7 Reasons to Use Predictive Modeling For Your Direct Mail Program

7 Reasons to Use Predictive Modeling For Your Direct Mail Program

(Updated: February 19, 2019)

As a marketer, you’ve got a tough job and all eyes are on you. As you wade through your sea of options, you’re constantly thinking about optimizing your marketing. You’ve heard the buzz about predictive analytics and modeling, but you’re not sure how it applies to direct mail. You may not even know how or why to apply it to direct mail.

When you examine it, incorporating predictive modeling into your direct mail marketing is a better strategy than maintaining the status quo and doing things the way you’ve always done them. According to a recent Forbes Insights study, “Predictive marketing is emerging as the best strategy to embrace data analytics to guide decisions and increase the visibility of markets.”              

If you’ve never considered why you should incorporate predictive modeling into your direct mail program, here are some reasons to explore that route.


1) Focus on only the best opportunities.

Predictive modeling allows you to target your audience better and estimate the likelihood of outcomes and responses. This enables you to target prospects likely to become customers. Analyzing good data and implementing predictive modeling will lead you to pinpoint these good opportunities and ignore bad ones.

“Predictive marketing is emerging as the best strategy to embrace data analytics to guide decisions and increase the visibility of markets.”
— 2015 Forbes Insights Study

In the 2015 Forbes Insights study, executives were asked to name the top benefits of predictive marketing. One stood out among the rest. 46% of executives stated the top benefit of predictive marketing was to identify better market opportunities. 

This kind of information empowers you to decide where to focus your efforts. You can concentrate on the right prospects. You can also identify which regions, states, and zip codes are more densely populated with likely buyers.

What kind of results would you see if you ignored the bad opportunities and focused on the good ones?

That's right:

You would see your marketing ROI improve using this approach.


2) Target for propensity.

Predictive modeling enables you to select by something that is not available on a regular mailing list, such as a response list. For example, with a regular list you cannot select households by a desire to open a new account, get a new credit card, or capacity to purchase a new car. However, based on other known attributes, one can build a model, score a database, and create a new proprietary field representing a propensity for any of those.

Sure, you can select a list of credit card holders or certain vehicle owners and assume a portion of them will respond to your offer, but you won’t know if they are likely to be interested in your specific offer.

46% of executives stated the top benefit of predictive marketing was to identify better market opportunities.
— 2015 Forbes Insights Study

Response lists can definitely be useful, but what about people who are not on the list and would be interested in your offer?

If you’re using a response list consisting of a couple million people, but there are over 240 million individuals in the US you can mail to, you’re leaving a lot on the table.

You have the opportunity to grab a share of the market before your competition does.

Automotive dealers often ask about consumers who are “in-the-market” to buy a car. With predictive modeling, you can identify which consumers can be persuaded into the market.

In other words:

You’re not limited to a list containing information on past behaviors because you can now predict future behaviors. This opens up untapped opportunities to you.

This leads to my next point.


3) It’s a scalable, flexible, and dynamic solution.

You’ll have a larger number of people to market to than a response list because you’re able to use the predictive model to score the entire country. This leads to a better “rollout” as you market to the best prospects over the course of several months. Then as results come in, you’ll be able to refine the model further and re-score the national database, adjusting who you target and thereby improving your campaign results.

You can also create models for cross-selling and upselling. You simply can’t do any of that with a static list. Even if there are new names added every month, there’s no matching the power of the predictive model.


4) Extract better insight from Your data.

With predictive modeling, you can gain a better understanding of your customer base.  When asked about their top priorities for 2016, 21% of marketers surveyed said enhancing their knowledge of their customers' needs, attitudes and motivations was number one—the highest rated answer.  

With predictive modeling you can leverage what you already know about your customers and enhance that knowledge by applying it to your entire customer base and even to prospects nationwide.

With predictive modeling you can leverage what you already know about your customers...

For example, if you’ve kept track of which customers have defected to the competition, you can use predictive churn/attrition modeling to create a “disloyalty” score. In other words, you can create a measure that indicates who else has an attitude of dissatisfaction towards your company and the degree to which that is the case. Then you can craft specialized programs or promotions for those who are at risk of leaving you.

Consequently, better insights lead to better decisions, which lead to better results. 

5) You don’t want to be left behind.

You can’t do the same things as you did last year and be successful next year. If you’re not incorporating predictive modeling, you may have trouble competing in your industry. If you’re not using a data-driven approach at all, you will very likely fall behind.

In a 2017 global review of data-driven marketing and advertising (DDMA), 79.6% of marketing practitioners believe customer data to be “critical” to their efforts. Over half of the panelists said they increased their spending on DDMA from the previous year.

Additionally, 46.7% stated that "predictive analytics and related segmentation offers the most potential to expand the contributions of DDMA in the coming years."

And there's more:

As you travel along your predictive marketing journey, your results will improve.

In the table below, you can see how organizations that have had initiatives underway for awhile experience increased returns even greater than those reported by new and advanced users.

Perhaps unsurprisingly, you gain wisdom and better results as you mature.

You don't want to get left behind while others are improving their results using predictive marketing.

6) You can create predictive multichannel marketing.

You can combine with email marketing for a multichannel approach. With this powerful one-two punch, there’s potential for a synergistic effect that helps boost response rate. In fact, according to a Canadian study, brand recall was over 40% higher when direct mail followed email.

Now integrate predictive modeling and you may unlock the potential to achieve unprecedented direct marketing results.

7) Making decisions based on your gut instinct is a bad way to do business.

You need evidence to draw the proper conclusions. Otherwise, you’re likely to fall victim to personal biases and an incomplete understanding of your customers. That will ultimately lead to you wasting a significant part of your marketing budget.

So, you need to start with good data and begin extracting insight from that data.

If you want to become an effective, data-driven marketer, you’ll need to make evidence-based decisions. Gut instinct won't cut it. And though descriptive analytics can be helpful in understanding the past, predictive modeling is far superior in understanding the future.



Your job as a marketer is important to your business. It’s also a tough job to do with the deluge of data, various direct mail technologies, and stiff competition. To optimize your direct marketing program, predictive modeling is the best way to go.


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