Predictive Analytics for First-Party Data

First-party data isn’t anything new, though it’s recently become more of a buzzword due to changes in the privacy ecosystem. First-party data simply refers to the data a company collects about its audience, prospects, and customers. It’s intensely valued today because of its accuracy, timeliness, and utility for personalizing customer interactions and refining marketing strategies.

Get Invited to the First Party

It’s essential to understand how first-party data differs from other data types. In contrast, second-party data is collected by another company (it’s their first-party data) and is then sold “second hand” directly to another organization. The two organizations are interested in the same customers or audience, but probably not competitors. They may also share data through a partnership arrangement. Data exchanges help match buyers and sellers for second-party data, and include companies like Lotame PDX, AudiencePrime, and Neustar’s Fabrick.

Third-party data is data collected by an aggregator that doesn’t necessarily have an interest in the people whose data it collects. However, the aggregator packages and sells the data to buyers who want to reach those potential customers directly. Examples include BlueKai, Snowflake Marketplace, Acxiom, LiveRamp, and the AWS Data Exchange.

You may even hear references to “zero-party data.” That’s the data that customers voluntarily choose to share with a brand. So, for example, if you’ve ever told an app which products or topics you’re most interested in, you’ve offered up zero-party data.

Among all these data types, today’s marketers are increasingly concerned with boosting their first-party data collection and putting that data to work most effectively. That’s because data from third parties is either now much less granular or may soon become much less accessible. Shifting privacy policies from major social media platforms and mobile devices have made it much harder to get information about specific individuals from third parties. Third-party cookies will also be phased out at some point, making cross-channel identification of consumers even more challenging.

First-Party Data Is More Important Than Ever

To accomplish their goals in this new environment, marketers need robust, trustworthy first-party data that they can use to design and refine campaigns. First-party data can be collected in various ways:

Survey results showing marketers' preferred way to grow first party data - bar graph

Your first-party data collection is critical because it is the best data you’ll have. Your brand understands your customers best. You are best positioned to trace the patterns in their behavior, whether in transaction data or in their activities across your different channels. Overall, you are most familiar with your customers’ journey. Your knowledge will be deepest and most accurate, compared to any other source of data about your customers.

Indeed, the evidence shows that using first-party data can be powerful. Research from BCG in 2021 showed that companies effectively integrating their first-party data generated 1.5 times the revenue from a single ad, communication, or outreach. Moreover, they doubled their cost efficiency when compared to companies with limited data integration.

Additionally, your first-party data is available to you pretty much immediately, without the delay involved in acquiring second- or third-party data. That means you can take action informed by first-party data far more rapidly. You won’t wait to obtain and combine other sources’ information in order to make decisions. Today, customer behavior is changing quickly, and staying on top of the latest trends is critical.

Overall, because first-party data is better and faster, your marketing performance will simply be better with faster ROI. What’s not to love?

Maximizing the Value of First-Party Data

So what are some of the specific applications of first-party data that can boost your marketing efficiency? Using first-party data, marketers can better:

  • Personalize messaging through refined customer segmentation
  • Refine email campaigns to make them highly relevant to distinctive customer groups
  • Test messages for specific audiences by A/B testing with targeted versions for defined groups
  • Target personas with paid ads across various channels
  • Develop efficient retargeting campaigns to draw in more of your ideal customers
  • Strengthen customer relationships with loyalty programs, special offers, or upgraded service
  • Launch paid ads targeting specific personas
  • Create look-alike audiences matching to their ideal customer profile
  • Unify data from varied sources in a DMP for identity management and cross-platform reach

 

Some of these applications involve the use of predictive analytics, which applies mathematical models to your customer data to identify complex patterns that predict certain outcomes. For example, you can predict customer lifetime value, optimize cross-sell offers, or predict the chance a customer will convert from a free trial to a paid subscription. AI-powered predictive modeling can reveal what customers are likely to do or prefer. You can then take action based on those reliable predictions.

First-Party Data Informs First-Rate Predictions

Automated, accessible predictive analytics platforms like Pecan are making it possible for far more business teams to gain foresight from first-party data. Learn more about what you can do with your first-party data and predictive analytics. Then, get in touch to see how you can boost your KPIs with this perfect blend. You’ll soon be planning your first party to celebrate your predictive analytics success (see what we did there?).

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