Likely buyers in MySQL

Why use predictions for likely buyers?

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 MySQL, 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 MySQL account.

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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 MySQL using Pipelines

Getting started with likely buyers in MySQL

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

Requirements for this likely buyers recipe

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

• A cohort representing your customers — or create one first

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

• MySQL — or create one first

Building predictions for likely buyers in MySQL

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

Describe your likely buyers predictions with outcomes

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

Creating an outcome for likelihood to buy.

Let's make an outcome for likelihood to buy.

• In the navigation sidebar, choose Outcomes.
• 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".
• 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.

Using Pipelines to deploy predictions to your stack

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

Create your pipeline for likely buyers in MySQL

• In the navigation sidebar, choose Pipelines.
• 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 MySQL".
• Click the Save pipeline button.

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

Deploying your pipeline to MySQL

MySQL

• In the Deployment area, find the MySQL module and click Add.
• Fill out the popup:
  • Provide the specified parameters for MySQL.
  • Click Next.
  • Choose the Hashed option.
• Click the Next button.
• Expand the Limit section of Advanced Settings
  • From the dropdown, choose an outcome from the list
  • From the next dropdown, choose Between (1-100%)
  • Set the minimum to 86%
  • set the maximum to 100%
• 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.

How to use your likely buyers predictions in MySQL

With your pipeline deployed, your likely-to-buy scores are loaded into MySQL 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.