Mastering Customer Data: Segmentation, Lookalike Audiences, and Retargeting

Retargeting with Mobile Banner Ads and Google Ads

“Remarketing” and “retargeting” are similar terms that are often used interchangeably. But while they refer to the same general concept—reaching out to consumers who have interacted with your brand before—there are differences between the two. Let’s begin with retargeting.

As you’re well aware, many consumers browse your product menu—or even load up their shopping cart—without actually making a purchase. Retargeting involves the use of a simple Javascript code—often called a “cookie” or a “pixel”—to track that consumer after they leave your site. This allows you to reach out to (or “retarget”) them later, typically by displaying a mobile banner ad or Google ad.

Because these consumers are already familiar with your offerings, the chance of closing a sale is greatly increased compared with consumers who have never interacted with your brand before. What’s more, these multiple points of contact—powered by a repetitive and alluring visual display—strengthens brand awareness.

“Remarketing” is similar, but it refers specifically to the strategy of reaching out to actual past customers rather than visitors who almost became customers. It typically involves strategies such as “brand reminder” emails or banner ads, or offers to renew a service or upsell an accessory. Because these consumers are already in a current customer bucket, there’s a better chance of re-engaging with them compared with a cold call.

Segmenting for Email and Text

We’ve written about segmentation before, but its central concept bears repeating:
We all want to believe that we’ve created a business or service that appeals to everyone, but the truth is that everyone is different and they shop differently.

In this light, segmentation is nothing more than sorting and classifying your customer data in order to deliver each consumer the most perfectly targeted content and offers. Some common organizing principles include:

• The location(s) a customer shops at most
• The products and brands they purchase most often
• The total amount each customer spends, as well as amounts spent on specific products and brands
• Special dates such as birthday and the date of their first purchase from you

Building on these basic attributes, it’s easy to build an increasingly detailed customer persona. Using criteria such as the customer’s age, demographics, marital and income status and more, you can constantly refine your marketing efforts. Used wisely, these tools deliver impressively personalized and targeted campaigns that speak to their specific needs and desires. And needless to say, that’s the name of the game when it comes to marketing.

Creating Look-Alike Audiences for Mobile Banner Ads and Google Ads

Finally, let’s take a page from the world of social media. “Look-alike audiences” are most commonly associated with the social giant Facebook, but the concept is readily importable to other platforms. Essentially, all it refers to is the practice of analyzing a custom list of existing customer attributes, then creating a “look-alike audience” that markets to potential customers who are similar to pre-existing ones.

Here’s an example of how that might work. Let’s say you create a custom audience by pulling the customer data from your CRM about everyone who’s purchased a tincture in the last two years. Then, you create a look-alike audience based on the customer attributes in the first audience. In essence, your goal is building a profile of consumers most likely to buy tinctures based on the detailed characteristics of those who have in the past.

As you can see, this is a great way to cast a narrowly targeted net for new customers. And it can be used to focus the standard tools we’re already used to such as mobile banner ads and Google ads.

There’s more to all three of these basic uses for monetizing your customer data; much more. If you’d like to learn how to leverage this information for yourself, we’d love to talk. Reach out anytime.