Outcomes

In Outcomes, you'll describe the business goals you'd like to predict. Let's say a major business goal of yours is that you want to acquire more high-quality leads (who doesn't?)–you'll plug the outcome's cohorts in, and Faraday will create a likely-to-buy predictive model for that goal by applying dozens of strategies to build a range of candidate models, then selecting the one that most accurately predicts your outcome.

Getting started

Inside Outcomes, you'll find a list of your current outcomes if you have any, with columns for:

  • Performance: the performance score of the predictive outcome. Look at the analysis tab inside an outcome for more info.
  • Eligiblity: the cohort chosen for who you want to be able to achieve this outcome.
  • Attainment: the cohort selected as attainment represents the "finish line" for the prediction. What cohort do you want the eligibility cohort to look like?
  • Attrition (optional): the cohort people will enter if they fail to enter the attainment cohort by achieving the outcome.
  • Status: whether the outcome is ready, queued, or errored.

Creating an outcome

  1. Select new outcome in the upper right of the Outcomes list view.

Screenshot of the Outcomes list view

  1. Next, select an eligibility cohort. The eligibility cohort is who you want to be able to achieve this outcome. An important note on eligibility cohorts, however, is that it should not be a subset of your attainment cohort: if your attainment cohort is set to Customers, your eligibility cohort should not be something like Customers with a basement.

Screenshot of a new outcome

  1. Next, select your attainment cohort. This cohort represents the "finish line" for your prediction. If you're predicting which leads are most likely to become customers, your attainment cohort

  2. Optionally, select an attrition cohort for users that fail to attain this outcome.

    For an overall example in using these cohorts, say you want to create an outcome that scores leads based on the likelihood that they'll convert and become customers. In your outcome, you'll select the attainment cohort Customers, as the goal of your outcome is that leads will enter the Customers cohort and become customers. You'll leave the attrition cohort empty, as you don't necessarily want to discard the leads who don't attain this outcome. Lastly, your eligibility cohort will be your Leads, as you're only interested in how likely it is that your leads will become customers. With these selections, Faraday will use your current customers as a baseline against which your leads will be scored on the likelihood that they'll become just like your customers.

  3. Optionally, select certain traits to block in this outcome. For example, you may want to ensure protected classes aren't used.

  4. Once your cohorts are selected, give your outcome a unique name.

  5. With your desired fields are filled out, click save outcome to save the outcome, after which you'll receive a popup telling you that your outcome is building. You'll receive an email when the outcome is ready for use, and its status in the Outcomes list view will display as Ready.

Analyzing an outcome

Once your outcome is complete and its status is &&, click the Analysis tab to analyze its performance.

Screenshot of an outcome's analysis view

Your model will receive a score based on what results you can expect when using this outcome in your campaigns. The score can range from:

  • Misconfigured: This can happen when your cohorts have too few people in them to make meaningful predictions.

  • Weak: Expect your results to have minimal improvement when leveraging predictions from this outcome.

  • Moderate: Expect your results to improve modestly when leveraging predictions from this outcome.

  • Good: You can expect good results when employing this outcome in most supported use cases.

  • Excellent: Your outcome is strongly predictable. You can expect great results in many use cases.

  • Warning: Your outcome is predicted better than we would typically expect. You should check that only predictors known prior to the outcome are included. In other words, the model's performance is too good to be true. This can happen when the model calls on first-party data that's directly related to the outcome.

For a full report on everything involved in creating this outcome, click the full technical report button. In the report, you'll find information on everything that went into creating the predictive model, including bias and fairness reports.