Likely buyers in GCS

Introduction

To be able to know which of your leads and prospects are most likely to buy your product is to have the leg up on your competitors.

The most effective way to predict likelihood to buy is with machine learning. You can take people just like that lead or prospect you're looking at–similar hobbies, income, lifestyle, and more–and use their historical actions to predict whether or not your they'll take that leap and convert.

Faraday makes predicting likelihood to buy for both individuals and geographies intuitive & easy, and delivering it to any channel in your stack a breeze.

With likely buyer predictions in GCS, you'll give your team the ability to focus on only those people that are most likely to buy, meaning time is never wasted on bad fits.

Follow the steps below to get your likely buyers predictions into your GCS account.


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

  • Organize your customer data into cohorts
  • Describe predictive models for likely buyers with outcomes
  • Deploy likely buyers predictions to GCS 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:

Screenshot of the cohorts listing that includes Customers You'll also need the following connections available in your Faraday account:

Screenshot of the connections listing that includes GCS

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 to buy

Let's make an outcome for likelihood to buy.

  • In the navigation sidebar, choose Outcomes. Screenshot of the outcomes list
  • Click the New outcome button.
  • Fill out the form:
    • Select Everyone. This option will be disabled unless you have a contract with Faraday. Contact sales for a to demo.
    • For Attainment cohort, pick the cohort that best represents your customers.
    • Leave Attrition cohort blank.
    • Skip over Trait blocking.
    • Enter a memorable name, like "Likelihood to buy". Screenshot of the new outcome form, filled out
  • 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 gcp_gcs_csv.

Create your pipeline

  • In the navigation sidebar, choose Pipelines. Screenshot of the pipelines list
  • Click the New Pipeline button.
  • Fill out the form:
    • For Payload, choose the following:
      • Outcome: Likelihood to buy
    • Enter a memorable name, like "Likely buyers in GCS". Screenshot of the new pipeline form, filled out
  • 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

GCS

  • In the Deployment area, find the GCS module and click Add. Screenshot of the ready pipeline with no targets yet
  • Fill out the popup:
    • Provide the specified parameters for GCS.
    • Click Next.
    • Choose the Hashed option.
    • Skip the advanced settings. Screenshot of the new target form, filled out
  • Click the Finish button.
  • Click the Test deployment button and confirm the results meet your expectations. Screenshot of a target after hitting its test button the first time 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 likely-to-buy scores are loaded into GCS and ready to be plugged into your favorite marketing activation platform, where you can kick off a campaign to target only the best fits.

🔒 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.