Likely buyers in LinkedIn Ads

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 LinkedIn Ads, 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 LinkedIn Ads 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 LinkedIn Ads using Pipelines

Getting started with likely buyers in LinkedIn Ads

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:

Screenshot of the cohorts listing that includes Customers

Building predictions for likely buyers in LinkedIn Ads

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. 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.

Using Pipelines to deploy predictions to your stack

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

Create your pipeline for likely buyers in LinkedIn Ads

  • 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 LinkedIn Ads". 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.

Deploying your pipeline to LinkedIn Ads

CSV

  • In the Deployment area, find the CSV module and click Add. Screenshot of the ready pipeline with no targets yet
  • Fill out the popup:
    • Choose the Hashed option.

    • Choose Human friendly column headers.

  • Click the Next button. Screenshot of the new target form, filled out
  • Expand the Filter section of Advanced Settings
  • Click Add payload filter
  • From the dropdown, choose an outcome from the list
    • In the dropdown on the left, choose Greater than or equal to
    • On the right, enter the value 86.
    • Click Add another condition.
    • In the dropdown on the left, choose Less than or equal to
    • On the right, enter the value 100.
  • Expand the Structure section of Advanced Settings
    • From the dropdown, select the linkedin preset. Screenshot of the second page 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.

How to use your likely buyers predictions in LinkedIn Ads

With your pipeline deployed, it's time to plug your likely-to-buy scores into LinkedIn. Follow the steps below to create an audience in LinkedIn Campaign manager.

Creating a new audience in LinkedIn Campaign Manager

  1. In your Faraday pipeline, click the Download CSV button under the deployment to download your likely buyers as a CSV.
  2. Navigate to your LinkedIn Campaign Manager account, and select Plan, then Audiences in the left-hand navigation bar.
  3. Click create audience to open the type selector, then select company / contact.

Image of LinkedIn audience creation

  1. Give your new list an appropriate name, and ensure list type is contact list.
  2. Format your Faraday CSV deployment of likely buyers to match LinkedIn's specified template.

LinkedIn's template requires no extra columns, so you may need to open the CSV in an editor like Microsoft Excel or Google Sheets to remove any columns that aren't in LinkedIn's template. Note, however, that LinkedIn only requires one of email, first and last name, company, mobile device ID. Faraday exports include email and first & last name, and will already be filtered to include only the top scoring indivudals–feel free to delete any columns other than email and first and last name.

  1. Select your CSV deployment from Faraday, then click agree & upload. Your audience can take up to 48 hours to build in LinkedIn Campaign Manager, after which you can use it to launch campaigns 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.