How to Use Direct Mail Attribution in Campaigns

Attribution is a retrospective analysis that makes it easier to target and measure the impact a suggested action or interaction has on customer behavior.

With digital marketing mediums, such as email, multi-touch attribution is much easier to measure. You simply look at the conversion event and then reverse engineer the role every touchpoint had in causing the action. However, attribution analysis tends to be less clear cut with something like direct mail—which falls into both the digital and offline categories.

Knowing this, you must understand direct mail attribution and the various models to drill down into the details.

Direct mail enables the precise targeting of digital channels and the increased reach of offline channels. But, to see whether your campaign is running effectively, you must understand attribution. This enables you to:

As mentioned, attribution isn’t as simple as seeing who clicked what. And the equation becomes even more difficult if a customer visited multiple channels or encountered multiple touchpoints that directed them to an intended action.

For direct mail, there are front-end and back-end tracking methods with varying degrees of accuracy and information.

Some of the most common front-end tracking methods include:

While front-end tracking can give you a broad overview of your campaign’s performance, back end tracking will give you the clearest, 1:1 read on your overall campaign.

The front end tracking methods we mentioned attempt to copy the click-through behavior you’d experience with an email marketing campaign; however, there isn’t a one-to-one relationship between the two marketing materials. This is where back end tracking comes into play. 

When we refer to back end tracking, we are talking about matchback analysis, which is perhaps the most effective way to measure your campaign performance by tracking direct orders and where they came from. 

Matchback reporting pairs untracked sales with your mailing list. Although this type of analysis can be done in real-time, it’s most commonly done after the fact. It compares the addresses of the customers who purchased to the addresses of the people who received the mail. 

For this reason, matchback analysis is the most effective method for measuring the success of your campaign. This strategy allows you to directly look at untracked sales and orders and match them back to your campaign file. Linking sales to campaign files in this way lets you more accurately track your campaign’s success.

For any type of analysis to be valuable and actionable, it must have a delineated timeframe. The same goes here. 

And for matchback analysis, where only a small percentage of direct mail responses come through a directly attributable feature such as a promo code, it's important to only count matches during the campaign’s attribution window. Which is typically, between 60-90 days. 

Think about it, not everyone opens their mail daily; if they do, many will hold onto marketing material until they are ready to use it. Another factor and benefit to direct mail is that people might put their mail on their counter for weeks and continue to walk by the same postcard - eventually convincing them to take the desired action.

This is why we typically see 60% of direct mail’s benefits in the first month of the campaign and the rest between the next 30 and 90 days.

In recent years, custom attribution models have become more popular. Since there are so many interactions along the conversion path as well as a variety of tools that enable impression tracking, many companies have decided to create customized attribution models where various factors are treated differently. 

But such direct attribution models can create serious issues, particularly since they are subjective and dependent upon how certain factors are weighted.

They use the following example to highlight the potential issues. An observation model determines that people who get married and purchase a home have a 2% chance of having a baby within two years. But if there are storks living in the neighborhood, that probability increases to 3%. 

Drawing on this example, does it appear that storks increase pregnancy likelihood? 

With correlation, you could wrongfully conclude that storks actually increase your chance of being pregnant by 50%. And this is the exact reason why there is a movement from custom attribution modeling to lift analysis.

Lift analysis, also known as conversion lift tests, makes it easier to conduct a controlled experiment and then gauge the incremental value of a direct mail campaign. Using indirect attribution, it focuses on two groups:

This makes it possible to perform an analysis where you compare the downstream actions of both the test and control groups. 

In other words, with lift analysis, you can see causation. It allows you to identify the people who didn’t convert, the people who converted without the campaign, and the people who converted as a result of the campaign. That final group is the conversion lift. 

This is especially important for a channel like direct mail because your online tracking won’t take into account your offline efforts. For example, if a customer converts after seeing your emails, Facebook and Google ads, and a direct mail piece, you can’t be sure which channel drove the conversion. With holdout groups, however, you can run a lift analysis to see in the aggregate how many recipients of your direct mail campaign converted vs the holdout (control) group of people who only received your emails, Facebook and Google ads. If the holdout group has a 1% conversion rate and the mailed group has a 2% conversion rate, you know your direct mail campaign doubled your conversion rate over the control group.

So, what’s the best approach to attribution? 

The answer is—it depends on your company’s goals. For precise attribution, direct attribution modeling can highlight a specific action's performance. But if you want a broader look at a campaign’s efficacy, lift analysis is the better solution. 

Chances are, you’ll want to leverage elements of both methodologies via iteration to find the optimal approach. But if you’re going to incorporate direct mail attribution into your current marketing mix, there are several steps you can take to make it more impactful, including:

Direct mail attribution is an essential way for marketers to measure their success and optimize campaigns. When done right, direct mail attribution can help marketers determine whether they've reaped the benefits of direct mail marketing or not. But, as we’ve noted, they aren’t without their flaws. Even leveraging both an indirect and direct attribution model may not produce clear results. 

That is unless you use Poplar. With our platform, you can build direct mail campaigns easily and then perform automated multivariate testing and optimization. This way, you don’t need to worry about running these analyses manually.

Together, we can create personalized mail campaigns with a full range of retargeting and CRM options. What does that look like?

Get in touch about our direct mail solutions today!