Adaptive discounting quickstart

Interface

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

Add data representing your transaction events using a dataset.
Describe your Customers group formally using a cohort.
Create a predictive model for forecasted spend using a forecast.
Deploy your adaptive discounting predictions using a pipeline

Let's dive in.

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.

Transaction event stream

Add data

Create a transaction event stream

Using CSV as an easy example

This section describes how to add data securely using a CSV file that you export from your data infrastructure.

Most Faraday users eventually update their configuration to pull data directly from their data warehouses, cloud buckets, or databases. To do that, you’ll add your source as a Connection and then choose it below instead of CSV.

For more, see our docs on Datasets and Connections.

Extract a CSV representing your transaction from your systems, or download the sample transactions file from Farazona. Either way, save it someplace memorable.

curl

First, upload your data with a POST /uploads request:

curl --request POST \ --url https://api.faraday.ai/v1/uploads/transactions/YOUR_CSV_FILE.csv \ --header 'Accept: application/json' \ --header 'Authorization: Bearer YOUR_API_KEY' \ --header 'Content-Type: application/octet-stream' \ --data-binary "@YOUR_CSV_FILE.csv"

Then use a POST /datasets request to register the data:

curl https://api.faraday.ai/datasets --json '{
  "name": "Transaction",
  "identity_sets": {
    "shipping": {
      "city": "shipping_city",
      "house_number_and_street": [
        "shipping_address1",
        "shipping_address2"
      ],
      "person_first_name": "shipping_address_fn",
      "person_last_name": "shipping_address_ln",
      "postcode": "shipping_postcode",
      "state": "shipping_state"
    }
  },
  "options": {
    "type": "hosted_csv",
    "upload_directory": "transaction_data_files"
  },
  "output_to_streams": {
    "transaction": {
      "data_map": {
        "datetime": "created_at"
      },
      "value": "total"
    }
  }
}'

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

Customers cohort

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

Confirm your data

Event streams

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

Transaction

What’s an event stream?

Predicting a certain customer behavior requires historical examples of customers exhibiting that behavior. Faraday works best when that data comes in the form of “events” — specific actions or occurrences that happened at specific times.

Formulating data this way helps you define cohorts more expressively.

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 /streams request. Your response should look like this:

[{
  "name": "Transaction",
  "id": "$TRANSACTION_STREAM_ID"
, ...}]

Make note of the IDs of the necessary streams.

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

Transaction

→

Create cohort

Create a customers cohort

curl

Use a POST /cohorts request:

curl https://api.faraday.ai/cohorts --json '{
  "name": "Customers",
  "stream_name": "transaction"
}'

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

Forecasted spend forecast

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

Confirm your data

Event stream

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

Transaction

What’s an event stream?

Formulating data this way helps you define cohorts more expressively.

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 /streams request. Your response should look like this:

[{
  "name": "Transaction",
  "id": "$TRANSACTION_STREAM_ID"
, ...}]

Make note of the IDs of the necessary streams.

If the required event stream isn't there, follow the instructions using this button, then return here to resume.

Transaction

→

Configure your prediction

Create a forecasted spend forecast

curl

Use a POST /forecasts request:

curl https://api.faraday.ai/forecasts --json '{
  "name": "Forecasted spend",
  "stream_name": "transaction",
  "stream_property_name": "value"
}'

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

Adaptive discounting deployment

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).

curl

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.

Customers

→

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.

curl

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.

Forecasted spend

→

Deploy your predictions

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

Create a pipeline for adaptive discounting

curl

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

curl

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.