This blog was written by Harald Fanderl, Nicolas Maechler, and Jesko Perrey from McKinsey & Company

While making this aspiration a reality is difficult, companies that deliver great customer experiences are finding they can both boost profits and reduce costs

That begins with understanding the increasingly complex journeys customers take when they want to buy something or get something done. Consider what it takes to open a bank account today. A typical customer embarks on a multichannel journey: researching online; downloading an application; speaking to a call centre 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 fact that  data are typically collected at each touchpoint and in huge quantities—US companies store at least 150 terabytes of it—makes gaining insights into customer behaviour a serious challenge.

Parsing this data, however, is critical to improving 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 interactions between a consumer and a brand that are needed to accomplish a transaction. Our analysis shows that companies acting on journey insights have seen a 15-20% reduction in cost to serve, a 10-20% 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 on each of their customers. The reality is that in general there are three to five customer journeys—such as installing cable service or resolving an issue with a newly-purchased mobile phone—that matter most to both consumers and the bottom line. Focusing on those journeys allows companies to cut through the data clutter and prioritise.

One cable television player, for instance, used advanced data analysis of multichannel customer behaviour to focus on where drop-offs in the journey occurred so they could address nagging customer retention and loyalty issues. They focused on two journeys—the series of interactions needed to set up a new customer (onboarding) and resolving issues with the service (problem resolution). The data team identified important service trouble spots, which led to the creation of a “learning lab” that trialed and refined new approaches. The changes improved customer satisfaction scores by more than 20 percent.

2. Don’t wait for perfect. Companies often hesitate to take action because they believe data are missing or existing data are a mess. While that’s often true, we’ve found that successful organisations tend not to overthink all the details and instead just roll up their sleeves and get to work. Most companies, in fact, already have the data they need. The challenge is pulling the data together. 

Since data are held by a number of functions internally, companies need to bring together operations, IT, in-store sales, and marketing people to identify the touchpoints along the most important customer journeys. We've seen companies create small “SWAT” teams from across functions to break through bureaucratic logjams. These track performance in serving customers from the outset, mistakes and all, since that experience helps teams test, refine and learn, and ultimately accelerate the benefits to consumers.

3. Focus on journey analytics, not reporting.  Companies tend to focus on generating reports from their data about what has happened. Much greater value, however, comes from analysing 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 operations. It was only when the team connected the dots across touchpoints, however, that the bank discovered behaviour patterns that highly correlated with imminent risk of default. These included changes in how often customers checked their accounts online, the number and type of call centre inquiries and branch visits as well as credit line use. Analysing those complex patterns allowed the bank to develop an early warning system that flagged high-risk customers.

Big Data impact doesn’t come from the data; it comes from people and their analytical skills. Big Data leaders are integrating analytics into daily performance discussions from the front lines all the way through to top management. Employees need to not only understand the insights (on segment needs, product preferences, communication performance, etc.) generated by analytics but they also need to change and improve procedures based on them. Only by combining Big Data and people with analytical skills can companies hope to produce the right stuff. 

The views and opinions expressed in this article are those of the authors and do not necessarily reflect the views of The Economist Intelligence Unit Limited (EIU) or any other member of The Economist Group. The Economist Group (including the EIU) cannot accept any responsibility or liability for reliance by any person on this article or any of the information, opinions or conclusions set out in the article.