Player reinvestments and return on marketing spend for online gaming companies can be a tedious task. Without comprehensive insights from the data collected, wasteful expenses are incurred and potential clients lost. We have seen this in the past with one of our clients. This blog talks about how we helped the client optimize its marketing spend and incentivize players more effectively.
The client’s existing ways to allocate marketing spend was based on limited analysis of limited data from players’ gaming history. We developed a detailed multidimensional player segmentation framework and methodology for assigning scores based on behaviour and gaming history. This helped the client to increase revenue by 133.33 %.
The client’s existing methodology to allocate marketing spend on player reinvestment was primarily based on analysis of only a few attributes like average player bet amount with the data set being considered for a very limited time series. This resulted in an ‘average player’ approach and sub optimal allocation of player reinvestment spend and wrong set of players being incentivised. A large proportion of the client’s annual marketing budget was spent on player reinvestment as player bonus for retention.
Improvement in return on marketing investment
The client saw a scope for improvement in the return on its marketing investment which we helped them accomplish by developing a structured framework which comprised of the following:
- Contrary to the existing approach of limited attributes, an exhaustive list of customer demographics, gaming history, client’s revenue at player level, were considered for creating 4-5 player segments. This helped in gaining a better understanding of the players but still was not completely actionable.
- New RFM segments were created based on players’ Recency, Frequency & Monetary values. This scoring model assigned scores to players based on their RFM values.
- This helped the client identify most valuable players in each player segment and differentiate them from less valuable ones and use targeted player reinvestments more effectively.
- Player’s Demographic
- Gaming History
- Client’s Revenue at Player Level
Identification of most valuable
customers based on RFM
(Recency, Frequency & Monetary value)
The Implementation Result
The implementation resulted in gaining a better understanding of the players based on their demographics and behaviour which enabled the client to target and engage players better. It also, resulted in targeting player reinvestments (Ex: Special Bonus) based on their individual RFM scores rather than ‘average approach’ resulting in enhanced targeted marketing. This approach can be easily scaled for any number of customers and gaming products. The model follows machine learning where the daily results are fed back into the model for improvement as a continuous process. The client through their CRM solution is able to allocate the player retention spend almost on a daily basis, based on player’s RFM score rather an inaccurate one time exercise of identifying the player segments. This along with other Analytical approach resulted in 133.33% increase in the client’s revenue.