Dynamic Pricing in Mobile Games
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Transcript of Dynamic Pricing in Mobile Games
© 2015. Company Confidential and Not for Redistribution. [email protected] 07/22/15 1
Dynamic Pricing In Mobile Games
July 22, 2015
© 2015. Company Confidential and Not for Redistribution. [email protected] 07/22/15 2
About Me
• CEO and Founder, Scientific Revenue
• Previously CTO / SVP Product for Live Gamer (Payments Aggregator focused on F2P Gaming )
• Long history in data and artificial intelligence
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The Last AI Conference I Spoke At ….
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About Scientific Revenue
Scientific Revenue provides a dynamic price management solution for mobile games that boosts in-app purchase revenue. We match the right prices with the right players at the right times, to keep players engaged, increase conversion, and grow profits for game publishers.
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What Heather Said
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Earlier Today ….
• The heavy lifting going on around knowing what players are doing has to do with prediction and classification
• Classic territory
• Systems today are more strongly on detection and diagnosis, not action side
• We’re starting to get solid predictive analytics
• Scientific Revenue is about a control framework
• Well, that and some pretty nice machine learning.
• This talk is mainly about control frameworks.
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What is a Price
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An Offer To Sell a Good or Service for “Real Money”
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An Offer To Sell a Good or Service for “Real Money”
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Lots of Decisions
Different:
• Prices• Coin amounts• (volume discounts)• Framing text and cues• Default selection• Different bonus types
Same:
• Call to action
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These are Also “Pricing Decisions”
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Not Just “How Much for How Much”
• Pricing decisions are also
• What additional inducements do you offer?
• When do you make the offer?
• What channels you make the offer in?
• What messages accompany the offer?
• How long the user has to act on the offer?
• ….
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The Problem
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Increase LTR (“R” = “Revenue”)
• Pricing optimization is a tool to revenue maximization
• Without causing adverse reactions
• NOT Looking at things at the “individual transaction level”
• For games with very high churn and very short retention times, these approaches overlap
• But if you’re keeping your users around and hoping for more revenue later (second purchases, advertising, …) then there are other aspects to consider
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LTR
• 20% of all purchases occurred on day 1• All spending was done by day 40• 27% of all first purchases occur on day 1• 80% of all first purchases occur in week 1• 49% of purchasers bought a second time• Half of all purchases occurred in week 1• Second purchases were the same size as first
purchases
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The Settled Science isn’t Very Useful
• Classical Economics involves pricing to the demand curve (and, maybe, estimating demand curves using multi-armed bandits)
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Reality is Actually …. Kind of Unsettled
• Training effects?• Framing Effects?• Volume discounts? • Churn Impacts?• Community Impact?• Moral Hazard?
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Moral Hazard
This is absolutely disgusting. I'll be sure to tell everyone
about this creepy, exploitive tracking of players.
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Our Predecessors Made Many Simplifying Assumptions ….
• Non-negotiated pricing• Flexible return policy• Segmentable market demand• Highly competitive markets / little or no vendor
loyalty• Publically available ratecards
• Pre-existing anchoring on pricing and rates• Infrequent, large-dollar amount purchases• Customers return months or years later
• Low variable costs• Fixed capacity• Inventory can be changed from one product to
another• Perishable inventory
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The Architecture
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In Block Form
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Three Distinct Requirements
• Data Collection
• This isn’t an algorithm problem. It’s a modeling and feature problem and it requires a well designed data set informed by the machine learning goals
• Control Framework
• The point is to change prices. By itself, that’s actually pretty hard to do (in the “lot of code” sense)
• Asynchronous Evaluation Framework
• Most of our model building and training is done asynchronously
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Evaluation Framework: Global Cycle
Calibrate
Measure
Diagnose
Propose
Promote Test
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Evaluation Framework: Calibration
• Solving “Cold Start”
• Have a canned set of 70+ segments (that are “known” to exhibit pricing and behavioral differences).
• Have a predefined set of 250+ additional features
• Have a diagnostic framework that can exhaustively measure a large number of metrics and which can evaluate features for predictive power
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Example: Part of Day / Day of Week
• Left: Number of new users by date and hour, lighter = more
• Right: Number of purchasers by date and hour, lighter = more
• People who join at noon are 4 times more likely to spend than people who join at 5 AM (in this game)
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Evaluation Framework: Calibration
• Run for three weeks to train the models against the initial segments and get baseline performance data
• Compare the initial segments to each other to get an idea of variation and benchmark good performance
• Spot “underperformers” and “overperformers”
• Automated diagnostics to explore reasons why
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Directional Metrics
• Traditional KPIs can indicate issues, but don’t help much with action
• ARPU dropping? What is the automated, or partially automated, outcome?
• Part of the power of our approach comes from putting features against finer-grained behavioral metrics
• And then automating the sifting
• Example:
• Purchase Index• Default Acceptance Ratio• Upsell %• Downsell %
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Evaluation Framework: Segmentation
• Predictive Analytics
• Churn Prediction, Likely To Purchase, Potential Whale, …
• Custom Models
• Disposable Income, Affluence, Gamerness, Mobile Native Ness, …
• “Possibly Important” Features
• Lots of these
• Propose Segments and Pricing Policies
• Based on important features, create segments and compare
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Evaluation Framework: User Lifecycle
(start) JoinInitial Profile
Baseline Modeling
Baseline Prediction
Observe Adjust
Initial Pricing Set
Reprice
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Dealing With Intuitive Ideas
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Intuitive Physics
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Intuitive Economics
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What is Stickershock
A feeling of surprise and disappointment caused by learning that something you want to buy is very expensive.
Astonishment and dismay experienced on being informed of a product’s unexpectedly high price.
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Formalizing Stickershock
Before:
• D0 to D3 timeframe• A user visits a payment wall (or purchase opportunity) early in their lifecycle (and unusually
early)• We have other signals that they are likely to buy (usually behavior-oriented)
During:
• They don’t buy• They abandon relatively quickly
After:
• They don’t come back to the payment wall• They either grind or leave the game entirely
Supporting Evidence:
• Low affluence signals
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Options for Dealing with Stickershock
• Give out more currency early
• Initial Framing Offer
• Targeted Intervention
• Reorder baseline prices and reset default
• Different set of Baseline Prices
• Different set of Baseline Prices with windowing