Facebook Custom Audience: How to Clone Your Customers

Want to increase the effectiveness of your Facebook ads?

Looking for better quality audiences to show your ads to?

Lookalike audiences built from a source audience of your best customers allow you to find highly targeted, responsive audiences.

In this article, you’ll discover how to clone your best customers by combing Facebook’s custom audience feature and Lookalike audience targeting.

The different audience types on Facebook you need to know

It is important to understand the three types of audience on Facebook. There are Saved, Custom and Lookalike audiences that you can use at the Ad Set level.

The effectiveness of these audiences can vary depending on the “temperature” of the audience coupled with the positioning and messaging of your ads that are presented to that audience.

Saved audiences are the most basic type, which use demographic information and the detailed targeting feature to target people through interests and behaviors. These are “cold” audiences of people who are unfamiliar with your business.

Next, you have Custom audiences, which consist of people that have engaged with your business either on Facebook or on your website.  These are “warm” and “hot” audiences as people are familiar with your business due to the previous interaction they have had.

The third type of audience are Lookalike audiences. These too are “cold” audiences just like Saved audiences but they are better targeted. They use a source audience that you control, to find new people that share the same characteristics as the people in your source audience.

Create a customer file custom audience to match your database on Facebook

The first step in the cloning process is to create a customer file custom audience.

A customer file custom audience is where Facebook will try and match the people in your audience upload, to their profiles on Facebook.

First, navigate to the Audiences dashboard in Ads Manager.

Click Create Audience. You’ll see a dropdown list of the three audience types we covered earlier in the article. Select Custom Audience.

From the list of custom audiences select Customer file. This will open a new pop-up window with three options asking you how you want to upload your data.

The first is a manual upload of a customer file or copy and pasting the data. The second is directly from your lists through the MailChimp integration and finally, you can create a customer list custom audience that features the life time value (LTV) of your customers.

Select Add customers from your own file or copy and paste data. In this example, we are going to upload data that has been exported from our CRM system.

Preparing your data for optimal match rate

 Before you upload your file you need to prepare it to maximize the match rate.

You can include up to 15 different identifier fields. The more fields you include the higher your match rate will be, as there is more data for Facebook to match against.

When creating your spreadsheet, ensure that each identifier that you want to match is in its own column. This includes separating names into a First name and Surname column.

One key field to include that improves your match rate is the country location field. Even if all of your data is from the same country you’ll still want to include a country field because Facebook matches data on a global scale and this step helps them match as many people as possible from your customer file.

Finally, you want to ensure that your customer file is in a .CSV or .TXT format as these are the only two file types that Facebook accepts when manually uploading data to create customer file custom audience.

 

Upload your customer file and then map the identifiers

Once you have selected the Add customers from your own file or copy and paste data option, another pop-up window will appear with the four step process to creating your customer file custom audience.

The first step is to upload your list or you can copy and paste it using the option below that.

Once you have uploaded your customer file, formatted using the optimal tips mentioned above, the next step is to map your identifiers.

Select the relevant identifier to each column in your file. Once you have mapped all of the relevant identifiers click Upload and Create.

This will move you on to step three, where your customer list is hashed locally on your browser and then sent to Facebook. There, it is matched against Facebook’s existing list of their users’ hashed IDs and the matches are added to your custom audience.

Your customer file custom audience will now appear in your audience dashboard with a blue populating status. This means that Facebook is building your custom audience – it can take up to 30 minutes to populate.

 

Use your customer file custom audienceas the source for your Lookalike audience

After you have created your customer file custom audience, step 4 in the audience creation process will give you two options. The first is to create a Lookalike audience and the second is to create a campaign which will use the customer list custom audience.

Select Create a Lookalike Audience.

The Lookalike audience creation window will now appear. The customer list custom audience you have just created will already be selected as the source audience in the source field.

Next, choose the country that you want to build your Lookalike audience. For example, if your target audience is in the UK, then choose United Kingdom in the drop down list of locations.

Finally, select your Lookalike audience size. You have a sliding scale from 1-10 percent. This represents a 1-10 percent sample of the total Facebook audience size in your chosen country or region that you set in the location field.

Your 1% will be a smaller audience than your 10% but it will be closer matched to your source audience of your customer data.

Start by creating a 1% Lookalike and then to reach more people, repeat this process changing the audience size to 2%, 3%, 4% all the way up to 10%.

Your Lookalike audience quality will be impacted by the size of your source audience. The larger the source audience, so the more matched data in your customer file custom audience, the better quality your Lookalike audiences will be.

Having tested different source audience sizes for creating effective Lookalike audiences, we’ve found that a minimum of 1000 is our starting point.

Improve Lookalike audience quality by using the Life Time Value identifier 

You can use the life time value (LTV) of your customers as an additional identifier field when creating your customer file custom audience.

To do this, navigate to your Audiences dashboard and click on Create audience. From the drop down select Custom audience and choose Customer file.

Next, select the bottom option to include your LTV.

When preparing your data, as well as using the optimal guidelines outlined earlier, you’ll also want to include a field with the LTVsrelated to each of the customers in your file.

For eCommerce companies using platforms like Shopify or Woocommerce you can easily export your customer data and include purchase values.

Follow the same process to create your customer file custom audience as outlined earlier in the article. This time though there is an extra step to match the value column in your file to the LTV identifier.

When to use Lookalike audiences and how to scale with them

Now you have created highly targeted “cold” audiences of people that are similar to your customer database, what campaigns and ads should you show to these new Lookalikes?

As Lookalike audiences are “cold” audiences, it is best to use them at the Awareness stage of the ALL Framework to position valuable video content to start to “warm” up these audiences.

Here’s a list of Facebook ad examples that you can use for inspiration for targeting your new Lookalike audiences.

Conclusion

Cloning your existing customers by creating Lookalikes audiences from your database is one of the most effective ways of producing highly targeted audiences on Facebook.

With better targeted audiences you’ll see better results compared with using regular Saved audiences built from interests and demographics.

This is a great competitive advantage when comparing it to businesses that don’t have the customer data in the first place or don’t know the cloning process.

What do you think?