How to create insights from consumers’ click histories

• Updated on: 2017-05-22
• Read original article here

Without any action behind it, data is just a bunch of numbers. Clickstream data is particularly valuable, providing insights about what consumers are doing.

Data alone does not lead to insights. Analyzed data backed by a hypothesis and placed in the right context, on the other hand, does.

Clickstream information is a particularly good set of data for marketers to examine if they want to understand their customers better and connect with them based on their actions.

With clickstream data, you can examine not only how customers are interacting with your brand, but also what they are doing before and after they arrive at your site.

Clickstream information is based on consumers’ actual click and browsing behaviors, with records of click-throughs and URLs visited collected in the order they occurred, giving marketers important, industrywide insight into online behavior, the customer journey through the funnel, and user experiences.

Rather than providing simple numbers of visits or sales, clickstream information reflects consumer behavior based on their activity and identifies areas companies could improve where the competition might be doing it better.

The insights garnered from clickstream data may not always match your hypothesis, but they are always useful if you ask the right questions.

Don’t collect data just because numbers are nice to fall back on. Instead, focus on collecting information like click history that is directly tied to your business objectives and key performance indicators.

Identify what you want to learn, and focus your collection and analysis on that specific data subset.

Creating actionable insights out of your data is essential to portraying a full and accurate picture of the customer journey. Maximize the effectiveness of your clickstream analysis by employing these three tactics:

This is a minimum requirement for a data project to be efficient and lead to insights. Without a hypothesis, you’re just wasting time. The more specific you are in your data requests, the easier it is for your data team to pinpoint exactly what they need to pull, analyze, and provide.

You don’t have to be sure of the outcome, and the data may prove you wrong, but that’s OK. Just be sure your data team enters a project focused and that they reach a conclusion.

Let’s say you run a display campaign to drive awareness and clicks to your own site for a product. If you sell that product through third-party distributors, like Amazon or Target, your hypothesis might be that your display campaign is influencing purchase behavior and conversions on these third-party sites. Without clickstream data, it’s very hard to connect those two pieces and prove or disprove this hypothesis.

Your analysis might reveal plenty of information about how consumers reach and interact with your brand or with your competition, but not all information yields actionable insights. You might find that consumers searching your website tend to search three times. That’s interesting, but you don’t gain real insights from it without understanding how their search activity affects their subsequent behavior or how it differs from consumer search activity on competitors’ sites.

Structuring your hypothesis and analysis around KPIs diminishes the risk of reaching insights that are not actionable. If your leading KPI is, say, trial subscriptions, look into the trial conversion flow of your competitors, and reverse engineer their customer journey through the funnel to detect conversion and abandonment trends at each step.

If the vast majority of consumers bounce during step three of five on your site (but not on your competitors’ sites), test out consolidation steps to improve the user experience and increase conversions.

Without a clear goal for what you intend to do with clickstream data, you cannot transform it into actionable insights. Are you studying customer journeys to optimize conversions or user experience? Are you looking for details about PR or case studies to grow brand awareness and generate leads?

Answering these questions and setting intentions for your data will help you in many ways, from filtering data requests from the get-go to guiding your thought process when focusing your data request and analysis.

By analyzing customers’ online actions – clicks, purchases on other sites, and their browsing history — with specific output goals, you reveal a world of insight into how they interact with your brand’s web properties, your competition, and how they react to your offering.

Don’t collect clickstream data just for the sake of collecting it. Understand what you want to investigate and how you can benefit from it. Make sure it’s relevant to your company, and then analyze clickstream data to better understand your customers’ actions and optimize their experience.

Marketers need to go beyond just the numbers and patterns that data provides if they want to successfully understand and connect with consumers. Focusing on customer actions will lead to a better understanding of your audience and what resonates with them, increasing the success of your marketing efforts and, ultimately, creating a better business.

This is an abridged version of an article published earlier this week on our sister site ClickZ.