5. Experimentation and big data
Could the enterprise become a full-time laboratory? What if you could analyze every transaction, capture insights from every customer interaction, and didn’t have to wait for months to get data from the field? What if . . . ? Data are flooding in at rates never seen before—doubling every 18 months—as a result of greater access to customer data from public, proprietary, and purchased sources, as well as new information gathered from Web communities and newly deployed smart assets. These trends are broadly known as “big data.” Technology for capturing and analyzing information is widely available at ever-lower price points. But many companies are taking data use to new levels, using IT to support rigorous, constant business experimentation that guides decisions and to test new products, business models, and innovations in customer experience. In some cases, the new approaches help companies make decisions in real time. This trend has the potential to drive a radical transformation in research, innovation, and marketing.
Web-based companies, such as Amazon.com, eBay, and Google, have been early leaders, testing factors that drive performance—from where to place buttons on a Web page to the sequence of content displayed—to determine what will increase sales and user engagement. Financial institutions are active experimenters as well. Capital One, which was early to the game, continues to refine its methods for segmenting credit card customers and for tailoring products to individual risk profiles. According to Nigel Morris, one of Capital One’s cofounders, the company’s multifunctional teams of financial analysts, IT specialists, and marketers conduct more than 65,000 tests each year, experimenting with combinations of market segments and new products.
Companies selling physical products are also using big data for rigorous experimentation. The ability to marshal customer data has kept Tesco, for example, in the ranks of leading UK grocers. This brick-and-mortar retailer gathers transaction data on its ten million customers through a loyalty card program. It then uses the information to analyze new business opportunities—for example, how to create the most effective promotions for specific customer segments—and to inform decisions on pricing, promotions, and shelf allocation. The online grocer Fresh Direct shrinks reaction times even further: it adjusts prices and promotions daily or even more frequently, based on data feeds from online transactions, visits by consumers to its Web site, and customer service interactions. Other companies too are mining data from social networks in real time. Ford Motor, PepsiCo, and Southwest Airlines, for instance, analyze consumer postings about them on social-media sites such as Facebook and Twitter to gauge the immediate impact of their marketing campaigns and to understand how consumer sentiment about their brands is changing.
Using experimentation and big data as essential components of management decision making requires new capabilities, as well as organizational and cultural change. Most companies are far from accessing all the available data. Some haven’t even mastered the technologies needed to capture and analyze the valuable information they can access. More commonly, they don’t have the right talent and processes to design experiments and extract business value from big data, which require changes in the way many executives now make decisions: trusting instincts and experience over experimentation and rigorous analysis. To get managers at all echelons to accept the value of experimentation, senior leaders must buy into a “test and learn” mind-set and then serve as role models for their teams.
Podcast: According to Hal Varian, Google’s chief economist, companies that take advantage of “big data” and the new opportunities for experimentation that technology affords will gain a significant competitive edge. Download the podcast or listen in the player below.
Further reading:
Stefan Thomke, “Enlightened experimentation: The new imperative for innovation,” Harvard Business Review, February 2001, Volume 79, Number 2, pp. 66–75.
Stephen Baker, The Numerati, reprint edition, New York, NY: Mariner Books, 2009.
Thomas H. Davenport, Jeanne G. Harris, and Robert Morison, Analytics at Work: Smarter Decisions, Better Results, Cambridge, MA: Harvard Business Press, 2010.
David Bollier, The Promise and Peril of Big Data, The Aspen Institute, 2010.
Janaki Akella, Timo Kubach, Markus Löffler, and Uwe Schmid, “Data-driven management: Bringing more science into management,” McKinsey Technology Initiative white paper.
“Economist special report: The data deluge,” the Economist, February 25, 2010. |