Rep assignment

Predict which rep will most likely convert a lead.

Interface

In this tutorial, we'll show you how to:

  • Deploy your rep assignment predictions using a pipeline

Along the way, we'll point you to other documentation you need to configure prerequisites. If you'd rather have every step all on one page, see our Rep assignment quickstart

Let's dive in.

  1. You'll need a Faraday account — signup is free!

Confirm your data

Unless you’ve already created it for another quickstart or purpose, you’ll need to add the following cohort to your account:

  • Leads

Cohorts

What’s a cohort?

A cohort is Faraday’s term for a commercially significant group of people — for example, a brand’s customers, leads, or even “people who bought X and Y and then cancelled.”

Cohort membership is fluid — continuously computed by Faraday — and is defined by events its members must all have experienced and/or traits its members must all share.

For example, a Customers cohort could be defined as the group of people who have all experienced a Transaction event at least once.

For more, see our docs on Cohorts, Events, Traits, and Datasets (which define how events and traits emerge from your data).

curl

To verify, use a GET /cohorts request. Your response should look like this:

[{
  "name": "Leads",
  "id": "$LEADS_COHORT_ID"
, ...}]

Make note of the IDs of the necessary cohorts.

If the required cohort isn’t there, follow the instructions using this button, then return here to resume.

Confirm your predictions

Unless you’ve already created it for another quickstart or purpose, you’ll need to add the following prediction in your account:

  • Recommender: Rep assignment
Recommenders

What’s a recommender?

A recommender is what you use in Faraday to predict which of several options a customer is most likely to choose. Most commonly, this is used to recommend a specific product or category from your available offerings.

For more, see our docs on Recommenders.

curl

To verify, use a GET /recommenders request. Your response should look like this:

[{
  "name": "Rep assignment",
  "id": "$REP_ASSIGNMENT_RECOMMENDER_ID"
, ...}]

If the required recommender isn’t there, follow the instructions using this button, then return here to resume.

Deploy your predictions

Now you’ll create the pipeline necessary to deploy your predictions.

Create a pipeline for rep assignment

curl

Use a POST /scopes request:

curl https://api.faraday.ai/scopes --json '{
  "name": "Rep assignment",
  "population": {
    "include": [
      "$LEADS_COHORT_ID"
    ]
  },
  "payload": {
    "recommender_ids": [
      "$REP_ASSIGNMENT_RECOMMENDER_ID"
    ]
  }
}'

Your pipeline will start building in the background. You can proceed immediately with the next set of instructions. When your pipeline is done building, you’ll get an email.

Deploy your rep assignment pipeline

Deploying to CSV as an easy example

This section describes how to deploy your predictions to a CSV file that Faraday securely hosts (and continuously updates) for you to retrieve either manually or on a scheduled basis using your existing data infrastructure.

Most Faraday users eventually update their pipelines to deploy to data warehouses, cloud buckets, or databases. To do that, you’ll add your destination as a Connection and then choose it instead of Hosted CSV.

For more, see our docs on Pipelines and Connections

  1. In the Deployment area, find the CSV option and click Add.
Screenshot of Faraday showing pipeline detail view, no targets
  1. Choose the Identified option and click Next.
Screenshot of Faraday showing new target form, complete
  1. Skip the advanced settings by clicking Finish.
  2. Click the Test deployment button and confirm the results meet your expectations.
Screenshot of Faraday showing pipeline detail view after target test

Your pipeline will finish building in the background. You can proceed immediately with the next set of instructions. When it’s done, you’ll get an email—then you can return to this pipeline and click the Enable pipeline button to activate it.