alan avidan
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Welcome to:
Real-Time Optimization: Putting Facebook User Attributes to Work - Going Beyond A/B Testing and User Segmentation - Optimizing Open Graph
Alan Avidan − Executive Director [email protected] @beesandpollen
We’ll Cover:
1. The Playground: Games/Apps/Campaigns
2. Which User Attributes Can You Use for Optimization?
3. Predictive Best-Fit Optimization, and How Does it Lift
KPIs like Revenue, Virality, Engagement, Retention
4. Traditional Optimization Tools:
Analytics, A/B Testing, User Segmentation
5. Open Graph Optimization with Predictive Best-Fit
• Lots of Successful Apps, Games and Campaigns with
Millions of (Individual) Users
• Low Retention, Low %Pay, High User Acquisition Costs
• Notifications/Posts Can Become Spammy and Blocked
• KPIs Under Pressure – Need Lift - Perform or Perish!
• Vast Amounts of User Attributes
Terminology • Attributes • Elements (Events/Decision Points) • Options (Variants)
Low Range High Range
User DNA - Attributes Sources
Geo-Demographic attributes: age, gender, education, country, etc.
Open Graph: scores, achievements, published stories, custom actions, etc.
Behavioral attributes: level, spending, score, health, custom, etc.
Session attributes: time of day, day, duration, etc.
3rd Party attributes: income level, education, etc.
Facebook attributes: Friends, Influence, Likes, Interests, Posts, Events, etc.
Predictive Best-Fit Algorithms Find Correlations Between User DNA and Conversions
Predictive Best-Fit – Core Concepts
User User Social, open-graph
and Behavioral Data DNA Generation
Predictive Best-Fit Algorithm Real-
Time action
Analytics
Segmentation
Traditional Optimization Technologies A Quick Tour
A/B Testing
Define options Split traffic Measure results Deploy winner Max Result
Low range
High range
high range
A/B Testing
Upside
• Conceptually simple and understandable Can achieve good results – up to a point
Downside:
• One-size-fits-all
• Results may deteriorate over time
A/B Testing – Bottom Line
Define segments Define Options and rule base
Result
A Priori Segmentation
Low range
high range
Upside
• Can be effective if segmentation was meaningful
Downside
• Segments are predefined and cannot be changed during the analysis
• Different elements might require different segments
• Hard to scale in terms of data-set and number of elements
• Hard to fine-tune
A Priori Segmentation
Clustering Segmentation
Define options
A/B test options
Segment users based on result
Deploy winner
Low
range
High range
Upside:
• Highest Lift
• Discover correlations you never knew existed
Downside:
• Requires storage of terabytes of data
• Need really smart people to work on it
• Effort = Very High
Clustering Segmentation
• Can optimize in-app and open graph performance
• Automated end-to-end solution (Acquire data, analyze, predict, enact)
• Machine self-learning
• Real-time
• No user history required
• Numerous data sources
• In full compliance with facebook privacy rules
• Deep new insights
Predictive Best-Fit
Effort/Resources
Elements For Predictive Best-Fit Optimization
Monetization
• Payment Page: Ranges, Incentives
• Shop Order
Retention
• Message Timing
• Incentives
• Gifts
Engagement
• Offers
• Products
• Content
• Communications
Virality
• Share Messages
• Invite Friends
Look & Feel
• Colors
• Graphics
• Layouts
Open Graph
• Publish Yes/No?
• Timing
• Art and Copy
• Call-to-Action
• Story
Since revamping Open Graph stories with custom art and content, BINGO Blitz got 20% more likes and comments on news feed stories and 500% more unique clicks to the game.
SongPop Hits Major Milestones Just Three Months After Launch • 25 Million unique players to date • Has consistently received a coveted 5 start rating • 4 million people play every day, and growing
Big Impact Open Graph
Ford created an app that publish a story each time a user customized his dream Mustang and then battle others’ model. Although their goal was 2 million engagement they had more than 5 millions and more than 17,000 referrals.
The food finding and sharing app has seen a 3X increase in number of visits and activities shared by helping people share the dishes they want, try and ate with friends on Facebook
1 2
5
4 3
6
Open Graph Optimizations
Publish by User – Yes/No 1
Image 3
Landing Page 4
Time 6
Story 2
Action Verb Object 5
Yes No
Publish only by the right users!
Publish by User – Yes/No 1 Open Graph
Post with the right content to engage the viewer
• Publish achievements the player unlocked
• Publish scores the player achieved
• Publish custom activities:
Jeff E. finished Level 4 on MyGame!
• Publish extended custom activities:
Jeff E. won a game against Chris on MyGame!
Story 2 Open Graph
Publish using the most effective creative
Option A
Image taken from to
game
Option B
Image of real-world
landscape
Option A Image of song, leading to
clip
Option B Image of genre, leading
friends to songs/albums
recently listened to by user
Image 3 Open Graph
Publish with the best landing page to convert the viewer
Option A Landing page with the song playing
Option B Landing page with the latest songs of that genre listened by friends’ Option C Landing page of that album with a discount coupon
Landing Page 4 Open Graph
Option A Justin listened to [SONG X] by [SINGER-NAME] on Spotify
Option B Justin listened to Classic [GENRE Y] music on Spotify
Publish the most effective actions and objects
Action Verb Object 5 Open Graph
listen
Publish at the right time to get maximal exposure Friends newsfeeds
Timing 6 Open Graph
The Last Word
Consider optimization if you wish to become successful or stay relevant Consider Predictive Best-Fit Optimization All the Gain without the Pain
Welcome to:
Real-Time Optimization: Putting Facebook User Attributes to Work - Going Beyond A/B Testing and User Segmentation - Optimizing Open Graph
Alan Avidan − Executive Director, Business Development [email protected] @beesandpollen