Do the Math: The Mu-Sigma Customer Summit Celebrates Analytics

Last weekend, I attended the 2013 Customer Summit in Las Vegas hosted by Mu Sigma, a decision sciences and “pure-play” analytics company started by one of our Chicago Booth graduates, Dhiraj Rajaram. The company’s slogan says it all: “Do the math.”

The slogan effectively communicates to both the external world and employees what the company is trying to accomplish (see my earlier post on effective slogans). The summit provided an opportunity for current and prospective customers to understand the ways in which decision sciences in general, and Mu Sigma in particular, have helped address various business problems. Current customers shared their analytics-related experiences. Panel discussions then highlighted trends and possible future directions for the role of decision sciences in organizations.

The confab kicked off with an interesting discussion between Rajaram and Gary Reiner, an operating partner at General Atlantic and a former CIO at GE. Reiner provided an insider’s view on how GE came to have an office of the CIO, and on the role played by GE’s legendary former CEO Jack Welch in the evolution of the company. GE initially did not pay much attention to information technology, but it was not only at the forefront of this function, it was also an early mover in the trend toward raising efficiency by offshoring some of these operations. The fundamental point made during this session: note the importance of both the “art” and the “science” of decision-making.

To draw the contrast, Rajaram used a movie analogy and compared the movie Moneyball (starring Brad Pitt and based on the book of the same name by Michael Lewis) to the Clint Eastwood–Amy Adams movie Trouble with the Curve. While the first movie highlighted how data and statistics helped a baseball team that was resource constrained make better decisions, the latter emphasized the importance of baseball scouts in identifying talent, even in situations where data and statistics were not widely available. Ultimately, Rajaram noted, both were important in providing a more holistic picture of players and the game, as “the scout can see things that numbers cannot see”—or, in the case of Gus Lobel, Eastwood’s character in the movie, hear what numbers cannot hear—“and the numbers say things that the scout cannot say.” Similarly, decision sciences are, and will continue to be, an intelligent interplay between art and science. In other words, do the math, but do it wisely.

Another highlight of the summit was a keynote talk by Gary Loveman, an erstwhile Harvard Business School professor and current CEO of Caesar’s Entertainment. Loveman emphasized the importance of “profitably influencing consumer behavior” by engendering loyalty and increasing the customer’s share of wallet. He did the latter at Caesar’s by encouraging customers to visit properties operated by the company in destinations all around the globe. Contrasting the tendency of grocery stores to “reward” low-spending customers by having express lanes for those who shop little (think “10 items or less”) and not having special lanes for people who shop a lot and are likely to be more valuable to the store, Loveman emphasized that the more a customer or client spends at Caesar’s Entertainment’s properties, the better she is treated.

Granted, there are costs to investing in customer retention. To help determine the amount the company should spend on, say, coupons for a steak dinner, tickets to see Celine Dion, flights and hotel rooms, or even a night that recreates the experience of the movie The Hangover (if a high roller requests), the company looks at the data and uses analytics.

Loveman also emphasized the importance of the service-profit chain, the idea that satisfied employees lead to satisfied customers. To deliver superior service, he noted that Caesar’s human-resources system ensures that employees are ready (have the required information), willing (are given the appropriate incentives on the basis of customer satisfaction) and able (possess the requisite training) to help customers. This leads to employee loyalty and, by the very nature of its design, to higher customer satisfaction. The higher satisfaction then generates customer loyalty and, consequently, greater profitability and growth.

A point that both Rajaram and Loveman made in their talks was the central importance of learning. Often decision-makers have hunches or hypotheses about various aspects of the business—why sales are declining in Market X, or why customers for Product Y are defecting, for example. It is critical to test such hypotheses with data, otherwise they could lead to incorrect decisions or rules that simply end up exacerbating the situation. At Mu Sigma, this idea is ensconced in their DIPP (Descriptive – Inquisitive – Predictive – Prescriptive) framework to analytics. At Caesar’s, Loveman also advocated the use of randomized experiments that can determine whether price increases on drinks at the casino’s bars would be accepted by customers, for example.

The key, then, is not to be stubborn and stick to a hypothesis without testing it— instead, learn from it. To quote Eddie “Scrap-Iron” Dupris, the character played by Morgan Freeman in Million Dollar Baby, “all fighters are pigheaded some way or another: some part of them always thinks they know better than you about something. Truth is: even if they’re wrong, even if that one thing is going to be the ruin of them, if you can beat that last bit out of them . . . they ain’t fighters at all.”

Overall, I came away from the summit with the feeling that companies are increasingly open to the role of analytics and decision sciences to translate data into usable information. At the same time, if the model and the mind combine to extract a usable insight, that can help businesses move forward.

Pradeep K. Chintagunta is the Joseph T. and Bernice S. Lewis Distinguished Professor of Marketing at Chicago Booth.