Truths About Lookalike Targeting for Mailers

You’ve probably heard it all before: “target smarter,” “build a lookalike audience,” “optimize your list.” Under the surface of those common phrases lies a lot of variation, especially in how different agencies approach modeling. While most marketers are using lookalike models, far fewer are maximizing them. From how seed audiences are selected to how models are maintained over time, the small differences in execution are what separate a good campaign from a great one. This article surfaces the truths and strategies behind lookalike targeting for mailers, plus opportunities even the pros might be missing.

Prefer to listen? Check out our podcast The Direct Effect: Truths About Lookalike Targeting for Mailers

1. Great Lists Are Built, Not Bought

Traditional targeting often stops at broad demographics. This is an easy approach, but one that rarely drives meaningful results. Pulling names based on age, income, or publication readership might check a box, but it doesn’t go far enough if you want to maximize your direct marketing performance.

Taking a Step Back: What are Lookalike Audiences?

Effective lookalike targeting for mailers begins with identifying your best customers—those who will spend more, engage consistently, and stay loyal over time. From there, a modeler enriches this audience with thousands of data points to uncover what truly drives performance. The process identifies both positive and negative variables: shared traits of high-value customers and what differentiates them from lower-performing segments.

Lookalike Modeling as a Growth Engine

A leading home services brand was looking to challenge their existing mail performance and asked FM Direct to participate in a head-to-head direct mail test against their current agency. Both agencies mailed the same volumes. While the competitive agency relied on the existing control creative and list, FM Direct designed three unique creative packages and five custom lookalike models to challenge the controls. After two consecutive tests, FM Direct’s FactorTest™ methodology outperformed the control strategy – twice. All three of FM Direct’s creative tests beat the competitor’s control by more than 15%, and 4 out of 5 of FM Direct’s customer lookalike models outperformed the control data to generate 54% more sales than the competition.

Here’s what made the difference:

Model smarter, not broader: Each lookalike model was engineered to identify high-intent prospects, not just anyone who looked “similar.”
Creative that fits the model: FM Direct developed three unique creative formats specifically aligned with modeled audience behaviors.
Test to optimize: Using the FactorTest methodology, FM Direct deployed combinations of models and creatives in controlled test cells to maximize learning and performance.
Do more with the same volume: No extra spend, no extra mailers. Just sharper strategy and segmentation.

This level of precision allowed this home services brand to uncover new pockets of opportunity, far beyond what standard targeting can deliver.

“Pulling names based on age, income, or publication readership might check a box, but it doesn’t go far enough if you want to maximize your direct marketing performance.”

2. Strong Models Start with Even Stronger First Steps

A customer file is only as powerful as the insight behind it. Before modeling begins, ask yourself: Who are we trying to find more of?

It could be your most profitable customers, those with the highest lifetime value, or buyers who return frequently. Starting with this lens, combined with key data points like recency, frequency, spend, and product category, elevates the accuracy and impact of the model.

According to The State of the CMO 2025, marketers who leverage first-party data see a 2.9X revenue increase and 1.5X cost savings compared to those who don’t. That’s why refining your file before modeling is critical.

The Better Your Input, The Smarter Your Output

A foundational step prior to building a lookalike model is generating a customer profile report. This high-level analysis highlights which characteristics truly drive response, often uncovering insights that shift strategy. For example, Franklin Madison Direct’s data team might create a customer profile report for a new client, breaking down who their ideal customer is with multiple modeling variables. Although the information is presented at a higher level, it helps clients understand who their actual customers are instead of who they believed them to be.

3. Even Strong Models Expire

Using outdated data can cause marketers to make decisions based on obsolete information, resulting in significant issues such as stockouts or financial losses. Customer behavior shifts, products evolve, and market dynamics change. These shifts can gradually impact performance, often before it’s noticeable at the surface level. That’s why rebuilding models regularly (ideally at least once a year) is a smart strategy.

Rebuilding isn’t Always About Starting from Scratch

Sometimes, the best way to approach lookalike modeling is to test new seed audiences: frequent buyers, high-value lapsers, or seasonal responders.

In recent campaigns, Franklin Madison Direct identified models that performed exceptionally well but began to decline over time. However, the new version drastically underperformed after its rebuild. As a result, the data targeting team continued to provide new audience segments for testing, exploring whether these groups yielded stronger results. The analysts then compared the variables between the previous model and the newly rebuilt one to determine if the differences were significant.

A fresh perspective often unlocks new momentum, especially when timing and data relevance align.

4. The Quality of Your Model Depends on Your Data Partners

Even the best algorithm won’t deliver results without high-quality data and the right partners behind it. Effective modeling is as much about process and collaboration as it is about math, and strong agency–data partner relationships allow for early identification of shifts, quick rebuilding, and campaign alignment with changing market conditions.

According to Wiland, the best data partners have developed robust “data factories” supported by extensive identity graphs that underpin their solutions. Qualified partners also invest in acquiring data from various sources and utilize sophisticated logic to integrate all this information in meaningful ways.

Our Agency Doesn’t Rely on Guesswork

At Franklin Madison Direct, we work closely with modeling partners to monitor real-time performance and proactively refresh models when trends shift. That responsiveness ensures campaigns don’t just maintain performance, they continue to grow. We transform modeling from a one-time setup into an ongoing advantage.

For example, this fast-growing home-security company wasn’t satisfied with the performance of their previous direct mail test launch. In a three-month test period, FM Direct tested five different models, 16 affinity files, three offers, and three unique creative concepts to generate 189 cells of data via indexing. The test doubled the brand’s sales rate compared to previous mailing efforts. In just one year after the re-launch, the brand found eight different control models. Direct mail is now one of the brand’s largest marketing channels, and FM Direct has produced over 100,000 new leads with the modeling changes made.

Here’s why this worked:

Collaborative testing: Over three months, FM Direct worked with the client and data partners to test five models and 16 affinity files, combining these with creative and offer variations to explore the top-performing combinations.
Shared testing structure: The teams co-developed and executed 189 indexed testcells, allowing for a controlled environment to evaluate results.
Iterative planning: The depth and breadth of the testing effort reflected a coordinated approach that brought together external data assets, internal goals, and strategic execution from FM Direct.

This multi-party collaboration laid the foundation for a direct mail program that met the company’s six-figure sales goal and scaled rapidly into a core revenue channel.

“Effective modeling is as much about process and collaboration as it is about math, and strong agency–data partner relationships allow for early identification of shifts, quick rebuilding, and campaign alignment with changing market conditions.”

When Did You Last Challenge Your Model?

Lookalike lists aren’t just for when results dip. It’s a powerful tool for any program, whether you’re troubleshooting, scaling, or simply looking to optimize. Beyond fixing problems, modeling can help uncover underdeveloped segments, identify traits that predict long-term value, and guide expansion into new verticals. It’s also a strong validation tool, confirming whether your marketing instincts are aligned with actual performance data.

Whether fine-tuning a well-performing program or exploring new audiences, modeling gives you the clarity to act with confidence, not just intuition.

It’s Time to Test Your Assumptions

Lookalike targeting for mailers is now a baseline expectation, but how it’s built and how often it’s refreshed makes all the difference. The strongest programs are built on thoughtful testing, evolving data, and a clear understanding of what’s really driving response. If your current approach hasn’t been reviewed in the last 12 months, or if performance has plateaued, it might be time to take a closer look.

Partner with Franklin Madison Direct for Smarter Lookalike Audiences

When your lookalikes get smarter, your results usually do too. Test your models with Franklin Madison Direct to discover what’s possible.