Customer Retention vs. Prospecting: How to Strike the Right Balance with Data Enrichment

Data enrichment is a key requirement for brands looking to optimize both customer retention and prospecting initiatives. By enhancing the depth and quality of existing customer data, organizations can unlock valuable insights, enabling them to better understand customer needs, anticipate preferences, and personalize interactions. Moreover, leveraging enriched data empowers businesses to identify new prospects with characteristics similar to their most valuable customers, effectively expanding their customer base while maintaining a focus on high-value relationships.

Through a strategic approach to data enrichment, businesses can enhance their marketing efforts, drive revenue growth, and cultivate lasting customer loyalty in an increasingly competitive landscape. Let’s look at how to strike the right balance when it comes to data enrichment for customer retention and prospecting efforts.

Enrichment for Customer Retention

When marketers have first-party data about their brands’ customers, they know certain things about them: their name, their purchase behavior and how the brand helps them fulfill their needs. Outside of that, there’s very little knowledge of the customer. What are their other interests? What does their socio-demographic background look like? And what might their other wants and needs be?

Let’s look at a common retail customer example.

Fran regularly purchases with a certain luxury retailer, so that retailer has a few data points on her. The retailer knows her name, her address, and the fact that she buys an expensive skin cream every month. She comes to that retailer because its cream gives her the best results of all the ones she’s tried.

The retailer knows those tidbits. But it doesn’t know that Fran is also interested in cooking, that she’s a mother, and that she works in marketing. But if it did, it could significantly up-level the value of its communications and offers.

By using audience data, the retailer can create a better view of what motivates Fran and start personalizing her experience. It can start cross-selling into additional categories while increasing brand engagement. In other words, with first-party data enrichment, the retailer can extend that facial cream loyalty into other categories, deepen the relationship with Fran, and retain her loyalty.

Enriching first-party data with audience data enables marketers to take scattered information about customers and pull it together to start forming a clear, more-holistic picture. The enrichment process fills gaps and enhances the broader scope of a consumer’s interests, behaviors, and intent. In other words, it allows a brand to get deeper insights into what customers are thinking and doing. By engaging customers outside of the existing direct relationship, brands can open avenues for more-personal conversations with customers.

As brands obtain more data on customers, they can spot patterns in behavior and identify attributes that they have in common. Some might be around purchase intent, based on audience data collected from product review sites, price-comparison websites, or sites where a customer filled a cart but didn’t make a purchase. Beyond purchase intent, there are general interest insights that can be gleaned from the news, blogs, and other content sites people are visiting, as well as audience data around product ownership and brand affinity. And, underpinning all of this, there is also classic socio-demographic information — not just age and gender, but also education levels, employment status, and more. Each dimension opens new insights that can be used to deepen customer relationships.

Data Enrichment for Prospecting

Customer retention and loyalty represent one facet of first-party data enrichment. But how can businesses then leverage their customer knowledge to also increase new customer acquisition? In combining data-driven retention and prospecting efforts, companies can maximize the value of their first-party data with audience data.

The beauty of audience data is that it can be integrated seamlessly across a brand’s tech stack — via DMPs, CDPs, or other data collaboration platforms — to power insights for both personalization and targeting. Equally important, the data should be available so that it can be linked with many identifiers (and flexible enough to adapt to the ever-changing field of available identifiers). With this approach, data becomes a common currency for insights and targeting that brands can use across all their advertising and marketing strategies.

When approached correctly, data enrichment feeds the entire funnel. By overlapping a brand’s own first-party data, marketers can identify hidden customer dimensions, as well as unique and shared traits that those customers have. Then, that data can be segmented so that brands can bucket customers into different personas based on enriched consumer insights. From there, marketers can begin to personalize campaign messaging and content based on target audience characteristics.

Meanwhile, in the background, marketers can be building enriched lookalike modeled audiences, or cohorts, based on the personas of current customers. These audiences can help brands acquire prospects with similar traits and attributes, and the enriched lookalike modeled audiences can be activated within demand-side platforms in a way that engages with prospects and target customers across all digital channels.

This is what a future-focused data strategy looks like — and it sits at the heart of today’s future-focused brands.