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Setting Up Calculated Customer Attributes Based on Purchase History/Game History

Natalie Samuels
  • Updated
Using Calculated Customer Attributes, you can create customer attributes based on existing Game/Product History data.
 
This is an easy and simple solution that allows you to independently add more attributes to your site. By using the Purchase History Calculated Attribute, you will be able to explore and segment your customers based on how much they spent on a specific game/product or how many bets/orders they made on a specific game/product, in order to improve the granularity of your target groups.
 

How To Set Up a Calculated Attribute

To add a new calculated attribute:

  1. Go to the Data Studio in the navigation bar
  2. Click the Attributes section
  3. Choose "Add a New Attribute"
  4. Choose Purchase History as the data that you'd like to use
 
Please Note: You can create up to 15 attributes using Purchase History data
 
 
datastudiopurchase.gif
 
  1. Choose a name and a description for your new attribute.
  2. Set up the attribute calculation
     
    Selection Element  Examples
    Function 
     
    • First: the first game played/item purchased
    • Last: the last game player/item purchased
    • Most Frequent: what product/game customers bought/played the most frequently 
    • Most Spent: what product/game customers spent the most on
    • Sum: the total amount purchased/played
     
    Of (if applicable)
    • Amount: Bet/Order Amount (amount of money deposited/spent on the chosen game/product)
    • Quantity: Number of Bets/Order (number of bets/orders made on the chosen game/product)
    Where 
    • Product Type = Leggings
    • Game Type = BlackJack  
    • Product Department = Sport
    Timeframe
    • During the previous X days
    • Exactly X days ago 
    • Last calendar month/quarter
     

    Screenshot - 2024-02-14T103122.749.png

    7. Click Save

    Your new attributes will be added to the list of calculated attributes under the "Not Published" status. 

     

    Please Note: Calculated Attributes take snapshot dates into account since the creation date only. 

     

    If you wish, you can add additional attributes. Once done, go to the list of calculated attributes and click "Publish Attributes".
     
    The new attributes will be available for segmentation and analysis in a matter of minutes, under the Customer Attributes selection criterion.
     

    Example Use Cases

    To give you a better idea of how you can leverage the Calculated Customer Attributes, here are some common use cases that can be addressed using this capability. 
     
    Communicating with your one-timers to encourage them to make additional orders is crucial to increasing customer lifetime value (CLTV). To achieve this, exploring their behaviors and personalizing your communications accordingly is key.
     
    First, in Customer Explorer create a target group of customers in the ‘New’ lifecycle stage. Next, select the First Category Purchase attribute to analyze what products your ‘New’ customers are purchasing.
    Picture4.png
    In this example, you can see most customers’ first category purchase is Swimwear. With this insight, you can now use the Purchase History Calculated Attribute to understand how much these customers are spending on swimwear in the last calendar quarter. Here’s how you’d set it up:
     
    • Function = Sum 
    • Of = Total Order Amount
    • Where = Product Category Equals Swimwear
    • Timeframe = Last Calendar Quarter 

    Screenshot - 2023-12-17T145437.595.png

    Now you can add this attribute to your target group and analyze the distribution of how much your “New” customers spent specifically on swimwear. Based on this data, segment customers that spent above the average total order amount and send them higher discounts, since they may be more likely to spend more money with you in the future. 
    Let’s say you are running a churn prevention campaign with a ‘Deposit $50, get $50 extra’ offer.
     
    However, to use this offer you want to make sure you only target players that have deposited $50 or less and send a different offer to players that have deposited over $50. 
     
    First, in Customer Explorer create a target group of customers at Critical and High Risk of Churn.
    Next, under Key Group Characteristics select the Product Preference attribute to analyze what games your high-risk customers are betting on.
    Picture6.png
    Here we can see the majority of high-risk players bet on sports games, with a preference for football.
     
    Based on these insights, you can create a calculated attribute to understand how much players are betting on football games in the last two weeks. Here’s how you’d set it up:
     
    • Function = Sum 
    • Of = Sport, Bet Amount
    • Where = Discipline = Football
    • Timeframe = During the previous = 14 Days 

     

    Screenshot - 2023-12-17T153645.462.png

    Now you can add this attribute to your target group and analyze the distribution of how much these players bet specifically on football. Based on this insight, use your new Purchase History attribute to segment players that have deposited $50 or below on Football, and target them with your discount campaign.

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