See how player clusters work within Optimise
Overview
This Player Cluster Highlights page is part of the Player Behaviours module.
This page helps you to view your player behaviours across multiple KPIs over set time periods. As a highlights page, it is designed to surface high-level data that can be explored in more detail in the Player Cluster Comparison page.
Visualisations
This page contains the following visualisations:
- A summary of KPIs across your business, which can be filtered by your player clusters
- A ribbon chart showing the distribution of your player clusters
How to utilise player clusters
Players are grouped together using a popular machine learning technique known as clustering. This technique considers your entire player base and organises them into clusters based on their behaviour.
How often player clusters are updated
Amplifier AI's models run on a daily basis and allocate players to a single or multiple clusters, based on their behaviour for every game they play.
As such, the total number of players in all clusters will exceed the number of unique players, and therefore it is intended to be used to understand the proportionate distribution between clusters.
Attributes of player clusters
The model considers the following attributes when assigning a player into a cluster:
- Number of spins
- Bets
- Average bet
- Average session length
- Bonus stakes (if available in data)
Using this information, players are assigned per game into one of the following categories:
- High Value Multi Session — these players tend to have highest number of rounds, sessions, bet value, and often have higher RTPs. They are termed 'Multi Session' as they play multiple sessions of the same game per day.
- High Value — these players share similar attributes to High Value Multi Session players, however they only play one session per day.
- Mid Value Multi Session — these players tend to play fewer rounds, place smaller daily bets, have smaller session lengths and less RTP than High Value Multi Session players.
- Mid Value — these players share similar attributes to Mid Value Multi Session players, however they only play one session per day.
- Tourists — these players contribute low amounts of GGR, play one session on average and tend to place few bets with low RTPs.
- Low value — these players have the lowest contribution to all the metrics compared to other clusters. It is likely that bonus abusers would appear in this cluster.
Visualisations
Summary Figures
The summary figure provide you with a performance overview of all clusters.
The metrics available are GGR, NGR, Turnover (or Wagering), Rounds, Players and Payouts. You can filter with the dropdown in the top right of the screen.
By default, all clusters are selected.
Helpful tip
Use the filter in the top right to see data from a a specific player cluster.
Player Cluster Distribution
In the ribbon chart on the lower half of the page, you will see the contribution of a selected KPI made by each cluster.
If you hover over one of the ribbons, it shows the ranking in terms of which cluster contributed the most towards the KPI, and the total change from the previous day.
The KPIs available are GGR, NGR, Payout, Turnover, Rounds & New Players. Toggle between KPIs using the orange buttons on the right hand side.
Helpful tip
On top of the page, there are toggles to help you select time periods to view data for that specific period. Each time period can be understood as follows:
Time periods
Yesterday - View data for the previous day.
Week to date - View data starting from Monday until the day before the present day.
Month to Date - View data starting from first of the current month until the day before the present day.
Last 30 Days - View data for the last 30 days.
Quarter to Date - View data starting from first of the current quarter until the day before the present day.
Year to Date - View data starting from first of the current year until the day before the present day.
How do I get support or make a feature suggestion?
Visit our 'here to help' form and we will respond accordingly.