Improving lead quality 22% with Adrsta AI’s Bid Optimizer

Disruptive Digital is a digital performance and marketing agency with a deep background in optimizing lead generation campaigns and conversion flows, particularly in industries like financial services and health tech.

As a longtime Facebook employee before starting Disruptive Digital, founder Maurice Rahmey is an expert in using data to lean into ad platforms’ machine learning and optimization algorithms to drive returns for his clients.

But as the availability of online advertising signals declined over the last five years — especially after Apple launched the App Tracking Transparency (ATT) framework in 2021 — Maurice saw that, while he could help recover lead volume by leaning even further into Meta’s capabilities like Conversions API, lead optimization, and conversion modeling, it came at the cost of:

1. giving up even more control of first party data and campaign optimization,
2. time-intensive, technical CRM integrations, and
3. a decline in lead quality that hurt total return on ad spend and made it especially challenging for lead-based businesses to scale.

Maurice was experiencing firsthand what we at Adrsta hear about so often from our customers: Meta and Google’s modeling capabilities deliver efficient conversion volume out of the box, but require additional training and insights to deliver value and ROI at the same scale — training and insights that only advertisers in control of their own data can derive.

With this in mind, Maurice reached out about setting up a test of Adrsta AI’s Bid Optimizer product for one of his financial services clients that had struggled to acquire quality leads from their Meta advertising.

How it works

In the old world, sophisticated strategists like Maurice would have planned and optimized these kinds of campaigns with a very manual process:

1. Work with a data science team (if one even existed!) to integrate data systems and dig into the customer tiers.
2. Manually interpret insights about those customers to build out possible targeting combinations for testing in ad platforms like Facebook or Google.
3. Create dozens (or more!) of different ad sets or ad groups to manually test targeting, then manage and optimize bid values based on proxy, short-term metrics.

It was a massively time consuming process, and while it would offer Maurice a lot more control over the cost of individual leads, the control would come at the expense of both time and campaign scale because of audience over-segmentation.

Fortunately, with Adrsta, Maurice could resolve that trade-off fast, driving higher performance while using far fewer resources. Here’s how it worked:

1. Using his client’s first party data and Adrsta’s proprietary model, he built a customized algorithm that predicted which leads would be most valuable and which available targeting parameters were most likely to predict a high-value customer.
2. Then, using Adrsta’s Bid Optimizer, he translated those insights into thousands of pairs of targeting parameters and bid values and applied them to his Facebook campaign via API — allowing him to automatically enter the right bid value in every auction without over-segmenting his audience.

And the results were profound:

• 22% Increase in Lead Quality (30% higher likelihood to convert)
• 50% Higher Lifetime Value
• 20% Reduced Cost per Conversion
• 35% Improved ROI

In Maurice’s words:

“Using Adrsta's bid optimizer allows us to lean into more efficient media buying for our business goals without having to sacrifice the optimal machine learning ad platforms like Meta provide.”

If you are interested in learning more about how Adrsta’s suite of Marketing Science tools can help drive ROI for your business, please click here to schedule a demo!