Math Magician: At IBM’s Watson Research Center, Brenda Dietrich helps connect the work of the company’s research mathematicians to its consultants’ projects, generating enormous amounts of new business.
Five years ago, Brenda Dietrich started to investigate how IBM’s 40,000 salespeople could learn to rely a little more on math than on their gut instincts. In particular, Dietrich, who heads the company’s 200-person worldwide team of math researchers, was asked to see if math could help managers do a better job of setting sales quotas. She assigned three mathematicians at IBM’s Thomas J. Watson Research Center in Yorktown Heights, NY, to work on new techniques for predicting how much business the company could get from a given customer.
The mathematicians collected several years’ worth of data about every sale IBM made around the world. They compared the results with the sales quotas set at the beginning of the year, most of which were developed by district sales managers who negotiated them with sales teams on the basis of past experience. To spot opportunities the sales teams didn’t recognize, the researchers collected external data on IT spending patterns by industry and combined that information with the internal sales data. Then they used a technique called high-quantile modeling–which tries to predict, say, the 90th percentile of a distribution rather than the mean–to estimate potential spending by each customer and calculate how much of that demand IBM could fulfill.
Armed with these predictions about how much equipment IBM should be able to sell to each customer, Dietrich’s mathematicians looked at the size and makeup of the sales team on each account and compared its actual performance with the theoretical maximum. Some teams were so small they couldn’t sell enough to meet that potential demand. Other teams were unnecessarily large. So the mathematicians advised the sales department to shift its staff around, taking less productive salespeople off the big teams and putting them on teams that had been too small. Sales in the latter accounts quickly grew.
The two-year project had a tremendous payoff for IBM. The corporate controller concluded that it generated $1 billion in additional sales through 2008, the year after the team finished its work, says Dietrich, a 50-year-old PhD with a sneaking suspicion that the world would work better if it were run by mathematicians. Since then, IBM has incorporated high-quantile modeling into its workforce analytics practice, a service it offers to help clients make decisions about human-resources issues such as how best to deploy their salespeople.
And the company drew a more general lesson from the experience: it came to believe that its mathematicians’ innovations were something for which other businesses would pay handsomely. Last year, the company created a major new business analytics and optimization group within IBM Global Business Services, and the group has already trained 4,000 consultants. IBM hopes to eventually do as much business in analytics as it already does in enterprise resource planning, which helps companies coördinate their information technology across separate departments; that service is a leading source of revenue in the $17.7 billion business services unit and has been one of its fastest-growing areas over the last 10 years. The two groups already complement each other: while enterprise resource planning tracks and organizes business processes, analytics maximizes their performance.
Dietrich, whose name is on 13 patents, thinks she and her team can create models that accurately describe activities far outside what is normally considered the realm of mathematics. For example, stochastic optimization algorithms, which incorporate random elements rather than assuming that all values are exact, have been used for decades to help manufacturers and financial markets adjust to changing conditions. But IBM’s mathematicians are applying the techniques to problems in human resources and marketing. They are using mathematical models to help the company find new customers and figure out the right mix of veteran and junior programmers to assign to a big software project. They are analyzing data to determine whether it is worthwhile for IBM to advertise in specific magazines or on certain television shows, or to attend particular trade shows. “We’re able to predict the impact of certain advertising programs on revenues,” Dietrich says–though, she concedes, “not with the precision I would like.”
Even if they are imprecise, Dietrich believes, these analytic techniques can be hugely helpful to many companies, which she says often don’t fully understand their internal processes and business models. Studying all the available data about sales and manufacturing could reveal bottlenecks that might be cleared or uncover opportunities that have been missed. She and her team are increasingly getting involved directly with customers. For instance, because of her reputation as a scientist and head of a math research team, she was recently invited to talk to a big pharmaceutical company’s executives about whether mathematical modeling could improve their process for allocating funds tovarious drug development efforts.
Such activities are a big departure from what IBM mathematicians used to do. In the old days, they were an odd breed among the scientists and engineers, who worked on science and technologies that might eventually lead to new semiconductor materials, new storage devices, or parallel-processing supercomputers. The mathematicians sometimes modeled IBM production processes, but they were judged mostly by their theoretical work and their publications in academic journals.
That started to change in the early 1990s, when IBM racked up huge losses. The board