Use your first-party customer data to reach new audiences

Customer data platforms (CDPs) are primarily associated with retargeting, retention, and other post-conversion use cases. But the deep vein of customer intelligence in a CDP is valuable for new customer acqusition as well.

Now that customer acquisition costs are rising higher than ever, it’s more and more important to fine-tune your targeting from the beginning. The better you can narrow your target audience, the more cost-efficient you can be.

One of the great advantages of CDPs is that they expand the scope of “known” customer data. It’s no longer limited to CRM data and existing customer records. Thanks to first-party IDs and cross-channel ID resolution, even anonymous website visitors are “known,” to a degree. This means you have a much larger data set from which to glean insights.

So how can you use your Velocidi CDP to reach the right audiences? Here are a couple of suggestions.

Create a Facebook Lookalike Audience based off of your “likely buyers” machine-learning audience

Velocidi provides a segmenting tool that captures website visitors who are predicted to be likely buyers. The primary use case for this segment is retargeting, but our clients have also reported positive results when using it as a source for Facebook Lookalike Audiences. It’s a more precise approach than using all of your website visitors as the source. Instead, you can start with an audience of only those website visitors who are likely to convert.

 

 

A common recommendation for brands to reach a similar outcome is to upload a custom list from their CRM. In other words, export a list of customers, select those with high CLV, and then upload that list to Facebook. That list is then used by Facebook as input on what good users look like, facilitating the creation of an audience with similar characteristics.

Using your Velocidi “likely buyers” audience is an easier option because it’s activated to Facebook via the Facebook pixel. And the process of identifying high-value individuals is already done without manual work.

This is a good approach for any platform that provides similar solutions to Facebook’s Lookalike Audiences. However, not all ad platforms offer this kind of functionality, and when they do, they are not always customizable. In those cases, we have something else you can use.

Use Velocidi’s acquisition campaign recommendations to choose high-performing targeting parameters

To help you get to know who your best customers are, Velocidi provides machine-learning recommendations for acquisition campaign strategies you can use on external platforms.

Essentially, we show you the common rules that have tied your best customers together for the previous 30 days. Are a large chunk of your customers and website visitors in a few specific cities? Are they in a specific age band? These recommendations are generated daily to keep up to date with current activity.

The campaign recommendations show up to ten rules on common customer attributes, with between one and five attributes per rule. You’ll see what percentage of your current users and customers match the given rules. And you’ll see a score for how well the machine-learning model expects a campaign using these rules to perform. As you experiment with these rules over time, the model will improve and become more accurate.

 

 

The default attributes are very basic, such as geographic, demographic and device information. This is to provide targeting parameters that are widely applicable. However, it will be possible to add attributes that are relevant to your brand and your preferred platforms. This is just a matter of making a request to our team.

Thanks for reading! Are you using other strategies to reach new customers based on first-party data? Keep in touch with us by signing up for email updates in our footer.

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