Myth: Many see customer journey analysis as something that can be approached like a piece of technology that is plug and play. While some products do offer out-of-the-box analytics – to unearth specific issues in the customer journey, further customisation is needed.
Reality: True customer journey analysis requires the identification of the processes, people, and technologies involved. Typically, customer journey analytics starts with a consultant, gathering information by talking to the user groups of specific journeys the business wants to map. This journey does not just comprise touch points from a consumer perspective, but also all back office and IT processes. Businesses must avoid starting with a data first approach, instead starting with a process first approach. This means that the process for making the changes to a customer journey should be agreed from start to finish before taking the first step and augmenting this plan with data.
Myth: All it takes is data to improve a customer journey. Many organisations believe rich data and good analysis is all it takes to make customer journeys easy to analyse and improve. The truth is that many businesses fail to decide what they want to achieve and set out on improving their customer journeys without having a destination in mind.
Reality: While data and analysts are useful, what is most important, is to figure out what you want to achieve. Organisations must take a step back and put plans in place at the beginning of the process that will allow them to achieve their goals. In this case, trying to work out what they want to achieve or what the business thinks is broken before they go about improving or fixing these elements.
Myth: Another myth is that Artificial Intelligence and automation have come so far, that customer journey analytics tools can learn, auto-correct and modify parameters based on data that is collected all by itself, giving a truly ‘hands off’ service to companies.
Reality: Unfortunately, to date, no tools in isolation can automate the improvement of customer journeys. There are systems that can self-learn and give customer journey, but independently. There will be several occasions where businesses must make the analytical models unlearn, re-learn, improve, etc. based on various outcomes. What seems to be a perfect fit in one model may not be in six months’ time. Whether this is due to the change in business operations, product behaviour or customer segments – this isn’t something that can be automated - yet.
Myth: Some businesses are sceptical that measuring and improving the customer journey is achievable – and in some cases, they even think it is totally impossible.
Reality: Improving the customer experience is an ongoing process that can be difficult to measure. However, good customer experience leads to a higher revenue growth – so surely this isn’t something that should be ignored? According to Forrester, organisations that provide a better customer experience are the ones that grow their revenues most successfully. Thus, one way to measure the effect of an improved customer journey is to link it to changes in revenue and customer satisfaction scores each time a change is implemented.
When it comes to making the most of any new technology, businesses should ensure they have the right information and processes available to help them benefit. Implementing customer journey analytics is no different; to succeed, businesses must have the right team, tools and processes place, so they can glean accurate insights. Customer journey analytics can prove instrumental in improving the customer experience and ultimately, the bottom line, but business must be prepared to put the legwork in to truly benefit over the long-term.