“By 2020, only one-third of sales organizations will have embraced predictive and robotic technologies that guide and automate actions to achieve sales goals.”
– Mark Smith, CEO & Chief Research Officer, Ventana Research
Fueled by new levels of sophistication, processing power, and AI solutions in the digital landscape, it is time for enterprise executives and sales leaders to fully embrace AI to empower their sales organizations. Innovations in AI, robotics, and chatbots are expanding at an accelerated rate. As a result, there are more capabilities and solutions available today than the ability of sales organizations to adapt. Moreover, many sales organizations appear to be lagging in their efforts to utilize AI as an enabler for digital sales transformation.
Sales drives the engine of growth and represents your front line in creating and sustaining lasting customer relationships. So why are only one-third of sales organizations adopting predictive analytics, AI, and robotic technologies? Sales organizations also represent the largest and most expensive labor pool in most organizations so any gains in productivity or effectiveness will have significant impact to business performance, shareholder value, and enhance customer experience.
This white paper provides a roadmap you can utilize to embrace AI in your digital sales transformation and realize the full potential of your most powerful employees – front line sales! There are six critical steps in the journey:
- Understand your customer journey and engagement model
- Activate your data
- Move from many channels to omni-channel
- Embrace predictive analytics
- Stop “pulling data” and start “pushing” information and insights to your sellers
- Reduce complexity – Utilize AI and digital solutions to simplify sales processes
The following discussion and examples are focused on B2B enterprises and sales organizations. There are many more use cases which can be discussed for planning and optimizing your sales organization and the design of your sales force (both “direct” and “indirect”). However, the focus of this white paper is empowering your salespeople to be more productive and effective using AI and emerging digital technology solutions. It may also be useful to review the Fractal Analytics’ white paper titled “Accelerating AI enterprise-wide to achieve a competitive edge” to learn more about key ingredients and strategies for successfully implementing AI and analytics in enterprises.
Many of these suggestions are not new. However, what is new is the rapid growth in innovation and available solutions, and an increasingly competitive landscape that is adopting AI solutions for sales at varying rates of speed and success. Enterprise executives and sales leaders who employ these best practices will be at a competitive advantage over those who do not or cannot. Perhaps easier said than done, but there are significant rewards for enterprises that lead the way in driving digital sales transformation through AI.
CUSTOMER JOURNEY AND ENGAGEMENT MODEL
According to Forrester1, “Modern B2B buyers want to buy from modern sellers; they want to interact fluidly across channels, and when doing so, they expect to have a consistent brand and engagement experience. B2B companies that fully embrace this journey will thrive, and those that delay risk disintermediation from competitors and/or buyers themselves.”
There are obviously massive implications with this trend, but one clear message is that there is more data than ever before being generated by these digital buyers through an ever-increasing number of channels. The “art” of selling is being augmented, if not replaced by the “science” of selling.
Therefore, whether you are early in your digital sales transformation, integrating a new acquisition, or establishing standardized sales processes, the critical first step is to understand and document the customer journey across these various channels and touchpoints. Identify the key touchpoints, events, or actions that shape or influence decisions, perceptions, actions, and customer experience. From there, you can map your sales engagement process to understand where you can make the greatest impact from a sales perspective. Likewise, this will help you identify where you also have the most significant gaps.
Once you’ve identified these critical events, you can then map your data sources and begin to determine where, and how, you can employ AI and new technology to deliver the information your sales teams need, and when they need it. You may also determine where you may have glaring gaps in your data models and information flow, or where you rely on excessively manual processes and corporate knowledge. Once understood and documented, you can then prioritize where you need to invest in process redesign, tools, or capabilities to remedy the gaps.
1 Mary Shea, “Sales Digital Transformation: It’s Now or Never!”, Forrester, January 8, 2018.
Using a simplified and generic customer journey map for B2B customers helps to illustrate the point. By utilizing a customer journey map, you can identify what events or actions your sales team should either be aware of, influence, or drive to win business and satisfy customers. It will also help to expose where you have excessive complexity, where your salespeople require better support and collaboration by specialists, support, sales operations, or with external partners, including channel partners. The exercise will also help you determine your future- state model to improve the end-to-end process to remove complexity and make it easier for your salespeople to sell and to satisfy your customers.
Finally, the exercise will also provide you with critical information to help you identify where you may be able to invest in new applications, tools, or redesign your legacy sales processes to utilize data-driven or AI-enabled sales processes. By leveraging AI-driven sales process software and tools, you can redesign, automate, and standardize how you perform activity management, opportunity and deal management, prioritization, pipeline prediction, and sales forecasting. By mapping your customer journey, you can also enable the implementation of more comprehensive quote-to-cash (QTC) and configure-price-quote (CPQ) processes. In all these cases, you will be in a better position to leverage advance analytics, including machine learning, to compare actual vs. desired activities or actions and make appropriate recommendations and drive the right behavior throughout the sales engagement process.
FIGURE 1. B2B Customers Illustrative Customer Journey Map
Mapping the complete customer journey through all channels-digital and non- digital-is obviously an extensive and lengthy exercise. However, even if you do some basic mapping and understand critical events or activities along the journey, you can use these to begin charting your digital sales transformation. In other words, you can take a “crawl, walk, run” approach vs. “boiling the ocean” to get started. This will also allow you to begin to design or redesign your workflow around digitally enabled processes and tools which can improve customer experience, sales productivity, and improve win rates or conversions. As an example, in analyzing your online customer engagement or “clicks”, you can apply advanced data engineering and anomaly detection to identify unique customer journeys, determine “drop-offs” in the journey, and establish improved or automated processes to reduce drop-offs and improve conversion rates.
Once you have identified your key processes and touchpoints, it is likely that the data rests in many various sources and in many different forms and formats (e.g., structured, unstructured). As a result, this requires a salesperson to spend valuable time aggregating this information from these various systems and tools to prepare for or meet new or existing clients, address challenges or questions, and update sales management on progress vs. plans and objectives. This administrative work is obviously unproductive and prevents the salesperson from spending more time in the field or with customers. It can also force the salesperson to determine the appropriate or next best course of action without the benefit of predictive analytics to help guide the process or the decision. Predictive analytics can provide recommendations or guidance based on analysis of historical events or activities that can yield better results than traditional methods or gut instinct. Augmenting the “art” of selling with “science” can make it easier for the salesperson and can generate more effective and productive outcomes. Taking this a step further, if you can complement the use of predictive analytics with technology solutions which can aggregate and simplify the delivery of this information to sales, then you can eliminate these administrative tasks and free up your sales teams to spend more time where it matters-with your customers.
ACTIVATE YOUR DATA
Once you’ve defined your customer journey and critical touchpoints, the next obvious question is “where is the data, and what do we need”? Data is the “fuel” for AI and empowering your sales teams to successfully manage the customer journey. Delivering effective AI-driven recommendations and results requires readily-available clean and trusted data from your source systems.
Starting with your CRM, begin to identify where source data resides in your organization. You will quickly find out that only a portion of the data you need resides in your CRM. You may also need to access data in your ERP system, support systems, customer success, content management, CPQ (configure- price-quote), and other transactional systems. A significant amount of data will reside in your data warehouse, data lake, or the data may reside in “shadow IT” data sources, which are the most challenging of all. Regardless of where the data resides, you need to map each of these data sources to the critical events you defined in the customer journey and determine how easy, or difficult, it will be to integrate, access, cleanse, transform, and deliver the data you need to be successful.
FIGURE 2. Enterprise Date Strategy & Governance Program
As you identify these various sources and map them to the critical touchpoints you’ve defined from your customer journey work, you then need to determine the quality of the data. You must understand if you have consistent, well-defined, and agreed-upon data definitions and standards. Based on these definitions, how clean is your customer data? How are inputs created, by whom, and how frequently? What are the chances you may clean your data only to find that errors creep back into your data due to lack of controls, data stewardship, or data governance? Frequently overlooked, an ongoing data governance framework is vital to ensure that once you clean your data, it remains accurate and reliable.
AI can also provide a very useful benefit here as well. It can improve how you capture customer data and information (e.g., scanning business cards vs. manual data entry, or ingesting LinkedIn data). AI can also help rationalize and cleanse customer data records like names, job titles, addresses, phone numbers, company names, etc.
Since the foundation for much of your customer data is your CRM system, it is imperative to drive adoption and use of your CRM systems to ensure timely, accurate, and reliable data. Historically, this has been a significant challenge for many sales organizations due to many factors. Updating CRM systems often requires significant administrative time to modify or add records, update opportunities, etc. There is also often an unwillingness or reluctance to provide accurate opportunity or pipeline data due to quota or compensation concerns, complexity, or other factors. As a result, legacy CRM solutions can present a drain on productivity due to the manual data entry required to provide accurate and timely information for sales management. There is also some reluctance to provide too much information, which can then be “micro-managed” by sales leadership. Nevertheless, the more accurate your underlying sales and customer data, the better and easier it will be to make decisions for your sales organization and your company. Given these challenges, how can you improve the accuracy of your customer data?
There are many tactics you can utilize to drive adoption, use, and compliance. Traditionally, these methods work with varying degrees of success, but industry-wide CRM systems remain plagued by low adoption, infrequent usage, complexity, distrust, and erroneous or missing data.
Through innovations in AI, a new class of products and solutions has recently emerged to address this challenge. These solutions are generally described as “Virtual Digital Sales Assistants (VDSAs)” or “Intelligent Assistants”. VDSAs combine AI algorithms with touch, talk, or text interfaces with leading CRM and Salesforce Automation (SFA) solutions, marketing content/automation systems, customer support, legacy third-party databases, and other data sources. VDSAs simplify how sales can interact with these source systems to provide updates on contacts, leads, opportunities, and gather critical information at pre-determined times or on demand. Ultimately, VDSAs remove “friction” from current sales processes to allow sales to be more productive and effective. When coupled with predictive analytics or AI-delivered insights as described elsewhere in this white paper, it can become an even more powerful element of your digital sales transformation.
Not only can VDSAs simplify and streamline data integration for sales and help reduce administrative time and friction, there is another very strong value proposition. By making sales’ jobs easier and delivering information and insights to sales to help them perform their jobs better, there is a strong incentive by sales to use these solutions. As they do so, the data in the underlying CRM will be better, more accurate, and timelier. This means that all downstream data- driven processes, including forecasting, supply chain demand planning, and predictive analytics, will also become more accurate and trusted. An example from GE illustrates this point. At GE, “sales, technology, and finance executives have been collaborating on an app to reduce time that sellers spend inputting and addressing forecast questions. This app allows sellers to enter information on the fly, through text and voice solutions, and has eliminated multiple rounds and levels of management inspection of the numbers. Early pilots point to significant ROI as sellers spend more time on customer-facing selling activities.”2
To conclude, as you aggregate and deliver clean, reliable customer data through your CRM and source systems, you can then apply AI predictive models and algorithms to help your sales teams make sense of the mountain of information available to them. Ultimately, this will enable you to make it easier for your sellers to succeed in their core mission, which is to serve your customers and drive growth for your business.
MANY CHANNELS TO OMNI-CHANNEL
According to Forrester,3 “the explosion of content-rich B2B marketing and commerce sites has made buyer research easier than ever. This digital maturation is in stark contrast with the experience buyers receive working with human sellers. More than 90% of B2B buyers prefer to make their purchases online rather than interact with a salesperson, yet highly considered purchases often still require seller involvement. AI in human-assisted sales needs to match the buyer experience of self-service if sellers hope to stand a chance in long sales cycles.”
Buyers today are more educated and connected than ever before, and accustomed to a digital experience in their personal lives. As a result, their expectations for achieving the same experience in their working lives has increased dramatically. When considering new products or services, they will engage multiple channels to explore and learn about products and services, frequently doing so before they ever engage a salesperson. They will go online and read marketing literature, white papers, customer testimonials, watch videos or demos, or perhaps even download sample or trial software. As a result, it is essential to connect all these various channels to ensure not only a consistent experience for the customer, but also arm your sales team with the information they need to be successful.
2Mary Shea and Jacob Milender, “B2B Sales Force Digital Transformation: Three Global Leaders Share Best Practices”, Forrester, July 26, 2017.
3John Bruno, “How AI Will Transform Sales”, Forrester, December 18, 2017.
This means that coordinating and collaborating across organizations, partnerships, and through various channels is more critical than ever. It’s no longer adequate to have your marketing organization collect and distribute recommendations and actions through email, powerpoint, or other standalone data sources to your inside or outside sales teams. Data-driven information must be available and delivered as “real-time” as possible to exploit opportunities before they are lost. A recent article by Boston Consulting Group also stated that “the big problem for most companies is that marketing and sales operate in their own silos, each function having its own organization processes, incentives, cultures, and in many cases, objectives”. Furthermore, they state that “companies that do not cooperate closely across their organizations may suffer because of poorly executed customer buying journeys, misaligned objectives, misallocated resources, and poor team morale”, resulting in the potential for “customer alienation, loss of market share, and slowed or no growth”.4 As a result, it is imperative to close the historical gap between marketing and sales through integration of data and insights into common language, actions, and insights delivered through a single, integrated platform. Many CRM systems attempt to provide this connection, but all too often, marketing teams and sales ops or support teams deliver additional insights or recommendations through alternate means like email, chat, powerpoint, or excel. Frequently, these recommendations may not align due to underlying data differences, inconsistencies in definitions, KPIs, or analytical models. Ultimately, this requires the salesperson to consolidate and decide what course of action he or she needs to act on. This can exacerbate the administrative burden and reduce confidence in the predictive analytical models.
One excellent example of addressing this challenge is Cisco’s 2020 initiative5. Cisco’s sales and marketing teams partner on Customer360: a data-driven collaborative effort to better understand buyers and provide guidance and recommendations for sales to take appropriate action. By employing data engineering, predictive analytics, and “connecting” marketing and sales data and insights, it means the seller doesn’t have to look for the information and can focus on selling and their customers. Not only does this mean your reps are more effective, but you can also realize significant improvement in your sales cycle time, conversion, win rates, and ultimately revenue.
It’s also imperative to be aware of and align your efforts between your “direct” and “indirect” sales teams or your channel partners. It’s no longer acceptable for the sophisticated B2B buyer to have discrete engagements by both sales organizations, particularly if unaware of the potential “conflict”, or worse, uninformed and openly competitive. It’s equally important to collaborate and align with your support organization (i.e., are there any open service tickets or escalations?) and your specialist teams with deeper knowledge of your product and services capabilities and features. Your salespeople need to know who is calling on their customer, what have they bought, and what is their experience with your company, your products, and services? All of this requires knowledge and information from various sources and organizations-both inside and outside of the company.
4Phillip Andersen, Robert Archacki, Basir Mustaghni, Roger Premo, “Building an Integrated Marketing and Sales Engine for B2B”, The Boston Consulting Group, June 2018.
5Mary Shea and Jacob Milender, “B2B Sales Force Digital Transformation: Three Global Leaders Share Best Practices”, Forrester, July 26, 2017.
“We take marketing sentiment data, pair it with sales data, and create insights that tie to opportunities and actions that reps can take. This collaborative effort fosters higher- quality interactions with customers and better rep prioritization of selling activities.”
– Forrester describing Cisco’s 2020 initiative
To meet the need to share and exchange information and ideas across these various channels, there are many new collaboration software and solutions in the marketplace. These solutions are increasingly powered by AI and address various use cases such as team collaboration and communication, content or document coordination, edit and approvals. Collaboration tools and applications can help sales work seamlessly with other teams and customers to accelerate deals and provide a superior customer experience. If designed and implemented with the salesperson at the center, you can facilitate rapid collaboration by diverse teams to enhance communications, enable the use of file sharing and document annotation capabilities (e.g., proposal content review and coordination), support live meeting capabilities through video and audio, and incorporate electronic signature capabilities for faster approvals of proposals and contract documents. Ultimately, by improving collaboration through these tools, you also remove friction and administrative burden from your selling process.
“Sales force digital transformation requires new and more creative ways of collaborating.”
EMBRACE PREDICTIVE ANALYTICS
It’s an unprecedented era for B2B sales organizations. Buyers and buying patterns are changing. Traditional sales models are being augmented if not supplanted by digital channels and expanded routes-to-market. There is a staggering amount of data, computing power, and technology solutions available to sales. However, if left unmanaged, it merely adds to the burden a salesperson has, which is to navigate the growing amount of data and information being sent their way. Sophistication in advanced analytics and machine learning provides the ability to augment traditional sales “instincts” with data-driven information at a scale never possible before. Due to these trends, B2B sales is also rapidly evolving from an “art” to a “science”. To many, this is not an entirely comfortable conclusion given that “science” does not completely replace human knowledge, intuition, experience, and “gut instinct”. However, there is so much data and information available for the salesperson today, it is not humanly possible to make sense of it all without the benefit of big data, analytics, and AI, particularly if utilized effectively. Like the “needle in the haystack” analogy, it is critical to discern what is important and insightful from the mountains of available data. This is where predictive analytics can make a significant impact. Furthermore, according to Forrester in a recent Forbes article6 “companies that opted to blend AI with human insight report improved satisfaction on the part of sales reps (69%), as well as heightened operational efficiency (68%), agent productivity (66%), and customer satisfaction (61%).
Therefore, embracing advanced and predictive analytics can benefit sales organizations in many ways. From the massive amounts of data available to enterprises today, advanced and predictive analytics can rapidly evaluate the available data faster and better than a human is able to. By employing predictive analytics, you can provide recommendations on campaigns to maximize impact, accounts, customers, or opportunities to prioritize. You can also provide guidance on accounts with a higher propensity to win or buy, help optimize product, and pricing strategies, next product to buy, and more.
6Falon Fatemi, “4 Ways AI is Transforming Sales Organizations”, Forbes, February 28, 2018.
The table below illustrates some predictive analytics use cases that can be applied against each step of the customer journey to provide guidance and recommendations to the salesperson throughout the process. Moreover, as you implement these predictive analytics solutions and apply machine learning, the output will become more comprehensive, refined, and accurate. A fundamental principle of AI and machine learning is the ability to learn, improve, and increase in accuracy over time through repetition and feedback. As you compare actual results vs. predicted outcomes through machine learning processes, you can refine your algorithms. Therefore, as you implement, explore, test, and learn, the software algorithms will improve as well. This means the entire ecosystem gets smarter—sales reps, sales managers, executives, and those who rely on sales to drive (and predict) the engine of growth and customer experience for the enterprise. It also means the outcomes and recommendations gain trust as they improve in accuracy; and based on Forrester’s findings, your sales reps will also be more satisfied, productive, and efficient.
FIGURE 3. Sampling of Predictive Analytic Use Cases
As you implement these various use cases, you will find that not only can you provide recommendations or guidance based on the algorithms, but you may even be able to introduce proactive measures. For example, you could alert your support teams to act when you observe an event, trend, or pattern that requires attention or action while notifying your salesperson of a potential issue. As an illustration, if you have a shipment that may be delayed, you could notify your sales operations and supply chain organizations to proactively address or mitigate the issue to prevent or minimize the impact. Your salesperson would be informed and can help manage the event or issue with your customer. This is far better than not knowing, missing the shipment, and finding out about the issue through customer escalation. It could be as simple as a quote is expiring, or an opportunity at 90% in your pipeline is “stalled”. If you measure and track customer sentiment, satisfaction, or NPS, you could also proactively alert your sales rep that one of their accounts is at risk of leaving and recommend actions. If your account is “healthy” and/or you are expanding your presence in the account, you could also identify opportunities for cross- selling or up-selling complementary products and services or proactively capture renewal opportunities.
Depending on your business priorities or challenges, there are an infinite number of use cases, which can be designed and implemented to improve your sales execution and customer satisfaction. As you employ these predictive analytics, learn and improve the underlying algorithms, you can improve your ability to proactively provide recommendations or guidance to your sales teams.
Proactively suggesting actions avoids not only missing critical events or opportunities to win business, expand relationships, or retain accounts, but it also moves you away from the reactive and time-draining cycles of managers, support staff, or executives asking what happened along with the ensuing emails, phone calls, and escalations. Even worse is it means a salesperson will be forced to spend more time dealing with internal issues and questions as opposed to spending time with customers. You may even go beyond data science and apply “behavioral science” in order to understand the drivers of behavior (customer and/or salespeople) and design levers which “nudge” or encourage the desired action or outcome.
To improve or accelerate the use of predictive analytics, you may also consider how you organize your analytics and data scientists (internal, or external partner) to support your sales organization. While there are benefits to a shared service function like analytics and data science, you will get significantly more impact, relevance, and buy-in from sales if you align your talent to the sales organization they support. This does not suggest you utilize a fragmented organizational model for your analytics talent. They may (and likely should) still report into a central function. However, they should be embedded into the business they support. In this manner, you can benefit from both the advancement in analytics and AI knowledge and skills developed within the central function while learning more about the business processes and challenges. The more domain knowledge the analytics professional or data scientist has, the more relevant and accurate the predictive analytical models. From there, it is imperative to experiment, test, and modify the original hypothesis with actual results and continually refine the algorithms.
As you advance in your knowledge, use, and trust with data-driven recommendations, it will only be natural to see the increase in use of AI, chatbots, and robotics to automate repeatable activities or transactions, freeing up your high-cost, and highly-skilled, sales talent to focus on more complex engagements and opportunities.
From a sales leadership perspective, these predictive analytical models should be used in conjunction with “gut instinct”, not instead of. The more accurate and useful the prediction, the lower the variability in actual results vs. predicted results. This is a significant improvement over purely “gut instinct” or even worse, “gaming”. As discussed above, algorithms and models will mature and improve. As they do, sales and sales leadership can rely even more on these data- driven recommendations. This also means sales managers and sales leaders can provide more effective coaching and training based as much on facts and data as behaviors and acquiring knowledge. The emergence of “behavioral sciences” to complement “data sciences” may provide additional benefit as the field matures and more use cases become evident. By applying behavioral science to determine the way decisions are made (by sellers and buyers), identify optimal sales candidates, assist in sales coaching, sales communication and goal setting, and improve sales processes to encourage or “nudge” the appropriate behavior, there is great promise in what behavioral sciences can yield.
As discussed earlier in this white paper, it is essential to have good, clean, and reliable data from trusted data sources for these predictive models to work.
PUSH INFORMATION AND INSIGHTS TO YOUR SELLERS
Once you’ve defined the critical events in your customer lifecycle, the single “source of truth” for the data, and have begun utilizing predictive analytics to provide guidance to your salespeople, what is the best way to deliver that information to your front-line sales teams when they need it?
Traditionally, it was up to the sales individual to access multiple data sources to plan, manage, and report on their business and performance vs. plans and quotas. In many enterprises, sales planning teams, sales operations, marketing, or other organizations are sending either emails, texts, tasks, or actions in your CRM or using any available means to communicate a promotion, new offers, events, or recommendations for your sales teams, ultimately overwhelming them with information.
In addition, sales management and executive leadership want to be kept informed. They request status updates through email, chat, text, or most critically – the CRM. As a result, the salesperson would access their CRM system to enter contact data, account information, update opportunities, enter trip reports, and keep their management updated on all assigned accounts. The salesperson may need to create reports, charts, or slides for management review. While this helps keep management informed and allows them to keep track of appropriate actions and activity, it’s very time consuming for the salesperson.
If they are geographically or territory-assigned salespeople, they may need to access multiple data sources to plan their customer visits. They may use D&B, Hoovers, Aberdeen, and LinkedIn to gather information on the customer and account information or gather some competitive intelligence. They may access their CRM and other internal data sources to understand buying patterns, product history, outstanding quotes, and any existing relationships. They might use a mapping program like Google Maps to efficiently plan their route.
These examples highlight the fact that the salesperson must access disparate tools, applications, or data sources and aggregate the information in a manner that is relevant and useful for them to do their jobs. Perhaps work like this is being performed by your inside sales, sales support, or sales operations organization. However, all this work requires manual effort and can divert the salesperson from what they should be doing—spending more time with customers and less time doing administrative or data entry work. Moreover, most modern business intelligence (BI) tools and applications used to gather this information often requires a lot of training or require the end user or seller to drill into raw data, create pivots, or query databases, which contributes to the challenge in how you effectively deliver information to sales.
Fortunately, there are emerging platforms to make it easier for sellers (or anyone for that matter) to acquire this information. By using GUI search tools similar to Google, BI and analytics providers are beginning to make it easier to work with BI and analytics applications. Therefore, instead of asking your end users or sellers to navigate dense databases to search for information, they can query these new applications through search or voice commands and obtain the information they need. In return, they will receive real-time, contextual answers to their questions without having to spend valuable time mining databases. Behind the scenes, these search solutions employ AI technologies like NLP, machine learning, and chatbots to query, ingest, and deliver information to the end user or seller in an intuitive, relevant, and contextual manner. While GUI and cognitive search engines simplify how your end users or sellers can acquire valuable information or insights, it is even better if you can proactively communicate or push this information to your sellers, so they don’t have to look for it.
To further advance the thought, what if you not only provided data and insights to sales when they need it, but relied on tools and processes to remind or “nudge” them to act? For example, you just left a customer meeting, what if your sales application asked you to document your meeting, provide a trip report, and enter all of this into your CRM seamlessly?
Even better, what if you alert your salesperson before they enter the meeting that there is an update on pricing for an outstanding quote, expiring warranties, or software licenses that are up for renewal, or products which may be nearing “end of life”. Furthermore, by leveraging your predictive analytics engine, you can also provide recommendations on next best buying opportunity (e.g., if you are a B2B enterprise selling products and services, are there additional products, peripherals, accessories, or services that you can offer to complement a recent quote, or better, order?). What if the customer has experienced a recent product outage or is having troubles with your services organization? Obviously, it would be better to be armed with this information in advance to avoid surprises, better manage the customer experience, and take advantage of selling opportunities.
All these scenarios are possible (and many more) if you have defined your customer engagement and sales process, the underlying data sources, and leveraged your Analytics Center of Excellence or your analytics partner to develop and push these insights and recommendations to sales. As discussed in the next session, ideally you can do so through simple, intuitive mobile solutions.
UTILIZE AI AND DIGITAL SOLUTIONS TO SIMPLIFY SALES PROCESSES
The final piece of the transformation is delivering the data, information, and insights you have generated into an easy and intuitive interface for your sales organization-how they need it, when, and where. Mobile business intelligence, salesforce automation (SFA), or analytics solutions represent an improvement over legacy PC or browser-based applications. Mobile SFA solutions provide an easier way for sales people to interact with their CRM systems through mobile devices. While these mobile solutions represent an advancement in making it easier for sales to utilize technology to aid them in their work processes, they fall short of truly empowering the salesperson and solving the dilemma of poor adoption, use, and accuracy of data in CRM systems. As discussed earlier in this white paper, contributing to the challenge is the fact that critical data is typically stored in different source systems, data warehouses, or data lakes requiring the salesperson to aggregate the information.
As described by The Boston Consulting Group7, “while companies have made massive investments in technology, they haven’t focused on true integration – that is, integrating tools with the way people actually work”. The resulting paradox they claim is the “complexity trap” that most companies face. They further assert that “digital technologies and methods are supremely flexible. They enable businesses, end users, and IT departments to design applications and user journeys that are “just right” and adapt processes accordingly-in the end, reducing or eliminating this complexity trap. Naturally, this is also the dilemma of the average salesperson who is asked to navigate internal complexities to do their jobs.
Yet another challenge is that sales reps notoriously delay putting a deal opportunity into a CRM because they don’t want sales managers learning about it and constantly asking what they are doing to move the opportunity further in the sales cycle. In addition, it took time to translate notes or recall into CRM databases, which means sales reps will often wait until they work from home on Friday or prepare on Sunday evening for the week ahead. This means the data may be inaccurate, stale, or forgotten. While this is useful to provide information to sales while they may be traveling or visiting customers, it falls far short in empowering your sales teams with the data and insights they need to be successful in performing their work, generating sales, and spending time with clients.
As suggested by Gartner’s Tad Travis8, this challenge can be addressed by employing a Virtual Digital Sales Assistant “VDSA” solution for your sales organization, particularly when utilized in conjunction with predictive analytics use cases described in this white paper. VDSAs can integrate data across the CRM, support systems, content management, legacy data sources, and external data sources like LinkedIn, email, calendar, mapping, and other sources to deliver frictionless and contextual insights. Many of these VDSA solutions utilize Natural Language Processing (NLP) and chatbots to enable your sellers to interact with these source systems and your CRM through voice command similar to how you may use Alexa, Siri, Cortana, or similar applications in your personal life.
7Vanessa Lyon and Anne-Francois Ruand, “Take Control of Your Digital Future”, The Boston Consulting Group, 2018.
8Tad Travis, “2016 Recap: The Third Wave of Sales Automation is Here”, Gartner, January 3, 2017.
“While companies have made massive investments in technology, they haven’t focused on true integration – that is, integrating tools with the way people actually work.”7
– Vanessa Lyon andAnne-Francois Ruand, The Boston Consulting Group
As a result, you could have an AI-powered sales digital assistant proactively deliver information to you in advance of a meeting that you’ve scheduled, or your sellers could ask for information through voice command. The VDSA can then deliver information on the account, contact, prior sales, open opportunities, open service tickets, partners or competitors who may be engaged, etc. All of this means your salesperson is significantly more knowledgeable going into a customer meeting, and with significantly less effort than trying to gather this information on their own. Moreover, you don’t need to employ large teams of support staff to gather and generate information like this either—further freeing up your talent and operating budget for more value-add work and more time with customers. When your salesperson leaves the meeting, their personal digital sales assistant can “nudge” them to capture and document key findings, agreements, next steps, or actions. It can then automatically update your contacts, account information, or opportunity status in your CRM system, meaning the information is timely, accurate, and readily available to your supporting organizations in sales operations, support, supply chain planning, or even your financial forecasting team. Using The Boston Consulting Group’s analysis, this would also allow you to remove complexity from your sales processes, and free up your salespeople.
Ultimately, instead of salespeople being expensive data entry clerks, they can enjoy a seamless, intuitive AI-powered interface (“touch, talk, text”) with their CRM and various data sources, allowing them to focus on the work of selling. The seller becomes the center of the selling universe, not the CRM. Coupled with the use of predictive analytics along the customer journey to deliver insights and information to your salespeople when they need it, you will greatly empower the ability of your entire sales organization.
Your sales organizations can enjoy higher productivity, win rates, and faster sales cycles, while improving employee engagement and customer experience.
“VDSA will become the primary interface by which sales representatives manage their work. When combined with artificial intelligence systems, VDSA will become the cognitive system that removes much of the inefficiencies common in B2B sales processes.”
– Tad Travis Research Director Gartner
CLOSING AND CALL TO ACTION
Unfortunately, there are no complete, end-to-end solutions which adequately address all of the challenges and inefficiencies that exist within legacy sales operations. Naturally, there will be convergence and consolidation as vendors and service providers mature in their application of AI solutions to solve these sales process challenges.
The good news is many of these emerging applications, tools, and solutions are focused on simplifying and improving the seller’s (and customer’s) experience through the application of advanced analytics and AI. This is a vast improvement over legacy applications that largely focused on the collection of data and information for management, oversight, and inspection of sales activities.
None of these steps are easy and may be too large of a leap for many firms. However, these strategies are pivotal to success in driving digital sales transformation in today’s rapidly evolving, complex B2B selling environment. Therefore, the sooner executives and sales leaders adopt these strategies, or embark on a roadmap to do so, the more competitive they will be in the digital era.
- Define your customer journey and identify your critical touchpoints. Use this to determine how and when your salespeople should engage customers (your methodology or desired process) and where you can apply AI solutions to aid them.
- Map your touchpoints to your data and your data sources. Leverage open source or API solutions to accelerate access to necessary data sources.
- Embrace all channels-online, offline, direct, indirect, support, and social media. Utilize collaboration tools to break down internal and external silos.
- Aggressively employ advanced and predictive analytics-adapt and modify algorithms to improve accuracy and confidence. As your organization learns what is possible, new methods will become apparent, including proactive, predictive, and prescriptive solutions.
- Take the burden of data entry and data consolidation away from your sellers. Streamline, integrate and “push” information and insights to your sellers when they need it.
- Utilize AI-powered digital solutions to deliver the insights and information in a simple, intuitive manner.
Analytics and AI are beginning to make a significant impact in enterprise sales organizations. Leaders in adopting AI to enhance their sales processes are already reaping the rewards of their investments.
As stated by Gartner in a Forbes article9, “30% of all B2B companies will employ AI to augment at least one of their primary sales processes by 2020. The most effective companies, though, will use AI to augment multiple parts of their sales processes”. There will be significant rewards for companies that do so. According to McKinsey10, “companies that have embraced what we call the ‘science of B2B sales’ have already started to pull ahead of their peers in terms of revenue growth (registering 2.3 times industry average revenue growth), profitability (3 to 5 percent additional return on sales) and shareholder value (8 percent higher total return to shareholders than the industry average).”
9Falon Fatemi, “4 Ways AI is Transforming Sales Organizations”, Forbes, February 28, 2018.
10Tim Colter, Mingyu Guan, Mitra Mahdavian, Sohail Razzaq, Jeremy Schneider, “What the future science of B2B sales growth looks like”, McKinsey&Company, January, 2018.
Advisory Board Member, Fractal Analytics
Doug provides advisory services to help advance Fractal Analytics’ capabilities, services, and offerings to empower enterprise clients. Doug leverages his knowledge and experience to help Fractal Analytics and clients accelerate the use, adoption, and value creation with data, analytics, and AI in the enterprise. Previously, Doug held various leadership roles at Dell for more than 19 years. In his most recent role at Dell, he was responsible for providing global data, reporting, and analytics services to support Dell’s sales, marketing, finance, services, e-commerce, and operations business units. He was also responsible for leading a transformation strategy for improving the use of data, BI, and analytics across the company to enhance decision-making. Doug also led a digital sales transformation for Dell’s global sales operations by partnering with IT to create a big data platform for sales, enabling the use of enterprise- wide KPIs and BI solutions. He also led the creation and implementation of predictive analytics forecasting solutions for the global sales organization.