In today’s world, marketers are being asked to work magic with limited resources. They’re the ones responsible for keeping the stream of new customers flowing, which is basically the heart and soul of long-term growth and profit. No pressure, right? Cutting down on how much it costs to get new customers on board and transforming those promising potential buyers into super-engaged, loyal fans – those are the big targets. So, what’s a marketer supposed to do in the face of all this?
Smart marketers today are on board with the idea that data is the foundation of success. They get that segmenting, targeting and personalization are going to be the factors that move the needle. A great way to utilize the data at a marketer’s fingertips is “look-alike” modeling. This technique helps you find potential new customers that have similar behaviors, interests and demographics of your best customers. These models help you:
This step is critical to the success because it requires that we clearly define the prospect audience we want to identify and engage, which can prove very tricky. Is it customers with the highest spend? Or those who purchase from a specific product category? Or, perhaps, those in a specific geography or age range (i.e. Millennials, Gen X)?
After you’ve isolated the customer segment you want to clone, you’ll need to separate the “best” customers from the rest of the pre-defined segment base. This would typically be done by using a key financial metric, like net or gross sales, gross margin or operating profit. Using this financial metric, you’d rank each customer from high to low performance.
Appending 300+ 3rd party data elements (like, age, household income, education, likely to enjoy hiking or play tennis) to every customer record identified in steps 1 and 2, is the next step to building the final “look-alike” model.
From among the 300 appended data elements, between 7 and 12 individual attributes will represent the key attributes in the “best” customer “look-alike”, or cloning model.
There are more than 280 million individuals in the U.S. that can be used for new customer prospecting initiatives. To determine which of these prospects are most likely to look like your best customers, you’d apply the model built in step 4 to score each of the prospects. The ones with a score in the top 10% or top 20% are typically selected for your acquisition campaign (depending, of course, upon your budget and marketing acquisition objectives).
After you’ve identified the pool of prospect names from the scoring process (step 5), you should always match your existing customer base against this list and suppress any matching records. The inclusion of any existing customers in a new customer acquisition campaign is a waste of advertising dollars and will skew the results of the “look-alike” model’s performance.
You can utilize a variety of messaging strategies to engage these prospects through any addressable marketing channel: direct mail, email, display, social and/or mobile digital media. Don’t be dissuaded from using digital media to reach and pinpoint your target prospect universe. Digital media can be a very effective reach strategy, depending upon the intended target audience.
Data Axle’s analytics team recently built a look-alike model for a retail client who wanted to increase membership in its loyalty program. Using a randomly selected prospect group to benchmark the results, we determined that the prospects identified from the custom model had a:
Developing a data-driven customer acquisition program has consistently shown to be a significantly more effective approach to funneling new customers through the pipeline than the traditional “scatter gun” methodology used all too often. This strategy not only increases conversion, but we’ve seen time after time that these new customers are also significantly more likely to spend more than the randomly targeted prospects.
Using data, analytics, and “look-alike” modeling methodologies is enabling marketers to gain a competitive advantage by identifying the “right” prospects and engaging them to create more sales, better CPAs and higher marketing ROI.
Editor’s Note: This blog was originally published in August 2020 and has been updated for accuracy and comprehensiveness in August 2023.
As Content Marketing Manager, Natasia is responsible for helping strategize, produce and execute Data Axle's content. With a passion for writing and an enthusiasm for data management and technology, Natasia creates content that is designed to deliver nuggets of wisdom to help brands and individuals elevate their data governance policies. A native New Yorker, when Natasia is not at work she can be found enjoying New York’s food scene, at one of NYC’s many museums, or at one of the city’s many parks with her two teacup yorkies.