1. Metrics for Viral Tuning
By: Jeffrey Tseng
Kontagent
Facebook Developer Garage SF 2009

2. PHAME
Growing the application efficiently
Problem
Virality can be engineered
Hypothesis
Testing variants, iterating quickly
Action
Viral factor, conversions
Metrics
Different contexts, call to actions, messaging
Experiments

3. SuperPoke Viral Loop
Invite Page 1 Invite Page 2
New users
notification/invite
Viral loop Viral loop 2

4. The Levers
A/B Testing
Call to Action
A/B Testing
Message
Social
Context
Product and User
Experience Design
Application

5. Sender View
Social
Context
Call to Action
# of Messages
Recipients
Sent

6. Sender View
Social
Context
Call to Action
# of Messages
Recipients
Sent
Avg msgs
Metrics sent/event

7. Recipient View
Application
Sender Message
Acceptance
Installs

8. Recipient View
Application
Sender Message
Acceptance
Installs
Msg % new users Conversion to
Metrics
Conversion invited Installs

9. Application View
A/B Test
# of Messages Social
Avg msgs
Call to Action
Sent Context
sent/event
Sender
Application
Recipient
Msg
Acceptance Message
Conversion
A/B Test
% new users Conversion to
Installs
invited Installs

10. The Metrics
Avg msgs
• Average message/event sent/event
• Msg conversion rate Msg
Conversion
• % of New Users Invited
% new users
• Conversion to Installs invited
Conversion to
Installs
• Repeat visits to the event!
Repeat Visits
to the Event

11. How do you calculate virality?

12. The Viral Co-efficient
Day 1 Day 2 Day 3
3 1
1
1
1
1
2
2
Simply the “average branching factor”

13. Viral Rate Co-efficient
average branching factor
average response time
0.5 1.0 0.5
days days days

14. Viral Rate vs. Viral Co-efficient
• Viral co-efficient
– Has no time dimension
– Can be used to tell if an app is viral
– Can be used for viral tuning (trending)
– Cannot be used to compare growth rate
• Viral rate
– How fast does the app grow?
– Can be used to compare growth rate of diff apps
– Can be used for projections

15. Why Track the Viral Tree?
• Absolute long-term measure of effectiveness
• Lifetime Network Value
• Tracking sources of installs (attribution)
– Paid source vs. organic
• Identify the most socially active users
– Message or treat them differently

16. What User Demographics Are
Most Viral?

17. Answer: It’s very app specific

18. Application 1 Application 2
24%
33% Female
Female
42%
Gender Male
Male
56%
20% Unknown
Unknown
25%
6.2 7.6 9.6
8.8
Invites
Sent/User
6.7 7.2
40.03% 39.40%
12.17% 12.66% 32.13%
10.46%
Acceptance
Rate
Female Male Unknown Female Male Unknown

19. Application 2
Application 1
7% 15%
0-17 (School)
0-17 (School)
24%
40% 47% 14%
Age 18-24 (College)
18-24 (College)
25+ (Work)
25+ (Work)
Distribution Unknown
Unknown
24%
29%
7.8 7.8 5.4
8.8 0-17
0-17 (School)
Invites 18-24
18-24 (College)
6.3
Sent/User 25+
25+ (Work)
Unknown
5.6 Unknown
10.7
10.1

20. Demographics for Viral Tuning
• Demographics
– Distribution does not equal behavioral distribution
– Measure the behavior of the
demographic/segment
• User segmentation is useful
– You can’t test EVERYTHING
– Segment users allows you to focus your testing

21. Final Notes on Viral Metrics
• Viral Metrics CAN
– Provide framework for test and experiment
– Allow you to iterate quickly
– A/B testing for small changes
Viral Metrics CANNOT build you a good product
The hard work is being creative

22. Long Term vs. Short Term
• Short term
– A/B for copy is a short-term local metrics
• Optimize for long-term metrics
– Time on site,
– LTV
– LNV
– ARPU