How to make your product go viral

How to make your product go viral
Dimitry Rotstein
August, 2016
1

 
1
11/
0




k
k
NtN
Tt
Nuclear chain reaction
Where:
N – number of atoms
T – reaction cycle duration
n – number of fission parts
p – hit probability
2
pnk 

 
1
11/
0




k
k
NtN
Tt
Viral chain reaction
pnk 
Where:
N – number of users
T – viral cycle duration
n – avg. number of shares
p – adoption probability
3

k>1
k=1
k<1
Significance of the k-factor
Ntotal
t
N(t)
4
   







totalN
tN
nptk 10

k>1
k=1
k<1
Ntotal
N(t)
t 5
   







totalN
tN
nptk 10
Significance of the k-factor

Nmax /Ntotal
k
Optimally, 1.5 ≤ k ≤ 2.5
1.0
0.5
1 2 3
6

7
dN/dt
t
Day-to-day growth
  Tt
kNtn /
0
k>1
k=1
k<1

Viral loop/protocol
8
4. Persuasion (L→A)
1. Motivation
3. Contact
2. Selection (TA,Ch)

Viral loop/protocol (realistic)
9
1. Motivation 2. Selection (TA,Ch)
3. Contact 4. Persuasion (L→A)

  PCBobSM PPNPPk 
Viral protocol quantified
Where:
PX – probability of passing stage X of the protocol
NBob – number of Bobs selected by average Alice
10

Sharing creates/increases value for you
M1. Natural
11
L
? ‫מיניין‬

Sharing/on-boarding gets you a knick-knack
M2. Synthetic
12
L
 1+1
 Group discount
 Friend brings a friend
 Good luck chain letters

You are pressured to share (NOT use)
M3. Coercion
14
L
E

15
M3. Coercion - examples L
E
 Bad luck chain letters
 “Maximum repost”
 “No soup for you”
 Public shaming
 Soft peer pressure

Share to help someone else (including the product itself)
M4. Altruism
16
L
E

Share to say “Look at what I got/know”, elevate social status
M5. Vanity
17
L
E
“How good are you in X” quiz

Share product/content just because it’s amazing or exciting
M6. Wow-factor
Copyright © New Line Productions Inc.
18
L
E

Share product/content just because it’s amusing/entertaining
M7. Fun-factor
19
L
E
“What kind of X
are you” quiz

Share warm, fuzzy feelings (adorable, touching, nostalgic, etc.)
M8. Happy
20
L
E
“80s cartoons” quiz

Share “bad” feelings (fear, sadness, anger, disgust, envy, etc.)
M9. Unhappy
21
E
Copyright © Disney Enterprises Inc./Pixar

Share just by using the product (perhaps without realizing it)
M10. Parasitic
Copyright © Universal City Studios Inc.
22
U

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http://www.eset.com
Share just by using the product (perhaps without realizing it)
M10. Parasitic - examples
23
U
“Share if you agree”

24
Anti-motivator
S V U C N W F H A P
Fear


x
xxaMM
Motivation formula
• Motivators are not created equal
• Motivators are independent of each other
25
 PUHFWVACSNx ,,,,,,,,,

“Word of Mouth” study,
Ernst Dichter (1966)
= W
= V
= A
= W + F + A
26

Get the word out about causes or brands (84%) = A
Grow and nourish our relationships (78%) = A + W + F + H
Self-fulfillment (69%) = A + V
Define ourselves to others (68%) = V
Bring valuable and entertaining content to others (49%) = W + F
Survey of 2500 heavy sharers,
“New York Times” (c. 2010)
27

F = 30-40%
W = 25-30%
H = 10-15%
U = 5-10%
Analysis of 100 million articles,
BuzzSumo & OKDork (2014)
28

Mx=[0,1] Description ax
Natural Create|increase product value for the sharer 4|2
Synthetic Reward sharing by artificial knick-knacks 2
Coercion Force to share via hard|soft pressure 3|1.5
Altruism Help others solve acute|ambient problem 3|1.5
Vanity Enable bragging 2
Wow Amaze|impress by functionality or UX 3|1.5
Funny Amuse|entertain by product or content 3|1.5
Happy Induce fuzzy feeling (touching, nostalgic, etc.) 2
Unhappy Induce anger, envy, disgust, fear, sadness, etc. 1
Parasitic Automate sharing
Motivators summary
29
8

Objective Vote (M=A+v+w+U=6.5)
Mar 2015
Election
Totalvisitors
30
http://objectivevote.org.il/?page=platforms&lang=he

More examples
8.5 (N+A+w)
4.0 (N)
3.0 (A)
3.5 (n+f)
7.0 (N+A)
31

32
M
PM
Motivation probability (PM)
4 100
1

33
Motivation probability - tips
 Spell out motivation explicitly
  AwarenessMPM  
4
tan 1
 Remind to share at suitable time
 Run A/B tests
 4 < M < 10

34
A/B testing
<?php if(rand()%2): ?>
<div>Invite your friends to have fun</div>
<script> var ab=1; </script>
<?php else: ?>
<div>Share your awesome results with your friends</div>
<script> var ab=0; </script>
<?php endif; ?>
Example: vary motivator awareness (for some game)
Note: A/B testing must always be randomized

35
Selection probability and size (PSNBob)
ChTAMPS 
M – Motivation
TA – Target Audience understanding
Ch – Marketing Channels awareness
 Increase M
 Define target audience explicitly
 List channels (social networks, forums,
email, phone, face-to-face, etc.)
 Run A/B tests

36
Selection probability – channel listing
 Share/like bar
 Share/like buttons
 Email invites
 Custom/embeded URL

37
Contact probability (PC)







TotalTotal
Leads
C
N
N
N
N
Filter
M
P 1
M – Motivation
Filter – Probability of being blocked
 Increase M
 Use filter-proof services, e.g. MailChimp
 Prefer channels with less filtering
 Tighten the Target Audience OR
 Address a very common problem

38
Persuasion probability (PP)
2
MessageTrustMPP 
 Increase M
 Optimize Target Audience for Trust
 Optimize Message
 Supply the Message explicitly/directly
 Run A/B tests
Message = Simple+Memorable+Interesting

39
<head>
...
<meta property='og:url' content='url' >
<meta property='og:type' content='website'>
<meta property='og:title' content='line1' >
<meta property='og:description' content='line2' >
<meta property='og:image' content='img_url'>
<meta property='fb:app_id' content='app_id' >
</head>
Supply the message via Facebook Preview
Then test/refresh values using Open Graph Debugger:
https://developers.facebook.com/tools/debug/og/object/
Metadata for the Facebook Crawler:

40
Bad example (“Objective Vote”)
http://objectivevote.org.il/?page=platforms&lang=he

41
Good example (“Objective Conflict”)
http://objectivevote.org.il/?page=conflict&lang=he

42
Measuring k-factor analytically
  t
T
k
NkNn Tt

ln
lnlnln 0
/
0
ln(n)
t
ln(k)/T
  Tt
kNtn /
0

43
ln(n)
t
A real world example (“Objective Vote”)
Problems:
 Different channels
 Need to know ‘T’
 Need to track individual referrals (MS)

44
Measuring k-factor directly
 Ask for referrals:
How did you learn about us?
1. Google
2. Bing
3. Facebook
4. Other: ___________
 Identify referrals by unique coupon ID
 Embed referral key into URL…

45
URL-embedded referral key
<div class='fb-like' data-share='true' data-width='350'
data-show-faces='true' data-ref='num'
data-href='url-to-like[?id=xxx]'
style='height:80px;margin-top:10px'>
</div>
FB ‘like’ button with referral key:
Disadvantage: no social validation (disconnected “likes”)
Solution: random polling
See more at: https://developers.facebook.com/

46
Everything put together
  LUCKMessageTrust
N
N
N
N
Filter
ChTAAwarenessMMk
TotalTotal
Leads








  2
2
13
1
4
tan

47
Contact me on:
Dimitry Rotstein

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