What do you believe more: what your customers say, or what your customers do?
While a definitive “I am looking for <insert very specific model number here>” or “I prefer the color orange, so please don’t show me anything that isn’t orange” is incredibly helpful in connecting customers to what they want quickly, it’s very rare that they come right out and say it. In some cases, people aren’t entirely sure what they want in the first place.
Behavioral analytics is all about teasing out the nuggets of truth about what customers feel, want, and need — and how all of that unfolds during their time with you — based on the actions they take throughout your digital presence. And yes, that behavioral data includes the words they say (or type) to you… but it also includes the unspoken cues that can be just as informative in creating a great customer experience.
But that doesn’t mean collecting and using behavioral analytics in your organization is a sure-fire solution or easy-as-pie to implement. Like any strategy you roll out in your company, you may encounter some roadblocks — but the benefits often make conquering them more than worthwhile.
Untangling the Complexities of Behavioral Data Challenges
With so many potential sources of customer behavior data and so many people involved in bringing it together, it should come as no surprise that there are a few hurdles to clear if you’re going to tap into your customer’s behavior.
Locating your behavioral data
It’s two sides of the same coin: on one, you likely already have a lot of behavioral segmentation data available to you; on the other, you need to identify it, track it down, and make sure the data owners are on board with the initiative.
That means taking stock of all the systems you use, across multiple departments in your organization — an endeavor that typically requires investment in time and resources.
Bringing your behavioral data together
It’s one thing to track down those sources of customer data, both behavioral and otherwise; it’s quite another to connect them with each other. Unifying your data sources means bringing information about user behavior that you have spread across different data management tools and systems under one umbrella so that it’s easier to identify patterns and glean valuable insights (or make informed inferences throughout the customer journey).
Unifying consumer data can also help reduce the number of smaller challenges, like duplicate entries you have across your organization (a struggle for anyone who maintains your systems), stale or incomplete data, low data quality, and unstandardized data structures.
The “chicken and egg” problem of behavioral analytics
Depending on the tools you use for data collection and behavioral analytics, you may find yourself in a catch-22: you need a healthy stockpile of customer data before you can draw useful conclusions from your user behavior analytics, but you can’t build that stockpile until stakeholders see the value in doing so.
(Thankfully, solutions like the Coveo Platform™ that are powered by machine learning don’t require a lot of data — or a degree in data science — to start providing value!)
Start with a proof of concept in one area that won’t impact others. One of the benefits of modern AI search platforms that help you unveil insights from behavioral data is that they offer robust connectors into many popular enterprise SaaS programs. Meaning you can expand, bring in and truly entwine journeys that already affect one another.
Proving your case for behavioral data analysis
Some stakeholders may need a little more convincing than others before they’re willing to commit time and resources to update processes and workflows. That’s okay — but it means you may have to start small, with a focused pilot project that shows the difference that data collection and behavioral analytics can make in customer experience and company performance as a proof-of-concept before rolling it out on something more large-scale and company-wide.
(We’ve done some of the legwork for you: Here’s a framework for building a business case for investing in Artificial Intelligence.)
Data security for behavioral analytics
Sure, 61% of customers already expect that companies have information about them ready at their fingertips—but they also expect those companies to keep their data safe against cybersecurity risks and data leaks. If you’re using additional tools and technology to bring your consumer behavior data together, that’s an added reason to make sure the ones you choose are secure.
Behavioral Data: Good for One, Great for Many
In our second article in this series, we talked about the types of applications we’ve seen from customers who collect and use behavioral data. But there’s more to it than that, because at a higher level, a behavioral data analysis strategy can have an impact that goes beyond achieving one goal; it can improve operations across your organization.
360 Degree View of Your Customer
It provides a 360 view of your customer, especially when paired with other kinds of data, like demographic or firmographic information. Imagine a customer calls into your contact center and a customer service rep is able to pull up insights about their customer journey – their last website visit, the kind of company they work for, and the kinds of products they are interested in. From there, your rep could provide more relevant support in solving his problem or helping him find exactly the right product or service. Connecting the dots of behavioral data across touchpoints leads to a better overall customer experience and happier consumers in the long run — and increased customer retention.
Collect and Use Behavior Data In Real Time
It allows you to collect user behavior data in real time… and use it in real time, too.If you’re relying solely on historical data, it takes time to build up those logs. But if you can make use of online behavior data as it flows in—think predictive search results, targeted content and the like—then providing a better user experience through personalization doesn’t need to wait.
Applies to Anonymous and Known Users
It applies to anonymous users and known users alike. Even on a first visit, AI-powered behavioral analytics allow you to trawl through the overall customer journey for actionable insights that help you make the next move. Each user interaction can inform the next—searching “running shoes” on a store page, for example, might help you prioritize casualwear over formalwear on their next search.
Does Not Require Big Data
It doesn’t actually require truckloads of data. If you pick the right behavior analytics tools, that is! Because behavioral data collection can happen in real time, and because every customer interaction informs what comes next, you don’t need a database of historical, personal data to provide a personalized experience. You just need behavioral analytics tools that are smart enough to make the right predictions—and one that can take a lot of the automation off your hands with machine learning.
Get a Head Start with Behavioral Data Analytics
It only takes customers a matter of seconds to decide if they want to stick with you—or simply switch tabs to a competitor if they can’t find what they need. Creating a behavioral analytics strategy to provide a personalized customer experience online, and doing so quickly from first contact and for every customer who interacts with you, is a key differentiator that can set you far ahead of your competition.
Dig Deeper
Wondering if your audience matches market trends? Looking for solutions to problems you’ve identified with your skill of drawing insights from behavioral analytics? Snag a copy of our 2024 Commerce Industry Report, where 4,000 consumers tell you their expectations in the ChatGPT era, their frustrations, and how to win their loyalty.
