The Importance of Using Multiple Data Sources When Building Direct Mail Audience Lists

By Patrick Carroll

More than ever, your potential customers want to be recognized. The challenge lies in identifying just who your best potential customers are, so you can effectively target them with your direct mail campaigns.

The optimization of your direct mail list is a crucial stage for the success of your campaign, whether your strategy involves direct mail, digital, or a multi-channel approach. When creating audience lists, there are several types of data sources available. You can employ first-party data (leads, cancellations, cross-sell), personas, demographic data, and more. So which data source or sources should you use when building your direct mail list?

Based on the 2022 industry report, Direct Mail: Signed, Sealed, and Still Delivering Results, a troubling data pattern has been recurring: nearly 50 percent of brands are relying on basic demographic selections rather than lookalike models or personas. Despite the ease of access to demographic and first-party data, lookalike and persona data have superior targeting capabilities which allow for more intricate audience segmentation.

Not only that, but more marketers should be using multiple data sources when creating direct mail lists instead of relying on just one. To help you expand your horizons and find your best customers, let's diver deeper into lookalike modeling and personas.

Through modeling, you can understand your best customers even better.

Unless you are a new company with little to no sales, you should be modeling. Marketers can employ models to assess hundreds to thousands of customer-related characteristics and decide which ones are the most important for forecasting behavior. Because success in modeling is influenced by a variety of significant elements, you should ensure your modeling process is seamless.

One of the most important things to remember is to identify your primary KPIs and marketing metrics before modeling. Find the ideal customer subset first, then model the data using that subset. Next, pick your top data partners, databases, and modeling strategies. The two most popular modeling strategies are "lookalike" (which identifies non-customers who look the most like your current customers), and "two-stage" (which identifies prospects that both resemble your previous customers and have a positive response history with direct mail). Finally, identify target prospects who are most likely to become customers using demographics, way of life, and preference.

A real estate investment company might learn, for example, that many of its clients are married, between the ages of 30 to 55, and have lived in the area for more than two years. That might be used by a marketer as an easy selection criterion. But how may those choices suggest a consumer is trying to sell their house? A model could show that customers shop for home remodeling materials in the months before listing their house. This would be a more accurate predictor of consumer tendencies.

Testing new models allows you to scale more efficiently.

Throughout a campaign's lifecycle, the top-performing lists are assembled from a variety of sources and tested against fresh lists. Marketers should collaborate with their agency partners to obtain access to persona and lookalike data sets, and then incorporate those models into continuing testing plans for ongoing scale and conversions.

With a model, you have equally sized ranks or deciles – there's no need to manually expand the selection criteria to grow volumes. Models allow for repeatable performance and predictable results from depth testing. As a best practice, keep refining your models against fresh, effective lists all the way through the campaign lifecycle.

Reputable direct mail companies may combine, add to, and test data from a variety of external sources to provide pertinent and varied marketing models that continuously grow the audience. According to your purchasing algorithm, every customer on a mailing list is often graded from "most likely to buy" to "least likely to buy" when the list is formed. You can go deeper into the model as your campaign develops and new data sources are added, maximizing the effectiveness of the campaign.

You can use predictive modeling to strengthen your personas.

To make sure your mailing list is filled with your ideal prospects, you can take advantage of a process known as predictive modeling. Depending on your desired outcome (high response rate, low CPA), you can build lookalike models to meet your goals. Then, continue to tighten your variable ranges based on your buyer personas. Narrow in on your target audience's household income, net worth, and age. These may seem like obvious data points, but they are often set too broadly.

Even if you are a new company without any sales, a persona will likely work better than a demographic selection. Personas today have improved greatly from the addition of digital and social scraping. Rather than hypothesizing about who your customers are, you can build a persona of people interacting with your competitors to inform who your target audience should be.

If your program is only using demographic selections and you aren't seeing the performance you'd like, focus on a more holistic data approach and you will find better results. The point is that the best performing lists are compiled from a variety of sources. Throughout the campaign life cycle, don't forget to be data-driven and test continually against new lists. While using only demographic and first-party data might be tempting, do your campaign a favor and engage in thorough modeling.

The views and opinions expressed are solely those of the contributor and do not necessarily reflect the official position of the ANA or imply endorsement from the ANA.

Patrick Carroll is the director of sales and strategy at SeQuel Response, an award-winning direct response agency based out of Minneapolis, Minn. As an experienced direct marketer, Patrick has a strong history of launching, scaling and optimizing direct response campaigns for a diverse client base. You can connect with Patrick at, email him at, or find him on LinkedIn.