What’s the big data secret behind Disney’s magical customer experience? Or any other retailer relationship, for that matter?
Whether shopping for clothes, sniffing-out cheaper utilities, or strapping on apersonalised bracelet for a visit to the Magic Kingdom, we expect the earth in return for our custom.
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. RecentMcKinsey 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.
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.
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.
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