Article sections

    You can get access to a wealth of new data about your customers by using our Google Analytics integration.

    This will help you investigate which channels generate purchases from which RFM groups and assess what campaigns have performed the best in terms of reactivating some segments.

    Be aware!
    The implementation below will only work for users who authenticate on your website at least once.

    Requirements

    For this integration you will need to have the Reveal RFM information available in the front end code of your page (usually as a Javascript variable).

    • If you use one of the platforms we have integrations with (like Shopify), these variables are automatically published into javascript variables, so you can use them directly in the tutorial below.
    • If you are creating a manual implementation of Reveal:
      • Make sure you have already imported the RFM information from Reveal, then please publish that information into Javascript variables that you will name as you like and will use them following the steps in the tutorial below.

    Please follow this article to see how to import Reveal RFM data into your project: https://www.omniconvert.com/help/kba/how-to-export-data-from-reveal/.
    When the data has been imported please revisit this article for the next steps.

    Step 1: Create Custom Dimensions in Google Analytics.

    Access Google Analytics and go to Admin -> Property -> Custom Definitions -> Custom Dimensions and click the “+ New Custom Dimension” button.

    In the next screen please provide a unique and relevant name for the new dimension you want to register. Please make sure you select the User option in the Scope field. Then hit the Create button.

    You will need to create a custom dimension for each type of Reveal RFM information type: RFM score, RFM group, last known RFM group and so on.

    Step 2: Push information into the new Custom Dimension.

    After the dimension was created in step 1, Analytics returns an example of a code you need to insert into your website in order to send information to your new dimensions.

    It should look like this:

    var dimensionValue = ‘SOME_DIMENSION_VALUE’;ga(‘set’, ‘dimensionX‘, dimensionValue);

    Where X is a number assigned to your own dimension.
    dimensionValue should be replaced with your javascript variable that will hold the information from Reveal, for example: RFM Group or RFM score.

    In case you missed this code, you can find it by clicking on your dimension in the Custom Dimensions listing interface: Admin -> Property -> Custom Definitions -> Custom Dimensions.

    You will need to insert this code in your website before a pageview event set to Analytics, for example:

    var revealRFMScore = 555;
    ga(‘set’, ‘dimensionX’, revealRFMScore);
    ga(‘send’, ‘pageview’);

    When using our Shopify integration and the user is logged in the RFM variables are available on the window object. You can access them like this: 

    window._Reveal.rfm_groupwindow._Reveal.rfm_score

    Use Cases

    Having RFM data available in GA can prove extremely useful – however, keep in mind the caveat at the beginning of this article – you will only have data for users who have logged in at least once.

    That being said, depending on how you set up your tracking, you can:

    1. Channel Performance – Investigate which channels generate purchases from which RFM groups.
    2. Campaign Performance – Assess what campaigns have performed the best in terms of reactivating some segments.
    3. Location Performance – Discover whether your Soulmates (or any other group) in one location behave differently to Soulmates in another location.
    4. Buying Behavior – Understand how many times a user in an RFM group needs to see a product before they buy it.
    5. Search Intent – Analyze the filters / searches used by your RFM groups.

    The first 2 use cases are the most robust because they’re reliant on a purchase being made (so users have to be logged in). The others, however, should be taken with a grain of salt and any assumption made based on them should be A/B tested.

    Was this post helpful?

    Related Articles