High spenders in RDS (SQL Server)

Introduction

Knowing which of your leads and prospects are most likely to spend big gives you the opportunity to serve them just the right offers at just the right time to drive revenue.

The most effective way to predict high-spend customers is with machine learning. With machine learning, you can constantly keep your lists of leads and prospects up-to-date with high-spend predictions based on the historical data of similar shoppers, and plug your high spenders right back into your stack, no PhD required. With your high spenders in your stack, you're ready to kick off a campaign to drive them to conversion.

Faraday makes predicting your high spenders intuitive & easy, and delivering them to any channel in your stack a breeze.

With high spend score predictions in RDS (SQL Server), you'll give your team the ability to target not just those likely to become customers, but those most likely to spend big.

Follow the steps below to get your high spenders predictions into your RDS (SQL Server) account.

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

• Organize your customer data into cohorts
• Describe predictive models for high spenders with outcomes
• Deploy high spenders predictions to RDS (SQL Server) using Pipelines

Getting started

Make sure you have a Faraday account (signup is free!) and that it's not in test mode.

Prerequisites

You'll need the following cohorts available in your Faraday account:

• A cohort representing your customers — or create one first
• A cohort representing your high spenders — or create one first

You'll also need the following connections available in your Faraday account:

• RDS (SQL Server) — or create one first

Objectives

Now you'll create the prediction objective(s) necessary to complete this use case with Faraday.

Outcomes

Outcomes use machine learning to predict whether or not people will exhibit a certain behavior.

Likelihood for high spend

Let's make an outcome for likelihood for high spend.

• In the navigation sidebar, choose Outcomes.
• Click the New outcome button.
• Fill out the form:
  • For Eligibility cohort, pick the cohort that best represents your customers.
  • For Attainment cohort, pick the cohort that best represents your high spenders.
  • Leave Attrition cohort blank.
  • Skip over Trait blocking.
  • Enter a memorable name, like "Likelihood for high spend".
• Click the Save outcome button.

Faraday will do some magic in the background, so you can proceed with the rest of the instructions. When your outcome is done building, you'll get an email, and you can review your outcome.

Pipeline

Now you'll configure the pipeline that deploys your predictions to aws_rds_sql_server.

Create your pipeline

• In the navigation sidebar, choose Pipelines.
• Click the New Pipeline button.
• Fill out the form:
  • For Payload, choose the following:
    • Outcome: Likelihood for high spend
  • For Population to include, choose the following:
    • A cohort representing your customers
  • For Population to exclude, choose the following:
    • A cohort representing your high spenders
  • Enter a memorable name, like "High spenders in RDS (SQL Server)".
• Click the Save pipeline button.

Your pipeline will start building in the background. You can proceed immediately with the next set of instructions.

Deploy your pipeline

RDS (SQL Server)

• In the Deployment area, find the RDS (SQL Server) module and click Add.
• Fill out the popup:
  • Provide the specified parameters for RDS (SQL Server).
  • Click Next.
  • Choose the Identified option.
  • Skip the advanced settings.
• Click the Finish button.
• Click the Test deployment button and confirm the results meet your expectations. Faraday will finish building your pipeline in the background. When it's done, you'll get an email—return to the pipeline and click the Enable pipeline button to activate it.

Conclusion

With your pipeline deployed, your high spend scores are loaded into your Google BigQuery warehouse and ready to be plugged into your favorite marketing activation platform, where you can kick off a campaign to target not just those likely to become customers, but those most likely to spend big.

🔒 It's a best practice to permanently delete any file that contains personally identifiable information (PII) after use. Any deployment from Faraday that is unhashed contains PII, and should be deleted after uploading it to your destination for security purposes.