Listening to the data is important… but so is experience and intuition. After all, what is intuition at its best but large amounts of data of all kinds filtered through a human brain rather than a math model? “Big data” can drive competitive advantage if companies follow a few timeless principles. (An excerpt from an article in Strategy+Business by David Meer).
“Big data” — the explosion of quantifiable information, much of it generated by people’s behavior on the Internet and social media — has captured the imagination of companies, academics, and the media. Executives are rightfully intrigued by the idea of drawing conclusions about their customers’ buying propensities from details of their activity: who they’re connected to, what they like. This promise is born partly of the fact that the data for Internet-based analytics is already being captured on computers, there to be sorted, filtered, and modeled.
Undoubtedly, big data will be the next game changer for marketers, but some will gain more advantage from it than others. Because of the automated tools that are available for mining data today, many executives assume that they will be able to easily uncover trends that weren’t previously visible. But analytics involves more than just knowing the facts. It requires the right analysts asking the right questions to make the right decisions. Any analysis of data that stops after asking “what,” which is already a big undertaking isn’t analytics. You have to ask “why?” and “what next?”
To answer these questions, and to leverage big data’s full potential, companies need to go back to basics. Three pragmatic lessons that have always been at the core of a strong analytics program, and should guide your initiatives today: Rely on theory-based approaches, not blind data mining; strive for a holistic view of your customers and markets; and learn by doing.
It Starts with a Theory
Without a theory about how consumers form preferences and act on them, an analyst will quickly be overwhelmed by the amount of data available, and all the processing power in the world won’t help. The starting point should be an explicit hypothesis about the needs of your customers and how you create value for them. It may be a new product in the lab that you think has the potential to be a runaway hit. Or it may be that there are customers in your market who aren’t really loyal to anyone — “undecided voters” whom you could capture with some slightly altered proposition. Once you’ve gathered the data required to test your hypothesis, the analysis will usually lead you to specific ideas for developing winning value propositions and taking them to market. Superior segmentation — clustering your customers and prospects based on similar behaviors or preferences — can lead to much more effective targeting strategies.
One of the key lessons from the history of marketing science is that when a new data source becomes available, everyone is quick to fall in love with it. But smart companies take a step back and strive for a holistic view of their customers and markets. They enthusiastically mine the new data source without discounting other information that may provide critical missing pieces to the analysis.
Like the flawed marketing ROI models of the barcode’s early years, the newest big data analyses can be misleading. Many retailers say, “I know everything about what moves off my shelves. I know a lot about my customers who have loyalty cards. But when we put more things on the shelves that resemble the things they’re already buying, we don’t see the growth we’re expecting.”
What’s missing? It’s likely that by focusing on the newest source of data, the retailer has unintentionally developed a one-dimensional view of its customers. What it needs, however, is the broadest possible view of a customer’s path to purchase. Call this perspective “a day in the life.” It means understanding more completely how your interaction with a customer fits in with all the other interactions he or she has with other retailers or (as the case may be) other businesses, shopping channels, or activities. Without that insight into what is prompting a customer to go somewhere other than to you, your growth initiatives can become a crapshoot.
Learning to Walk
The first steps you take to acquire, harmonize, and mine new data sources almost always lead to exciting new insights. As you gather these insights, it will be important to be open to new approaches and to challenge sacred cows. You may learn things about your customers that cause you to question certain products, services, or strategies. It can be a lot to take on. Rather than go whole hog with analytics all at once, undertake a few pilots. Learn to walk before trying to run: You can pick a product, a geography, and a problem that you want to focus on, and see if the return on effort and cost justifies the undertaking.
Getting Back to Basics
Many executives are interested in using big data but have relatively little direct experience with the latest analytics tools and techniques. Right at the start, they typically ask what it will cost. The response to give is: “What’s the cost of making the wrong decision?” Analytics can require a major investment, beginning with just assembling and harmonizing the data. On top of that, companies need specialists who are trained to do the more advanced work to find hidden patterns, interpret them, and turn them into insights the company can put to use.
Analytics becomes a self-funding way for companies to improve their position in the market: A manageable process with the potential for significant rewards.
- Big Data To Become Powerful Driver Of IT Spending – Gartner (misco.co.uk)
- Big Data Infographic | How Big is Big Data? | Domo | Blog (domo.com)
- ‘Big Data’ is more than just a buzzword (kcinconversation.com)
- Why data scientists are in demand and how they enable big data (zdnet.com)
- How Are You Managing Big Data? Data, Data Everywhere | Domo | Blog (domo.com)
- The Ecommerce Guide to Big Data [Infographic] (getelastic.com)