How familiar are you with digital behavioral data? Let’s put it another way.
You know that feeling when you walk into your favorite café—you know, the one that makes the perfect latté every time? You ask for “the usual” and the barista knows exactly what to make, just the way you like it.
This, in essence, is personalized service created through analyzing and understanding human behavior. The person behind the counter knows who you are, what your intent is, and how to get you what you need, just by observing you, listening to you, and interacting with you. In other words, they’ve honed their expertise, picking up on the behavioral cues you’ve laid down to make their service more relevant.
So how can you take the same level of personalization that you get when you order your morning cuppa joe, digitally enable it, and scale it using the online behavior of thousands of customers—without hiring whole teams of people to make those human-to-human connections?
A Quick Primer
Digital behavioral data is generated by the things people do—their online engagement with certain topics or activities. In terms of your business, that could be clicking a link, entering a search query, reading an article, watching a video, making a selection, rating or liking an item, calling your customer service team for help, filling out a form, adding something to a cart, or placing an order.
In the search biz, we call each of these actions an “event.” In and of themselves, events describe the “what” of user behavior. When stitched together across different touchpoints during one or several sessions, this behavior describes the customer journey that brought a user to a result. And when mapped with other kinds of data analytics, they help fill in the “why” or the user’s intent.
Intent—that word’s important, because the more accurately and quickly we can anticipate the intent of our customers, the more effectively we can deliver relevant experiences. And the more often we deliver a relevant customer journey to a user, the more likely they are to convert. A great customer experience also increases the chance of long-term customer loyalty.
The big tech players know just how key a role behavioral analysis plays in this equation. Amazon, for example, tracks the items you view and purchase, along with those that others view and purchase, to create product recommendations (think “you may also be interested in…”). Google analyzes your queries along with queries from others to help predict what you’re looking for in their search bar. And Netflix notes what you watch, so that it can recommend the shows you’re most likely to enjoy without asking you to browse through their extensive catalog.
Those examples are backed by the numbers. McKinsey points out that organizations that use behavioral data analytics enjoy 85% more sales growth than their peers, along with a boost of more than 25% in gross margin. Customers expect companies to have behavioral data, too, with over half expecting brands to know what their buying habits and preferences are to better anticipate their needs.
And with so many of us using services like these in the course of our everyday lives, it’s no wonder that 80% of consumers have come to expect this level of personalization that’s largely driven by the things we do.
What Behavioral Data Is—and Isn’t
So what kinds of behavioral data should we be collecting if we’re going to meet customer expectations and reap the rewards that come with doing so?
First, it’s important to distinguish behavioral data from personal data. Think of personal data as demographic or firmographic information that answers the question, “Who?” It’s the stuff you’d typically find in persona descriptions (and we have a few words to say about personas), such as location, age, education level, or job title.
While this kind of information can be useful, it doesn’t mean much on its own. Take it from Todd Yellin, Netflix’s VP of Product: “It really doesn’t matter if you are a 60-year-old woman or a 20-year-old man because a 20-year-old man can watch Say Yes To The Dress and a 60-year-old woman could watch Hellboy.”
Yellin’s getting at an important point: what people do will tell you more about what they want. And, it turns out you can learn a lot about what customers want from sources like:
- Site or application navigation. Are customers finding what they’re looking for? Are they reaching their goals? Using behavioral analytics to track the way they navigate your digital platforms can give you a more complete view of their individual experiences.
- Search queries. “Google is like a truth serum for our most personal thoughts,” quips American data scientist and author Seth Stephens-Davidowitz. The queries customers type into search can provide valuable information, in their own words, about what they’re looking for… things that people won’t disclose to even their closest family members or friends.
- Your customer service channels. Every customer interaction with your self-service portal, your on-site or in-app chatbot, or your customer service reps is a goldmine of behavioral segmentation data waiting to be unearthed.
- Shopping carts and purchase histories. Knowing which products each customer is most interested in purchasing – and knowing how other customers are purchasing as well – is the fuel that powers many recommendation engines.
- Ratings and reviews. Reviews and ratings are all about giving customers a voice. The content is critical information for your organization, but analyzing the interactions themselves can also tell you how their experiences – positive or negative – fit into their journey as a whole.
- Click-through rates. What marketing resonates most with your customers? And if you make this change here, and this tweak over there, will you see any changes in your results?
The lack of desired behaviors is, interestingly, also important in behavioral analytics. If customers aren’t doing the things you’d expect them to do, behavioral data can provide insight into where customers encounter barriers to conversion (or whatever your KPI may be).
Behavioral Data in Action
You have all of this customer behavior data, but what might it look like in action? Let’s explore by looking at two similar customers searching for the same thing—say, a pair of shoes.
Customer A and Customer B are both female, both roughly the same age, and live in the same state. But while their demographic data may look similar, one receives a page with a selection of running shoes while the other sees a list of pumps.
Why the difference? Well, if we look through their past clicks – their behavioral data trail, if you will – we see that Customer A was browsing windbreakers prior to her search, while Customer B just added a designer purse to her cart. In other words, our fictional website served each one a personalized customer experience based primarily on her behavior through her journey.
In fact, in a move akin to mind-reading, a great site search engine would be able to leap to this conclusion based on a couple of letters alone, and fill in the rest, even for first-time visitors. (Want a detailed breakdown of how this works? We’ve got you covered.)
Leveling the Playing Field
Companies like Amazon and Netflix may have been early adopters of behavioral analysis for providing more personalized, relevant experiences to their customers, but that doesn’t mean it’s a strategy reserved only for big tech.
With artificial intelligence infusing everything from our websites to our toasters, collecting and using behavioral data is now an achievable (and inexpensive) goal for companies of all sizes, in all sectors. Meaning you, too, can get started learning what your customers’ “the usual” is and deliver it more quickly each time.
How? We’ll discuss that in our next article in this series.
Want to see how companies are already successfully using behavioral data in real life? See how Caleres, Formica, and Life Extension are giving their customers data-driven, real-time personalization in our Guide to Delivering Intelligent Shopping Experiences.