumer goods industry before moving to consulting about 10 years back.

Read Interview of Amit Khanna

Ashish Singru – Director eBay India Analytics Center

Ashish has over 18 years of experience in driving analytics and customer insights into strategic planning and decision-making in a wide range of organizations. He currently heads eBay’s global center of excellence for analytics in Bangalore, with the aim of building cutting-edge capability to solve eBay’s hardest business problems.

Ashish has a unique perspective on how the application of data, technology & business analytics has evolved in organizations in the last 2 decades. When he started his career in mid-1990’s, analytics was primarily used by Financial services organizations. Other sectors like FMCG, Retail and Automotive were much more on mining customer survey data to generate business insights. He was one of the early pioneers in driving behavior-based analytics in these sectors. Subsequently, with the rapid growth of internet and enhanced database technologies, he has led the development of cutting-edge analytics practices to understand how online and offline customer behavior can be analyzed, as well as how to drive analytics deeper into the financial investment planning process for high-stakes decision on sales, marketing and pricing for major product and marketing campaign launches at global scale.

As an organizational leader, Ashish has extensive experience in creating and developing talent pool across multiple markets. He has mentored and coached numerous individuals in the analytics profession ranging from college-intern stage to senior leadership roles in different industries. Ashish believes in being the change you want to see in the world, and constantly works on developing himself along various professional dimensions in the rapidly changing industry so that he can help lead others in their growth paths as well.

Arnab Chakraborty – Managing Director, Analytics at Accenture Consulting

As a managing director at Accenture Analytics, now part of Accenture Digital, Arnab Chakraborty speaks analytics fluently. He serves as the Global Lead for Industry Analytics in Accenture’s advanced analytics practice and is also responsible for driving the Big Data Analytics practice.

Arnab has over 15+ years of rich experience in consulting and business analytics across diverse industry sectors. Before joining Accenture, Arnab played a pivotal role in scaling up enterprise wide analytics capabilities globally for the past ten years at Hewlett Packard (HP) and GE Capital (Genpact). Prior to that, he held leadership positions at KPMG Consulting, Wipro Technologies, and Larsen & Toubro. He has worked across High-Tech and Electronics, Financial Services, Consumer Goods and Manufacturing industries.

Arnab is recognized as an Edelman Laureate by the prestigious Franz Edelman Committee/INFORMS for his contributions in the field of e-commerce/ online analytics and has been voted by SSON in 2012 as one of the top 6 Global Sourcing Think-tank (G6) in Asia. Arnab is a regular author in the field of analytics and operations research in leading journals and magazines. He is also one of the key industry leaders driving the NASSCOM Analytics Special Interest Group

Arnab holds an MBA (Gold Medalist) from National Institute of Industrial Engineering (NITIE), Mumbai, India and a degree in Mechanical Engineering from National Institute of Technology (NIT) Rourkela, India. He is certified in Production and Inventory Management from APICS, USA.

Anil Kaul – CEO and Co-founder at Absolutdata

Dr. Anil Kaul is a well-known expert in the industry with over 16 years of experience in marketing research, strategic consulting. and quantitative modeling. He has consulted over 20 Fortune 500 companies during the 4 years he spent with McKinsey & Co. in New York. Thereafter, he joined Anubis Inc., an innovative data warehousing start-up in San Francisco Bay Area, as a part of the four-member top management team. The company was acquired by Personify.

He then co-founded AbsolutData in 2001.

Anil is a PhD and MS in marketing from Cornell University. He is also a voracious reader and an intuitive writer. He has published many articles in leading management and academic journals such as McKinsey Quarterly, Marketing Science, Journal of Marketing Research, and International Journal of Research in Marketing. One of his papers was nominated for “Paul Green Award” by the American Marketing Association.

He has also been an invited speaker at McKinsey & Co., Dartmouth College, Cornell University, Yale University, Columbia University, Wharton, UC Berkeley, New York University, IIFT, IMT Ghaziabad, and IIM Lucknow.

Dr. N.R.Srinivasa Raghavan, Senior Vice President & Head of Analytics at Reliance Industries Limited

Dr. N. R. Srinivasa Raghavan is Sr. Vice President & Head of Big Data Analytics/Data Science at Reliance Industries Ltd. Working closely with the Chairman and the Senior Business Leadership, he is setting up the Centre of Excellence for Big Data Analytics at RIL capable of delivering deep business insights across Oil & Gas, Petrochemical, Telecom & Life Sciences businesses. He is responsible for strategizing and realizing the value proposition for Data Science and Data Governance within the group companies while building and mentoring a world class team of data scientists / business analysts to deliver for high performance.

He has traveled widely, and has extensive industrial experience prior to RIL in strategizing and architecting large scale, global, and complex data science implementations in the Financial Services, Automotive/Semiconductor/PC Manufacturing and Supply Chain verticals.

He holds award winning Ph. D in Computer Science and M. Tech in Operations Research both from Indian Institute of Science, and is a gold medalist in his B. Tech in Mechanical Engineering from Sri Venkateswara University, Tirupati. He began his career with 8 years of academic research/teaching/consulting experience as a Professor at the Indian Institute of Science. In his past 7 years in industry, he has held senior leadership roles in advanced analytics delivery and innovation at Dell, Fair Isaac, General Motors and Citibank. He has 85 technical publications in leading International Journals/Conferences with 2 International patents filed and has also supervised many award winning doctoral students while at IISc.

He has received many awards including the Outstanding Young Associate of Indian Academy of Sciences, Outstanding Young Engineer of Indian National Academy of Engineering, and Young Scientist Fellowship from Department of Science & Technology, GoI.

How much Big Data is enough? Many people are under the impression that the more data you have, the more accurate your analysis will be. Rather than basing research off a small sample population, why not study the entire population? Many analysts who work with big data are now saying this is a false assumption. At some point, it does not matter how much more data you have. Moreover, the quality of the resulting knowledge you glean from analyzing Big Data is less dependent on the quantity of data and more the result of sound analytic processes.

Rather than spending time amassing as much data as possible, discerning data analysts are looking for clean data, which is data that has been purged of inaccuracies and vetted for authenticity. With Big Data, cleansing becomes a larger problem, and the more data you have, the bigger your mess can become. Therefore, it is better to start with a clean approach to gathering the data rather than haphazardly mining any and all data, only to have to sort through it later.

Predictive analysis can play a big role in ensuring that data gathering methods will produce clean data. Someone with skills in soft data science can determine the most reliable variables that will strengthen a data model. It would therefore be shortsighted to suggest that a computer system, even a current generation artificial intelligence, can take the place of a good analyst who can start with clean data rather than having to cleanse data as an afterthought.

Data is always messy

Fractal Analytics founder and CEO Srikanth Velamakanni agrees that it is possible to have too much data but also maintains that working with samples is “always risky”. Regarding data hygiene he said, “Data is always messy. Everybody has messy data. Having said that, we’ve never considered this a problem performing analytics and getting results. You can always get enough from it that it’s useful. If you’re digging for gas, you don’t mind the more expensive process because the value of the gas you finally get to.”

Ultimately, Velamakanni explains, you must find a balance between the two. You do not want to work with old data from ten years ago if you know it is no longer relevant to your research. At the same time, you do not want to ignore current data because it is larger than a traditional sample size. Quality Big Data analytics is found somewhere between the extremes.

Another risk data analysts take with big data is attempting to quantify human behavior. After all, business always deals with human behavior on some level, whether it is with customers in a retail environment or prisoners in a correctional facility. You can amass quite a bit of quantitative data about people but still not have a true understanding of why they are doing what they do or the circumstances surrounding their actions. Many businesses still gather plenty of qualitative data about customers and their opinions. That may take the form of interviews, focus groups and other softer approaches to gathering data. This can actually help them fine tune their quantitative predictive analysis.

Big Data is not going away, but as organizations actually start to sift through their mountains of information, they will soon find that much of it is not what they actually need. They will need to make better decisions regarding how to gather clean data, and there may be an opening in the Big Data market for vendors and service providers that can help businesses do just that. How much Big Data is enough? The answer may be in the data itself.

Thanks to Crunchbase’s downloadable database, we can report that in 2013 investors poured more than $2 billion into Analytic startups, up 38% from 2012.  Crunchbase reports 2013 funding for Analytics ventures more than five times greater than in 2009.

Palantir led the pack in new funding, going to the well twice, in October and December, to raise a total of $304m based on a valuation of $9b.  As a point of reference, at 4X revenue, industry leader SAS is worth about $12b.

Funding flowed to companies that build advanced analytics into focused vertical or horizontal solutions.  Examples include:

• Ooyala (analytics-driven solutions for video and digital TV): $43m
• WorldOne (global health care data): $35m 
• Drillinginfo (oil and gas exploration analytics): $25.1m
• DemandBase (B2B targeted marketing): $15m
• RetailNext (retail in-store analytics): $15m
• Tidemark (performance management): $13m 

Investors paid special attention to vendors who specialize in social media analytic platforms:

• DataSift (social media analytics): $42m
• Dataminr (social media analytics): $30m
• Networked Insights:() $9.4m

Capital also flowed to companies offering general-purpose software, platforms and services for analytics, including:

• Arria (natural language generation): $40.3m
• Ayasdi (advanced analytics platform): $30.6m
• Opera Solutions (specialized techniques for analysis of Big Data):  $30m
• Fractal Analytics (analytic services provider): $25m
• Skytree (machine learning software): $18m
• Databricks (Apache Spark-based analytics): $14m
• Guavus (streaming analytics): $9m
• Revolution Analytics (analytics based on open source R): $7.8m

Investors continue to fund startups offering easy-to-use interfaces for the business user, including:

• Datameer: $19m
• Alpine Data Labs: $16m
• Alteryx: $12m
• RapidMiner: $5m

Top investors in Analytics for 2013 include:

• Founders Fund (Palantir)
• Lightspeed Venture Partners (Evolv, EdgeSpring, MapR, DataStax, Numerify, Jut)
• Redpoint Ventures (Moogsoft, MapR, Tidemark, Looker, Heap, Datameer)
• Scale Venture Partners (Demandbase, DataStax, DataSift)
• Khosla Ventures (Ayasdi, Evolv, Bidgely, Kyron, ParStream)

Clearly, investors are placing bets on a robust future for analytics.

Experts say around 35,000 analytics-related jobs would be generated in India in 2014, with Bangalore alone generating 12,000 jobs.

Analytics has emerged as one of the most sought after professions in the city over the past year. The number of youngsters joining the stream more than doubled in 2013, the reason being a steady increase in the number of jobs coupled with attractive salary.

Bangalore continues to be the prime destination for those seeking a job in analytics industry. Of the 90,000+ analytics professionals in the country, the city recruits about 30%.

“Bangalore is the biggest recruiter in India for analytics, followed by Delhi NCR, Mumbai, Pune, Kolkata and Chennai,” says Gaurav Vohra, CEO, Jigsaw Academy, an organisation that trains people in analytics.

With global demand for analytics talent increasing on the backdrop of business requirements growth, mainly in the US, job growth in this segment is expected to go through the roof in India.

According to experts, the overall analytics-related employment generated in India will be about 35,000 in 2014. In Bangalore, the figure is expected to hover around 12,000 jobs, while in Delhi NCR, it will be about 10,000.

Pure play analytics firms such as Mu-Sigma, Fractal, and LatentView would be among the top hirers, while firms such as Accenture, IBM, Cognizant and EXL services would be among the top when it comes to IT/BPO and other firms, says a report by Knowledgefaber.

Rohit Trivedi, associate professor, marketing at MICA, seconds the report. The institute provides specialisation in marketing research and analytics. “During recruitment time, we see maximum placements happening in Bangalore. Another city that is fast coming up is Hyderabad,” says Trivedi.

Sectors and salaries

Analytics professionals have an array of options. They can chose to be in a KPO, or an IT company or join any niche analytics firm. Additionally, some companies have in-house analytics teams as well.

A report by Jigsaw Academy says with an average salary of Rs5.6 lakh at entry levels, KPOs are the most financially attractive option to enter analytics. Financial benefits aside, KPOs offer some other options that make them a good place to work. KPOs offer analytics services to a wide variety of clients. This means they are able to offer their employees opportunities to work on different clients and different industries.

In the IT industry, an analytics professional can earn Rs5.5 lakh at the entry level, which can go upto Rs23 lakh at director level. When it comes to city-wise breakup of salaries, Mumbai leads the pack at Rs11.49 lakh, followed by Bangalore at Rs11.34 lakh.

“If we adjust these numbers for the cost of living, Bangalore will actually have the highest salaries,” says Vohra.

Galileo once said that “All truths are easy to understand once they are discovered; the point is to discover them.” Great marketers uncover those obvious, but unexpected truths to win consumers hearts and sell products.

Marketing is, after all, the art and science of discovering what people want and offering it to them.  The right product, combined with the right message, can achieve wonders.  It can transform even a mediocre enterprise into a dominant market leader.

Historically, marketers operated by instinct. They would see an opening and pounce.  Over the years, numbers crept in—quantitative research, focus groups and analysis became basic tools of the trade. The next great shift, however, will be automation and it will require not just new skills, but a new perspective and that will be a much harder bridge to cross.

The Magic Of Vision

Very few brands have enjoyed the success of Marlboro, which has been a marketing icon for generations.  But it didn’t start out that way.  In fact, at one time it was struggling brand.  As a filtered cigarette, it was considered to be a mild tasting product for women and sales were sluggish.

Yet where others saw failure, advertising genius Leo Burnett saw an opportunity.  Health concerns were making filtered cigarettes popular with men and, because of its small consumer base, Marlboro was ripe for a radical repositioning.  With that insight, the Marlboro Man was born.

Leo Burnett came up with the most masculine image that anyone could think of—a cowboy.  Featuring real ranch hands in his ads, he portrayed Marlboro as a product that rough hewn men would smoke, perched atop a horse, for a bit of respite from a long day tending to their herds.  “Welcome to Marlboro Country” the headline read.

That blend of insight and image became Leo Burnett’s hallmark.  By 1972, Marlboro was the #1 selling cigarette.  Campaigns for other brands, like the Pillsbury Dough Boy, the Jolly Green Giant and Tony the Tiger found similar success.  In advertising’s golden age, a marketer with good instincts could move a brand from worst to first.

The Road To New Coke

Alas, the world never stays so simple.  As the consumer culture matured, markets fragmented and marketers soon learned that they didn’t need to make everybody happy, but could win sales by targeting a particular segment of the market.  In 1984, Pepsi launched its enormously successful “Choice of a New Generation” campaign.

Coke, as the market leader, felt it had to do something.  Pepsi had already made inroads with its long-running “Take The Pepsi Challenge” effort and this new generational attack was putting it within striking distance.  No longer dominant in the category, Coke felt it had to do something radical.

So it began to develop a new version of its legendary formula, one that was sweeter and would appeal to the younger demographic that favored Pepsi.  After extensive testing which involved over 200,000 consumers, it looked like they had a winner.  All the data lined up: The taste tests, the marketing research and the sales numbers.

Of course, we all know what happened next.  New Coke was probably the biggest disaster in the history of marketing.  Many might have liked the taste of the samples in tests, but nobody wanted their icon taken from them.  In just a few short months, the company beat a hasty retreat and the old formula was reintroduced.

It’s hard to imagine Leo Burnett making the same mistake.  He might not have had reams of data or armies of statisticians, but he had what’s known as “marketing guts.”  He was in tune with the consumer zeitgeist.  He looked beyond their wants and needs and communed with their spirits.

The Rise Of The Planners

Marketers continued to adapt.  They would no longer be beholden to doing things by the numbers, nor would they operate by instinct, but would form a hybrid of highly trained planners that could do both.  They would still rigorously research the consumer, but to enhance creativity rather than stifle it.

Demographic groups, such as “women 18-34” or “professional men”, gave way to psychographic groups like “mainstreamers” and “young aspirers.”  They would no longer look at statistics, but try to understand behaviors.  After all, how somebody feels about herself and her life has a lot more to do with what she will buy than her age or profession.

Planners soon became immensely powerful within marketing organizations, but before long it became clear that many were falling prey to confirmation bias.  In effect, they were using research much like a drunk uses a lamppost, for support rather than for illumination.

In a sense, they can hardly be blamed.  Humans are miserably bad at information processing.  So, we naturally learn to filter and focus on the information that seems important.  And what could be more important than data that confirms what we already know to be true in our heart?

Simulation Marketing

There is a new approach evolving that I call simulation marketing in which machines, rather humans, analyze data.  After all, computers can process far more information than we can and new machine learning techniques allow them to recognize patterns in the data that can alert us to valuable opportunities.

One company, Fractal Analytics, provides a solution they call consumer genomics, which can combine a variety of different sources, including store level sales data, social media and competitive actions to come up with new segments that marketers would have never thought of.

Concentric uses an altogether different approach called agent based modeling in which they simulate not consumers, but entire markets.  This allows marketers to come up with “what if” scenarios and test them virtually rather than in-market.  Its models have proven to be over 90% accurate.

So the key performance attribute for marketers of the future will not be insight, but awareness, adaptation and a willingness to experiment.  If Leo Burnett were alive today, he would use algorithms to plow through terabytes of data, create a series of Marlboro Men, and A/B test them to determine the most effective version for each customer segment.

Time To Welcome Our New Computer Overlords

Up till now the story seems like a happy one.  A continuous progression from folk wisdom to technologically driven insight.  The old, disheveled, merchants of the past have given way to black-clad, data-driven, socially-networked growth hackers.  The past is gone, the dinosaurs are dead and tribal superstitions no longer need to be abided.

Yet, the story is not so happy.  As MIT’s Andrew McAfee points out in a recent HBR post, the challenge of big datais that we need to learn to trust our judgment less, to relinquish the “marketing guts” that we so admire in marketing legends like Leo Burnett—and like to think we see in ourselves—and learn to collaborate with machines.

In fact, he points to research which shows that rather than trying to use data to inform our judgment, we would do much better by putting our energy into building better models, but taking their answers at face value.  Applying our own subjective judgment after the fact is much more likely to worsen results than it is to improve them.

And that’s what many marketers will be unable to accept.  We like thinking of ourselves as experts with an intuitive feel for the marketplace.  We like being respected for our judgments and our special gift of insight.  In the end, we will have to decide what’s more important: our intrinsic feeling of self-worth or winning in the marketplace.

I’m afraid that for many that will be an easy choice.  It is far more glamorous to be a failed romantic than to be a successful practitioner.