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.
Marketers often fail to use their data to create actionable insights – that is, insights that are clear and relevant to their businesses – to better understand and connect with customers.
Instead, they are viewing data as simple numbers, forgetting to turn it into actionable steps. According to Forrester research from earlier this year, only 29 percent of companies successfully extract actionable insights from the data they collect.
The difference between data and insights is as blatant as the difference between a fake connection and a genuine human interaction, and it can play a big role in customer interactions. 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.
The many benefits of clickstream data
It’s common for businesses with web analytics to examine their own customer flow and navigation path funnel. While this information is useful in identifying customer segments, optimizing user experience, and minimizing drop-offs through the conversion funnel, it’s like examining one section of an industry with a magnifying glass: When you pull back the magnifying glass, 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. Partnering with a third-party allows businesses to bridge the gap between knowledge of their own sites and knowledge of other companies in their space.
Listen to what your clickstream data says
The insights garnered from clickstream data may not always match your hypothesis, but they are always useful if you ask the right questions. At Jumpshot, we examined click history to help an airline that was very interested in understanding its frequent flyers’ loyalty so it could increase its share with infrequent flyers.
The hypothesis was that the airline’s frequent flyers must be loyal customers and should be catered to because of their dedication to the brand. Infrequent flyers were seen as loyal to other airlines, and efforts should be made to switch them.
We compared the clickstream data of three customer groups of the airline – frequent flyers, infrequent flyers, and those who had never flown with this airline – to see their flight transaction activity both on this airline’s site and those of its competitors.
The behavioral group that had been deemed most loyal turned out not to be; those customers just flew the most overall. They bought competitor airlines’ flights at a higher rate than the general population, too. We also found that infrequent flyers were not just infrequent with this company, but were infrequent overall.
Our insights from the clickstream analysis taught the airline’s business leaders to focus on increasing the rewards program to persuade frequent flyers to be more loyal to their brand. That could result in a widespread influence on the airline’s marketing messaging, branding, perks, and even its product growth.
Frequent flyers offered a bigger pot and opportunity to increase market share than infrequent flyers, who just didn’t fly as much, creating a smaller pot).
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.
Make the most of your clickstream data
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:
1. Have a hypothesis
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.
2. Tie your analysis to KPIs
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.
3. Identify your output goals
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.
As an organisation, finding the right marketing channels is an essential part of your marketing strategy.
When measuring the effectiveness of discount codes, retailers often get it wrong. In this article, we'll look at how data-driven attribution can help businesses better understand where discount codes produce the best ROI.
The term ‘marketing cloud’ has gained significant traction in the last few years as major software companies have sought to monetise the growing importance of technology for marketing teams.
Marketers create personas to better understand their target audience and what it looks like. If marketers can understand potential buyer behaviors, and where they spend their time online, then content can be targeted more effectively.