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 HubSpot, 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 HubSpot account.
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 HubSpot using Pipelines
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
Now you'll create the prediction objective(s) necessary to complete this use case with Faraday.
Outcomes use machine learning to predict whether or not people will exhibit a certain behavior.
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:
- 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.
Now you'll configure the pipeline that deploys your predictions to hubspot.
- 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 HubSpot".
- For Payload, choose the following:
- Click the Save pipeline button.
Your pipeline will start building in the background. You can proceed immediately with the next set of instructions.
- In the Deployment area, find the CSV module and click Add.
- Fill out the popup:
Choose the Identified option.
Choose Human friendly column headers.
- Click the Next button.
- Expand the Structure section of 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.
With your pipeline deployed, it's time to plug your high spend scores into HubSpot. Follow the steps below to see each HubSpot contact enriched with their high spend score.
- In your Faraday pipeline, click the Download CSV button under the deployment to download your high spend scores as a CSV.
- Navigate to HubSpot's import wizard via Contacts > Contacts > Import, select start an import on the left, and click next.
- Select file from your computer on the left, since you're uploading a contact list that you intend to reach out to, rather than the other option of an opt-out list. Click next.
- Select one file and click next.
- Select one object, as we're only handling contact data. Click next.
- Select contacts, and keep selections in "activities" blank. Click next.
- Select your CSV, and click next.
- In data mapping, ensure email is matched correctly, as it is used by HubSpot to match contacts in your CSV to their HubSpot record.
Under the "Import as" column, key rows to map to "Contact properties" are email and the percentile row outlined in step 9.
- In the final row, "fdy_outcome_propensity_percentile", ensure the "Import as" column selection is contact properties, click choose or create property, then create a new property.
- Under "Group", select contact information, as the property you're creating is for the contact card.
- Give the new property an appropriate label, such as "high spend score."
- Optionally give the new property a description,, such as "Faraday's high spend score, or the percentile the customer is in based on their predicted likelihood to spend big." Click next.
- Change "field type" to number under "values," as the percentile is numeric. Click next.
- Click create to finish creating the new HubSpot property for percentile, and ensure the new property is selected in the final row (step 9).
- Click next to move onto the final stage of the import, where you can name the import.
- If desired, tick the box for create a list from this import, which will save you a step when preparing to launch a campaign to target these high spenders. Additionally, you can tick set these contacts as marketing contacts so that they are able to receive communication from you.
- Click finish import to wrap up. If you ticked the box to create a list in step 16, you can begin to plan a campaign to intervene just in time to keep your customers on board.
🔒 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.