What’s the big data secret behind Disney’s magical customer experience? Or any other retailer relationship, for that matter?
And why not? Shaped by new technologies and channels, this complex marketplace no longer allows commerce to pay lip service to customers. It forces retailers to lionise them. To put customers slap, bang, at the heart of operations.
No wonder browsers are becoming more picky about the buying process and the brands they choose to buy from, rather than the actual products and services themselves.
These days, customer loyalty is hard-won – earned and nurtured by caring for each individual, personally. In other words, by optimising the customer experience. And for businesses with a traditional silo-view of channels, products, and services, this presents a real challenge. To remain competitive, they have to grow out of the habit of concentrating on single moments-in-time and develop an end-to-end understanding of customer interaction.
The traditional means of measuring customer satisfaction – asking customers to provide feedback after a single transaction – is misleading. Although a customer might have had a successful conversation with a call-centre agent, his or her journey to that call may well have been confusing and stressful. However, by combining attitudinal data (NPS scores from customer satisfaction surveys and complaints) with behavioural data from multi-channel journeys, companies uncover the real story complete with negative, cross-channel customer experiences.
And it pays off in spades. Recent McKinsey research showed that optimising customer journeys can increase customer satisfaction by 20 percent, lower the cost of serving customers by 20 percent and, most importantly, boost revenue by as much as 15 percent.
Mapping the journey, understanding the number of touch points plus the length and time between interactions as well as assessing the outcomes, provides a more insightful view of the customer experience. It allows businesses to identify areas that need improvement and optimisation, and to work out the best time to engage individual customers with the most timely and appropriate messages, too.
The zig-zag route to brand stickiness
Previously, analysis relied on mapping customer interactions to a linear journey (e.g. AIDA – attention, interest, desire and action). However, today’s consumers leap from stage-to-stage and channel-to-channel, making it impossible to map a linear decision-making process.
Consequently, the future of journey analysis involves moving away from a business-only view of journeys, towards analysing the actual routes, paths, and processes followed by the customer. This makes it easier to pick up on unexpected switches between channels, friction or failure within a journey, and leakage.
You might be surprised by how long a customer journey really takes
A predicted 10-minute process for obtaining a new credit card can actually take weeks – from Googling providers, booking appointments, visiting branches and completing applications, through waiting for back-end processing to run its course and mail-out, to online activation.
During such a complex journey, the customer has many opportunities to drop out (e.g. where a simple two-step process becomes ten steps with the customer looping through several attempts to complete an application form, or checkout process). These leakage points are a key source of analysis, highlighting business challenges and the need to re-engage those customers by other means.
Even if the customer completes their intended action, this is no longer a seamless or positive experience. Worse still, these friction points can lead to failure (e.g. if a customer is unable to complete their transaction online and is forced to visit a branch or store). Channel-switching requires additional effort from the customer, eroding the customer experience.
That said, channel-switching is a strong focus for analysis. It is important to find out whether the switch is down to choice (perhaps they moved from a digital platform to a more personalised channel for 1-to-1 advice), or whether it suggests something has gone wrong (e.g. an active internet banker abandoned an online application to complete in-branch). Root-cause analysis can help determine the reasons for the switch.
Stop selling – start helping
By understanding the complete end-to-end view of a customer journey, businesses have a basis for analysing and improving customer service. The goal is to provide consumers with an omni-channel experience, starting an interaction in one channel and seamlessly picking it up and completing in another.
Banks are making great strides in this area, allowing customers to interrupt an online application process at any point and complete in the call centre, without having to provide the background details of the application again. This saves time and effort for both customer and bank. And earns the bank valuable loyalty points.
The fact is, businesses have to be totally committed to improving the customer experience if they want to still be around in years to come.
As Walt Disney said: “Do what you do so well that they will want to see it again and bring their friends.”
Over the years, I have worked with many businesses to identify challenges and understand business context, providing an analytical perspective for achieving solutions. This has meant using new, or untapped, sources of data coupled with innovative analytics to enhance competitiveness. Techniques have included event-based analytics, predictive modelling, natural language processing, time-series analysis, and attribution strategy development. My work takes me to all parts of the globe, covering a wide range of industries including finance, retail, utilities, and telecommunications. I speak regularly at international conferences and events. After an undergraduate in computing, I took a tangent into the world of Life Sciences, completing my PhD in Data Management, Mining, and Visualisation, at the Wellcome Trust Centre for Gene Regulation and Expression. Following this, I worked as a data scientist, building analytical pipelines for complex, multi-dimensional data types. Along the way, I’ve provided leadership, training, and guidance, for many analytical teams, creating actionable insights and business outcomes through the development of analytical use cases. I have also lectured on Masters programmes for BI and Data Science. I grew up in Scotland, and love the great outdoors. Consequently, I’m an avid hiker (the Scottish Munros are perfect) and sea kayaker. Oh yes, and a keen traveller, too.