In the digital world, personalization often holds the key to better marketing performance. According to Instapage, personalized emails deliver six times higher transaction rates. But only 38% of the largest companies currently have the infrastructure for personalization in real time, and even fewer have the strategy to take advantage, according to Econsultancy and Adobe. That’s why marketers desperately need tools and techniques to not only personalize their message, but also to target that message to their 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.
In simple terms, once you’ve completed a market segmentation project, you have a new set of customer groups to target with marketing campaigns. You also have an existing database of your own customers. Database scoring enables you to more accurately “score” the likelihood of each person in your database belonging to a particular segment. Using the same principle, some database scoring techniques can also help you target and acquire new customers.
Traditionally, brands have been challenged to take the insights from a few (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 “right message to the right person at the right time,” you don’t just need techniques to understand your customers. You also need tools 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 leads directly to improved usefulness and success of a customer segmentation project.
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:
First, have a sample of actual customers take the segmentation typing tool. Then, using the behavioral and demographic data in the CRM database on those customers, build a predictive model of segment membership that can be extrapolated to your entire database of millions of customers.
For increased accuracy, you can also purchase third-party data to augment internal CRM data, and the model can still be used directly within a company’s database to target through email and other dynamic content and communications.
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 (e.g. segment membership, etc.). 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.
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. No doubt, 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.
For more information, reach out to our Digital Analytics team at info@lrwonline.com.
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