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Customer intelligence: a Machine Learning Approach: Dynamic talks Atlanta 8/21/2019
The document discusses a machine learning approach to customer intelligence in marketing operations, emphasizing the automation of micro-decisions in a complex retail environment. It presents case studies on targeting, timing, and outreach strategies for optimizing marketing efforts, along with methods such as propensity scoring and reinforcement learning. The document highlights challenges in traditional models and advocates for advanced predictive and dynamic optimization techniques to enhance campaign effectiveness.
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Customer intelligence: a Machine Learning Approach: Dynamic talks Atlanta 8/21/2019
- 1. Customer Intelligence: A MachineLearning Approach Ilya Katsov Head of Practice, Industrial AI Grid Dynamics ATLANTA AUGUST 21 2019
- 2. ML-based Decision Automationin Marketing Operations ● Billions of micro-decisions in real-time: who, when, how, what, ... ● Complex environment: human behavior, complex business models, hidden factors ● Many building blocks: propensity scoring, recommendation algorithms, multi-armed bandits, etc. ● How to design a system that can make micro-decisions based on business objectives?
- 3. Case Study: Environment Retailer1 Manufacturer 1 Manufacturer NRetailer M purchases, clicks, loyalty IDs ... ... Promotion targeting system ● Drive traffic ● Improve loyalty ● Increase market share ● Acquire/grow/retain clients ● Improve loyalty
- 4. Objective Selection Plan andForecast Review User Experience Execution and Measurement Privileged and Confidential 4 Case Study: The End Goal
- 5. Case Study: Decisionsto be Automated ● Targeting – who ○ Exploits variability in tastes, price sensitivity, propensity to buy ○ Optimize short-term or long-term outcomes ● Timing – when ○ Exploits variability in price sensitivity ○ Exploits individual purchasing cycles ● Outreach/budgeting – how many ○ Exploits variability in propensity ● Promotion properties – what ○ Aggregated view on a promotion calendar
- 6. Approach Retailers Brands Product • Willingness topay • Stages of journey • Affinities to brands • Affinities to channels Predictive Models (Digital Twins) • Propensity • Life-time value • Demand Economic Models • What-if analysis • Optimization • Opportunity finding • Business objectives • Constraints Controls • Offers • Channels • Messages • Prices Signals Decisions
- 7.
- 8. 8 Incremental revenue Acquisition MaximizationRetention time New Cardholder $/brand current non-buyers + high propensity to buy new product current buyers + high propensity to buy more current buyers + high propensity to buy less Product Trial Replenishment Category Stretch Retention Alarm Com petitive Defence Look Alike Modeling and Survival Analysis
- 9. 9 Look Alike Modelingand Survival Analysis time no purchase Model training Model scoring purchase no purchase behavioral history outcome Customer profiles for training Customer profile to be scored score
- 10. 10 Look Alike Modelingand Survival Analysis: Target Metric Design behavioral history outcome Unconditional propensity: Expected LTV: click/purchase/CTR 3-month spend Response/value uplift:
- 11. Challenges with BasicPropensity Scoring 11 Retail ● Does not take into account product sequences ● Does not optimize offer sequences (i.e. not strategic) ● Requires separate models for different products/offers/objectives Checking Account Credit Card Brokerage Account Banking / Telecom Customer maturity Product maturity level time
- 12. profile value (LTV/ ROI)M Offer 3 Offer 2 Offer 1 profile value (LTV / ROI)M Offer 3 Offer 2 Offer 1 Next Best Action Model - Naive Approach 12 profile value (LTV / ROI)M Time Offer 1 Offer 2 Offer 3 Offer 3 Offer 2 Offer 1
- 13. Refresher - ReinforcementLearning 13 ● Most basic scenario - Markov decision process (MDP) ○ State ○ Action ○ Reward ○ Value ● Most basic solution - Dynamic programming (DP) ● Two major challenges: ○ The number of states and actions can be large or infinite ○ States and rewards are not known in advance action s1 s2 s3 reward Time
- 14. Next Best Actionwith Reinforcement Learning 14 Customer state, t action1 action2 action3 reward32 reward33 reward34 Customer state, t+1 Customer state, t+2 Customer state, t+3 Expected LTV / ROI Q(s, a) One timer Churner Repeater Loyal customer Multi product ● Need to estimate an action-value function given a certain offer policy: State (customer feature vector up to moment t) Action (offer feature vector) ● Use Q-function to optimize the offer policy s1 s2 s3 s4 s5
- 15. Next Best Actionwith Fitted Q Iteration (FQI) 15 Purchase Visit No action Offer 1 Offer 2 Offer 3 2. Initialize approximate repeat 1. Generate a batch of transitions (each trajectory corresponds to 4 transitions): { (state, action, reward, new state) } A simplified test dataset is shown for illustration 3. Initialize training set 4. For each 5. Learn new from training data
- 16. Next Best Actionwith FQI 16 Offer 3 Offer 2 Offer 1 (default) Low state V High state V Customers who got Offer 3 in early Customers who got Offer 2 early Customers who got Offer 2 -> Offer 3 Customers who did not get offers or got Offer 1 ● Max value for each state: ● Next best action for each state (policy): A simplified test dataset is shown for illustration
- 17. Next Best Actionwith FQI 17 ● A generalization of the look alike modeling for multi-step and/or multi-choice strategies ● More control over LTV/ROI metrics ● Can evaluate performance of a new policy based on historical trajectories ● Batch-online learning trade-off: multi armed bandits
- 18. Budgeting Models and DecisionAutomation
- 19. Privileged and Confidential19 Targeting Thresholds: Static Optimization High propensity Low propensity
- 20. Privileged and Confidential20 Targeting Thresholds: Dynamic Optimization time $$ campaign duration target budget Decrease propensity threshold Increase propensity threshold
- 21. 21 Campaign Parameters Optimization Purchase trigger buy<X buy X+ buy 0 buy 1+ Announcement Buy X or more units and save on your next shopping trip! Promotion Y% off 1. Estimate demand elasticity 2. Estimate how many consumers will buy more, how many will redeem offers 3. Do break-even analysis for costs and benefits
- 22. 22 Solution Design: TechnicalPerspective Marketing Manager Campaign Template ● Steps ● Offer types ● Forecasting logic Targeting Score (Look Alike or Next Best Action) Timing Score (Replenishment) LTV Score (Monetary) Offer Database Profile Database Campaign Planner Targeting Server Forecasting Optimization Targeting decisions Budgeting decisions request response Marketing Manager (merchant) Decision automation Customer models
- 23. Objective Selection Plan andForecast Review User Experience Execution and Measurement Privileged and Confidential 23 Solution Design: Marketer’s Perspective
- 24.