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Mapping the Customer Journey with Data Analysis

For brands, the customer journey is increasingly complex as customers interact in stores, online, with call center agents and in a myriad other ways.

shutterstock 61669849 300x199 Mapping the Customer Journey with Data AnalysisWhile the data generated by this growing number of actions may seem overwhelming, firms need to corral it to keep customers and grow the business, according to a Harvard Business Review post from a trio of authors from McKinsey & Co.

In stark contrast to the days when interactions with customers were entirely in person, today more than half of all customers move through three or more channels to complete a single task, the post notes.

Companies acting on the insight they gain from the data analysis of this customer journey have seen a 15%-20% reduction in repeat service visits, a 10%-20% boost in cross-selling and a drop of 10-25 basis points in churn, according to McKinsey’s research.

McKinsey advises companies that would like to improve customer journeys to follow these three recommendations:

1. Isolate the journeys. While many companies might aim to study the wide array of data available to them, McKinsey’s analysis shows that across industries, 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,” according to the post. “Narrowing the focus to those journeys allows companies to cut through the data clutter and prioritize.”

For example, a cable television company used the data analysis of multichannel customer behavior to focus on where it was losing customers during two key journeys: on boarding and problem resolution. The data analysis team helped the company pinpoint key service trouble spots and ways to improve the on-boarding 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 post notes. “The changes improved customer satisfaction scores by more than 20 percent.”

2. Don’t over-think it. While companies may be tempted to wait to begin data analysis until all the missing data is found, McKinsey notes that successful organizations tend not to over-think all the details and just begin the work.

“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 touch points,” the post notes. “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 touch points.”

For example, a European energy company wanted to unearth insights trapped in data silos within the web team, call center and marketing. The team analyzed the journey customers took when they changed addresses; it found that the moving process alone accounted for 30%-40% of all churn.

By fine tuning the change-of-address journey, the company reduced churn by 40% and increased upsell opportunities through the journey.

3. Focus on analytics, not reporting. While many companies may be tempted to focus on generating data analysis reports about what has happened in the past, they should be using data analysis to pinpoint cause and effect and make predictions related to the customer journey.

“Big data harbors big opportunities to improve customer journeys and value,” McKinsey concludes. “What it requires is a commitment to focus on what really matters.”

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