Gaming Marketing

Gamer Acquisition and Lifetime Value

author Josh Miramant December 12, 2017

The Challenge:

In marketing and sales, new ways to reach out to customers and prospects come out every year. It’s exciting and frustrating at the same time. With limited resources, how do you make strategic choices about which marketing methods, media, and technologies to use? You can guess and hope for the best. Or you can adopt a data-driven methodology.

Extermax, a game company, wanted greater predictability in their customer acquisition process. Without that in place, meeting their growth and financial goals would become much more difficult. Blue Orange helped them get answers with a data-driven methodology. While acquiring customers effectively was their primary focus, that was not the only area of interest.

The secondary goal was to predict their highest value customer engagement touchpoints.

By developing an accurate model for Customer Lifetime Value (CLTV), they can optimize their sales and marketing efforts. As a bonus, other departments ended up using the CLTV model to guide their company-wide benefits programs, figuring similar strategies could be used retaining customers as retaining employees. 

Company: Extermax

Sector: Gaming

Vertical: CLV/Market Segmentation

Model: K-NN/PCA

Model: NDB/Pareto

They aimed to measure and determine optimal solutions for the following:

  • How much should I spend to acquire a customer? 
  • What types of customers should sales reps spend the most time on trying to acquire?
  • What is the most effective marketing touchpoint and at what frequency?

The Solution:

We developed a user acquisition tracking platform for a mobile gaming client. 

Built a predictive tracking tool to customize each marketing touchpoint for potential customers. Additionally, we calculated the Lifetime Value of each individual user among several cohorts segmented by each network.

  • Designed and implemented customer based prediction models (Linear Regression models, NDB/Pareto model) to calculate the Lifetime Value per user.
  • Applied user segmentation for enhanced user acquisition. 
  • Determined the specific efficiency (profit/loss) of individual ad-networks.
  • Performed several user segmentation techniques: K-Neighborhoods + PCA and customized RFM analysis.
  • Automate data ingestion, processing, and reporting with RPA

The Results:

By evaluating each ad network, the marketing department could make smart decisions about the budget more quickly. That means less wasted money and more profit for the bottom line.

Improved user segmentation helps the company improve customer retention. Rather than treating all customers in the same way, the company can recognize their “best” customers early and keep them. For example, casual gamers can be engaged differently than long term gaming fans. The company has also developed different segments to guide marketing. 

The key takeaway is clear: Marketing optimization is an important strategic capability that leverages data assets to streamline campaign performance and improve customer acquisition efficiency. Above and beyond the bottom line benefits, there is more to this business transformation study. By equipping the company with customer lifetime value data, every department now speaks the same language. Sales and marketing professionals can measure their performance with a similar yardstick. Increased revenue predictability helps management plan budgets and avoid costly reliance on financing for operations. 

Can marketing optimization or CLTV solutions be developed for your business use case? How to get started with a CLTV solution? What processes can be automated via RPA?

Do you have any related questions? From private equity to health care, the Blue Orange Digital team has extensive experience with RPA and CLTV based solutions.

Get in touch and we are happy to provide you with answers!


Consumer Analytics Customer Lifetime Value Gaming Marketing Predictive Analytics
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