Inspiration:

Current solution to churn in game marketing is to simply detect possible churn and start chasing the customer and offering monetary deals. This is result of defining customer as raw numbers and variables. In an industry like gaming whose customers are all about passion, overlooking emotions and physiology is a big mistake.

What it does:

Simplify Analytics combines data science with years of psychological research to help gaming companies find the right customer, reduce the churn, improve the products and identify possible account theft. Our approach to solve your problem is much more accurate and above all, requires less computation power.

How I built it:

Our solution is based Python and scikit-learn. We also use some stats from activity manager to identify the times customer has launched and closed the application.

Challenges I ran into:

It was hard to find a data-set that captures this simple parameters. Available data sets are captured to invent the wheel again and analyze the customer behavior through a large number and complex gaming parameters.

Accomplishments that I'm proud of:

We are proud that Simplify Analytics respect gaming fans as a human and tries to understand their behavior through psychological pattern.

What I learned:

We learned to make a change, sometimes you need to look at the problem from a different angle instead of focusing on optimizing the existing solutions. While we appreciate the power of Data Science, we think we do not need to invent the wheel again. We can use years of psychological research to understand customer's behaviors.

What's next for Simplify Analytics:

We plan to implement a data-extraction tool where we can build our own data sets.

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