Everything You Need to Know About Offline Direct Mail Data

When you commit valuable advertising dollars towards a direct marketing campaign, it’s crucial that you’re confident in the accuracy of the data that fuels it. In today’s digital world, many direct marketing campaigns are charged by online programmatic data, making it easy to forget about the industry pioneer: offline marketing. Direct mail is still a customer acquisition and retention powerhouse, heavily supported by the polished offline data that is available for use during predictive modeling.

What is offline data?

Offline data is sophisticated consumer data collected from an offline source, such as proprietary customer data or publicly available information. These data types are traditionally sorted into three categories: first-party (your data), second-party (someone else’s data), and third-party (aggregated) data. Due to the assortment of consumer data available, there are several ways to build a strong performing mailing list relative to your campaign goals and budget.

Four reasons offline data delivers:

1. It is dependable and accurate.

Offline data is anchored to an individual’s name and physical address, a simple yet vital element in any direct mail campaign. The reliable demographic, behavioral and even psychographic data available from offline sources ranges from life stage and homeowner status to hobbies, interests, and past purchases. These valuable and quantifiable variables are used during audience segmenting and predictive modeling to ensure your message is both relevant and appropriate for each consumer on your list.

Online data, on the other hand, is typically anchored to browsing history or search intent, making it difficult to accurately identify an individual consumer’s key data points. For example, if you have a visitor at your house and they connect their device to your internet, their digital activity is now associated with your online footprint. Additional consumer privacy protections, such as the withdrawal of Facebook’s third-party data and the impending elimination of cookie-based advertising, continue to tighten up online data gathering and usage, making offline data even more attractive to marketers.

2. It allows for precise targeting.

One of direct mail’s unique attributes is that it’s hand-delivered to the list of recipients who are most likely to respond, a list that’s derived using predictive modeling. Predictive modeling is a process that leverages key customer insights and data science to identify the prospects who are most likely to engage based on your campaign goals. First, the attributes of your best customers are used to build an ideal customer profile (or purchase algorithm), then those attributes are paired up with third-party data to build a hyper-targeted mailing model.

The two most popular modeling processes are “lookalike” and “two-stage.” The lookalike model identifies which non-customers look most like your current customers. Two-stage, or logistic regression modeling, is more predictive as it identifies prospects that both look like your previous customers and have a positive response history with direct mail.

3. It is scalable.

Credible direct mail agencies can match, append, and test data from an array of third-party sources to create relevant and diversified mailing models to continually expand the audience. Traditionally, when a mailing list is created, all the consumers on the list are ranked from ‘most likely to buy’ to ‘least likely to buy’ based on your purchase algorithm. As your campaign grows and new data sources are introduced, you mail deeper into the model to optimize campaign performance.

Through complex analytics and campaign tracking, data scientists can evaluate and transform buyer variables from the original database to create new attributes to model against. Another option is a proprietary machine learning algorithm. This scaling technique is even more powerful than AI or human scientists, as it can constantly review, sort, transform, and update variables to forge new data points.

4. It can boost online campaign performance.

Combining offline attributes with online intent not only provides deeper insights into consumer preferences, but it is proven to increase response rates and improve customer retention, while reducing the customer acquisition costs (CACs). The comprehensive prospect models that were built with offline data can be onboarded into online environments (i.e. social media and data management platforms) to create a synchronized omnichannel experience for your prospects. An integrated online and offline strategy can be implemented throughout the customer journey — from priming the prospect digitally prior to the mail drop, to sending a direct mail piece after a digital interaction to recover an abandoned cart. This coordinated strategy will ensure you reach the right prospects, at the right time, in the right channel, without wasting ad spend.

When considering the versatility and precision of offline data, it’s obvious why so many brands rely on it to effectively and efficiently reach their audience both offline and online. Regardless of your data source or strategy, it’s important to continually test models throughout the campaign lifecycle against new, high-performing lists to optimize your program for continued conversions and scale.

Featured in BRAND United on 9.15.2020