Integrating Text Mining with NPS: The

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Integrating Text Mining with NPS: The Story Behind the Score at Sage Software

Rex

Sage North America, a provider of business management software, has long embraced the notion of customer centricity. This fact is evidenced in our reliance on Net Promoter Score (NPS), a score obtained by asking customers about their likelihood to recommend a product or service. Our product general managers gauge the relationship health of our business by monitoring NPS surveys, as well as other empirical diagnostics. But it wasn't until the adoption of a broader Customer Experience Management (CEM) program that also leverages text mining that Sage North America became capable of discerning the true voice of our customers.

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The recent rise of text mining and CEM at large Fortune 1000 companies speaks volumes about the importance these companies are placing on making their customers happy. While CEM isn't necessarily a new philosophy, the way that companies are now able to capture and analyze customer opinions on products and services has changed drastically with the introduction of text mining. Drawing from survey verbatims and other text-based customer channels, text mining enables a company to tap into the true feelings of each of its customers. Text mining can automatically convert any text-based feedback from across and beyond the enterprise into timely, actionable business intelligence.

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In the case of Sage North America, complementing NPS with text mining-fueled CEM has uncovered the root cause behind the scores our customers give us. This has also given us the ability to engage new, high-value customers early in their relationships with Sage North America, which has resulted in dramatically higher NPS scores. Plus, text mining and CEM have significantly enhanced our ability to address the specific, qualitative comments of dissatisfied customers, "detractors," and convert them into "promoters."

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A Commitment to Customer Centricity

Sage North America provides customizable business-management software and services for accounting, customer relationship management (CRM), human resources, merchant services and time tracking, as well as specialized solutions for customers in construction, distribution, healthcare, manufacturing, nonprofit and real estate, among other industries. About 2.9 million small and medium-sized businesses rely on our products, and their needs vary depending on applications and industries. We are dedicated to providing superior customer experience to the entire range of our clients, and that requires staying attuned to their unique and changing needs and desires.

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This is one of the key goals of our Customer Relationship Program. We strive to contact and survey 200,000 customers quarterly, to ensure we stay in touch with what they need and how they feel about Sage North America's products.

When the Customer Relationship Program was launched three years ago, it was exclusively quantitative data on which we based our primary metrics for trending, ranking and assessing customer feedback. But the open-ended comments that were captured in the surveys could not be efficiently processed. The general managers for Sage CRM Solutions and our other products had to manually read and categorize this qualitative information. The process was complex and time-consuming, and it also was nearly impossible to accurately link customer loyalty metrics to customer behaviors.

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This issue was certainly not unique to Sage North America at the time. In 2006, when we started the Customer Relationship Program, CDC Respond reported that, while 95 percent of surveyed companies collected feedback in some form from their customers, only 35 percent used the data in some way. Furthermore, only about 10 percent made changes in response to the feedback—and just 5 percent told their customers what they had done in response to the suggestions.

At least we were leveraging the NPS scores and other quantitative data that we were collecting. And we always understood the potential value of the qualitative, textual feedback gathered in our surveys; we just didn't have a good, efficient way to convert that feedback into dependable, timely business intelligence.

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Implementing CEM and Text Mining

For years at Sage North America, we've felt that data are like garbage—you don't want to collect it unless you know what to do with it. CEM and text mining are what we now do with the open-ended responses we receive in combination with our customers' NPS scores.

We implemented Clarabridge's Content Mining Solution service in the fourth quarter of 2007. Clarabridge's text mining solution augments NPS by illuminating the "why" behind a customer's scores.

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Text mining can capture and analyze valuable customer intelligence found in various text-based feedback channels and indirect sources of consumer opinions, such as call center notes, qualitative survey feedback, Web 2.0 content, online consumer forums, social networking sites and even customer warranty forms. Aberdeen Group in July 2007 estimated sources of unstructured data such as these to account for 85 percent of all of the data available to a company.

Our text-based customer feedback is automatically categorized, and associated sentiment is determined. The qualitative feedback is integrated with quantitative via a single report system, and this allows us to efficiently rank the issues that customers are talking about in relation to their NPS scores and identify the aspects of our business that cause our customers to recommend us to others. It is a fully automated, end-to-end approach to CEM that enables us to make more intelligent business decisions on behalf of our customers.

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We have come to see that the most useful insights from our surveys are to be found in the verbatims from open-ended questions. Consequently, we have overhauled our surveys—cutting back from 29 to five questions, three calling for open-ended responses. Our customers have more freedom to tell us in their own words exactly what they think, and text mining and CEM are the tools that enable us to respond accordingly.