• Mar 17, 16
  • Direct Agents

If you’re anything like me, scrolling through Facebook, Instagram, and Twitter is a part of your daily routine. We use social media not only to stay connected with our high school friends, but also to follow and engage with the brands, publications, TV shows, and celebrities that we care about. Through our social media platforms, we share the knowledge of our interests, activities and favorite brands with our followers and friends.

Because we voluntarily reveal so much information about ourselves through social media and spend so much time engaging with our News Feeds and Timelines, Facebook, Instagram, and Twitter (and Snapchat, LinkedIn, among others) have become an integral part of any marketer’s media plan. There is seemingly a goldmine of self-declared user data that we can tap into when creating social media marketing campaigns. We can target users by demographic, behavioral, and psychographic data with simply a few mouse clicks.

However, when presented with such robust targeting options on paid social media channels, many marketers and agencies abandon the most meaningful data they have access to: first party customer data. Leveraging your first party data is essential to maximizing your social media marketing strategy, both for customer acquisition and customer retention.

In this series, I’ll outline some successful concepts and strategies that I’ve utilized to help my clients gain the most value from their social media advertising campaigns.


With billions of people using Facebook, Instagram, and Twitter daily, these platforms are a natural fit as a tool to prospect for new customers. However, it is also a challenge to filter through these countless users while making sure you are spending efficiently and gaining visibility from the right audience.

Lookalike audiences on Facebook and Instagram (Tailored audiences on Twitter) are two of the most powerful tools for your social media marketing campaigns, and through the daily conversations I have with marketers (both in-house and within agencies), I’ve come to realize they are highly underutilized.

Lookalike Audiences are an effective way to find the users on a social platform that most closely resemble your customers or site visitors, based on demographic, behavioral, and psychographic similarity. In other words, you upload a CRM list, and the social channel creates a custom audience of users that have shared traits and behaviors.

Because the resulting Lookalike Audience is completely dependent on the file you upload, it’s crucial to be strategic in deciding which CRM data to use as the input. I’ve found that when first testing Lookalike Audiences, marketers often use CRM data sets that are far too broad. For example, take a list of all active customers in the last 12 months. While a Lookalike Audiences modeled on this list could potentially help hit your social media marketing KPI’s, campaigns prove to be much more successful when CRM data is strategically segmented and multiple Lookalike Audiences are built.

Next time you create Lookalike Audiences for your social media campaigns, take a more sophisticated look at your CRM data. The objective here is to uncover which subsets of your customer data are the most relevant for each specific campaign. To get started, here are 3 simple approaches to segmenting your customer data for Lookalike Audiences:

  1. Purchase Value. Not all customers are equally valuable. Maybe you have a campaign promoting a product that costs $200. Try targeting a Lookalike audience modeled after all users that purchased similar products costing $150 in the last 12 months. These are people that you know are interested in the type of product you’re promoting, and have shown a proclivity for spending in the target amount.
  2. Number of previous purchases: Some customers are more loyal than others. It’s possible that people who purchased from you 6 times are more valuable, but a Lookalike Audience modeled on people who purchased from you 3 times is more effective at achieving a low customer acquisition cost. Test both! Discover what the sweet spots are for your business.
  3. Similar Product Purchases: This one seems simple, but is often overlooked. Let’s say you’re launching campaigns promoting a new product. Try targeting a Lookalike Audience modeled on your customers that have purchased your most similar products. Again, we want to be sure that the CRM data we use for Lookalike Audiences is the most relevant data for your campaign that is available.

Facebook’s (including Instagram) and Twitter’s custom audience modeling are powerful, but they’re not magic. Facebook’s Lookalike Audiences typically include at least 1 million users. More often than not, this giant pool of users will be too broad for your campaign objectives. Narrow down your Lookalike Audiences by leveraging behavioral, interest, and demographic targeting.

Let’s say you’re a marketer for a watch brand and you are launching campaigns to promote a new casual watch that will sell for $100. Start by creating a Lookalike Audience modeled after your customers that have purchased products from you that cost $75+ in the last 12 months. Narrow down this audience with behavioral and interest targeting. For example, target users who buy men’s fashion accessories, or users that follow your competitors or similar brands.

You can significantly improve your social media marketing campaigns by properly leveraging your customer data. Think about what your specific campaign goals are and carefully consider which of your customers are the most relevant for building Lookalike Audiences. Remember to test many different audiences until you find the ones that perform best for you. Let us know how this approach works for you!

In part two of this blog series, I’ll outline some strategies for using your customer data for improving customer retention and maximizing the lifetime value of your clients. Check out our blog and stay tuned!

For more information on how you can improve your social media marketing strategy, please contact us.

By Jackson Richards, Senior Yield Analyst