Player Feature - Predicted Bet Amount on Slots
Let's take an in-depth look into the Predicted Bet Amount on Slots Player Feature; how it can be used, the objective of using it and the logic of how it has been set up.
Predicted Bet Amount on Slots is a system Player Feature.
✅ This means that it has been created by FT and is available to use as part of the Singularity Model.
🧠 Please note that system Player Features cannot be edited or deleted. If you want to make changes, you must create your own version of the Player Feature.
⚙️ Feature Type
All Player Features must be connected to a Feature Type. Think of the Feature Types as the settings that define the language that we use to talk about important pieces of information. The Player Feature uses these settings and relates them to a player.
The Player Feature: Predicted Bet Amount on Slots is created based on the Feature Type: Casino Stake Range Bracket.
The classes and slugs that are required by the Player Feature, are created and defined in the Feature Type.
📚 Further reading;
The objective of the Predicted Bet Amount on Slots Player Feature is to be able to look at all sessions the player had during their last Active State and use this information to predict what their future stake size will be. The Active state is determined in the Lifestage Player Feature.
Possible outcomes (Classes)
The possible outcomes (Feature Type Classes) that a player can belong to are;
- Nano (€0.01c and Less)
- Micro (€0.01c - €0.20c)
- Tiny (€0.02c - €0.50c)
- Small (€0.50c - €2)
- Medium (€2 - €5)
- Large (€5 - €10)
- Big (€10 - €25)
- Huge (€25 - €50)
- Mega (€50 or More)
Let's look more closely at how these classes are calculated and how players can qualify to belong to a certain class 👇
Movements define the way in which players can be moved from one state to another.
They can either be real-time movements, that occur when a real-time action occurs (such as a payment or registration), or a time-based query. Time-based queries occur at a set time of the day and evaluate the player base to determine if a player should move class.
📚 Read more;
For Predicted Bet Amount, there is one Active Process or movement that has been set up to manage player movements between states;
Evaluate Casino Stake Range Bracket
- This movement is a Time-Based Query that is set to run at a set time of 'Everyday at 03:00 UTC'.
- The computation checks data from when the player last was in the Active State, as defined by the Lifestage Player Feature.
- Following the previous point, the data that is being looked at is the number of casino bets from a player's most recent Active State. Players are grouped into one of the stake amount categories, where the stake amount category with the highest number of casino bets will be assigned to the player.
- Blocked and excluded players are also included.
🧠 Important to Note
The amounts in this player feature are predefined.
The bet amounts are in your base currency. If you use a different base currency from €, you will need to set up your own version of this Player Feature.
Most of the Player Features in the Singularity Model make use of time-based queries. Queries are good for determining states of player inactivity, something a real-time movement is unable to determine.
Our queries are created using ClickHouse and are included in the Singularity Model for you to use.
🧠 Please note that the slug from the Feature Type class must match inside the query.
If you want to write your own queries, you can use the Query Editor or ask Fast Track for assistance. You can find the query editor in; Insights & Analytics menu - Data Studio - Query Editor.
🏁 What's Next
After some time, once the computation triggers have fired, you'll be able to see that players have now been assigned to one of the classes of Player Feature. You can see this happen in the Player Distribution dashboard inside the Player Feature:
Following this, you will be able to find Predicted Bet Amount on Slots in the segment field list to be used for Activities and Lifecycles.