Dynamic Pricing

A hyper-casual gaming apps company in Germany predicted willingness to pay and showed their users a dynamic price, increasing revenue and user satisfaction.

Software
Growth
75,000

rows of data

3 hours

time to value

81.3%

prediction accuracy

23%

increased revenue

Background

Dynamic pricing enables several businesses to price their user based on various market changes. Dynamic pricing is a way to base prices on current market conditions. Uber does it. Airbnb does it. Airlines do it too. Basically, anything that has rising and falling prices is probably using a dynamic pricing method. However, using Machine Learning, you can super charge dynamic pricing by charging users what they're willing to pay vs. a standard cost.

A hyper-casual gaming apps company in Germany decided to turn to this method of pricing to optimize revenue growth from existing users while still aiming to maintain user satisfaction. They decided to turn to Obviously AI to build their dynamic pricing model.

The Solution

The company conducted extensive market research and chose Obviously AI’s predictive analytics platform for their needs. 

The Growth team used Obviously AI’s platform to train a model to analyze their customer’s demographic and behavioral data to answer the following questions:

1. How much is a customer likely to pay for extensions?
2. Which customers are most likely to pay more for in-app purchases?

The Results

The company increased revenue by 23%.

Obviously AI’s platform allowed the existing Growth team to build the AI model within a few hours, versus 4 months it would require a backlogged data science team, and at a significantly lower cost.

Most notably, they empowered their existing non-programmers with the capability of building state of the art AI predictive analytics models, without the need for an on-call data scientist.

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