j. tseng app engagement dynamics social developer summit
DESCRIPTION
Social Developer SummitTRANSCRIPT
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Increasing Application Engagement
Presented by:
Manu Rekhi, GM of MySpace Games, Developer Platform & Content
Jeff Tseng, CEO/Co-Founder of Kontagent
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MySpace Users Love Games
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MySpace…
• Has more than 113 million monthly active users
• Sees 100,000 signups every day
• Users spent a total of 9.1 billion minutes on the site
MySpace Gamers…
• Spends 2x longer on MySpace than non-game players
• The top 100 apps on MySpace by DAU see avg. usage of over 7 minutes a day per user.
• Some games see as high as nearly 25 minutes per user a day.
• We have tens of thousands of Games and Apps total.
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Kontagent is the leading provider social analytics for social games
Tracking 500+ social games and applications
Tracking over 12M DAU
Tracking over 50M MAU
Kontagent
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4. TRACK
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Funnel Approach to A/B Testing for engagement (exclude new users)
Messages Sent/Posted - # of outbound messages/per user Used to optimize engagement if messages are sent to existing
users A/B test to optimize the call to action
Acceptance or click-through - ratio of user interactions with a viral msgs Used to optimize engagement A/B test to optimize CTR
Engagement w/ Viral A/B Testing
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3. EXPERIMENT
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• Test assumptions every day• Organize around small nimble teams• Be results oriented• Define success metrics
Experiment
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Viral Message A/B Testing
Min Average Max Max vs. AvgInvites Sent/Event 3.9 4.3 4.9 +12%Conversion 6% 8% 11% +35%Accepted/Event 0.25 0.35 0.47 +34%
Application A - A/B Tests (50K+ MAU)
Min Average Max Max vs. AvgInvites Sent/Event 3.5 6.7 9.5 +42%Conversion 6% 10% 17% +73%Accepted/Event 0.31 0.63 1.17 +85%
Application B - A/B Tests (700K+ MAU)
Kontagent App - A/B Tests (50K+ MAU)
Min Average Max Max vs. AvgInvites Sent/Event 1.2 11.7 17.2 +48%Conversion 4% 11% 22% +103%Accepted/Event 0.9 1.2 2.0 +61%
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2. LISTEN AND LEARN
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Research shows that users are more likely to invite friends to apps with high production values
Focus on Quality
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Graphics
PLOT
story
refe
ren
ces
challenge
battles
interaction
propertiesch
ap
ters
fightsentertainment
customization
leisureleisure
competitioncompetition
• Graphics and plot are this games strong and engaging features
• Theme is also a strong value.
• Competition and game mechanics also seem to be important for engagement.
• Needs work on customization
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"It’s a bit of an addictive game, competitive, and does have a social feeling to it being able to communicate to my "mob" friends etc.“(female user on why she likes Mobsters)
“Once you were the top player it felt like a good achievement." (female user about games)
"I like it because I have a lot of friends that play and I’ve met a lot of new people playing." (male user about why he likes games)
"These games (...) are always improving and offering something new to the games which urges you to return." (male user about games)
User Feedback
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1. ITERATE
Lather, Rinse and Repeat
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CASE STUDY
Track and Experiment - Deep dive
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Complex flows can’t be analyzed using funnels
Split A/B Testing can be used to see if a change is effective
Split users cohorts by behavior, user attributes, time, random
General Idea: Group users and look for differences in behavior then to select the optimal one
Using Split A/B Testing to Improve Monetization
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Case Study: Split A/B Testingwith User Timelines
Observation: Initial users pay a lot, but the majority of money is made later
Hypothesis: Increase monetization touch points further in the game
Methodology: Make changes to one group of users and split A/B test
0 100 200 300 400 500 6000%
20%
40%
60%
80%
100%
120%
Cumulative Monetization vs. Visit #
Visit #% M
on
eti
za
tio
n E
ve
nts
0 100 200 300 400 500 6000
20
40
60
80
100
Monetization Events vs. Visit #
Visit #
Mo
ne
tiza
tio
n E
ve
nts
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Selecting Cohorts for Testing
Test Setup: Compare 2 time based cohorts 392K average DAUs for Original Cohort 381K average DAUs for Test Cohort
0 5 10 15 20 25 30 -
100,000
200,000
300,000
400,000
500,000
DAUs
Original CohortTest Cohort
Days
Use
rs
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Test Results:Cumulative Monetization Events
0 5 10 15 20 25 30 -
2,000
4,000
6,000
8,000
10,000
Cumulative Monetization Events
Original CohortTest Cohort
Days
Mo
ne
tiza
tio
n E
ve
nts
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Test Results:Monetization Timeline
0 100 200 300 400 500 6000
20
40
60
80
100
Monetization Events vs. Visit #
Original CohortTest Cohort
Visit #
Mon
eti
zati
on
Even
ts
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Test Results:Cumulative Monetization Timeline
Results Original Cohort: 6,943 monetization events Test Cohort: 8,088 monetization events
Improvement: +16% increase monetization events in Test Cohort
0 100 200 300 400 500 600 -
2,000
4,000
6,000
8,000
10,000
Cumulative Mon. Events vs. Visit #
Original CohortTest Cohort
Visit #
Cu
mu
lati
ve
Mo
n.
Eve
nts
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1. Simple Funnel/conversion A/B Testing Optimize viral behavior
2. Split A/B Testing Used to measure the effect of more complex
behaviors
Summary of Testing Methodologies
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Improving Engagement: What Makes a Sticky Game?
- Quality is king- Graphics, theme, and game plot are key
- Ability to play with and without friends- Strengthens bonds, allows you to maintain your social ties.
- Game play is intuitive and easy- Emotional attachment- Ability to level-up- Achievements
- Make them public
- Enable self expression- Customization is specially important among female gamers and RPGs.
- Competition and bragging rights- No ending to the game, ever changing and growing.
- keep users interested, adapt the game.
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QUESTIONS?