๐ŸŽ๏ธPLG: Entity-level scoring

By the end of this tutorial, you will understand how to set up entity-level scoring in Cargo, use AI to enhance your scores, and effectively create segments based on those scores


Introduction to Entity-Level Scoring:

Typically, scoring mostly happens for the workflow level where records are processed and scored one-by-one.

However, you will come across use cases where itโ€™s not efficient to leave the scoring to the workflow execution level and instead you want to create entire segments as a function of their score. There will be times when a simple numeric score wonโ€™t cut it and instead youโ€™d use AI to use categorisation to create the scoring.

Picture this: Youโ€™re running a PLG motion want to identify high-potential users by scoring if the person is a key decision maker and whether theyโ€™ve sufficiently used the product automated score for each entity. You want to build a segment of high-potential users and engage them in a nurturing sequence

This is where entity-level scoring comes in. Let's delve deeper with an example of how weโ€™re doing this for ourselves at Cargo

Setting Up Entity-Level Scoring with Cargo:

Begin with the Basics:

  • In your Cargo workspace, navigate to the entities tab and select the contacts entity (for instance)

  • Click the three-dots on the top-right of your screen and select the 'columns tab'

  • Add a computed column and click add to insert new fields

  • Now, you can start adding your scoring criteria

    • Choose the data type for the new column. For this tutorial, weโ€™ll choose #Number

    • For the expression input, click on the small Library icon as in the image below to choose from one of the available recipes

    • Now you can start adding your scoring criteria

Build a Score Using Defined Parameters:

  • Choose the attributes (columns) and assign weights to them based on their importance to your score

  • For example, here weโ€™re taking the column app_run_count that tracks how many cycles of runs a user has performed on Cargo. Every time a user has executed more than 15 runs, we add a score of +20 to their score

  • Similarly, here we can attribute a positive score of 20 whenever the user signed up with a corporate email rather than a personal email

  • Other attributes we could have leveraged

    • Deployed workspaces in the last month

    • Total members on workspace made

    • Engagement on marketing emails

    • Duration since the last purchase

Enhancing Your Score with AI:

  • So far we were using columns or attributes that were easily classifiable using simple logic. What if we wanโ€™t to use a score on something not so straightforward. We had a use case where we wanted to classify whether a user was a decision maker inside a team, and not just an individual contributor.

  • Setting one up is pretty simple. Here we chose an AI recipe from the library

  • In the configuration, pass the name of the column that contains the the job title of the user

  • Pressing the check button on the top right and resyncing the entity will now create a new column with the output of your AI classifier

  • You can now use this column in your points-based scoring as shown above

  • You can set up your own recipe if youโ€™d like to play around with the prompt. Hereโ€™s the kind of prompt we were using.

Creating a Segment Based on Your Score:

  • Filter on a threshold of score that suits you

  • Click on "Create Segment" and youโ€™re good to go.

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