DIRECT MAIL DATA & TARGETING SERIES – PART 3: TRANSACTIONAL DATABASES

27 October 2020 / By Alexa Sundberg
Reading time: 5 minutes

DIRECT MAIL DATA & TARGETING SERIES

PART 3: Transactional Databases

In our last database blog we described and discussed the compiled databases largest data source for direct mailers.

In part 3 of this series on data types, we delve into transactional prospect databases, a unique source of data that helps us select prospects based on their transactional histories.

Transactional Databases

Models based on transactional databases are often among the most responsive data options for mailers. The key difference between databases with transactional data and other databases is that they provide behavioral prospect attributes based on actual marketplace transactions.

Many large databases are compiled data from multiple sources and based on demographic data. For example, you can target 24–49 year-old women, living in households earning $60,000–$100,000. That could be a key target for a Nike running shoe.

But it would be even more valuable to know who among that target spends money on gym memberships, workout clothing and the like. That’s what a transactional database adds — things people do with their money that can serve as a proxy for interest in a product or category.

This begs the question; can Nike buy data on people who purchased Adidas shoes and try to convert them? It doesn’t work like that. The database owners protect their data sources. They keep things general and anonymous.

That brings us to how these databases are developed.

A transactional database is built from many businesses contributing their customer files and corresponding customer online and offline transactional data, as well as other data attributes like demographics and survey data, into a single managed database. Billions of daily transactions are used to update a database every month. This results in a rich dataset that represents how individuals are spending their money in terms of amounts, recency and frequency.

Sometimes these databases are called cooperatives (COOP) and require participation (contribution of data) to the database to have access to the database. However, often there are industries that are exempt from contribution. Also it is possible for non-exempt mailers to have a slightly restricted access to the database without contributing.

A typical best practice for using this type of database is to build a prospect model using your company’s active customers that is then applied to the transactional database. Modeling allows us to pick out the prospects in the database that are most like your current customers. The theory being that people with transactional histories most similar to your existing customers are most likely to be your next customers. You can read more about modeling and how it’s done here.

Here are some other pluses and minuses of transactional databases:

Advantages:

  • They are large. Next to compiled databases, which represent virtually every household in America, these databases are your next large universe option.
  • They outperform demographic data in most cases. We’ve seen lifts as high as 60% over non-transactional data sources.
  • They are useful for a wide range of categories. Pooled contributors expand the available universes for transactional data.
  • Free modeling for a data commitment. If a mailer commits to realistic test quantities and rollouts then the model build is typically no charge.
  • Lists “refresh” regularly. Your prospect spends 15 years in the 25-49 year-old age demographic bracket. But, they are spending money daily and that reflects changing interests based on buying behavior. That means you can find a large pool of new prospects based on recent transactional activity every month.
  • Often the best option for multichannel efforts. Transactional databases are more likely to have good email addresses on a significant portion of their database that can be used to target prospects in social media, through digital ad buys and via email.
  • A B2B source, too. Transactional databases (especially COOPs) started as consumer databases, but have more recently expanded to offer quality B2B data.

Challenges:

  • In the case of cooperatives, you must give to get. Most COOPs require mailers to add their customer file to the COOP database. That can be a challenge for some clients and is not permitted at all in certain industries. To offset that:
    • You can test before you commit.
    • Only multi-sourced data is made available for mailers from the COOP. Often it’s sourced from at minimum 7+ other businesses, so that no truly unique customers are available for other mailers.
    • Contributors are kept anonymous.
    • Some categories (like financial and healthcare) are exempt from this requirement, but are still allowed to purchase COOP data.
    • And there are some options that will allow use of categorical (less specific) transactional data without COOP participation.
  • Monthly universes can be less than the available universes for modeled compiled databases. To offset that, transactional database model builders offer multiple model builds, exploring multiple views into households that may respond equally well and provide multiple sets of rollout universes.

In summary, transactional databases offer high-performing data sets for mailers with the added benefit of offering large quantities of refreshed data for ongoing large mailer campaigns.

Read our other blogs in this series on data types:

DIRECT MAIL DATA AND TARGETING PART 1: The Two Key Types of Data

DIRECT MAIL DATA AND TARGETING PART 2: Compiled Databases

ABOUT GUNDERSON DIRECT

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 Sundberg
About The Author

Alexa Sundberg

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.