Creating the most seamless, friction-free customer journey has become an ever more critical aspect of customer experience, acquisition and retention. Yet some customers still appear to behave in ways that user Interface designers did not plan for (or hoped to avoid), causing undesired behaviours such as abandoned baskets and calls to the customer service helpline. This final instalment of a four-part blog series explains the value of customer journey analytics in revealing the true customer experience.
Finding friction points
Journey analysis has become a fundamental component of online development for every business. Mapping the way customers flow through a multi-step process is key to revealing areas of friction that are damaging the customer experience. However, discovering that 40% of customers abandon the check-out process at stage two, while interesting, does not help the company address the problem. Why did they leave? What is it about the process that doesn’t work?
The only way to understand the triggers for this behaviour is to understand the root cause and look to where these customers went when they abandoned the process. For example, did they go off to check shipping charges and fail to return because the charges are too high? Indeed, are these customers even following the four-stage process as designed or are they actually having to take six or more steps to find additional information? For example, are customers forced to manually enter address details because the address locator isn’t working or up-to-date?
Beyond drop-off points to underlying causes
Not all customer journeys actually follow the static model defined by the business. Using data to reveal the true journeys, the number of steps taken and other pages visited during the process is key to understanding fully the customer experience, not just what the drop-off rate is, but the underlying cause. By progressing from the static journey to a more complex model that analyses all paths and identifies areas of friction, weakness, repetition and looping, a company can begin to optimise and create the perfect customer experience.
One bank, for example, recently discovered that a product application process, which included third-party credit checking, was not performing efficiently because the credit checking site was down when a significant proportion of visitors were using the website. Every time this occurred, an additional three-step process kicked in.
So, while the bank perceived it was offering a slick, four-step application process, the customer experience was a very different and somewhat frustrating seven-step model. Joining together the traditionally qualitative approach to customer experience with the data-driven customer journey model provides a significant step towards data-driven decision making that is underpinning ever more sophisticated activity.
Whether an organisation starts with segmentation, attribution, customer journeys or another marketing priority, the key is to start somewhere. As this blog series has shown, leading companies are beginning to gain measurable benefits from this activity and the rest cannot afford to get left behind.
Creating a cross-functional team to identify opportunities and prioritise activity - as described in the first blog - and developing a culture that actively supports data-driven decision making and getting senior level commitment enables gains through tangible change. Start small, build skills and gain confidence.