What Is It?
Facebook, Linkedin, and Twitter all have great capabilities when it comes to creating custom audiences based on previously generated lists. What exactly does this mean though? Let’s say you have a successful blog with 1,000 subscribers. Sure, they’ll get an email every time you post a new blog, but wouldn’t it be nice if you could reach them across different social media platforms as well? Or, again using ecommerce as an example, when a customer makes a purchase, they need to enter an email address to complete their order. It would be awesome to target people with similar interests and social media profiles as the person who just became one of your customers. This is where custom audiences come into play. While not all social media platforms give you the ability to upload email lists, Linkedin, Twitter, and Facebook all have the ability to leverage lists to enhance the audience targeting.
Why Is It Important?
As mentioned in the Persona Development for Advanced Targeting section, you can target specific companies on Linkedin. If you have a list of Sales Qualified Leads (SQLs), and have already established a buyer persona, you can create an audience of 100 companies and then target specific job titles within that company. This is an incredibly specific audience that you won’t get across any other advertising medium, and from there we can create campaigns that are directed at these specific people. This is incredibly important because you can create specific campaigns aimed at individual companies or companies that are in the same industry. Again, with social media, the promoted content or ads shouldn’t feel intrusive but should blend in as just another part of the user’s feed.
Twitter can take a list of email addresses, mobile phone numbers, Twitter usernames, Twitter user IDs, or Mobile advertising IDs. Once you upload a .csv file of any of these records, Twitter then matches up the data to actual Twitter user’s profiles. This in itself is an awesome feature, but there’s even more you can do with it. Once you’ve created an audience based on any of the five pieces of information above, you can create what Twitter (and Facebook for that matter) calls a “Lookalike Audience”. Twitter creates a new, broader audience of Twitter users who have are most like your best-existing customers. With this audience, it’s possible to then layer even more detailed targeting on top of what Twitter already deemed is a good match to your current customers. For example, if you sell hiking shoes, and you upload a customer email list to Twitter and create a Lookalike Audience, you can then go in and target people who have an interest in hiking, the outdoors, and have recently tweeted about hiking or hiking shoes. Now that’s specific!
Facebook is also ahead of the game when it comes to using lists to create custom audiences. Much like Twitter, Facebook gives you the option to upload a list of email addresses, phone numbers, Facebook user IDs or mobile advertiser IDs. If you’re using MailChimp, you can also import an email list straight from there into Facebook. Facebook then matches the data imported to actual Facebook users. While it obviously varies by industry, (B2B usually has a lower match rate due to people using personal emails for Facebook rather than their business email) you can expect to get anywhere from a 30% – 70% match rate. Also much like Twitter, Facebook then allows you to create a Lookalike Audience. Where Facebook differs from Twitter though is in choosing the specificity of that audience as well as the country to target. You can choose to create a Lookalike Audience comprised of profiles located in a number of different countries around the world. It also gives the option to match up profiles from 1% of the country (The profiles most similar to your list. In the United States this is around 1.9 million people) to 10% of the country (Broader profiles but still similar to your list. In the United States this is around 20 million people).
At Directive Consulting, we take these customer lists and create a number of different audiences, then test which ads perform best when targeted at a 1% Lookalike Audience compared to a 10% Lookalike Audience. We also go in and narrow down these lookalike audiences even further by adding detailed targeting that’s relevant to the specific ad or piece of content we’re promoting. We put hours and hours into split testing these audiences to determine which one does better for each of our clients, rather than assuming it’s the same all across the board.