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Lead targeting

Lead rejection

Avoid buying leads that won't convert — using Aurora (MySQL)

You’ll need a Faraday account to use this template. It’s free to sign up and you can use sample data to start.

In today's competitive landscape, many brands—especially those in home and financial services—buy leads from third-party sources. These leads are often purchased through a process called ping-post, where lead bids are placed in real time.

Unfortunately, many of these leads don't convert into customers, leading to wasted marketing spend. But what if there were a way to ensure you're only purchasing leads with the highest potential to convert?

The Challenge

Brands face a common challenge when buying leads: the inability to distinguish good leads from bad ones. Without clear insight into the likelihood of a lead converting, brands often waste money purchasing low-quality leads. The inefficiency of this process is a huge financial drain.

Faraday's Lead Rejection Solution

This is where Faraday steps in. Our AI platform can optimize your lead buying process by predicting the probability of each lead converting. Here’s how it works:

  1. Scoring Leads in Real Time: Faraday scores your eligible population, which often includes a broad “everybody cohort,” on their likelihood to become customers.
  2. Real-Time API Integration: When a lead becomes available for purchase, your brand can request a conversion score using Faraday’s real-time Lookup API. This score allows you to make an informed decision about whether to buy that lead, ensuring you're only investing in leads with strong potential.

How to Implement Faraday's Lead Rejection Strategy

You can seamlessly integrate Faraday’s lead rejection solution into your current lead management system (LMS). Here’s a simple three-step approach:

  1. Set a conversion probability threshold based on Faraday's score.
  2. In your LMS’s ping-post facility (like LeadConduit), set up a rule to automatically reject any lead that falls below that threshold.
  3. Enjoy better quality leads without lifting a finger.

The Benefits of Rejecting Bad Leads

By using Faraday to automatically reject poor-quality leads, your brand will:

  • Save Money: Stop wasting your budget on low-probability leads.
  • Improve Conversion Rates: Focus on high-quality leads that are more likely to convert, increasing your overall success rate.

Take Control of Your Lead Buying

Faraday empowers you to take control of your lead purchasing process, ensuring that your money goes to leads that are truly worth it. With our AI-driven lead rejection solution, you can optimize your marketing spend and improve your customer acquisition strategy.

Aurora (MySQL) logoIf you're using Aurora (MySQL), integrating Faraday's lead rejection predictions could be a real game-changer for managing your customer data. Instead of spending time and money on leads that are unlikely to convert, these predictions help you focus on the ones that show promise. By seamlessly fitting into your existing Aurora setup, you can easily identify which leads to prioritize, making your marketing and sales efforts more efficient. It's just a smarter way to cut through the noise and allocate resources where they’re most likely to make a difference.
  1. Step 1

    Connect your data sources

    Use the link below to connect Aurora (MySQL) to Faraday. You can also skip this step and use CSV files to get started instead.
  2. Step 2

    Ingest your data into event streams

    This allows Faraday to understand what your data means. These links will guide you through ingesting the data necessary to power this template.
  3. Step 3

    Organize your customer data

    You'll create groups, called cohorts, that are the essential building blocks of Faraday and allow you to easily predict any customer behavior.
  4. Step 4

    Declare your prediction objectives

    With your cohorts defined, it's easy to instruct Faraday to predict the necessary behaviors. Follow the docs with the link below.
  5. Step 5

    Define your lead scoring pipeline and deploy to Aurora (MySQL)

    Finally, deploy your prediction with the link below.
  6. Step 6

    Deploy to Aurora (MySQL)

    Create a deployment target using the Aurora (MySQL) connection you created above. Or, get started by simply deploying to CSV.