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Decision Automation in
Marketing Systems
Ilya Katsov
Head of Practice, Industrial AI
Grid Dynamics
SAN FRANCISCO
JULY 17 2...
ML-based Decision Automation in Marketing Operations
● Billions of micro-decisions in real-time: who, when, how, what, ......
Case Study: Environment
Retailer 1 Manufacturer 1
Manufacturer NRetailer M
purchases, clicks, loyalty IDs
...
...
Promotio...
Case Study: Decisions to be Automated
● Targeting – who
○ Exploits variability in tastes, price sensitivity, propensity to...
Approach
Retailers
Brands
Product
• Willingness to pay
• Stages of journey
• Affinities to brands
• Affinities to channels
P...
Targeting and Timing
Models
7
Incremental revenue
Acquisition Maximization Retention
time
New Cardholder
$/brand
current non-buyers
+
high propensity ...
8
Look Alike Modeling and Survival Analysis
time
no purchase
Model training
Model scoring
purchase
no purchase
behavioral ...
9
Look Alike Modeling and Survival Analysis: Target Metric Design
behavioral history outcome
Unconditional propensity:
Exp...
Challenges with Basic Propensity Scoring
10
Checking
Account
Credit
Card
Brokerage
Account
Banking /
Telecom
Customer matu...
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 - N...
Refresher - Reinforcement Learning
12
● Most basic scenario - Markov decision process (MDP)
○ State
○ Action
○ Reward
○ Va...
Next Best Action with Reinforcement Learning
13
Customer state, t
action1
action2
action3
reward32
reward33
reward34
Custo...
Next Best Action with Fitted Q Iteration (FQI)
14
Purchase
Visit
No action
Offer 1 Offer 2 Offer 3
2. Initialize approximate
...
Next Best Action with FQI
15
Offer 3
Offer 2
Offer 1 (default)
Low state V
High state V
Customers who got
Offer 3 in early
Cus...
Next Best Action with FQI
16
● A generalization of the look alike modeling for multi-step and/or multi-choice strategies
●...
Budgeting Models and
Decision Automation
Privileged and Confidential 18
Targeting Thresholds: Static Optimization
High
propensity
Low
propensity
Privileged and Confidential 19
Targeting Thresholds: Dynamic Optimization
time
$$
campaign
duration
target budget
Decrease...
20
Campaign Parameters Optimization
Purchase
trigger
buy <X buy X+
buy 0 buy 1+
Announcement
Buy X or more units
and save ...
21
Solution Design: Technical Perspective
Marketing
Manager
Campaign Template
● Steps
● Offer types
● Forecasting logic
Tar...
Objective Selection
Plan and Forecast
Review
User Experience
Execution and
Measurement
Privileged and Confidential 22
Solu...
Thank you!
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Decision Automation in Marketing Systems using Reinforcement Learning: Dynamics talks SF July 17th 2019

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In this talk, we will discuss automatic decision-making and AI techniques for customer relationship management. First, we will present a methodology that helps to develop highly automated promotion and loyalty management systems. Next, we will walk through practical examples of how predictive models can be used to characterize customer intent, and how optimization and reinforcement learning techniques can be used to build next best action models that incorporate targeting, budgeting, and pricing decisions.

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Decision Automation in Marketing Systems using Reinforcement Learning: Dynamics talks SF July 17th 2019

  1. 1. Decision Automation in Marketing Systems Ilya Katsov Head of Practice, Industrial AI Grid Dynamics SAN FRANCISCO JULY 17 2019
  2. 2. ML-based Decision Automation in 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. 3. Case Study: Environment Retailer 1 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. 4. Case Study: Decisions to 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
  5. 5. Approach Retailers Brands Product • Willingness to pay • 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
  6. 6. Targeting and Timing Models
  7. 7. 7 Incremental revenue Acquisition Maximization Retention 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
  8. 8. 8 Look Alike Modeling and 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
  9. 9. 9 Look Alike Modeling and Survival Analysis: Target Metric Design behavioral history outcome Unconditional propensity: Expected LTV: click/purchase/CTR 3-month spend Response/value uplift:
  10. 10. Challenges with Basic Propensity Scoring 10 Checking Account Credit Card Brokerage Account Banking / Telecom Customer maturity Product maturity level 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 time
  11. 11. 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 11 profile value (LTV / ROI)M Time Offer 1 Offer 2 Offer 3 Offer 3 Offer 2 Offer 1
  12. 12. Refresher - Reinforcement Learning 12 ● 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
  13. 13. Next Best Action with Reinforcement Learning 13 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
  14. 14. Next Best Action with Fitted Q Iteration (FQI) 14 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
  15. 15. Next Best Action with FQI 15 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
  16. 16. Next Best Action with FQI 16 ● 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
  17. 17. Budgeting Models and Decision Automation
  18. 18. Privileged and Confidential 18 Targeting Thresholds: Static Optimization High propensity Low propensity
  19. 19. Privileged and Confidential 19 Targeting Thresholds: Dynamic Optimization time $$ campaign duration target budget Decrease propensity threshold Increase propensity threshold
  20. 20. 20 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
  21. 21. 21 Solution Design: Technical Perspective 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
  22. 22. Objective Selection Plan and Forecast Review User Experience Execution and Measurement Privileged and Confidential 22 Solution Design: Marketer’s Perspective
  23. 23. Thank you!

In this talk, we will discuss automatic decision-making and AI techniques for customer relationship management. First, we will present a methodology that helps to develop highly automated promotion and loyalty management systems. Next, we will walk through practical examples of how predictive models can be used to characterize customer intent, and how optimization and reinforcement learning techniques can be used to build next best action models that incorporate targeting, budgeting, and pricing decisions.

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