This document discusses the use of machine learning for predicting customer lifetime value (CLV). It argues that while machine learning is well-suited for classification and descriptive tasks, it falls short for long-term CLV prediction because it tries to explain all customer behavior patterns. Instead, CLV models should embrace the inherent randomness in customer actions. The document then presents the standard new view of using CLV models to make forecasts, and then applying machine learning to explain differences across customers based on those predictions. It provides examples of layering machine learning on top of CLV models for B2C and B2B customers.

1. Customer Lifetime Value in a
Machine Learning World
Presenter: Peter Fader
Professor of Marketing at The Wharton School of the University of Pennsylvania
Co-Founder of Zodiac
Author of Customer Centricity: Focus on the Right Customers for Strategic Advantage

2. 2
Machine learning is HOT right now!

3. 2 Classic Use
Cases for
Machine
Learning

4. 4
Use Case 1:
Classification
Yes or no questions or a number of
different “buckets”: Which one does this
person belong in?
Examples
Is this person going to be a good credit risk?
Is that person likely to vote for our candidate?
Will this customer churn or stay with us next year?

5. 5
Use Case 2:
Description / Profiling
By looking at a lot of different
descriptive variables, find which ones
do the best job of explaining which
bucket a person should be in.
Examples
Will social media usage or website visitation be a better
predictor of brand preference?
Will hobbies or income level be a better predictor of
spend in a product category?

6. Ideal Tasks for
Machine
Learning
Will customer X make a purchase next quarter or not?
Will customer X churn next year or not?
Will customer X’s next category purchase be with us or a
competitors’ brand?
Does customer X tend to be a high or low spender for
her purchases?
When will the next purchase for customer X likely occur?
How long will it probably be until customer X churns?
How many purchases is customer X expected to make
over the next three years?
What is the expected spend (in dollars) for customer X’s
future purchases?
Where Machine
Learning Falls
Short
6

7. If the goal is to ask long-run
questions (and that’s what lifetime value
is all about) then don’t start with
machine learning.
What do you do instead?

8. Embrace the “Randomness” of
Customer Behavior
• Don’t try to explain away every little wiggle and jiggle in a purchase pattern
• Aim to uncover the true/underlying/unobservable propensities that drive the
observed behavior
• Capture the inherent variation across people and over time
• Result: better forecasts and diagnostics than a model that focuses on
explanation
8

9. Looking at Regression
𝛽0 + 𝛽1 𝑋1 + 𝛽1 𝑋1+ 𝛽2 𝑋2+ 𝛽3 𝑋3 + …
9
Customer
Spend
ε
? ?𝑡
𝑖=

10. Embrace the “Randomness” of
Customer Behavior
• Don’t try to explain away every little wiggle and jiggle in a purchase pattern
• Aim to uncover the true/underlying/unobservable propensities that drive the
observed behavior
• Capture the inherent variation across people and over time
• Result: better forecasts and diagnostics than a model that focuses on
explanation
10

11. Embrace the “Randomness” that
Always Exists in Customer Behavior
• Don’t try to explain away every little wiggle and jiggle in a purchase pattern
• Aim to uncover the true/underlying/unobservable propensities that drive the
observed behavior
• Capture the inherent variation across people and over time
• Result: better forecasts and diagnostics than a model that focuses on
explanation
11

12. The New Standard View of CLV
12

13. The New Standard View of CLV
13

14. Where Machine Learning
Fits into CLV
• We don’t use machine learning for forecasts
• We use it once we’ve already done our CLV prediction. Once we have these
forward-looking projections, now we can put people in buckets and find how
they are the same or different from each other.
• Machine learning approaches explain cross-sectional differences
14

15. 15
Layering on Machine Learning- B2C

16. 16
Layering on Machine Learning- B2B
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
NAIC3 Desc. Specialty Trade Contractors
NAIC3 Desc. Professional Scientific & Technical
NAIC3 Desc. Chemical Manufacturing
NAIC3 Desc. Transportation Equipment Manufacturing
NAIC3 Desc. Other
Month 3
NAIC3 Desc. Nonmetalic Mineral Product
Month 2
NAIC3 Desc. Fabricated Metal Product
NAIC3 Desc. Mining Except Oil & Gas
CTM Size Desc. Unknown
NAIC3 Desc. Merchant Wholesalers Durable Goods
Quarter 4
NAIC3 Desc. Machinery Manufacturing
Weekday Monday
Quarter 3
Weekday Thursday
Weekday Wednesday
NAIC3 Desc. Unknown
CTM Size Desc9 701k
First Trans Units
CTM Size/Desc.7 200k - 700k
CTM Size/Desc.5 71k - 200k
First Trans Spend
First Trans Cost
CTM Size/Desc. 21k - 70k
Attribute Importance
CTMAttributes
CTM Attribute Relevance in Early Prediction of High LTV

17. 17
Can You Feed
the Machine?
A machine learning model requires many descriptive variables.
• Can you really trust this data?
• Can you get it over the long term, i.e. the next 5 years?
• Can you link all these variables together at the customer
level?
• Where should these variables fit in the CLV modeling
process?
Location Demographics
Website Behavior Social Media

18. RFM: The Earliest
Machine Learning
Example?
Direct marketers in the 1960’s-70’s invented
early data mining/machine learning
They compared explanatory factors to see which
were most predictive of actual activity
(Recency Frequency Monetary Value)
Let’s not reinvent the wheel—RFM works!

19. The right CLV platform
allows organizations to
predict
customer behavior
on an individual level
and explain

20. 20
Explain (Machine Learning)
Predict (Probability Models)
Which customers will come back How long the relationship will last How much they’ll spend
Which products the best
customers bought first
Which acquisition channel
produced the best customers Which factors best predict churn

21. Let’s Talk
@faderp
@ZodiacMetrics
pete@zodiacmetrics.com Zodiacmetrics.com
Thank you!