Using Predictive Data Modeling to Amplify Direct Mail Efforts

24 September 2019 / By Mike Gunderson
Using Predictive Data Modeling to Amplify Direct-Mail Efforts on
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What if you could see into the future and ensure only the best results for your direct mail campaigns? Well, you can, and predictive data modeling makes it possible.

Direct mail marketing may seem like a thing of the past to some, but it remains one of the most effective marketing mediums out there. Why? Just think about your email inbox. HubSpot reports that 91% of consumers feel overwhelmed, interrupted, and shadowed by digital ads, which means more and more people are installing email filters and ad blockers to help minimize the amount of marketing they have to sift through.

A trip to the mailbox, on the other hand, is a welcome reprieve. As email inboxes continue to overflow with irrelevant advertising, people are eager to check their mail again. Recent surveys by the Data & Marketing Association (DMA) found that direct mail gets around a 5% response rate, which eclipses comparable email rates of about 1%. And, according to recent research, 73% of people prefer direct mail marketing over digital.

But, what if you could see into the future and ensure even brighter outcomes before sending a direct mail campaign? What if you predictively knew who in your audience would be more likely to open your mail and which ones would instinctively trash it? Well, you can, and predictive data modeling makes it possible.

What is predictive data modeling?

In vague terms, predictive data modeling uses data and statistics to accurately predict outcomes and increase ROI. When you apply predictive data modeling to your direct mail approach, it eliminates the old-school method of “spray and pray” distribution.

Today, the power of data is being harnessed to create hyper-targeted direct mail marketing, and it is transforming the way marketers communicate with customers and prospects alike. With the use of predictive data, companies can tailor their direct mail strategy based on factors such as:

  • What’s important to prospective customers
  • Which prospects are most likely to identify with the company’s branding
  • How much money prospects realistically have to spend on the company’s products or services
  • Who is most likely to connect with the company overall

Knowing more about your customers will help you determine who is most likely to respond to direct mail efforts.

Reasons to explore predictive data modeling for direct mail campaigns

1.  Give your existing data a boost

Predictive data modeling allows marketers to take what they already know about their customers and apply it to their entire existing customer base. For example, let’s say you lost a chunk of customers to a competitor. You could then use predictive data modeling to measure the indicators of dissatisfaction within the existing customer base, and then customize a direct mail campaign to proactively address those at risk of leaving for the competition. Better insights lead to more informed decisions, which result in better outcomes.

2.  Take “gut feeling” out of the equation

It’s easy for marketers to believe that their ideas are the best. Unfortunately, though, making strategic decisions without evidence or data is an easy way to fall victim to your own personal partialities. For anyone wanting to be a proactive, data-driven marketer, gut instinct isn’t the compass they should be following. They need to make evidence-based decisions, and predictive data modeling is one of the best ways of ensuring campaigns are successful.

3.  Reduce the amount of time wasted

According to the 2015 study by Forbes, 46% of executives said the main benefit of predictive data modeling was the ability to identify better market opportunities. Predictive data modeling allows marketers to stay focused on targeting prospects who are most likely to become customers and to keep from wasting time and money on people who were never going to convert. By knowing where to concentrate on marketing efforts, you can focus on the right prospects, and also identify which states, regions, and ZIP codes are most densely populated with probable buyers.

4. Remain competitive

In marketing, it is nearly impossible to be a successful one-trick pony. What worked last year is not going to work next year. Those who want to remain competitive need to employ a data-driven approach. To optimize results and help ensure year-over-year success, predictive data modeling is arguably the best way to go.

To learn more about how to use predictive data modeling to help you amplify your direct mail marketing efforts, just drop us a line. We’re pros at that.

About The Author

Mike Gunderson

Mike Gunderson is the founder of Gunderson Direct, Inc., a direct marketing agency that helps businesses drive new leads and close more sales through traditional offline channels, especially direct mail.

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