Upcoming SlideShare
×

# Viral loops

1,092 views
933 views

Published on

This is a presentation about the different kind of technology consumer products, where viral loops can fit in, and how to design an efficient viral loop.

6 Likes
Statistics
Notes
• Full Name
Comment goes here.

Are you sure you want to Yes No
• Be the first to comment

Views
Total views
1,092
On SlideShare
0
From Embeds
0
Number of Embeds
5
Actions
Shares
0
24
0
Likes
6
Embeds 0
No embeds

No notes for slide

### Viral loops

1. 1. Value Loops, Viral Loops & NetworkAnurag Jain Effects
2. 2. Structure of Talk• Value Loop• Network Effects• Viral Loops• Designing viral loops
3. 3. Types of products• Inherent value or dependent on network effects?• Primary value => Inherent – even a single user can be a long-term user• Primary Value => network effects – No community => all users leave
4. 4. Value loop• More value + less cost = value loop• Usually Word of Mouth• Typically takes a while• Concentrate on channels of distribution• E.g. - mint.com, google.com, early days of facebook
5. 5. How do we measure value?• Net Promoter Score – Users rate from 1-10 – 10 = best – Promoters (9-10) – Passives (7-8) – Detractors (1-6)• NPS = % Promoters - % Detractors• High NPS (>40%) => No need for virality
6. 6. Network Effects• 100 users or 10,000 ?
7. 7. Network Effects• 100 users or 10,000 ?• It depends!!
8. 8. Network Effects• 100 users or 10,000 ?• It depends!!• Imagine 10,000 facebook users with 0 friends each
9. 9. Network Effects• 100 users or 10,000 ?• It depends!!• Imagine 10,000 facebook users with 0 friends each
10. 10. Network Effects• Metcalfe’s law: Value of network = c(P^2)P – no of connected users• If n sets of connected users, Value = c(P1^2 + P2^2 + … + Pn^2)
11. 11. Network Effects• Metcalfe’s law: Value of network = c(P^2)P – no of connected users• If n sets of connected users, Value = c(P1^2 + P2^2 + … + Pn^2)• E.g. facebook, linkedIn, twitter
12. 12. Network Effects• Metcalfe’s law: Value of network = c(P^2)P – no of connected users• If n sets of connected users, Value = c(P1^2 + P2^2 + … + Pn^2)• E.g. facebook, linkedIn, twitter• Cold start problem
13. 13. Social Layer?• Facebook -> People/People• Twitter -> People/News• Foursquare -> People/Places
14. 14. Social Layer?• Facebook -> People/People• Twitter -> People/News• Foursquare -> People/Places• Your product -> People/????
15. 15. Primary benefit• Why should I care?• What’s in it for me?
16. 16. Primary benefit• Why should I care?• What’s in it for me?• Sharing?
17. 17. Psychology of sharing• Facebook -> Socialite• Twitter -> In the know (journalist)• Yelp -> Critic• Quora -> Expert• Instagram -> Photographer• Foursquare -> Person-about-town
18. 18. Psychology of sharing• Facebook -> Socialite• Twitter -> In the know (journalist)• Yelp -> Critic• Quora -> Expert• Instagram -> Photographer• Foursquare -> Person-about-town• Your product -> ?????
19. 19. Friendship graphs• Symmetric or asymmetric?• Friend or follower?• Friend = more private• Follower = public, but higher growth – More specific to intent-based vertical networks
20. 20. Building Network Effects• Critical Mass – community that sustains itself – 150 users in each community• Alpha Users – High Social Networking Potential (SNP)• Cold Start problem – If I have only a few friends, where is the value for me?
21. 21. Building Network Effects• Social Learning – Watch what others are doing
22. 22. Building Network Effects• Social Learning – Watch what others are doing• Singling Out – Messaging from others
23. 23. Building Network Effects• Social Learning – Watch what others are doing• Singling Out – Messaging from others• Feedback – How does my network respond to my actions?
24. 24. Building Network Effects• Social Learning – Watch what others are doing• Singling Out – Messaging from others• Feedback – How does my network respond to my actions?• Distribution – Extent of reach of my actions. Who all sees it?
25. 25. Network Effect example• Credit for user @a all along the chain• More motivation to write funny/interesting tweets
26. 26. Building Network Effects• Network Effects – Alpha users will help, but only for a while – Slow growth => death – Need viral growth
27. 27. What is viral?• Anything that grows exponentially, like a virus
28. 28. Why do we want viral growth?• It’s cheap (and fast)
29. 29. What is Viral?• Anything that grows exponentially, like a virus• Word of Mouth propagation can be viral under certain circumstances
30. 30. What is Viral?• Anything that grows exponentially, like a virus• Word of Mouth propagation can be viral under certain circumstances – But typically slow• How do we know when something is viral?
31. 31. Viral Co-efficient• Let’s say each user invites 5 friends – on avg 22% of invitees register – viral co-eff => 22% * 5 = 1.1• > 1 = viral
32. 32. Viral Co-efficient• Let’s say each user invites 5 friends – on avg 22% of invitees register – viral co-eff => 22% * 5 = 1.1• > 1 = viral• If 1 user gets 1 more, can get the entire world
33. 33. Viral Cycle Time• If 1 user gets 1 more, can get the entire world• But, how fast?• Until market is exhausted, how fast does it grow? – Viral Cycle Time – Depends on each product• Measurement is important!!
34. 34. Measuring Viral Cycle Time• Cohort Analysis• Weekly (or daily) cohorts of users• Track how users behave over period of time, based on when they joined• Idea: Don’t make decisions for new users based on metrics from more experienced ones
35. 35. What parts are viral?• Invitations• Engagement• Trick is to invite other users as part of the core user experience• Not to be confused with network effects
36. 36. What is the viral loop?• User A takes an action
37. 37. What is the viral loop?• User A takes an action• User B is notified as part of that action
38. 38. What is the viral loop?• User A takes an action• User B is notified as part of that action• As part of the response, user B takes SIMILAR action
39. 39. What is the viral loop?• User A takes an action• User B is notified as part of that action• As part of the response, user B takes SIMILAR action• User B therefore invites User C
40. 40. What is the viral loop?• User A takes an action• User B is notified as part of that action• As part of the response, user B takes SIMILAR action• User B therefore invites User C• … and so on
41. 41. What is the viral loop?• User A takes an action• User B is notified as part of that action• As part of the response, user B takes SIMILAR action• User B therefore invites User C• … and so on• Integral part of the product, not a layer
42. 42. Designing Viral Loops
43. 43. Designing viral loops• Single core activity• Single entity for user to interact with• Can have multiple types of users, but with great difficulty• Channels for virality
44. 44. Single activity• What is the core activity?• Can we add messaging to this activity?• The new user who sees this responds to this activity by taking the SAME action• … and so on
45. 45. Single activity• What is the core activity?• Can we add messaging to this activity?• The new user who sees this responds to this activity by taking the SAME action• … and so on• Viral growth
46. 46. Single type of user• Multiple sets of users – VERY HARD• Motivations are very different for different groups• E.g. Seller != Buyer
47. 47. Single type of user• Multiple sets of users – VERY HARD• Motivations are very different for different groups• E.g. Seller != Buyer• Design loop for each set separately
48. 48. Channels for being viral• News Feed• Notifications• Email• Profile• Invites• Non-user pages• Profile Actions
49. 49. Other Techniques for Viral• Game Mechanics – Badges, scores, etc.• Not core value, unless game is the core itself• Rewards• Contests
50. 50. Steps for viral loop• Design loop with core activity• Build product• Test on small set• Optimize the loop
51. 51. Conclusion• Feature depends on network effects? – Cold start remedies• Need viral loop• Measure, measure, measure• World dominance