Marketing directors can’t make decisions in a vacuum. They need information about their customers, their channels and all the touchpoints that help them to connect with each other. How can they deliver what the customer wants unless they can see through the customer’s lens?
Customer interactions with a brand are now defined as their ‘journey’ and it is insight into that journey that helps organisations to plan product roadmaps or launch new services. McKinsey calls it ‘journey analytics’: the combination of big data technology, advanced analytics, and functional expertise, which come together to develop a comprehensive view of the end-to-end customer journey that gives marketing departments all the information they need.
Journey analytics captures customer feedback from everywhere, doing the hard work in bringing together the ways in which customers engage, what they are trying to accomplish and where there are areas of friction that need ironing out. Their journey can be seen clearly, and shared across the company so that changes can be made and ideas implemented with confidence.
Using analytics to enhance the customer journey is a process that can be taken in stages. Here is an introduction in five steps:
1. Gather the data
When customers interact with a brand they leave clues about their levels of satisfaction and engagement that can be acted upon by marketers.
If you think about the number of touchpoints, from loyalty programme information and purchase behaviour through to online reviews, social media references and conversations with customer service representatives in contact centres, these interactions deliver data that helps marketers to visualise the customer’s journey, assess their responses and uncover sentiment.
The smallest detail can reveal the most interesting finding, and as the data accumulates across all of these areas, it provides an accurate, and often unexpected perspective.
2. Reshape Customer Feedback
Data relating to customer interactions is both quantitative and qualitative. Structured quantitative data, which might include when the customer last purchased from a brand, how old they are, where they live and the products they most frequently buy, together with qualitative feedback, such as the unstructured voice of the customer needs to be married together. This requires Natural Language Processing (NLP) technology. This transforms the unstructured information into something that can be analysed. NLP can reveal a customer’s sentimental response and spot discord based on how, and how often, the customer talks about the experience.
Sentiment analytics have the power to ascertain what customers like or what they don’t, and more importantly, why. It is the difference between quantitative data that informs a company that their customer service is rating 6 out of 10, and qualitative data that explains why, and it enables specific issues to be addressed with accurate information. This delivers the ‘moments of truth’ that for marketers are akin to the holy grail.
3. Analyse Customer Data in a High-Level Journey
Now it’s time to place the data into logical journey touchpoints. A retailer, for example, could segment the data for the “Purchase Online” journey as follows: Research products available ➔ Open Account ➔ Select delivery options ➔ Pay for Goods ➔ Establish Online Connectivity. There would then be lots more steps, but maintaining a high-level allows marketers to place all data related to that area in one bucket to determine which areas to drill into.
This enables the emotional high and low points to be assessed using customer sentiment as they move through the journey, and focus on extreme areas of positive and negative sentiment. A robust analytics solution will help marketers to include actual customer comments, such as those delivered to contact centre staff, in the charts that are produced, and which can be elevated to board-level.
This way, the company can see representative and statistically relevant comments in context rather than just anecdotal pieces of information that don’t accurately portray the bigger issues.
4. Take action
Of course, there’s no point in marketers doing any of this unless they then show their team, and the extended company, how to set a path to improvement, the reasons for doing it, and the key objectives. Interactive dashboards on analytics solutions allow marketers to illustrate the impact that all stakeholders have on the customer journey. This is empowering, helping to drive communication and process improvements, system enhancements, and policy changes. The insights can be easily consumed and deliver contextual understandings of trends. Interactive dashboards also allow for real-time root cause analytics that throw light onto issues being faced by customers. With this information, marketers can work with other departments to manage these issues and reduce the necessity for customers to call back into the contact centre.
5. Avoid pitfalls
As with any tool, unless journey analytics are used properly, they will not deliver. Forrester Research has said that this happens when journey analytics are used in one of three ways:
· to validate assumptions instead of to discover;
· in a silo, instead of throughout the enterprise;
· and as a one-off project instead of a as a change management tool.
Analytics should be fed with both qualitative and quantitative insights and with feedback from multiple sources so the results are based on how the customer actually interacts with the brand across all channels. Most importantly, the data should be shared on a regular basis with the key stakeholders via dynamic dashboards. And marketers should not forget, customer journey mapping too often ignores the unstructured feedback provided by customers, so customer testimonials, verbatim comments and what customers say to contact centre customer service reps in conversation, is vital.
By following these steps and implementing the right tools, journey analytics can become the lens through which a marketer views the customer’s journey and experience. When implementing this, it is important to map customer statistical data (demographic and behavioural) with the customer voice, their feedback and friction points. When this is done, it is possible to improve the parts of the business that matter most to customers.