By Fabrice Martin
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.