Improving the Value of Customer Experience Analytics

It is no surprise that many businesses are taking advantage of the power of analytics. In 2010, researchers from MIT Sloan Management Review and IBM found that organizations that used business information and analytics outperformed organizations that did not. Top-performing businesses were twice as likely to use analytics to guide future strategies and guide day-to-day operations compared to their low-performing counterparts.

The use of analytics in business is growing and sees much support by business leaders. In a 2012 survey of 600 executives from the US and UK, Accenture found that use of predictive analytics is up threefold (33% in 2012) since 2009. They also found that 68% of the executives rated their senior management team to be highly or totally committed to analytics and fact-based decision making. Additionally, within the last 18 months, two out of three companies have appointed a senior figure (e.g., “chief data officer”) to lead data management and analytics.

IBM CEO Virginia Rometty said last week, when addressing the Council on Foreign Relations 2013 Corporate Conference, “Data will be the basis of competitive advantage for any organization.” She likened data as “the next natural resource.” But while everyone will have access to this natural resource, Rometty continues, “what you do with it will make the difference.” She predicts that, for business, “decisions will be based on predictive analytics and not gut extinct or experience.”

Analytics and Customer Experience Management

According to Accenture, businesses are applying analytics in a variety of different areas that cut across different functional areas like Finance (59%), Customer Service (55%) and Production/Operations (54%) but many with a customer focus. For example, a majority of companies said they are using analytics to improve the customer experience (60%), improve customer retention and acquisition (69%) and monitor competitor performance/activity (65%). To support these different use cases, analytics will necessarily be applied to different kinds of customer data including attitudinal data (e.g., customer satisfaction) and behavioral data (e.g., renews, purchases, clicks).

Improving the ROI for Customer Experience Analytics

Even though businesses are utilizing the power of analytics, they still find it difficult to maximize the return on investment of analytics. In the Accenture study, only 20% of respondents were very satisfied with the business outcomes of their existing analytics programs. The lack of value that executives are experiencing with their analytics may be occurring for a couple of reasons. First, executives lack clarity regarding the meaning of their analytics; over half (58%) of executives said they were unclear of business outcomes from the data. Additionally, only thirty-nine percent of the executives said that the data they generate is “relevant to the business strategy”.

Second, the problem of data integration appears to be minimizing the value of analytics. Half of the executives indicated that data integration remains a key challenge to them. Data are coming from different sources in a variety of forms and companies need analytics to make sense of it all.

Accordingly, to optimize the ROI of customer experience analytics, companies need to focus on three areas:

  1. Measure the right customer metrics: Companies are measuring the wrong things or have gaps in the way they are measuring important variables. Businesses need customer metrics that provide reliable, valid and useful information about the customer relationship (e.g., satisfaction with customer experience, customer loyalty). Applying analytics to reliable, valid and useful customer metrics only serves to improve decision making. Customer metrics that are meaningful (linked to?) to business growth helps executives see the value of the outcome of analytics as they apply to their business.
  2. Focus on strategic issues: Businesses need to use analytics to enhance strategic decision making rather than focusing on tactical issues. This is best accomplished by applying analytics on customer metrics that impact company value (however you define it). By applying analytics on the right customer metrics, businesses will be able to solve strategic business problems (e.g., product development, resource allocation) that really address ROI at an enterprise level (e.g., improve revenue growth, profitability, return on capital, customer loyalty, customer value).
  3. Integrate business metrics: Executives need the correct insights to help in decision making. The right analytics need to help executives gain a cross-functional view of their business data. This cross-functional view is possible only when businesses tie different business data sources together. Integrating metrics from disparate sources is no simple task as different metrics are needed to address different kinds of business questions. For example, addressing questions about how employee metrics impact customer loyalty is a different data integration problem than when addressing questions about how call center metrics impact customer satisfaction.

Summary

The adoption of predictive analytics in business is on the rise and for good reason. Companies that use analytics outperform those that do not. The value received from analytics, however, can still be improved by businesses who have adopted an analytics approach to decision making. First, businesses need to focus on measuring the right customer variables using reliable and valid metrics. Second, executives need to deal with strategic business issues rather than tactical issues. Finally, businesses need to integrate their different data silos to answer different customer-centric questions.

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