How to responsibly scale first-party data

First things first, it's important to acknowledge the power of first-party data. It's no small feat for a user to willingly provide a brand with their personal information. As Spiderman famously said, "With great power comes great responsibility."

As consumers have become more digitally aware, they expect transparency from businesses regarding their use of personal data. This means brands must handle first-party data with care, as mishandling it could result in a loss of trust from consumers and, consequently, a decrease in future data collection.

Here are a few ways that advertisers can use their first-party data responsibly in order to get the most out of it.

Activate data with a CRM

To use first-party data effectively, you need a CRM platform to collect and store information about customers and prospects. Many marketers invest in a separate platform to target their CRM data, but this can take a long time. Marketers who need to access and use their data more quickly should use a platform with built-in integrations that allow direct uploading of CRM data. With Fetch by Subset, you can identify your target by any internal CRM list or specific criteria with an accurate holistic profile including social, professional, and personal information. Advertisers gain access to targetable audiences in hours, saving time and simplifying the process.

Create audience profiles

Audience profiling is crucial for advertisers to build strong relationships with customers, even if they've already collected first-party data. By leveraging strategic data partnerships and advertising automation, advertisers can create detailed user profiles based on demographics, interests, and buying behavior. For example, Subset’s various audience identification integrations allow marketers to enhance first-party data with machine learning technology, resulting in in-depth consumer profiles for targeted messaging.

Look-a-like modeling

Marketers can use look-a-like modeling to tap into new audience pools, in addition to audience profiling which helps them understand current and past consumers. Although third- or second-party data can also be used for look-a-like modeling, the most accurate results come from leveraging first-party data. For instance, machine learning technology can identify audiences that are like robust audience profiles created with a brand's first-party data. This addresses the scalability concern of first-party data by creating large-scale audiences from a smaller amount of data. To achieve this, marketers can provide a seed audience via their CRM or place a pixel on their site to collect first-party data that machine learning technology can amplify with additional audience pools.

Layering First-Party Data with Contextual Targeting

Another way to personalize the consumer experience is by combining contextual targeting with first-party data. Contextual targeting serves ads based on the content of digital environments. And while contextual is an effective strategy on its own, coupling it with first-party data can create an even more personalized consumer experience. How does it work? Let's use Subset's partnership with Peer39 as an example: To leverage first-party contextual targeting Subset analyzes the brand's media distribution and performance across contextual signals, pulling insights about where audiences are spending time and consuming content. Based on those insights, Subset creates customized contextual targeting categories that are optimized to reach target audiences.