Customer journey mapping is a technique central to the CX design process. It leverages customer insights data to develop a compelling visualization of the customer’s entire brand experience. This technique can have several uses:
- Defining the current state of your customer’s experience with your brand, including painpoints, softspots and opportunities to better engage.
- Building consensus among a CX team, stakeholders and leadership around where to invest in improving CX for a brand.
- Visualizing a future state where the customer’s journey connects with the brand in new ways.
- Creating a framework to help understand how customers connect with a brand through multiple channels (e.g., digital, social, physical, mobile, customer service and face-to-face interactions).
How does the collection of information and data become a story? And more importantly, how can that story inform an actionable plan for improving the customer experience, one where the customer will fall in love with the brand? Many customer journey efforts fall short in the analysis and interpretation of the data – and then in making smart, strategic decisions based on that information. Where should you start?
Purposeful Selection – What Are You Solving For?
How do you identify what part of your data is essential to your map? What is most insightful? Whether you are you trying to outline B2B buying scenarios or an omnichannel customer path in a retail environment, customer journey mapping should be seen as a way to help distill your dataset. First, consider who will consume your map and on which insights they will base their decisions. The finance team will likely see things through a different lens than your operations team. If you want to socialize the map with a team to gain consensus and a clear vision regarding next steps, you will want to make sure that the entire team’s needs are met. This can be tricky, because the complexity of your map may increase at the same time. What should be an easy story to tell all of a sudden is well hidden in a forest of data.
Seeing the Forest Through the Trees
Data visualization is an art and a science at the same time. For starters, your map will need to identify each touchpoint in the customer journey. There can be dozens (even hundreds) of touchpoints, and for each, your map will need to highlight what is happening at that time from a customer perspective. Each touchpoint can be seen as an entire ecosystem of interactions, with every key moment for the customer informed by physical, digital and inter-personal connections. Each of those connections is supported by infrastructure that can be entirely invisible to the customer.
Seen from the operations perspective, this can be an extremely complex – even overwhelming – matrix of channels, touchpoints and interactions. There is more than one way to visualize the information, and the good news is that you will be able to eliminate much of this data from your map depending on the story you are trying to tell. (Not every data point you have is useful in your map, so you can parse out what’s meaningful in telling the overall story.) Look for ways to reduce the complexity of the map by focusing on the key points relevant to the narrative. Ask yourself the following questions:
- Is the story easy to grasp for the audience?
- Is your map painting a clear picture of the situation you are trying to convey?
- Is your map overloaded with less relevant information?
Below is a simplified map we developed at Lenati to capture a customer journey across digital (social, web, mobile) and physical channels. To the customer, it’s just one experience. To the company, it might involve four discrete functional silos within the organization, each with different objectives. Visualizing the connections and interdependencies from a customer perspective quickly identifies any friction or opportunities in the experience.
After the map was developed, each of the touchpoints needed to be defined and designed by a cross-functional team, tested, measured and implemented. This also needed to be supported by a complex infrastructure of distribution, training and management to be enabled in the real world. However, this didn’t mean that all of that detail needed to be shown in the map. The key was to show the story of the customer’s interactions in enough detail to demonstrate that the journey is fulfilling, valuable and complete from the customer’s point of view. At a functional level, it also told the story of how the customer was interacting with the brand through several channels at once, connecting the customer’s experience with top-level aspects of the company’s operations. Importantly, it didn’t show much else; other layers were left out because they didn’t support the main story.
Before you start the data visualization, consider the journey mapping framework. Eliminate layers that don’t help you communicate the team’s vision and get feedback from across the organization while you create the map to build consensus before finalizing the vision (especially in those areas that will be impacted by the results of this effort).
One further note: Graphic elements like line weights, color, perspective or varying type styles can be useful, but should be dealt with sparingly. Every addition of a new style should tie to a new layer of relevant information, adding useful dimensions to the map. Some of the most compelling customer journey maps can be represented in black and white, with one font, and without any treatments like dropshadows or gradients – while still telling the story beautifully.
The objective of the customer journey map is to distill large and complex data and visualize it in a simple, compelling and relevant manner that is accessible to stakeholders. It needs to drive your team’s decision-making regarding the experience your customers have when interacting with your brand. Remember Einstein’s famous quote – it should be “as simple as possible, but not simpler.”
For a deep-dive on research toolsets for building a better customer experience, download The New CX Toolbox which provides insights on customer feedback, observational data, visualization and data validation.