Why Marketers Should Leverage Behavioral Data To Understand Customer Intent
Considered purchases is a common term in marketing. But many industries have a complex purchasing cycle. Marketing for considered purchases depends on understanding the customer and their position in the buying cycle better. Here, Matt Stone, SVP Marketing, Verisk, explains why marketers should leverage behavioral data in their efforts.
The term “considered purchases” is often associated with B2B marketing. But many consumer markets, such as insurance, banking, and lending, involve complex purchasing decisions that transcend a digital shopping cart or buy button. In these industries, organizations compete for leads from comparison-shopping websites and must carefully nurture customer relationships, from initial awareness to purchasing decisions and beyond.
Marketing for considered purchases is equally dependent on understanding who the customer is as well as where the customer is in the buying cycle. This combination of personalized and contextual marketing is the foundation for delivering an exceptional customer experience.
See More: Consumer Behavior Matters More Than Sales Trends
The Business Case for Behavior
Demographics and other static data are key components of personalization, but they can only go so far. Unlike demographic data, behavior reflects a precise moment in time. Leveraging behavioral data allows marketers to identify and respond to real-time purchasing intent rather than relying on a “spray and pray” method of outreach that is often ineffective and always inefficient.
Behavioral data includes actions like site visits, page views, form completions, and duration or frequency of activity. This type of data is usually created and stored in what is known as an “event,” meaning an action taken, with “properties,” or metadata used to describe the event conditions. For example, an event could be a website visit, and the property for that event could be the type of device used to access the site. It may help to think of events as the “what” and properties as the “when and where.” The “who” is the consumer that took action (including any static data you already know about them).
Before incorporating behavioral data into its marketing efforts, one international bank was using what is known as propensity modeling to drum up leads for mortgage refinancing. The problem? Propensity modeling determines customers that would benefit from refinancing based on changing interest rates but does not reflect any purchasing intent on the part of the consumer. In some cases, the lag created by data modeling had their teams scrambling to catch up. In others, it led to the sort of preemptive outreach that feels “creepy” — not the sort of interaction that makes a good first impression.
The bank began incorporating behavior into its marketing efforts using a clever CRM integration that delivered daily insights. These insights told loan officers who had started or were continuing to shop for a mortgage, what sub-category they were interested in, how many shopping events had taken place, and even what time of day each customer preferred to browse. Since 95% of consumers started their mortgage research online, the bank could reach customers at the initial point of interest with targeted emails tailored to actual intent.
Though the buying signals will change, the same basic principles apply in other industries involving considered purchases. Imagine being able to offer a bundled discount to a customer with an existing home insurance policy, not on a whim, but when they have been actively shopping on another website for insurance on a new car. This sort of personalization not only drives better customer experiences but also makes better use of limited resources, like a marketer’s ad spend and salesperson’s time.
Best Sources of Behavioral Data
Like demographic data, first-party behavioral data — the behavior detected on a company’s own website or app — provides only a limited view of the customer’s mindset. It is like driving with blinders. Think of third-party behavioral data as your team’s peripheral vision, arming you with insight into what customers are doing across the web. Let us consider another example from the mortgage industry.
After dating for a few years, two professional 30-somethings make the momentous decision to get married. Not long after, they begin thinking about buying a home and use a free online calculator that estimates how much they can afford and how much they should save for a down payment and closing costs. All this is in exchange for sharing their names, joint income, relationship status, and one partner’s email address. Several lenders purchase this data from a lead aggregator and add it to their CRM.
The couple gets sidetracked with wedding planning. For the next several months, the couple shows no signs they are in the market for a mortgage and marketers or sales teams dedicating significant resources to them would be futile. The wedding passes, happy tears are shed, and the newlyweds decide to put their generous monetary gifts toward a down payment on their first home. They consult a neutral comparison-shopping site like NerdWallet to learn about potential financing programs.
A savvy lender with a sophisticated marketing stack is notified in real-time that the happy couple is back in the market! What’s more, they are exhibiting behavior that signals they are now ready to get serious, taking them from “casual window shoppers” to a “high priority lead.” While the mortgage company’s marketing automation system kicks into high gear, a loan officer personally reaches out to learn more about the couple’s needs.
Though this was a hypothetical story, it is not theoretical. Marketing technology is only as powerful as the data that powers it. In an independent study conducted by Forrester, three financial services companies used third-party behavioral data to improve their customer acquisition, retention, and cross-selling efforts, achieving a combined ROI of 191%.
See More: 4 of the Best Predictive AI Tools to Improve Customer Experience
From Compliance To Enthusiastic Consent
Any conversation surrounding consumer data should include a footnote on responsible data management. Today, consumers are simultaneously demanding personalized experiences while voicing concerns about how their data is being used. It is essential that marketing and sales teams take great care to use behavioral data in compliance with privacy regulations and, perhaps more importantly, in a way that makes consumers feel comfortable doing business with them.
Fortunately, privacy and personalization are not mutually exclusive. Behavioral insights and third-party data encourage organizations to go beyond meeting the basic requirements of regulatory compliance. By focusing the bulk of their efforts on customers who have demonstrated purchasing intent, marketing and sales teams can skip aggressive and intrusive tactics in favor of building real relationships, turning leads into customers and one-off transactions into long-term relationships.
Are you using behavioral data in your marketing efforts? Let us know what benefits or limitations you have found on Facebook, Twitter, and LinkedIn.
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