As one of Lytics’ resident data scientists, I’ve been asked to share some of the many lessons I’ve learned in the last 6+ years at Lytics through this multi-part blog series, which complements our Snack Break webinar series. We start with one of the most important lessons of all: Not all data are created equal, which is in the Introduction video.
Marketers have access to more data than ever before. Customer data. Transactional data. Demographic data. Even third-party data to augment the data they already have. And more data is a good thing, right? Not necessarily.
The goal of data-driven decisioning is to drive outcomes: acquire more customers, add more subscribers, prevent customer churn, etc. But most data don’t have an impact on outcomes. In fact, at Lytics, we’ve discovered that about 92 percent of outcome variabilities can be traced to customer activities that can be measured through affinity and behavioral data. That means much of the time that marketers spend collecting, cleaning, and consolidating data doesn’t add value, but delays the time to value.
In other words, marketing teams that achieve better outcomes aren’t worried about collecting more data. They’re focused on collecting the right data.
I like to describe the right data as signals. These are data points that distinguish customers and predict their behaviors. For example, just using demographic data, my neighbor and I would probably get lumped into the same segment as lookalikes. We live in the same region, with similar incomes, similar ages, etc. But those data points don’t tell you much about our actual buying behavior.
Consider a company like Instacart, which many of us have become familiar with over the last few months. Instacart isn’t really interested in your age or income. They’re interested in what you buy when you shop on their site. I might be a vegetarian, while my neighbor might be a barbecue fanatic. Those are the kinds of data signals that really matter, because they help you set up the next best experience for that customer through relevant product recommendations. You can’t buy this kind of personalization. You need to build it using the data signals your customers are sharing through their interactions with your business.
Of course, Instacart is a unique use case, so let’s consider a more common marketing example: newsletters. As a marketer, maybe you’ve identified a problem with customer churn in your monthly newsletter. Twenty percent of subscribers may stop engaging after the first month, an additional ten percent the following month, and so on. The propensity to churn is what we would call a behavioral signal. You don’t need to overlay mountains of data to find those signals. Our customer data platform, for example, isolates this behavioral data out of the box so you don’t need to go digging through mountains of data to find it.
A lot of this valuable data has to do with content affinity. Understanding how customers engage with your content is vital to understanding what they’re interested in today and what they’re likely to be interested in tomorrow. These affinities not only drive the next best experience for content consumers but also help companies create more relevant content.
So, the question that marketers need to be asking themselves is, Are we investing in the right kinds of data? If you’re collecting data just to build broad segments of customers and align them with marketing campaigns, the answer is “probably not.” Data like that is too broad to be useful. You need behavioral data and affinity-based data that separates the individuals in a segment down to their atomic level. In other words, you don’t need data that tells you what a 35- to 54-year-old male in the Chicago area making over $150,000 is interested in. You need data that tells you what Michael Smith, who happens to be a 47-year-old father living in Elmhurst, is interested in based on his most recent interactions with your brand.
Being a data-driven business isn’t a bad thing, but don’t let the pursuit of ALL data drive you to distraction. Focus on data that can drive personalized experiences—behavioral data, affinity-based data, etc.—and you’ll be able to achieve the right marketing outcomes.
Stay tuned for my next installment: Unpacking value out of the box.
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