Dynamic Pricing in Mobile Games

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Dynamic Pricing In Mobile Games

July 22, 2015

mailto:[email protected]


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



The Last AI Conference I Spoke At ….



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.



What Heather Said



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.



What is a Price



An Offer To Sell a Good or Service for “Real Money”



Lots of Decisions

Different:

• Prices• Coin amounts• (volume discounts)• Framing text and cues• Default selection• Different bonus types

Same:

• Call to action



These are Also “Pricing Decisions”



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?

• ….



The Problem



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



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



The Settled Science isn’t Very Useful

• Classical Economics involves pricing to the demand curve (and, maybe, estimating demand curves using multi-armed bandits)



Reality is Actually …. Kind of Unsettled

• Training effects?• Framing Effects?• Volume discounts? • Churn Impacts?• Community Impact?• Moral Hazard?



Moral Hazard

This is absolutely disgusting. I'll be sure to tell everyone

about this creepy, exploitive tracking of players.



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



The Architecture



In Block Form



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



Evaluation Framework: Global Cycle

Calibrate

Measure

Diagnose

Propose

Promote Test



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



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)



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



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 %



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



Evaluation Framework: User Lifecycle

(start) JoinInitial Profile

Baseline Modeling

Baseline Prediction

Observe Adjust

Initial Pricing Set

Reprice



Dealing With Intuitive Ideas



Intuitive Physics



Intuitive Economics



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.



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



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


Dynamic Pricing in Mobile Games

Data & Analytics

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