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 advanced customer and content signals can be generated using predictive models, and how optimization and reinforcement learning techniques can be used for targeting, budgeting, and pricing decisions. This talk is for Data Scientists, Product Owners, and Software Engineers involved in marketing operations or development of marketing automation software and interested in ML-based decision automation techniques.

1. Customer Intelligence:
A Machine Learning
Approach
Ilya Katsov
Head of Practice, Industrial AI
Grid Dynamics
ATLANTA
AUGUST 21 2019

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. 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. Objective Selection
Plan and Forecast
Review
User Experience
Execution and
Measurement
Privileged and Confidential 4
Case Study: The End Goal

5. 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

6. 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

7. Targeting and Timing
Models

8. 8
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

9. 9
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

10. 10
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:

11. Challenges with Basic Propensity 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 - Reinforcement Learning
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 Action with 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 Action with 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 Action with 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 Action with 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
Decision Automation

19. Privileged and Confidential 19
Targeting Thresholds: Static Optimization
High
propensity
Low
propensity

20. Privileged and Confidential 20
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: 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

23. Objective Selection
Plan and Forecast
Review
User Experience
Execution and
Measurement
Privileged and Confidential 23
Solution Design: Marketer’s Perspective

24. Thank you!