The following is a guest post from Patrick Reagan, VP of Sales, Marketing and Client Services at Compu-Mail and a member of DMA’s Data Community Leadership Council.

While most marketers know that it takes 7-15x more effort and spend to acquire a new customer than cultivate and grow an existing one, acquisition continues to be their primary objective. Here, we will focus on how to maximize those efforts to make them as effective as possible.

The answer is within your current point-of-sale (POS) data, derived from a process called “Look-a-Like” modeling. Your database contains everything you need to know about your customers, both good and bad. If you can define who your “best” customers are, and find the unique attributes that separate them from your “worst” customers, Look-a-Like modeling can help you narrow your acquisition focus by locating and attracting more profitable customers with a smaller, more targeted net that filters out irrelevant or uninterested prospects. The result can be lower up-front marketing spend and an overall lower cost per acquisition for customers that will be more profitable.

Get into the mind of your customer or prospect.

There are many definitions of a “best” customer. Some of the most common are:

  • Visit/shop most frequently
  • Purchase most frequently
  • Highest average spend per order
  • Most referrals (if you track referrals)
  • Lowest cost to serve (fewest calls to customer service, fewest returns, buy on-line vs. in-store, etc.)

Buying lists to direct a campaign is an outdated approach. Accuracy issues in the compiling of lists and the inability to dive deeper than basic demographic segmentation diminish response rates. Getting more personal requires knowledge of more than magazine subscriptions, income levels, gender, or the type of car someone drives. You have to get into the mindset of your prospect to understand who they are as a person, how they think, how they go through the buying process, and how they make decisions. It’s as important with a prospect as it is with your current customer base.

Look-a-Like modeling helps identify currently unknown prospects that look like your best customers.

Personalization and understanding your prospects is the only way to grab their attention in a very congested space, especially if your primary acquisition medium is email. Today’s consumers see most advertising as white noise and have become accustomed to tuning it out.

It’s essential to understand this mindset and understand what your customers and prospects really want so you can cut through the clutter and deliver messages that are going to resonate with them.

Household Response Rates:

  • Direct Mail: 5.1%
  • Email: 0.6%
  • Social Media: 0.4%
  • Paid Search: 0.6%
  • Online Display: 0.2%

Source: 2017 DMA Response Rate Report

Look-a-Like modeling helps identify currently unknown prospects that look like your best customers. This type of modeling combines big data, psychographic analytics, behavioral data, demographic data, geographic parameters and various algorithms to generate a list of highly qualified, sales-ready prospects for your campaign.

This activity will do 2 things for you:

  1. Identify prospects that already agree with your value proposition but haven’t heard of your brand and just need to be introduced to it.
  2. When those prospects become customers, they are more likely to be like your best customers than your worst customers – they were chosen because they look like your best customers and are most likely going to follow similar purchase patterns.

The Bottom Line

Successfully employing Look-a-Like modeling into your direct marketing strategy will help your brand to increase visits to online and physical stores, grow revenue potential, and enhance good will and brand awareness in the marketplace.