Accelerate business value with Decision Intelligence & ModelOps
Accelerate business value with Decision Intelligence & ModelOps
Fractal is looking beyond carbon neutrality to create a positive impact on the planet
Behavioral science, backed by artificial intelligence, can help connect with consumers
Enabling US$ 10M in potential savings for a Fortune 500 insurer
Bringing together the dimensions of Agility, Intelligence & Trust to build on the foundational pillars of RTM.
Optimizing for the dimensions of comprehensive prediction of lifetime value.
A large telecommunication provider saw a continuous decline in the digital conversion rate. With Fractal’s digital optimization platform AIDE (Automated Insights for Digital Evolution) it helped to get over $1Bn in CLV with reduced friction on the digital journey.
SVP IT, Health at RB -UK
An effective way to enable clients to respond decisively, effectively and with confidence is handling workflow through triage. The crux of customer request triage is sorting jobs into multiple levels of effort required. Our approach to organize and manage workflow is achieved by:
Data annotation
- Build an exhaustive list of intents that could include all customer queries and resolutions
- Group similar queries, requests, and tasks into categories
Model development
- Develop a mechanism to verify and validate the manual labeling and categorization of user intent
- Create a baseline model to understand the predictive power within the chat logs
Evaluation
- Analyze the model outputs to classify and prioritize intents to improve routing effectiveness as well handle the volume surge
Stay on top of global coronavirus cases with Fractal’s AI Augmented Analytics platform.
It is critical to build a strong engineering capability that can mine big data in real-time, operationalize the solution and implement it at scale. This session will focus on how to build and deliver the essential capabilities (both technical and otherwise) to enable AI and scale algorithms to power decisioning in the enterprise.
Ambo has helped drive Fractal’s growth and stature to become the No.1 global provider of analytics solutions to the CPG and Retail Industry. Additional charge of incubating the Hospitality practice.
Seasoned analytics professional and business leader; 20+ years split equally in the CPG industry with P&G and PepsiCo and in the analytics industry. Incubated analytics start-ups as well as led global analytics practices for publicly listed technology and operations companies building business globally – the Americas, Europe, Pacific Asia and Japan.
He has an MBA in Marketing, Faculty of Management Studies, University of Delhi. Bachelor of Engineering, Electronics & Computer Architecture, NIT, Allahabad.
Anurag leads the intersection of AI, engineering, behavioral sciences and design at Fractal. He co-founded FinalMile Consulting, the world’s first Behavioral Sciences based design and strategy firm. He comes with an extensive experience in driving behavior change in complex contexts like digital learning, safety, HIV, financial decisions and health choices across US, Africa, Europe and India. He has developed the proprietary Design process – Behavior Architecture – that takes into account the predominantly nonconscious nature of human decision making.
Anurag has a keen interest in the intersection of AI, Engineering, Behavioral Sciences and Design to solve the most complex decision problems at scale. He has previously worked at DeBeers, Wipro, Bharti Telecom and IMRB. He has done his Masters in Business Economics from Delhi University and completed his study on Continuous Learning: Model Thinking, Gamification and Cognitive Sciences.
Role
Leading the intersection of AI, Engineering, Behavioral Sciences and Design
Experience
- Co-founded Finalmile Consulting, the world’s first Behavioral Sciences based design and strategy firm
- Extensive experience in driving behavior change in complex contexts like Digital Learning, Safety, HIV, Financial Decisions and Health Choices across US, Africa, Europe and India.
- Developed the proprietary Design process – Behavior Architecture – that takes into account the predominantly nonconscious nature of human decision making.
- Previously worked at DeBeers, Wipro, Bharti Telecom and IMRB
Education
- Masters in Business Economics; Delhi University
- Continuous Learning: Model Thinking, Gamification and Cognitive Sciences
Analytics Interests
Intersection of AI, Engineering, Behavioral Sciences and Design to solve the most complex decision problems at scale.
Senior Vice President, Data & Analytics
Global Head of Product Growth & Analytics, YouTube Marketing
Face recognition can detect and identify faces in real time for use in security surveillance, attendance management, criminal identification, and more. It can track people and log their attendance, identify suspicious people, and reveal insights such as demographic information and facial expressions.
AI algorithms can automatically identify actions performed in a video to create better customer experiences. For example, using video to see how a customer interacts with a product can help manufacturers design better quality products.
Detecting and recognizing objects in images and videos can reveal insights that drive value. Face-recognition systems on real-time video feeds can assist with security surveillance or managing attendance. AI can also identify brands and logos to detect specific products in shopping aisles.
Can you detect and identify suspicious person in an image or video? Can you track people and log their attendance using their face data? Our deep learning based face recognition algorithm can detect and identify faces in real time which can be applied to many real life application including security surveillance, attendance management, criminal identification etc. The solution can be extended to extract sentiments (happy, sad, angry etc.) and demographic information (age, gender etc.) attendance management, criminal identification etc. The solution can be extended to extract sentiments (happy, sad, angry etc.) and demographic information (age, gender etc.)
Hosting on September 28-29 in Chicago at the Swissotel Chicago. This private, invitation-only event comprised senior executive analytics thought leaders among Fractal’s strategic clients, representing some of the world’s most valued brands representing, in aggregate, over $600 billion of annual revenue.Hosting on September 28-29 in Chicago at the Swissotel Chicago.
Fractal hosted our 4th annual Client Advisory Board event on September 28-29 in Chicago at the Swissotel Chicago. This private, invitation-only event comprised senior executive analytics thought leaders among Fractal’s strategic clients, representing some of the world’s most valued brands representing, in aggregate, over $600 billion of annual revenue.
The event content was comprised of a keynotes, a panel and workshops with the following themes:
- Shared key trends in analytics and artificial intelligence (AI) and how these approaches are transforming our world
- Revealed new applications of Deep Learning (form of AI) among Member industries to shift from gut decisions to algorithms that
drive faster, more accurate, and more effective decisions - Share their views on the key drivers of analytics success within Member enterprises including how to organize and which people
are needed, the importance of data acquisition, influencing the C-Suite to drive the analytics mandate, and influencing business
users to adopt analytics, by considering: - Members shared case studies on successful analytics implementations and lessons learned
- Discussed the key considerations in building an analytics roadmap and adoption plan
Our analysts used advanced tools and text-mining techniques such as pattern matching, phonetic matching and approximate string matching to develop an algorithm for de-duplicating records and identifying unique customers and households.
We recommended a scorecard-based approach for extending credit, to score retailers’ behavior on PAE business transactions.
Our analysts used advanced tools and text-mining techniques such as pattern matching, phonetic matching and approximate string matching to develop an algorithm for de-duplicating records and identifying unique customers and households.
Jerry leads Customer Genomics and Trial Run product offerings at Fractal Analytics.
Prior to this, he was the Chief Strategy Officer of Olenick & Associates, the largest US-Headquartered software quality firm, focused on building the entire go-to-market strategy. Prior to Olenick, he served as the CEO of EIC Solutions, a niche electronics company that was transformed into an Internet marketing expert. Jerry executed a management buyout of NSTL, Inc. and built that company into the world leader of testing and certification of mobile software applications, prior to selling to a strategic buyer. Prior to NSTL, he served as Manager, Corporate Development, Advanta, Corp. and as a Senior Consultant in the Financial Advisory Services group of Coopers & Lybrand.
He has an MBA, Villanova University, Villanova, PA; BS, Finance, Drexel University, Philadelphia, PA.
Natwar is the business head of Cuddle.ai – AI based personal analyst for business professionals.
Natwar led Fractal as part of its leadership team through multiple growth stages over the past decade. He led the data-science and technology agenda for Fractal & its clients, built & grew CPG practice amongst others, conceptualized & delivered analytics technologies like Concordia & Trial Run. He was instrumental in building a top-notch data-science team providing the right foundation for the growth. Natwar is now building Cuddle.ai – a wholly owned subsidiary of Fractal Analytics – to bring the power of AI to the world of BI by building the world’s most intelligent BI platform.
He has an MBA, Indian Institute of Management (IIM), Calcutta and B.Tech in Mechanical Engineering, Indian Institute of Technology (IIT), Bombay.
At Qure.ai, our mission is to make healthcare more affordable and accessible using the power of deep learning. Deep learning is transforming how machines learn – from having to hand… -craft features to machines automatically learning features from labeled data
