Adaptive discounting
Adjust conversion incentive to match a customer’s predicted spend.
In this tutorial, we'll show you how to:
- Deploy your adaptive discounting 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 Adaptive discounting quickstart
Uses prerelease features
This document refers to features which are not yet available for general release: Forecast. Contact support if you'd like to request early access. Screenshots are disabled on this document.Let's dive in.
- 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:
- Customers
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).
To verify, use a GET /cohorts request. Your response should look like this:
[{
"name": "Customers",
"id": "$CUSTOMERS_COHORT_ID"
, ...}]Make note of the IDs of the necessary cohorts.
If the required cohort isn’t there, follow the instructions using this button, then return here to resume.
Confirm your predictions
Unless you’ve already created it for another quickstart or purpose, you’ll need to add the following prediction in your account:
- Forecast: Forecasted spend
Forecasts
What’s a forecast?
A forecast is what you use in Faraday to predict the number and/or value of events like transactions that an individual will experience over a certain timeframe.
For more, see our docs on Forecasts.
To verify, use a GET /forecasts request. Your response should look like this:
[{
"name": "Forecasted spend",
"id": "$FORECASTED_SPEND_FORECAST_ID"
, ...}]If the required forecast 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 adaptive discounting
Use a POST /scopes request:
curl https://api.faraday.ai/scopes --json '{
"name": "Adaptive discounting",
"population": {
"include": [
"$CUSTOMERS_COHORT_ID"
]
},
"payload": {
"forecast_ids": [
"$FORECASTED_SPEND_FORECAST_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 adaptive discounting 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
Use a POST /targets request:
curl https://api.faraday.ai/targets --json '{
"name": "Adaptive discounting in CSV",
"scope_id": "$ADAPTIVE_DISCOUNTING_SCOPE_ID",
"representation": {
"mode": "identified"
},
"options": {
"type": "hosted_csv"
}
}'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.