Improving Complex Service Systems
With rising complexity in serving customers, McDonald’s executives raised the question of how capable their overall system was in achieving their operational challenges. An intense analysis of existing data as well as numerous in-depth personal interviews with company leaders, HR executives and store managers surfaced a wide number of issues that affected the system. Areas such as the ability of store management and operations to make the right choices for their particular situations, training efficiencies and store technology and design were identified and judged as important for upgrading the operation. Following the report, internal workshops and brainstorming sessions took place in order to generate approaches and timetables for making adjustments.
Mining for Fresh Insights
Over the past decade, Blinds.com, the web’s leading window treatment and wallpaper retailer, saw tremendous growth through ground breaking web technologies, streamlined operations and calculated acquisitions. But when the housing market suddenly took a nosedive, senior leadership asked, “What’s next?” To provide the client with the most comprehensive understanding of their marketing channels and customers, we pooled customer data generated in-house with data acquired from outside sources. Rigorous data mining and statistical techniques were then applied to the combined data. Brand profile and geo-demographic segmentation analysis, allowed the client a rich visualization of high-reward opportunities and the ability to drill down into the data for maximum insight. We worked with client leadership around to the idea of innovating around a few key process and technologies and to test and refine the growth opportunities uncovered through our analysis. So far, we’ve seen some dramatic changes in the way the company does business online and continue to receive positive feedback on our ongoing analytic contributions.
Improving the Productivity of 1.7 Million Employees
McDonald’s, the world's second largest employer, wished to identify opportunities for improving labor productivity world-wide. The complexity of this task was compounded by the fact that varying metrics are used across countries to evaluate labor efficiency. Over a period of time, we facilitated an internal team in order to identify where opportunities existed and where common metrics could be efficiently employed. The result is a common language for metrics, prototyping where improvements can be made, field testing changes to employee engagement and recommendations for improvement and ultimate adoption.
The camera never lies…
...but lots of people do, especially when they’re talking to researchers or otherwise responding to surveys. A part of it might be attributable to the Lake Wobegon effect, from the mythical town of Garrison Keillor, where it is said all the children are above average. More technically, another driver is social desirability bias. This is where the respondent wants to provide an answer that will be looked at by others as favorable. • A recent poll asked Americans who they voted for in the last election. This poll showed Obama thrashing McCain by more than 20 percentage points -- far greater than the actual Obama margin of victory on Election Day. • When people are asked if they voted in a presidential election, the percentage of self-reported turnout is inevitably 10-20 percent higher than actual turnout. • About 40 percent of Americans say that they attend church regularly. Counting and tracking methodologies used to determine true church attendance found that about half that number can actually be found in the pews. • A number of years ago, a survey found that upwards of five million people claimed to be New Yorker magazine readers—an unlikely number given that circulation was barely above half a million. People want to be on the winning team, and want to look virtuous and smart. So when we ask them to self-report, we often get responses that are wildly inaccurate. Researchers are exploring tools such asanonymous online polling and expressionless computer avatars in order to obtain more accurate survey results. But no matter how sophisticated surveys become, there is no substitute for the careful capture of actual human behavior, as we do with video-enabled behavioral analytics to see into the realities of shoppers in the shopping aisles.
Can We Do an MRI in Aisle 11?
The search for the perfect predictor of advertising effectiveness continues. According to a recent story in the New York Times, a Yale undergraduate is using magnetic resource imaging to “study brain waves and determine why people respond to some advertisements but not others.” Emily Yudofsky became curious about the potential of neuromarketing in high school, when she worked in a laboratory that did research on the consumer response to Coke vs. Pepsi. Yudofsky’s neuromarketing company will specialize in research on public service advertising, hoping to develop anti-smoking or don’t-drink-and-drive campaigns. The article suggests neuromarketing is “tremendously controversial,” both because it is seen as “creepy” and, as scientists point out, “just because a neuron fires does not mean a consumer likes Coke better than Pepsi.” If neuromarketing is indeed effective, we will see it used for more commercial applications. It is tempting to believe that brain scans can provide a complete understanding of how consumers make decisions. However, no matter how refined this technology gets, it won’t be a substitute for the observation of behavior and the resulting insights that bring true understanding of the consumer. At least not yet.
