How to Use First-party Customer Data for Prospecting Campaigns

Can your first-party customer data help you reach new audiences?

Customer data platforms (CDPs) are primarily associated with retargeting, retention, and other post-conversion use cases. But the gold mine of customer intelligence captured in a CDP is valuable for prospecting 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.

So how can you use your Velocidi CDP to reach the right audiences? Easy. Just use the standard lookalike modeling features in ad platforms (Lookalike Audiences in Facebook and Similar Audiences in Google) but use your top-performing Velocidi audiences as the seeds for those models.

It’s a more precise approach than using 1% of the best matches for all your website visitors. Instead, you can start with an audience of only those website visitors who are likely to convert, or only those customers who have the highest lifetime value.

You may have creative ideas of your own for how to put this into practice. A few of our pre-configured audiences are intended for this exact purpose.

“Active potential buyers” machine-learning audience

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.

Your “active potential buyers” audience, which is also used for retargeting, is a great example of how your not-yet-converted customers can add value to your acquisition campaigns. These are customers who have indicated through their browsing behavior that they are in a buying journey but have not yet made a purchase. By using this as a seed for lookalike models, you’re reaching for new customers who might also be in-market for your products but have not yet visited your site.

This strategy is really about catching customers at the right moment in time. So the machine-learning function here makes sure the audience stays freshly up-to-date with current customer behavior.

“Best Customers” and “High Spenders” RFM Audiences

RFM stands for recency, frequency, and monetary value — three sliding scales on which each customer is scored. The “Best Customers” audience captures those who score in the top percentile on all three scales. In other words, your customers who purchased from you most recently, have a history of frequent purchases and have spent the most on your store.

“High Spenders” captures customers who score in the highest percentile on “monetary value.” It includes those of your customers who have spent the most and includes the whole range recency and frequency scores.

A common method to reach a similar outcome is to upload a custom list from your CRM to Facebook or Google. In other words, export a list of customers, run an RFM scoring process, filter out your top customers, and then upload that list to Facebook. That list is then matched to Facebook users using your customers’ email addresses, so Facebook can use it as the basis for their lookalike model.

Using your Velocidi audience is an easier option because it’s activated directly to Facebook. The process of identifying high-value individuals is already done without manual work. And the list updates automatically, so you never have to worry about it growing stale.

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.

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