In marketing, customer lifetime value (LTV, sometimes called CLV or CLTV) is a prediction of the net cash flow attributed to the entire future relationship with a customer. The prediction model can have varying levels of sophistication and accuracy, ranging from heuristic to the use of complex machine learning techniques. LTV in a non-contractual setting is widely accepted to be more difficult than in a contractual setting, in which the churn rate can be simplified as a constant. - As the world's largest concert promoter, Ticketmaster is focused on connecting live events to millions of our fans in a non-contractual and discrete setting. We adopted a paradigm called RFM (Recency, Frequency, Monetary) to make predictions of fans' two-year lifetime value to help make decisions including, for example - new product feature launch, SEM bidding automation, overall budgeting and essentially implement a winning strategy driven by customer lifetime value.- In this talk we will discuss RFM and other probabilistic models we used and how the results of the analysis helped drive business decisions. We will provide an overview of: (1) Customer lifetime value using RFM (2) Probabilistic Models: Bayesian Models, Beta-Geometric/Beta-Binomial Model (BG/BB) (3) A case study: how to use LTV for a new feature launch

1. Know Your Customers
August 17 2019
Confidential. Do not distribute.
Fiona Li
Manager, Analytics
Implement a Winning Strategy Driven by Customer Lifetime Value

2. 2
Agenda
● What is Customer Lifetime Value (CLV) and why do we care?
● How CLV is used for a business decision
● Deep dive on BG/BB probabilistic models
● Demo on an offline model: Implement BG/BB models in Python
● Select the right CLV model for your business

3. Imagine you are opening a boutique movie theatre...let’s say we
named it Popcorn & Soda, located in the University Village...
3
“The average life span of my potential customers is 5 years.
And on average, people visit similar theatres spend about $20 per month. ”
What is my Customer Lifetime Value?
$20/month x 12 months/year x 5 years = $1,200
How much should I spend on acquiring a customer in the first year,
if I would like to maintain a 20% profit?
$20/month x 12 months/year x 20% = $48

4. 4
Popcorn & Soda becomes a huge hit and you have earned
enough money for a MBA at Marshall...
m: the net cash flow per period (w/20% margin)
r: the retention rate (80% in this case)
d: the discount rate (assume 10%)
T: the time horizon for the calculation
What is my Customer Lifetime Value?
How much should I spend on acquiring a customer in the first year,
if I would like to maintain a 20% profit?
CLV (t=0) = $20/month x 12 months/year x 20% = $48
m = $20/month x 12 months/year x 5 years x 20% = $240
something = 2.91
CLV = $240 x 2.91 = $700
Reference: What’s Wrong With This CLV Formula? P. Fader, B.
Hardie
At the Marketing 101 Class:

5. Classifying Customer Bases
5
Contractual
customer “death” can be observed and often
modeled using survival-based approach
Non-contractual
customer “death” is unobserved and
lifetime distribution often modeled via
probabilistic models
Discrete
purchases
occur at fixed
period or
frequencies
● Streaming Services: Netflix, Hulu etc
● Magazine subscriptions
● Most insurance policy
● Movie, concert attendance
● Prescription refills
● Charity fund drives
Continuous
purchases can
happen at any
time
● Credit cards transactions
● Utilities
● Continuity programs: airline frequent flyer
discount program etc
● Retail purchasing, i.e.Starbucks
● Hotel stays
● Doctor visits

6. Typical Purchasing Journeys
Alice:
Brian:
Charlie:
6
Past Future
?

7. CLV Definition and Equation
7
Future Customer Lifetime Value =
Profit Margin % x Discounted Expected Residual Transactions x Predicted Order Value
Customer Lifetime Value is
“the total profit of the entire relationship with a customer throughout the customer journey, in the past and
future.”
Reference: Customer-Base Analysis in a Discrete-Time Noncontractual Setting P. Fader, B. Hardie, J.
Shang

8. “By sending a second email to our weekly newsletter subscribers, we observed a
80% lift in conversions! However, we also experienced a 20% increase of
unsubscription rate.
So should we continue sending out the second email or not? Given the numbers,
can we have a 1-year projection on the net impact?”
8
Popcorn & Soda is very successful, expanded to 20+ college
towns...now we are not only have a Marketing team but also
has a Data Science team...
In a recent memo from the Head of Marketing to the Head of Data Science:

9. “Two Coins” of BG/BB Model
a.k.a. Buy Till You Die model
9
Purchase Coin
Make a transaction!
Yay! I’m going to see a movie!
🤘🤘
No transaction!
I hate my coin!
😞
Death Coin
You “die”! No escape!
OMG! The Death Lord’s here!
☠️
You stay “alive”!
Angel to the rescue!
😇
Assume each customer has two uneven coins

10. Model Development
The dying (BG) and purchasing (BB) processes
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“Oh, End Game is on tonight 8pm at Popcorn & Soda.
Should I go see it?”
“Hmm, I just moved to Hollywood. I do like the theatre, but
should I drive 10 miles to see a movie at Popcorn & Soda?”
“Oh, I received an email from P&S and saying that there are lots of previous movies are on at P&S with lower rates. So…”
A customer makes a transaction at any given
transaction opportunity with probability p. This is
known as a binomial distribution:
A “living” customer “dies” at the beginning of a
transaction opportunity with probability θ. This is
known as a geometric distribution:

11. Brian:
11
● The order of given number of transactions prior to the most recent transaction doesn’t matter.But what matters
is when the customer is “dead” θ (Recency) and how many transactions p (Frequency) he/she has.
● We compute the probability of the transaction on each customer and then multiply it by the probability with
other customers’.
Brian’s transaction history - 100100
Model Development
An Example

12. 12
Beta Geometric Beta Binomial
BGBB Model
Model Development
Heterogeneity is captured in Beta Distribution and and the log-likelihood function

13. Demo in Python
13

14. Select a CLV Model for your business model
14

15. Deploy a Production ModelBusiness Context Select an Offline Model
Select a CLV model - suggestions and caveats
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● Depends on the nature of
customer base, the data
size can be huge.
● To decide if it’s a
streaming, online or AaaS
deployment.
● To deploy in a container i.e.
Docker to control package
versions.
● To write engineering
production code with
adequate test cases.
● For contractuals, we can use
survival based models, while in non-
contractual settings, we can
consider probabilistic models, i.e.
hierarchical Bayesian such as
Pareto/NBD, Pareto/GGG.
● Tools/Packages can be:
○ Python: lifetime, PyMC
○ R: BTYD, BTYDPlus
● Metrics to evaluate performance:
MAPE, tradeoff between granularity
and run-time if running MCMC.
● What are the values added if
we have CLV?
● To understand the business
context and business model.
○ Is the business model on a
contractual or non-
contractual basis?
○ Are the customer
purchases continuous or
discrete?
● How far we would like to
have CLV predicted?

16. 16
Popcorn & Soda successfully chose, implemented CLV models...
The Head of Data Science sends out a memo to the Executives and the Marketing Team:
“The estimated incremental revenue gained from the 80% conversion is about $3M. Based on the
10% increased number of unsubscribers we projected CLV for a year is about $1M. The Net Impact
from the calculation above is about $2M per year. Based on revenue impact calculations and cost
estimations, we believe that we will be better off if launching this product feature.”

17. Thank you
Confidential. Do not distribute.

18. Why do we care about CLV?
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From a strategic point of view:
● Company valuation based on customer equity
● Annual budgeting based on CLV
● Customer segmentation
Operationally:
● Identify most profitable customers, then
● Identify traits and features of valuable customers for engagement and growth
● Dictates allocation of marketing resources, i.e. acquisition cost, SEM spend at a visitor level
● To track sales forecasts
APPENDIX