The study concluded that marketing organizations need analytics professionals who understand data and the technologies that collect, house, and integrate it.1 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 more efficient 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.
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.”
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.
That targeted data science approach can give companies of any size a competitive advantage. Lenovo is a prime example of a marketing team that mastered the use of advanced technology and analytics tools, driving the company to create better value for its customers.
Ajit Sivadasan, Vice President and General Manager of Global E-commerce, realized that customer data was burgeoning and Lenovo needed to harness it. He began by establishing an analytics team in his e-commerce unit that today integrates and analyzes customer and marketing data from more than 60 sources worldwide. By integrating and analyzing Lenovo’s data, Sivadasan found that there are three main drivers of customer satisfaction that correlate to loyalty:
- Quality of the online experience. Sivadasan’s team tracks important variables such as how easy it is to find product information and whether Lenovo provides sufficient follow-up on the status of an order.
- Meeting commitments. This second driver includes how often the company misses promised ship dates.
- Experience with the product itself. By analyzing social media and direct customer feedback, Lenovo’s ecommerce team helps the company improve its products.
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, 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.”
Download “Measuring Marketing Insights: Turning Data Into Action,” an online Insight Center Collection of articles from Harvard Business Review, to learn more about using analytics to create customer value.
A version of this article first appeared as sponsor content on HBR.org in August 2016.
1. Harvard Business Review Analytic Services, "Marketing in the Driver's Seat: Using Analytics to Create Customer Value," 2015.