Presentation to Groundwork Labs incubator in Durham, North Carolina about LTV and data quality

2. PRESENTATION OVERVIEW
Relevant background
Why Lifetime Value
Importance beyond social media
Virality
Retention
Monetization
The Cost Side
LTV varies among customers
Uncertainty of LTV

4. THREE EXITS IN THREE YEARS
Co-founded Merscom CCO, led all marketing/sales/distribution
• Grew to top-5 casual game company
• Initiated and negotiated sale to Playdom, which was then rolled into $570 million Disney acquisition
GM of Playdom’s International Publishing team
• Responsible for Europe, Latin America, Russia and India
• Grew it from scratch to 25 percent of Playdom’s revenue
CEO of FiveOneNine Games
• Joint venture of EW Scripps and Capitol Broadcasting
• Launched Facebook and mobile games
Chief Growth Officer at Spooky Cool
• Helped lead sale of company to Zynga
• Lead UA, analytics, monetization and community
Senior Director, Zynga
• Social casino team
• Product, analytics, user acquisition

6. BASED ON THREE PERFORMANCE METRICS
Monetization
Virality
Retention

7. INTERDEPENCE

8. RELATIONSHIP BETWEEN LTV AND CPA
LTV > CPA
• Success
LTV < CPA
• Failure

9. THINK OF LTV STARTING DAY 1
Green-light
Design and
develop
focused on
LTV
Beta and
other testing
to optimize
LTV
Post launch
to focus on
improving
LTV

11. ONLINE OFFERINGS
Netflix
Ebates
Farmville

12. B2B
Square
Rackspace
Bronto

13. SAAS
Salesforce
HootSuite
Basecamp

14. RETAIL
Restaurants
Department
stores
Car dealers

16. DEFINITION OF VIRALITY
K-score
K=i*conv% (conversion percentage), where “i” is the number of invites sent
out by each new customer and “conv%” is the percentage of invites that
convert into costumers

17. AN ADVANCED LOOK

18. IMPORTANCE OF VIRALITY
Lowers cost
of customer
acquisition
Exponential
growth

19. IMPROVING VIRALITY
K=i*conv%
Increasing I
• Generate virality quickly
• Cater to Connectors
Increasing conv%
• Quality of communication
• Provide value for virality

21. DEFINITION OF RETENTION
Customer
lifetime
N-day retention
Churn rate

22. IMPORTANCE OF RETENTION
If they do not come
back, monetization
improvements are
virtually useless

23. IMPROVING RETENTION
Product quality
Get in their heads
Make it social
Make it global
Use email and
advertising for
re-engagement

25. DEFINITION OF MONETIZATION
ARPU (Average
revenue per
user)
ARPDAU
(Average
revenuer per
daily active user)
Average
transaction
Percentage of
customers who
monetize
Average number
of monetization
events per
customer

26. IMPORTANCE OF MONETIZATION

27. IMPROVING MONETIZATION
Product
quality
Promotions
and sales
Value
Shopping
experience
More
selection
Balancing

29. COST DRIVERS
Running Costs
•Hosting
•Support
Payment Processing
•Platform fees
•Payment processors
Software Royalties
•Engine, such as Epic’s UDK
•Analytics
IP Licensing
•Properties
•Talent

31. COHORTS
Time of year
Stage of product lifecycle
Holidays

32. SEGMENTS
Age
Sex
Income
Interests

33. SOURCES
Incentived
ads
Virality
Targeted
search ads
Television
Press
Cross
promotion

35. UNCERTAINTY PRINCIPLE
Quantum Mechanics
• The universe is random
• Perfect predictions are impossible if the universe is random
Not a function that creates a value
You are predicting a future event
Create a range, not a number
• Albert Pujols is likely to hit 30-40 home runs is more accurate than Pujols is likey to hit 36
home runs
Models are simplifications of the world

36. RISK VS UNCERTAINTY
Risk
Something you can put a price
on
Uncertainty
Risk that is hard to measure
Do not
confuse
uncertainty
for risk
Correlation of past data does
not create certainty

37. THE MAJOR DIFFERENCE BETWEEN A THING THAT MIGHT GO WRONG AND A THING THAT
CANNOT POSSIBLY GO WRONG IS THAT WHEN A THING THAT CANNOT POSSIBLY GO WRONG
GOES WRONG IT USUALLY TURNS OUT TO BE IMPOSSIBLE TO GET AT OR REPAIR,” WROTE
DOUGLAS ADAMS IN THE HITCHHIKER’S GUIDE TO THE GALAXY SERIES.
Wrong assumptions can have profound effects
• Independence of variables
• Mortgage industry
Chaos Theory
• Not a synonym for the game industry
• A small change in initial conditions can produce a large
and unexpected divergence in outcomes
• Major risk when modeling against past performance

38. DO NOT DISCOUNT QUALITATIVE INFORMATION
More data is better than less
This includes non-quantitive measures
Billy Beane has dramatically increased scouting
The Smell Test

39. AVOID OVERFITTING
Mistaking noise for signal
Fitting a statistical model to
match past observations
Test how much of the
variability of the data is
accounted for by your model

40. SOLUTIONS
Create LTV range
•Think probabilistically
•Distribution shows honest uncertainty
A/B Test
•Can validate assumptions
Surveillance
•Regularly (weekly or monthly) compare data with predictions
•“When the facts change, I change my mind”, John Maynard Keynes
Avoid Overfitting
Include qualitative data