High spenders in Salesforce

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 Salesforce, 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 Salesforce 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 Salesforce 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

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 salesforce.

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 Salesforce".
• 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

CSV

• In the Deployment area, find the CSV module and click Add.
• Fill out the popup:
  • 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, it's time to plug your high spend scores into Salesforce. Follow the steps below to see each Salesforce contact enriched with their high spend score.

Creating a Salesforce Contact Field

First, we're going to create a Salesforce Contact Field for our high spend scores so that we can assign them to our contacts appropriately.

1. Navigate to the Fields & Relationships tab under Lead in Salesforce Object Manager.
2. Click New in the upper right to create a new field.
3. Select the number bubble, then click next.
4. Name the field something unique like "high spend score." Feel free to leave the character length limit as the default 18.

5. Click next, then choose the appropriate permissions required per your company's guidelines for which Salesforce users can view the high spend score field–then click next again.
6. Finally, choose which page layouts you'd like the high spend score field to display on. We recommend including any layout that is used by your team. Click save to finish creating the custom field.

Importing your high spend scores CSV into Salesforce

Now that the custom field for high spend score is created, let's upload the CSV output from your pipeline.

1. In your Faraday pipeline, click the Download CSV button under the deployment to download your high spend scores as a CSV.
2. Navigate to Salesforce's Data Import Wizard and select either Accounts and Contacts or Leads depending on how your account is configured. For this example, we're selecting leads.
3. Select Update existing records, and ensure you're matching records by Email.

4. Select CSV in the right pane, choose the CSV you just downloaded from your deployment, then click next.
5. In the mapping step, map your contact fields appropriately, and select the high spend score contact field you created to map to the row for the high spend score. See the bottom row's "2, 69, 87" example below where each value is a score. The second-to-last row, the raw score, can be safely left unmapped and ignored.

For high spend scores, the higher the score, the more likely it is that the contact is going to buy.

5. Click next and review your mappings.
6. Click Start Import to begin the import. When the import is complete, your contact cards will be populated with your Faraday high spend scores, and you're ready to kick off campaigns targeting only the highest spenders.

🔒 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.