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05:17 PM

Toshiba Analyzes Customer Satisfaction

The diagnostic equipment maker wanted intelligence on customers' experience with its products. But how do you apply BI to qualitative data?

Toshiba America Medical Systems, a maker of diagnostic equipment such as X-Ray machines and MRIs, went live with software from Cognos about six months ago, part of what it sees as a novel way to use BI to keep its customers happy.

To do this, Toshiba, Tustin, Calif., had to quantify essentially qualitative data, what the company calls "experiential" information -- in other words, a hospital employee's experience dealing with a particular machine and with Toshiba's various departments, from sales to service. With an X-Ray machine, for example, a doctor can manipulate an image and retrieve diagnostic insights. But if the machine's software is difficult to understand, if the user interface is overly complex, Toshiba wants to know about it, and wants to know the exact reason behind it, so it can feed that information back to its engineers.

With typical BI analytics, said David Schwartz, senior manager of customer marketing at Toshiba, you can easily discover the "what" -- that sales for a certain product in a certain geographic region have suddenly tanked, say. "But you never know the 'why,' and more than anything else we wanted to know the why."

At bottom, he said, Toshiba wants to track the relative happiness of its customers over the long term, with the ultimate goal of gaining repeat purchases, and larger market share. After a sale is complete, "we live with that customer for five to seven years," said John Zimmer, vice president of marketing at Toshiba. If a hospital employee has a bad "touch" with a Toshiba machine, "that could very well cost us the next order." And since doctors talk to other doctors, throughout the country and the world, even one bad experience could hurt the company's reputation at large.

To generate the raw qualitative information, Toshiba employees go out in the field and query hospital workers, physicians and administrators, all of whom have different viewpoints about a piece of equipment. A doctor may OK a purchase based primarily on the quality of the diagnostics produced by the machine, for example, while an administrator may decide based primarily on price. Other queries include a customer's willingness to repurchase a machine, and, of course, the reasons for that willingness or lack thereof.

All of this information goes into the laptops of the field researchers, and the information is transferred to the company's data warehouse. Third-party research firms do similar field work for Toshiba, collecting data that result in a monthly scorecard of how Toshiba stacks up to its rivals across five or six categories. All in all there are nearly 40 different metrics, which took Zimmer and his department about six months to come up with and put into place.

Because the raw data is in the form of text, however, it needs to be translated into numbers. Much of the qualitative data is "force ranked" on a scale from one to 10. Other questions are yes-no, and therefore binary. All the numbers are then put into a hierarchy according to the Pareto, or 80/20, principle (which in a business context theorizes that 80% of a company's profit comes from 20% of its customers). When a metric scores low, Cognos delivers a warning to managers and to the relevant salespeople in the field. They can then drill down within that report to discover the reason why the score was low score, which theoretically allows Toshiba to go out and fix the problem as soon as possible, or to know why, precisely, they won a sale over a rival, so they can repeat the success later on.

Reports are web-based, with zero-footprint, so employees can get the information no matter where they are, and translate the reports into any language -- English to Japanese (and vice versa) of course being the most important. A static report also goes out to manager daily, detailing those 40 metrics.

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