Trend Prediction: Connections With Customers Matter

Data analysis shouldn’t overlook factors that are difficult to quantify

Published: Monday, October 31, 2016 - 16:55

Among the “10 top business trends that will drive success in 2016,” reported in an end-of-2015 Forbes article by author and consultant Ian Altman, was the point that “Top performing companies will focus on connecting customers.”

Citing examples that include Uber, AirBnB, Kickstarter, and others, Altman notes that these companies may own no real estate and have no funds to invest, and yet they are among highly successful firms in 2016. He attributes their success, in part, to the fact that in the case of Uber, for example, “they excel at connecting riders with drivers.” Baker predicts that, “The most valuable companies will connect buyer to seller, or consumer to content.”

Does this signal a return to customer service as a priority?

With emphasis on “big data,” what may be lost in a company profile is the importance of “small data,” or the success factors that may not be quantifiable. Responses of customers may lie in this category, since although customers can be surveyed and data collected, their reasons for specific responses or even their real feelings about products cannot be counted and charted. This is where small data come in.

Analysis of data related to an organization’s business is critical to understanding trends, predicting revenues, and exploring root causes. This analysis of appropriate so-called “big data” is essential to the success of organizations that need to optimize processes and pursue meaningful strategic planning and auditing, and to improve the quality of their products and processes. But what has become known as “small data” is increasingly creeping into the discussions of analysis, sometimes enhancing understanding of what big data may reveal and giving deeper insight into customer needs.

As consultant John Cook points out on his blog, “People are not completely described by a handful of numbers. We’re much more complicated than that.” Using data to make critical decisions is essential, to be sure, but relying on data alone may blind one from seeing aspects of a system that can’t be quantified.

Understood in a variety of ways, one definition of “small data” proposed by Allen Bonde, vice president of innovation at Actuate, offers a connection between “big” and “small” data. “Small data connects people with timely, meaningful insights (derived from big data and/or “local” sources), organized and packaged—often visually—to be accessible, understandable, and actionable for everyday tasks. “Sometimes the distinction suggests that “big data is about machines, and small data is about people.”

An NPR interview with branding expert Martin Lindstrom (in a March 23, 2016, issue of Morning Edition) provided an example of the ways in which small data can inform big data. When Lego, the Danish toy-brick manufacturer, saw a trend toward fewer sales in the 1990s, managers looked at the data that suggested the decline, and in their analysis, they speculated (guessed) that children, with diminished attention spans, were turning to other toys that did not demand so much time to create. Kids were, perhaps, becoming more satisfied by “instant” activities that they could complete quickly. In reaction, Lego began to make bigger bricks, to facilitate quicker assembly of objects. Still, they did not see measurable improvement in the sales data.

Turning to “small data,” the company began to interview children, and to watch them play. Asking one 11-year-old what he was most proud of in his playtime, interviewers expected a response related to technology—a fast-paced video game, perhaps. They were taken aback when he showed them grooved and worn sneakers that reflected his accomplishment on a skateboard. The boy, like other interviewees, saw the value in working toward a goal or completion that would produce satisfaction and pride. Lego returned to manufacturing small bricks, scrapping the large-brick theory. Its sales revenues bounded back.

Lego’s exploration of its customers’ preferences offers an example of the value of these kinds of data. Jamie Tedford, founder and CEO of Brand Networks, in the article “Small Data Can Help Businesses Be More Human” points out that “Small data, in essence, recognizes the value of fewer, more relevant data points. The value of data shouldn't depend on its volume but on its quality and how it is analyzed, interpreted, and put to use. Small data is about focusing on real-use cases, collecting only the right type and amount of data, and applying it with maximum efficiency and contextual relevance.”

Of course, one would hardly expect that a company’s operations might be completely changed by input from a small handful of customers. Data both large and small must be part of an analytical process that may include a variety of other data. The key lies in understanding context and applying appropriate analysis to any set of data.

Analyzing small data may mean exploring responses of a small set of customers, as this example demonstrates. It may also mean looking at a few of the factors that drive the sales process, for example, or identifying specific needs of a specific market segment. Customer-focused small data can provide real insight into the meaning of larger trends suggested by analysis of big data.

Clearly, understanding customers is essential to providing products and services that meet their needs and delight them with innovation. Collecting data based on interviews and focus groups can be a “small data” step in fostering this kind of relationship, and can fuel the trends that are represented by big data. Both kinds of data can be essential to improving outcomes. Of course, as with all data, the key to success lies not just in its collection, but in careful analysis of its meaning.

About The Author

Barbara A. Cleary

Barbara A. Cleary, Ph.D., is a teacher at The Miami Valley School, an independent school in Dayton, Ohio, and has served on the board of education in Centerville, Ohio, for eight years—three years as president. She is corporate vice president of PQ Systems Inc., an international firm specializing in theory, process, and quality management. She holds a masters degree and a doctorate in English from the University of Nebraska. Cleary is author and co-author of five books on inspiring classroom learning in elementary schools using quality tools and techniques (i.e., cause and effect, continuous improvement, fishbone diagram, histogram, Pareto chart, root cause analysis, variation, etc.), and how to think through problems and use data effectively. She is a published poet and a writer of many articles in professional journals and magazines including CalLab, English Journal, Quality Progress, and Quality Digest.