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

Boost your direct mail ROI with lead suppression

Direct mail is a powerful marketing channel, but it's no secret that it can get expensive. With printing and mailing costs often exceeding $1 per piece, brands need to ensure that every dollar spent is working toward driving conversions. One effective strategy to maximize your direct mail campaign’s efficiency is lead suppression.

In this post, we’ll explore how Faraday’s AI-powered platform can help you cut costs and improve conversion rates through lead suppression.

The challenge: mailing lists can be costly

Brands use a variety of methods to source mailing lists for their direct mail campaigns:

House lists – These include current or past customers and leads who have already shared their mailing address.
Co-op lists – Brands join a data cooperative, sharing their customer lists in exchange for access to other businesses' lists, such as Epsilon’s Abacus and Wiland Database Cooperative.
Credit bureau lists – Financial institutions often partner with credit bureaus to send pre-approved credit offers.
List providers – Data brokers rent out lists based on specific criteria, though the quality can vary significantly. Providers like Data Axel are commonly used in direct mail.

Each of these methods generates large lists of potential customers, but not every recipient will be a good fit. With direct mail campaigns, sending to a large volume of unqualified leads can eat into your profits. This is where lead suppression comes in.

The solution: smarter direct mail targeting

Faraday’s AI-driven platform helps you get the most out of your direct mail campaigns by refining your mailing list. Instead of blindly mailing to everyone, Faraday scores each individual on the list based on their likelihood to convert into a paying customer.

Once the scoring is complete, you can suppress low-scoring leads from your direct mail campaign, ensuring that you’re only reaching out to the most promising recipients.

Why lead suppression matters

By implementing lead suppression in your direct mail efforts, you can:

Reduce costs – By eliminating low-probability leads, you send fewer mailers and save money on printing and postage.
Improve conversion rates – By focusing on high-quality leads, your mailers are more likely to reach people who will actually convert, boosting the overall effectiveness of your campaign.

Get started with Faraday today

Lead suppression is a simple yet powerful way to maximize your marketing budget and improve campaign performance. By leveraging Faraday’s AI to score your direct mail lists, you can ensure that your message reaches the right people—without wasting resources on those unlikely to convert.

Ready to learn more? Contact us today and see how Faraday can transform your direct mail strategy.

If you're a Faraday user who also happens to use Google Cloud SQL (Postgres), incorporating Lead suppression predictions can be a straightforward yet effective way to manage your resources more wisely. By integrating these predictions directly into your Postgres database, you can flag leads that are unlikely to convert and focus your marketing efforts on those with a higher chance of success. This approach can help you cut down on unnecessary spending and streamline your workflows, all while making it easier to manage everything within your existing setup. It's a small step that can lead to more efficient resource allocation without complicating your current processes.

Step 1
Connect your data sources
Use the link below to connect Google Cloud SQL (Postgres) to Faraday. You can also skip this step and use CSV files to get started instead.
Google Cloud SQL (Postgres)
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.
Signup Transaction
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.
Leads Customers
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.
Likelihood to convert
Step 5
Define your lead scoring pipeline and deploy to Google Cloud SQL (Postgres)
Finally, deploy your prediction with the link below.
Lead scoring
Step 6
Deploy to Google Cloud SQL (Postgres)
Create a deployment target using the Google Cloud SQL (Postgres) connection you created above. Or, get started by simply deploying to CSV.
Google Cloud SQL (Postgres)