For the best reading experience, I’d highly recommend downloading the PDF here.

Data Science is a New Frontier for Retail Innovation

As techniques in Machine Learning begin to prove themselves, we’re beginning to see every major technology player make key strategic bets in the field of Artificial Intelligence.

Google is uniquely positioning itself as an “AI-first company” — organizing every product team to embody a data-centric approach.

Facebook is training every single developer in the field of Machine Learning enabling adoption at scale. Along with setting a 10-year roadmap with AI at its core.

Amazon has been rolling out product after product with a heavy focus on Artificial Intelligence, Machine Learning, Deep Learning, and Augmented Reality.

Microsoft is focusing on building its Azure cloud services, which is central to leveraging and democratizing Artificial Intelligence at its core.

Apple is infusing their entire lineup of products with Machine Learning — along with launching a range of processors that can take advantage of ML.

The benefits of Machine Learning — how it fundamentally works, what makes it an important area to invest in, and how it’s delivered at scale to impact customer experiences — will have a profound impact on business strategy by becoming a key differentiator for brands.

Retail Executives are beginning to take notice, recognizing that Machine Learning has passed the hype curve and is beginning to become a critical area of strategic focus.

Here, you’ll discover a list of comprehensive use cases across the End-to-End Customer Lifecycle that will help the retail executive gain a deeper understanding of how techniques in Applied-Machine Learning and Data Science can help tackle ongoing business challenges.

There is a plethora of opportunities across the customer journey that can be elevated through the use of Data Science — directly impacting key areas of business strategy, customer experience, and business outcomes.

This list focuses on areas that show tremendous promise, along with areas that in general are new ways to look at existing business challenges. It is meant to be used as guardrails to think about better, more meaningful outcomes — instead of being perceived as a comprehensive list of opportunity areas that mandate focus and investment.

Artificial Intelligence, Machine Learning, Deep Learning and Data Science all bring with them a unique advantage of building a modern data-centric organization. One that leverages data at its core to build meaningful customer experiences that drive both business and human value.

Emerging Technology Platforms

Virtual Assistants and Chatbots

Virtual assistants and chatbots are the most commonly understood use cases of AI. We’ve seen them in movies, and with the introduction of popular platforms like Amazon’s Alexa and Google Home these technologies are becoming a lot more accessible and familiar to the end user.

Natural Language Understanding

The most frictionless way for people to interact with computers is through natural language. This is why Natural Language Understanding is seen as one of the opportunity areas to elevate customer experience. Advances in algorithms focused on analyzing understanding and deriving meaning from human language are opening up new and profound applications for the end consumer.

Speech Recognition

As we begin to get better with Natural Language Understanding, so does our ability to understand Voice. Instead of forcing your customers to be heads down in a screen and constantly push them to enter their input in the form of keystrokes, we can use voice and allow users to speak out commands and opinions through voice.

Visual Recognition

One of the most powerful applications of modern AI is its ability to see — to visualize a user’s environment and identify the things they interact with. This opens up new avenues of customer interaction by understanding the things they’re holding, touching, feeling, and using. With visual recognition, the Retail industry is no longer confined to a physical store or a digital touchpoint — it can truly be anywhere.

Augmented Reality

With almost every big player heavily invested in AR (Google Tango, Facebook CameraEffects, Apple AR Kit), it is very well positioned to be the next platform for innovation. Snapchat and Pokemon Go have proven AR can work at scale and turned into something meaningful. If there has ever been a time to invest in AR it is right now.

Virtual Reality

Virtual Reality allows you to transport consumers into entirely new worlds that are limitless. The ability to build and curate environments, which you as a brand can dictate, is something that can fundamentally change how consumers think about a brand. While VR is achieving scale, it brings with it great promise and a whole new way to engage with the consumers.

Expert Systems

Expert Systems are automated systems powered by Artificial Intelligence to solve one or more complex problems. With intelligence at their core, an expert system can leverage the latest advances in Applied Machine Learning techniques to find solutions to existing problems in whole new ways — through the power of new computing standards and capabilities.

Robotic Systems

Advances in Robotic technology powered by AI are finding whole new ways to enter the Retail landscape. We’re beginning to see new applications ranging from Inventory Management Bots (Amazon), to Retail Service (Lowe’s Bot), to Delivery Bots (Starship Delivery Bot) enter the Retail environment and truly change how we go about designing consumer experiences.


Data Science & Artificial Intelligence being infused into the E2E Retail Customer Experience Journey

Stage 1 — Awareness & Inspiration

Proactively Engage & Build New Audiences

Look-A-Like Audience Modeling — Develop a model that discovers, engages, and builds new target segments based on a significant overlap of characteristics and patterns by cross referencing existing customers.

Audience Understanding

Social Insights And Listening — Manage social interactions around the globe by automatically analyzing and understanding audience sentiment, tone, nuances, and cultural attributes to inform business strategy.

Curate Love at First Sight

Intelligent Recommendations — Increase average order values by converting more anonymous visitors by recommending relevant products; tailor content and offers across channels based on visitor preferences.

Analyze Response to Marketing/Campaigns

Response Modeling — Maximize overall performance of marketing campaigns and promotional events by finding a set of promising segments that are most likely to convert based on promotional information.

Audience Intelligence Dashboard

Visualize Audience Relationships — Build an audience dashboard that allows you to visualize and monitor customer needs based on trends and behavioral patterns — driving confidence in decision-making during execution.

Tailored Audience Messaging

Content Analysis And Management — Optimize content based on formats that resonate the most with audiences. Personalize messaging by combining content analysis during campaigns with developed inferences.

Stage 2 — Consideration & Evaluation

Personalized Recommendations

Recommendation Systems — Build intelligent models to anticipate, predict, prescribe, rank, and refine tailored recommendations to users based on user tastes and preferences.

Identify Key Customer Segments

Customer Segmentation Modeling — Understand customers at a finer level by modeling key customer segments based on their digital footprints to engage with them through more meaningful experiences.

Infuse Product Intelligence

Product Attribute Graph — Rank products based on how they perform and resonate across key customer segments and use that ranking criteria to showcase the right products to the appropriate customers.

Stock Products and Maximize Returns

Category Management — Calculate the appropriate balance of products across varying physical shelf/window/aisle constraints in a way to maximize gross margins and substitute the right products.

Search Ranking

Recommendation Systems — Build models that rank product and multi-user taste preferences across a range of critical parameters in order to return accurate results by considering context and need.

Anticipate Customer Needs

Needs-Based State Machines — Build predictive models that consider the different states a user may belong to by understanding their needs within each state and possible triggers that generate new needs.

Pricing Strategy in line with Business Goals

Pricing And Profit Optimization — Model a pricing strategy by keeping in mind overall business goals and quarterly targets in mind. Assign individual price to each customer in order to maximize overall revenue.

Anticipate Future Demand

Demand Modeling — Recognize key trends that are both internal (customer/business related) and external (market/ industry related) that may influence the increase/ decrease of demand for key products.

Elevated Search Experience

Predictive Search — Enable data-driven search recommendations that allow the user to perform rich search queries that hand hold them to the right products in the most customized way possible.

Automated Catalog Curation

Catalog Ingestion — Automate SKU creation, curation, and management by recognizing patterns from available merchandising data, and use conflict resolution as a means to increase confidence in results.

Powerful Filtering Criteria

Collaborative Filtering — Allow customers to use meaningful and relevant filtering criteria to arrive at products they’re looking for — learn from patterns and behaviors to inform better product placement.

Stage 3: Shopping Cart

Facilitate Unanticipated Struggle

Struggle Detection — Automatically detect struggle with little or no human intervention by analyzing critical user flows and tasks and learn through observations to increase user experience where required.

Improved Cross-sell and Up-sell

Merchandising Analytics — Recognize patterns in customer behavior across buying patterns to improve product associations and recommendations in the shopping cart.

Market Basket Understanding

Basket Analysis — Analyze how customers make purchases and the behaviors that inform the products they buy to create relevant product associations enabling a smarter purchase.

Secure Transaction Platforms

Fraud Detection — Take advantage of cutting edge security solutions in fraud detection that recognize behavioral signals of fraudulent transactions and employ counter measures to void them.

Optimal Pricing Detections

Dynamic Pricing / Price Discrimination — Make optimal pricing decisions, keeping customer and business value in mind, by setting optimal prices to increase revenues and engaging with price-sensitive customers.

Identify Key Sales Drivers

Sales Value Decomposition — Identify how much proportion of sales is being driven by which driver factors that pertain to internal and external triggers. Use them to test hypothesis and validate through experiments.

Predicting Customer Behavior

Propensity Modeling — Conduct experiments and build models to predict the key shopping behaviors that determine likelihood of conversion versus likelihood of abandonment.

Stage 4 — First Purchase

Meaningful Customer Relationships

Response Modeling — Understand how customers respond to using your product by employing a mindset of collecting
feedback and analyzing the responses to inform the design and experience of products.

Stage 5 — New Customer

Rich Customer Profiles

Profile Insights — Build rich and informative customer profiles that allow a deeper understanding of customers, segmentation, preferences, and needs that inform product strategy and roadmaps.

Multi-taste Preference Understanding

Preference Modeling — Model multi-taste customer preferences based on collected data to understand what customers are looking for in products and services that can be used to inform strategic thinking.

Stage 6 — Second Purchase

Likelihood of No-Return

Customer Churn Analysis — Build models to understand why customers might not return to the brand, and fill the gaps across the user journey in order to better engage with customers.

Cross-sell & Up-sell Analysis

Propensity To Category Expansion — Increase likelihood of selling related or complementary products to customers by making data-driven recommendations that are highly personalized.

Incentivizing Next Purchase

Discount Modeling — Build unique models of discounting and incentivizing customer segments to increase the likelihood of their next purchase by understanding motivations and triggers.

Reach-out Programs

Call Back Analysis — Determine accurate, contextual, and highly personalized strategies to conduct reach-out programs with actionable triggers that can be measured.

Stage 7 — One and Done

Estimate Customer Lifetime Value

Customer Lifetime Value Modeling — Combine unique targeted models in varying granularity across different categories to determine the measurement in dollars associated with customers to identify their lifetime value.

Recognize Life-changing Events

Propensity To Change Shopping Habits — Identify likelihood of life-changing events (e.g., relocation, wedding, etc.) triggering new patterns of customer behavior and how that might open up new categories to purchase from.

Increase Customer Spend

Share Of Wallet Modeling — Increase percentage of customer spending within categories that resonate with them by understanding the context in which they engage with the brand and make purchase decisions.

Identify Customers Likely to Disengage

Risk Modeling — Build a process to identify customers early-on in the funnel who are most likely to disengage with the brand and most likely to not return to make another purchase.

Stage 8 — Repeat Customer

Serving Loyal Customers Differently

Uplift Modeling — Understand key patterns of repeat customers and how their behaviors may be different to regular shoppers in order to serve them differently, enabling a substantial sales boost.

Stage 9 — Loyal Customer

Recognizing and Retaining Loyal Customers

Advocate Definition — Building an acute understanding of what it means to be a loyal customer of the brand and what are the set of parameters that a customer needs to fulfill in order to sustain it.

Personalized Loyalty Programs

Gift Card/Loyalty Card Analysis — Go from Personas to People with a highly personalized loyalty program that keeps individual preferences in mind and engages in a contextualized relationship.

Stage 10 — Lost Customer

Retention Program

Churn Modeling — Quickly identify customers with high propensity to churn, and target them with retention campaigns that may bring them back through a discount, gift, or other monetary benefit.


Foundational Platform Elements

Supply Chain Optimization

Minimize distribution costs, warehouse assignment costs, route management, scheduling, product grouping, etc. by understanding the most optimal ways to manage your Supply Chain by leveraging Applied Machine Learning techniques.

Pricing and Profit Optimization

Build accurate pricing and profit models based on a range of parameters and pricing elasticity models. Predict likelihood of customer spend in line with business goals and profit margins.

Order Management

Automate order management, scheduling, delivery, and pick-up by understanding the crucial factors and risks involved to the overall business. Optimize models based on a dynamic data and cadence of smooth operations.

Inventory Forecasting

Predict optimal store or fulfillment center inventory based on historic and likelihood patterns of what customers may and may not order. Align with sales data to host the right products in the right geographies and store locations.

Sales Forecasting

Predict likelihood of sales and conversion across different properties by evaluating trends and sales pattern data. Analyze how you can do better and constantly implement new strategies to fuel new ways of selling.

Intelligent Sequencing

Detect which product combinations make sense for each customer segments and see how you can take advantage of a deeper understanding of the relationship-mapping of products to consumers.

Predictive IT

Predict abnormal behavior early-on in systems that may potentially lead to downtimes/crashes. Understand historical patterns/triggers to actively manage peak system performance — allowing you to constantly maintain efficient systems.

Anomaly Detection

Automate the detection of anomalies within systems for applications like Fraud Prevention, Fake Reviews, Coupon Transactions, etc. Build a deeper understanding of anomalies and employ counter measures to actively tackle them.

Warehouse Assignment

Calculate optimal warehouse assignment routes by understanding unique parameters that influence the matching of inventory to warehouses to either store or customer delivery.

Demand Prediction

Build a Demand Prediction model that analyzes product properties, sales events, shopping trends, and inventory status to model accurate demand and distribution.

Logistics and Optimization

Map the shortest, most optimal routes across various handoff stages for all logistical operations across the end-to-end customer experience in order to save costs or prioritize investment in crucial areas that have a direct business impact.

Sales Event Planning

Automate sales event planning for flash sales, seasonal sales, clearance sales, coupons, etc. by optimizing revenue management systems that predict and model sales events based on customer data and propensity to buy.

Cognitive Tagging

Build a cognitive tagging mechanism for individual products across all consumer segments that defines a multi-dimensional attribute model to build product intelligence, which can be leveraged across multiple use cases and applications.

Data Platform

Democratize data across the organization by building a robust, scalable data platform allowing seamless access for key departments to execute data and build a data-centric strategy and execution plan.