Ecommerce teams everywhere have been infected by the Data Rich, Insight Poor virus. With a vast range of analytics tools now available, digital teams have access to more data than they’ve ever been able to access before. But despite this wealth of information, businesses often fall short in being able to identify the relevant actionable data to most effectively optimise their user experience.
There’s no doubt that a data-driven approach is the future of marketing. The mass of customer data available to modern optimisation teams fuels the potential for a highly tailored - and highly effective - customer experience. But too often businesses rely on the assumption that more data equals more insight; simply collecting and processing data doesn’t generate the information necessary to deliver real results.
Extracting actionable insights that can inform optimisation efforts at scale is the first hurdle for UX and optimisation teams. Traditional analytics tools (such as Google Analytics) only provide limited insight into visitor behaviour online, and often it’s easy to be drawn to surface-level metrics, like click and bounce rate, that don’t answer the ‘why’ behind the ‘what’. This can lead to optimisation teams to run tests based on best practice or ‘gut instinct’ - neither of which are much better than guesswork compared to data-driven insight.
Some businesses have turned to other tools, such as session replays or heatmaps, to attempt to drill down into visitor behaviour and understand why they behave as they do online. But while these are good for edge cases, they don’t give a full picture of visitor behaviour, and come with their own problem: lack of speed.
Luckily, the DRIP virus has a cure: Using behavioural analytics to understand not just the ‘what’ of user behaviour, but the ‘why’. For example, using click recurrence (how many times on averages visitors click a particular field or content ‘element’) to identify user frustration at checkout, or using revenue metrics (identifying the revenue generated by a specific ‘block’ of content) to discover how much revenue your homepage banner generated this week compared to last.
Of course, data analysts and IT professionals may have the skills and tools to interpret large volumes of data in this way, but this presents its own challenge. Relying on only a few individuals to collect, process, interpret and share this information creates an organisational bottleneck and limits number of people who can access information needed to effectively optimise. Democratising this data - ensuring individuals can access relevant data, specific to their role, is critical to accelerating teams’ speed to insight.
While session replay tools can be used by several teams, they rarely provide aggregated data that is both easy to understand and representative of the visitor population. In other words, data that helps ensure any tests are data-driven and based on actual behaviour.
For example, Clarks, had replatformed and noticed a huge proportion of consumers going through multiple product pages, one after the other. ContentSquare’s insight paths provided a quick visual overview of the web and mobile journeys of specific segments across the site, allowing Clarks to identify an opportunity for optimisation. In-page zone analysis revealed that the colour swatches were driving this behaviour; customers were choosing the same product in other colours, and then continuing to navigate the site, but they didn’t always find a product that they wanted to add to bag.
Craig Harris, Data & Analytics Manager at Clarks found an answer: “We wanted to find a way to let customers see other colours without navigating away from the page. This gave us a view for a test that surfaces images of the alternative colours when people hover over the swatches so they can see what they look like without moving away from the page. We’re exploring the implementation of this option across all of our sites and it’s worth anywhere between £1.4 and £2.2 million ARR to us.”
Such small - but critical - optimisation opportunities like this are near-impossible to identify using traditional analytics tools or session replays. But with UX analytics, combined with recent advancements in Artificial Intelligence, these kinds of insights become accessible at speed and at scale.