Database Scoring for Market Segmentation: The Key to Personalization

In the digital world, personalization holds the key to better marketing performance. Personalization can significantly impact your bottom line – improving marketing spend efficiency by 30% and reducing customer acquisition costs by 50% – as well as boosting marketing ROI and overall revenue. But these kinds of results require a strategy that goes beyond just personalizing your content and messaging; it’s crucial you’re able to effectively target your highest-value audiences.

Database scoring is a predictive modeling technique that enables just that. It’s designed to predict which customers in your database (or out in the market) belong to which customer segment, without the need for all of them to take a short form survey. That way, you can ditch the spray-and-pray approach and deliver targeted messages to current, former and prospective customers that resonate with them.

How can database scoring improve your marketing efforts?

Traditionally, brands have been challenged to take the insights from a few customers (those who take a segmentation survey, for example) and extrapolate them to many (their entire customer database, or the entire market). In the age of “the right message to the right person at the right time,” you need more than techniques to understand your customers. You also need to help craft and personalize messages to your highest-value customer groups.

Though you can apply the technique to any audience with shared traits, database scoring is particularly useful in conjunction with a market segmentation study. And when implemented properly, it’s a technique that enables you to find segment members in the real world – to acquire high-value customers or to retain existing customers – and deliver personalized messages, targeted ads and a richer customer experience. For a growing number of brands, database scoring is a core component of their activation activities, and it leads directly to a more useful and successful customer segmentation project.

When should you use a predictive modeling technique, like database scoring, and with what audience?

Effective database scoring requires careful planning from the onset of a market segmentation study. Once the study is complete, database scoring techniques can be used for the following use cases:

1.      Engage existing customers to maximize lifetime value by cultivating deeper brand loyalty

First, have a sample of actual customers take the segmentation typing tool. Then, using the behavioral and demographic data on those customers in the CRM database, build a predictive model of segment membership that can be extrapolated to your entire database of customers.

For increased accuracy, you can also purchase third-party data to augment your internal CRM data, and the model can still be used directly within a company’s database to target through email and other kinds of dynamic content and communications.

2.      Discover prospective customers to increase new customer acquisition and conversion rates

When trying to reach new customers, it’s still important for a group of existing customers to take the segmentation typing tool to develop “seed audiences,” or smaller groups of consumers who exhibit shared traits. Data management platforms (DMPs) then utilize look-alike modeling, a form of database scoring using external data, to identify others in the broader population who are likely to “look like” those who belong to each segment. Marketers can then target those audiences programmatically across digital advertising platforms.

Then, by crafting segment-specific messaging and advertising, brands can utilize segmentations as the foundation for their personalization and activation strategies.

Who should execute a database scoring project?

Database scoring is a technical exercise requiring some mix of database administrators, data engineers and data scientists – unless it’s executed in a DMP, in which case the process is automated. Either way, it requires alignment across teams, including consumer insights and marketing.

In the early stages of the segmentation project, it’s critical for a survey designer and a modeler to know what non-survey variables are available; it’s also important for a company’s database team to understand the survey components. In database scoring and predictive analytics, a variety of factors – such as the quality and breadth of data – can drive better accuracy.

The benefits of database scoring are clear: by enabling targeted messaging at scale, this predictive modeling technique provides brands with an efficient tool to build deeper levels of engagement with customers, reducing churn through more relevant marketing, product and service strategies.

Are you ready to dive into a database scoring project? Our experts at Material are ready to help you get the most out of it.