Let’s face it, millennials love poached eggs on avocado toast. And did you know Generation Z follow a plant-based diet because they believe it’s better for the planet? Of course, these are sweeping generalizations. So why would so many retail marketers continue to categorize an entire audience based on something as arbitrary as generational segments? 

Even Pew Research, who has been at the forefront of generational analysis for years, recently admitted it has flaws. The reality is each customer is an individual and should therefore be treated as such. The good news is that with the right data strategy in place, effective audience targeting doesn’t have to rely on superficial stereotypes. However, not all data is created equal.

The First-Party Data Advantage

Most retailers have traditionally relied upon third-party data for audience targeting and engagement. However, the misuse of consumer data has set off a wave of new privacy protections from big tech and governments alike. Google, whose Chrome browser accounts for nearly two-thirds of the world’s internet usage, plans to phase out cookies completely in 2024, while Mozilla Firefox and Apple Safari have already done so. In addition, over 160 countries now have national privacy laws in place that regulate how consumer data can be collected and used. All of these factors are forcing retailers to rethink how they collect and use data to understand and interact with consumers.

But first-party data is more than just privacy compliant. Compared to third-party data that’s readily available to competitors, first-party data is unique to your business. When collected and unified into individual customer profiles, it can be used for segmentation, lifecycle marketing, and modelling to execute highly personalized and targeted experiences that no competitor can replicate.

Getting Granular with Segmentation

One of the primary advantages of first-party data is the ability to categorize customers based on key data points or attributes, including demographics (age, gender, location), psychographics (lifestyle, interests), behavioral data (purchase history, order frequency), and more. This granularity enables retailers to create precise audience segments that align with their specific marketing goals.

For example, cart abandonment remains a significant issue for many retailers, costing the industry an estimated $4.6 trillion a year. Using first-party data, retailers can identify and create segments of customers who initiated the shopping process by adding products to their carts but didn’t complete the purchase. These segments could include those who abandoned their carts within the last 24 hours, those who did so within the last week, or those who have a history of cart abandonment. 

Retailers can then tailor their cart abandonment strategies to each group. For instance, you can send timely email reminders or retargeting ads on platforms like Facebook, Google, and other networks to recent abandoners with incentives to return and complete their purchase. If the first-party data indicates that some customers often abandon carts with specific types of products (e.g., electronics, clothing), retailers can further segment and customize their messaging and incentives accordingly.

Delivering Relevant Messages at Every Lifecycle Stage

Robust segmentation is especially important for lifecycle marketing because customers have different needs, preferences, and expectations at different stages of their journey (e.g., acquisition, onboarding, retention, loyalty, churn). Using first-party data, retailers can ensure their messaging and offers are highly relevant based on a customer’s lifecycle stage. Examples include ensuring customers never see ads for product they already bought and exposing them to ads for related or complementary products instead, or sending promotions for items like a toddler bed to a customer who bought a baby crib two years ago. By delivering these types of highly tailored and timely messages and experiences, retailers can enhance customer satisfaction, drive conversions, and build long-term loyalty.

Expanding with Precision

When retailers are able to segment their customers effectively, it also makes it easier to find similar individuals in their target audience. For instance, retailers can focus on the most relevant characteristics or behaviors, such as demographics, interests, or purchase history, to create more precise and effective lookalike models. These lookalike models then identify individuals who closely resemble the characteristics of the existing segments. By creating these lookalike audiences, retailers can more effectively expand their reach to prospects who are more likely to engage or convert.

Getting Started

Most retailers have a wealth of first-party data collected from websites, mobile applications, ecommerce portals, POS systems, CRM systems, and more. But just because you can create a million segments from your data doesn’t mean you should. Managing and analyzing and excessive number of segments can quickly become overwhelming and complex. Moreover, crafting and implementing campaigns for each demands significant time, budget, and manpower. Start by identifying the most valuable segments and crafting your campaigns accordingly, and then expand from there. 

Tools that help you collect, unify, and activate first-party data, like a customer data platform, can also help drive operational efficiencies for retail marketers and the business at large. Growth-focused teams can build rich, multi-dimensional segments in minutes and instantly activate them through connections to other systems and channels, including email marketing platforms, advertising networks, social media channels, and more.

First-party data can give you a much better understanding of your customers and the ability to connect with them without making sweeping assumptions. So, let’s leave marketing based on generalized generations in the past where it belongs. 

About the author