Companies need to focus on three things to help turn their Big Data into effective action to improve the customer experience journey.
Back when there were a handful of channels, interactions between customers and brands were relatively simple. Today, by contrast, more than half of all customers move through three or more channels to complete a single task.
To open a bank account today, for instance, a typical customer embarks on a multichannel journey: researching online; downloading an application; speaking to a call center agent; linking brokerage accounts; visiting a branch; and installing the bank’s mobile app. Those steps leave a long and complex digital trail. That multichannel complexity, combined with the scale of the data—US companies store at least 150 terabytes of it—makes divining insights into customer behaviors a serious challenge.
Parsing this data, however, is critical to improving the customer experience and growing your business. In our experience, the most productive way to get there is not by fixing individual touchpoints but by improving the entire customer journey–the series of customer interactions with a brand needed to accomplish a task. (For more see the HBR article, “The Truth About Customer Experience”). McKinsey analysis finds that companies acting on journey insights have seen a 15-20 percent reduction in repeat service visits, a 10-20 percent boost in cross-selling, and a drop of 10-25 basis points in churn.
To put big data to work in improving the customer journeys, companies should keep three things in mind:
1. Focus on the top journeys. Companies may feel they need to study all the bits and bytes available to them. Our analysis across industries shows, however, that three to five journeys matter most to customers and the bottom line. They generally include some combination of sales and on-boarding; one or two key servicing issues; moving and account renewal; and fraud, billing and payments. Narrowing the focus to those journeys allows companies to cut through the data clutter and prioritize.
For instance, a cable television player used advanced data analysis of multichannel customer behaviors to focus on where drop-offs in the journey occurred in two journeys—onboarding and problem resolution—to address nagging customer retention and loyalty issues. The data team helped them identify key service troublespots and ways to improve the onboarding process. Those insights led to several policy changes, including creating a “learning lab” that effectively operated as a mini-company to trial and refine new approaches. The changes improved customer satisfaction scores by more than 20 percent.
2. Don’t wait for the data to be perfect. Companies often hesitate to take action for fear their data is missing or a mess. In our experience, however, successful organizations tend not to over think all the details and instead just roll up sleeves and get to work. Most companies, in fact, already have the data they need. The challenge is pulling the data together.
Companies need to figure out where that data is stored, and what it takes to extract and aggregate it so they can understand the customer journey across multiple touchpoints. Since data often lives in systems managed by various functions, bring the necessary operations, IT, in-store sales, and marketing people together to identify the touchpoints. We’ve seen companies create small “SWAT” teams from across functions to break through bureaucratic logjams. Track performance from the outset, mistakes and all, since that experience helps teams test, refine and learn and ultimately accelerate the benefits.
In one example, a leading European energy company generated a lot of data, but most of it was siloed within the web team, call center, and marketing functions. As a result, key insights were falling through the cracks. Using data the company already had, a marketing and operations team came together to analyze the journey customers took when they changed addresses. Looking into data patterns, the team found the moving process alone accounted for 30-40 percent of all churn. Customers were canceling their old accounts and not renewing at their new address.
In response, the company chose to manage customer expectations better along the core touchpoints of the journey. They fine-tuned its communications, providing a set of easy-to-follow instructions on its website with links that made setting up a new contract a matter of clicking a few buttons. That reduced churn by 40 percent and increased upsell opportunities throughout the journey.
3. Focus on analytics, not reporting. Companies tend to focus on generating reports from their data about what has happened. Much greater value, however, comes from analyzing data to pinpoint cause and effect and make predictions.
One bank, for instance, was looking for ways to use big data to spot early indications of loss risk in its small business lending and service operations. Touchpoint data revealed subtle changes in customer behavior that raised questions in the fraud team’s mind. It was only when the team connected the dots across touchpoints, however, that the bank discovered behavior patterns that highly correlated with imminent risk of default. These included changed behaviors in online account checking frequency, number and type of call center inquiries and branch visits as well as credit line use. Analyzing those complex patterns allowed the bank to develop an early warning system that flagged high-risk customers.
Big data harbors big opportunities to improve customer journeys and value. What it requires is a commitment to focus on what really matters.
This article originally appeared on the Harvard Business Review (HBR) Network Blog website