The impulse is always to want more. But when it comes to customer data, is more a good thing? Maybe not. After all, the data an organization has on customers and prospects is only as good as the insights that can be extracted from it and acted upon. More data, if it leads to fewer insights, is no good.
A recent study from Accenture concluded that one of the biggest challenges for marketing leaders today is not finding or hiring analytic talent, but rather it is finding the right ways to move the mountains of data into insights and then into action.
The study concluded that marketing organizations need analytics professionals who understand data and the technologies that collect, house, and integrate it. That’s a given. But beyond that, experts say, executives need to place more emphasis on data science than on data scientists. Put another way: They should pay more attention to analyzing and acting on what they have now because analysis paralysis doesn’t create customer value.
“Data scientists are technicians who are very good at managing and manipulating data,” says Peter Fader, the Frances and Pei-Yuan Chia Professor of Marketing at the Wharton School of the University of Pennsylvania and author of Customer Centricity: Focus on the Right Customers for Strategic Advantage. “But data science is about looking for patterns, coming up with hypotheses, testing them, and acting on the results.”
Machine Learning
That’s where machine learning can speed analysis and augment your analytics team’s work—by crunching massive amounts of data to identify patterns and anomalies.
A type of artificial intelligence that uses algorithms that iteratively learn from data, machine learning can surface insights without being explicitly programmed where to look for them. It makes it easy to crunch massive amounts of data, calling out issues before you see them and providing answers to questions you may not have even thought to ask. This speed to insight allows marketers and analysts to do more with the data that comes in and see the whole picture of the customer journey.
But instead of building data science capabilities, companies too often bring on increasing numbers of analytics specialists. The result is often what Fader calls a “data firehose” instead of a targeted set of insights that help answer specific questions about customer behaviors. Business leaders have practically no time to make good decisions about customer experience because so much data is being given to them.
Accenture Managing Partner Conor McGovern says, “If you can’t make the rubber hit the road with a disciplined approach to analytics, you will end up with customer experiences that aren’t as effective or engaging as they could be. As with any source of information, you need to embed and ingrain analytics into decision-making processes to obtain the desired results.”
Competing on Analytics
That targeted data science approach can give companies of any size a competitive advantage. One company that did that well was Harrah’s Entertainment (now Caesars Entertainment), says Fader. The company became an analytics legend through a rigorous approach to analytics when time was definitely not on its side.
“Competitors with deep pockets were handing Harrah’s their lunch, and the company was desperate,” he says. “They needed to figure out how to zig where competitors were zagging.”
Through analytics, Harrah’s aggressively experimented to find out who its best customers were and what would increase customer business with the casinos. For example, Harrah’s discovered that its best customers weren’t the high rollers most casinos targeted. Its best customers were retired professionals such as doctors and lawyers.
The focus paid off—the loyalty program ended up generating more than 80 percent of the company’s gaming revenue.
“Harrah’s prevailed in the end by using a disciplined approach to hypothesis development and experimentation,” says Fader, and then it was able to “move quickly and effectively.”
In order to pursue an effective analytics strategy, executives have to clearly define business problems and what the questions are that analytics can answer. If executives don’t do this, they risk getting back data that sends the organization in the wrong direction.
For example, companies frequently find themselves puzzling over a dip in conversions among a desired demographic. Organizations need to be able to study the data, ask customers and potential customers the right questions, and experiment with offering different solutions to optimize the customer experience. Answers need to come in quickly so the organization can act quickly—ahead of the competition.
The speed to insight that machine learning offers can help companies act strategically on the data they have, homing in on the insights with impact, allowing executives to make informed decisions.
Says Joerg Niessing, a marketing professor at INSEAD: “Executives still have to make the same strategic decisions that they have always made. They need to understand market dynamics and what competitors are doing—and then determine how the company should react. The only difference is that we now have a great deal more data and analytics to help make these decisions.”
To learn more about how leading companies are using marketing measurement and analytics to create customer value, download “Marketing in the Driver’s Seat: Using Analytics to Create Customer Value.”
