It’s an age-old question: how do you know… How do you know it worked? How do you know it will work? How do you know it’s broken? How do you know it’s not working as well as it could? I believe most if not all projects start with a question. That question could be a desire to understand, a need to resolve, a want or perhaps a value that inspires you to do whatever you do.
Optimising the performance of customer journeys and the users’ experience is very similar: aspiring to answer a question of confusion or friction from our users. But, how do we know we are asking the right question; the question that will lead and guide us into delivering an improved experience for our users?
I was recently asked this very question, how or where do you start in developing a plan to improve a customer journey? The question was in the context of an e-commerce purchase path, but the answer is consistent for any conversion path, be it a sales journey, data capture or one that presents guidance, help, information or advice. We must begin with what we know, which is absolute, a constant that we can rely on.
In the process of optimisation, our absolute focus should be on a key metric: a Key Performance Indicator (KPI) that demonstrates the overall performance of the user’s journey through an experience. Using a KPI as a North Star alongside a journey map will help guide us to build a Data Tree that provides a level of granularity to measure performance at touchpoints within the journey.
Having a level of granularity is important, firstly to provide us with a performance benchmark against each data point of the tree. These benchmarks help us develop a deep understanding of a journey at a micro level, plotting this across time provides a tremendous opportunity to identify areas of underperformance and to develop knowledge of trends or seasonality.
Secondly, and perhaps more importantly it empowers teams with knowledge giving them permission to ideate, innovate and focus on the touchpoints within a journey that will truly change the dial and improve the experience for your users.
Develop an e-commerce data tree
The majority of e-commerce data trees will be relatively similar, especially at a top level with the core business key performance indicator being financial measure. In the below example, you’ll see that I’ve used revenue and worked backwards to develop a hierarchy of performance metrics that are influenced at different stages of the customer journey.
Okay, so it looks like a little more like a cactus than a tree, and on reflection perhaps it should have made the diagrame green. That aside, most analytics tools can be configure relatively easily to capture this basic level of engagement for an e-commerce website, but is that enough to enable you to gather insight and ask those “how do you know” questions?
I believe from these twelve metrics you’ll get a good indication of any issues you may have throughout your e-commerce shopping journey, providing you have performance benchmarks or targets in place for each of them.
Just from these basic metrics you’ll see the impact of traffic entering your site, the frequency of visits, how active your users are in browsing and interrogating your product offering and all that prior to any signs of purchase intent. With a glimmer of intent from the Product Detail Page (PDP) you’ll understand how many users add to basket and navigate through into the purchase journey and lastly you’ll get a strong picture of what’s driving your bottom line: with the number of transactions, their average product quantity and value. I’ve purposefully not included any rate metrics here in order not to add an additional layer of complexity to the diagrame, introducing those and increasing the monitoring through the checkout steps will inevitably produce a much more rounded view of the journey and provide a fantastic opportunity to move forward and optimise your user’s experience.