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PART 2: Compiled Databases

Our previous database blog broke the world of data into house files and prospect data. Now let’s start breaking down individual sources of data; how they are derived and best used.

In this blog, we’ll take a deep dive into compiled databases, often associated with the most straightforward demographic targeting.

Compiled Databases

While the name might suggest that these databases are doing primary research to build large consumer and business files with detailed prospect information, that’s typically not the case. The vast majority of compiled databases are actually built on publicly available sources of data which are then combined to create a large “compiled” database of information.

Think of it this way. Thousands of data points on Jane Jones at 123 Main Street exist as a public record, based on surveys or questionnaires Jane completed, the registration of items she bought, etc. By purchasing or accessing this data, compilers match it back to Jane at her current address and create a prospect file. In most cases, compilers will access multiple sources to verify age, homeownership, income, and other attributes, so they can be pretty accurate on most of the important measures.

Compilers will often use their data to infer information about a prospect. This can apply to income estimates or more esoteric assumptions such as pet ownership.

There are many data compilers out there. What separates one from the other is strength of data in particular areas. While name, address, and homeownership may be pretty easy to obtain from any large compiler, more nuanced needs should be carefully researched.

If you are marketing a product to a certain type of car owner, for example, you’ll want to research list sources that are especially strong in that area. You’ll also want to know how current that specific data is and how often it gets updated, or “refreshed” in industry parlance.

At a base level, these lists are purchased using straight “selects” or based on demos, such as women, ages 25-39, married, and homeowner.

A best practice for using a compiled database is to create a company specific “look alike” prospect model using your active customers. The interaction of demographic attributes in the model is used as the criteria for selecting prospects who, well, look most like the customers that have been modeled. Applying a model to a compiled database generally results in superior response compared to pulling straight selects and is recommended if your company has enough customers (a few thousand) and can share data for analysis.

Here are some other pluses and minuses of compiled databases:


  • They are big. The largest compiled databases contain close to all households in America. That means successful tests can be rolled out to large numbers of prospects.
  • They cost less. Compiled databases are almost always the low-cost data option compared to transactional or other specialty databases. That can have serious cost implications for large quantity mailings. Even if response is not as strong, the lower cost may yield a lower CAC (customer acquisition cost) than other alternatives. Note that it costs more to buy records based on a model, but still less than many other options.
  • They are dependable. The well-known, comprehensive compilers are known entities and are respected sources of quality data. Always seek reputable data providers. (There is an industry adage worth noting, “Bad data is worse than no data at all”)
  • They help provide prospect data inventory. Mailers using smaller databases often run into a mail ceiling. Results are strong, but they’ve run out of prospects. A compiled database can augment their efforts with lower cost, but still good quality prospects.


  • Their ability to predict behavior is limited. Demographics tell us who, not what or when. Two men, aged 49-64, owners of a single family dwelling unit can have very different lifestyles, purchase behaviors and beliefs. Other databases may tell us that one of them travels frequently, purchases sports equipment and reads investment publications, for example.
  • Compiled data can fatigue quicker. Yes, compiled databases are larger. But, the information they compile often does not renew as frequently as other kinds of data. Once you’ve exhausted prospects with a particular attribute, you may just have to find another source of new prospects. In other instances, you can find yourself buying outdated data, too.
    • Models can help offset that issue by:
      • Rebuilding them on a regular basis.
      • Enhancing modeled data with other selects (attributes) to improve performance. For example, we may overbuy records in the highest indexing zip codes.
    • At Gunderson Direct, we sometimes acquire important key attributes, not present on a large database, and custom build the prospect universe required for our clients by combining the two data sources. For instance, if pet ownership is a mission critical factor for a mailing you can overlay a more recent source on pet ownership on a compiled file that has better sources for additional key attributes.
  • If you go the model building route, that comes with a fee. It can also add a week or two to timelines to generate a new model.

In summary, compiled databases incorporate virtually all U.S. household and are a lower cost way to generate large numbers of prospects than most other prospecting list alternatives.

Read the entire series on data types:

DIRECT MAIL AND TARGETING PART 3: Transactional Databases


Gunderson Direct is one of the largest independent full-service direct marketing agencies, providing strategy, data, creative and production expertise to B2C and B2B clients across the U.S.

Gunderson Direct does not own or compile data. We have a deep knowledge of data sources and the tools used to analyze their effectiveness. The goal is always the same: Deliver the optimal mix of prospect files to cost-efficiently test, learn and rollout programs to meet our clients’ business needs.

All our data sources are members of the ANA (Association of National Advertisers). As such, they are required to respect data privacy laws, have proper data privacy certifications and maintain a solid reputation in the direct marketing industry.

Alexa has over 30 years in Direct Marketing experience – B2B, B2C and government. From direct mail catalogs to marketing CRM databases, she has always believed in the power of targeting the right audience in the right way. She partners with clients in data strategy, measurement and stewardship. She is passionate about connecting marketing investments to a positive impact on sales.

When she is not digging into a new challenge, she likes listening to live music, practicing her short game, exploring the new marketing technology advancements coming out of the bay area, and visiting her family.