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Lead targeting

Lead rejection

Avoid buying leads that won't convert — using GCS

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.

GCS logoIf you're a Faraday user and also use GCS, incorporating Lead rejection predictions could really streamline your process. Imagine being able to seamlessly integrate Faraday's insights directly into your Google Cloud Storage workflows. You'll avoid spending money on leads that aren't likely to convert, helping you focus on the ones that matter. This simple addition could make your data operations more efficient without the need for extra steps or complicated procedures. It's just a handy way to make sure you're investing in the right leads straight from your existing GCS setup.
  1. Step 1

    Connect your data sources

    Use the link below to connect GCS 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 GCS

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

    Deploy to GCS

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