Leveraging Big Data Analytics to Create Value
Interview with Professor Peter C. Verhoef, a co-author of the new book Creating Value with Big Data Analytics: Making Smart Marketing Decisions.
Podcast Transcript
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Darima Fotheringham: Today we are talking with Professor Peter C. Verhoef from University of Groningen, The Netherlands. He is a co-author of a new book Creating Value with Big Data Analytics: Making Smart Marketing Decisions. Hello Peter!
Professor Peter C. Verhoef: Hello!
Darima Fotheringham: First of all, congratulations on your new book Creating Value with Big Data Analytics: Making Smart Marketing Decisions. Can you tell the listeners about you and your co-authors and how the idea of the book came around?
Professor Verhoef: I’m a professor of Marketing at the University of Groningen and I have been an expert in, specifically, customer relationship management and customer analytics. My co-authors have been working in practice and are now the founders of MetrixLab Big Data Analytics. They have extensive experience in customer analytics and marketing intelligence. We wrote this book because, first of all, the three of us wanted to share what we learned over the last two decades of our careers. Secondly, we saw that many firms are nowadays struggling with big data, specifically big data analytics and how to create value from these analytics. So we wanted to offer firms, service professionals and students, for instance MBA students, a book about big data and specifically big data analytics.
Darima Fotheringham: Great. Everyone would agree that value creation is the ultimate goal of big data strategy. At the same time, as you pointed out in the book, value has multiple dimensions. There is value to the firm, and value to the customer, also value to the society as a whole. Can you talk about these different perspectives on value? And what is the optimal way to balance these perspectives when developing your big data strategy and should that be your goal?
Professor Verhoef: Indeed, we make a distinction between these concepts. Specifically, value creation to the customer means that you provide customers with, for instance, brand new products, good customer service that creates good experience. You want to make your customers happy. Second, value creation to the firm means that, as a firm, you also aim to benefit from the things you do for your customers. You want to extract value from your customers. For instance, you want your customers to be more loyal or you want them to buy more products or maybe advertise for you and, in that way, bring in new customers. The last concept we talk about is the value to society. What that means is that you are not only focusing on delivering value to customers but also to the grand society. Consider, for instance, the concept of corporate social responsibility and, beyond that, more sustainable value for the long run, a more sustainable development of your firm in the society in the long run.
How do you balance these perspectives in creating your big data strategy? What you’d like to consider actually is, how your marketing actions or your service improvements can benefit your customer while, maybe not in the short run but maybe in the long run, your company can also benefit from that. So we observe, for instance, many firms in the online industry may have very satisfied customers but find it pretty difficult to earn money from these customers. That might work in that industry for some time but in the long run that might not be a sustainable way of doing business. In the end, what you want is to create value for your customers in such a way that you can also benefit from it as a firm by extracting value.
Darima Fotheringham: As you point out in the new book , data analytics have been around for many years, but the recent growth of big data has taken analytics to the next level. Can you talk about a few most important and maybe unexpected changes that took place and give some examples?
Professor Verhoef: Yes. A major change has been that we see the volume of data growing. While in the past we analyzed, for instance, four hundred customers, maybe a thousand customers, we are now analyzing data of one hundred thousand or even one million customers. That has an important implication. For instance, in terms of analyzing data, many things become significant. That means, actually, we are no longer interested in significance. We should move from significance of our results to focusing on the substantive differences. When we analyze a very large database, a small change of, let’s say, 0.001 % can already be significant but, at the same time, the substantive effect can actually very limited. That’s one major change.
Second change is that we are moving from structured data to unstructured data. We still have structured data, but we have more and more unstructured data, especially online. That means that firms have to learn how to analyze and how to interpret these data and learn new techniques. That means, for instance, that companies are using more text mining techniques. It also results in new metrics, digital sentiment indices, for instance, which can tell you more about how customers feel about your brand, about your service.
And the third point that we see changing, in terms of analytics is that are we moving from traditional methods more to computer science methods. You should think about, for instance, neural networks, Bayesian model averaging techniques. That’s a new area which marketers and traditional market analytics people are not as familiar with. So we also see new people, for instance, from computer science coming into our field.
Darima Fotheringham: Very interesting. When we are talking about this volume of data that companies have access to now, we know that questions of data privacy and security have been in limelight lately. You mentioned the case of Edward Snowden in the book and there was an Apple-FBI encryption dispute going on, which is widely discussed by experts but public reaction seems somewhat indifferent or at least so far. In the chapter discussing customer privacy and data security, you mentioned privacy paradox, which I thought was very interesting. Can you explain what that is and talk a little bit about that?
Professor Verhoef: Sure. Well, the privacy paradox suggests that consumers are worried about their privacy, they think it’s important, they think that firms should take care of it, etc. But when you look at their behavior, consumers frequently don’t behave consistently with their beliefs. So there is a strong discrepancy between how they, for instance, deal with their data, what they post on social media, and what they say about privacy. That’s kind of a strange paradox. So briefly, consumers do not behave as they say or they would like to behave when they talk about privacy. That’s an interesting phenomenon. Still, I think privacy is getting more important, as mentioned in the examples. Also, from a legal perspective or a government perspective, specifically in European Union, you see that firms are restricted in how they can use that data. For instance, we observed that some companies are throwing away data, especially nowadays they keep only one year of data in their database. They don’t want to keep the history of customers for long. One of the rationales behind that is the fear of all kinds of privacy regulations.
Darima Fotheringham: When customer data is the life line of the business, digital trust also becomes very important. Based on research in this area, what policies related to data privacy and security issues companies should consider adopting?
Professor Verhoef: Well, there are multiple recommendations I could give. One of the most important things is that you should give control to the consumers or at least they should perceive that they have control. They have to be able to see or be able to control, to some extent, how the firm uses their data. There is an interesting study by Catherine Tucker from MIT. It actually shows that after Facebook implemented such a strategy, the response rates to their commercial activities or some of their commercial approaches to consumers increased. It’s a very interesting phenomenon that when you give consumers more control, they are more likely to respond to your commercial efforts.
Darima Fotheringham: That’s a very interesting effect. In the chapter ‘Building Successful Big Data Capabilities’ you discuss four main building blocks of analytical competence: processes, people, systems and organization. Can you talk about the competences that are most critical yet most challenging for the companies?
Prof Verhoef: In terms of systems, you see that firms now can choose from a wide variety of systems, where in the past you had only a few suppliers. Now you see many suppliers of all kind of databases, cloud solutions, analytical solutions, dashboards, etc. In one way or the other, you should try to build a comprehensive big data ecosystem. The organization aspect looks at how you organize your big data analytics within the firm. Is it for instance, a very centralized staff department? Or are big data analytic teams available in several business lines, several business units? And how do you incorporate their analytics in your decision making? What for instance we see nowadays is that many companies are adopting a multi-disciplinary approach where the big data analytics play a major role.
The people aspect is very important, that’s also where firms face the most challenges. There are some studies, for instance by McKinsey, which actually show that it’s very difficult to find good data scientists. There is a shortage of data scientists on the market. And firms find it very difficult to find these people. In terms of capabilities, they need to have IT capabilities, they need to have data capabilities, know how to deal with different data sources, how to integrate them. They need to have analytical capabilities to be able to do sophisticated analytics, and finally they also need to have some business sense. An important question is, of course: Are the people that have all these capabilities available? Or should you work in big data teams where each of the team members brings some of these capabilities; and, together, they form a very powerful big data analytics group.
Darima Fotheringham: In your book, you actually have an example of a company that created this special program internally to close that capability gap. Can you share that example with us?
Professor Verhoef: Yes. That was a Dutch Telecom company. At the time they had a problem of not having sufficient number of highly trained analytics people. They set up a program called the Marketing Intelligence (MI) Academy together with a consulting agency to train new people. It was an in-house training program, where a part of the time participants were doing coursework, part of the time they were also working within the company. Most of the people who entered that program just came out of a University. They were trained in doing analytics but another important aspect was that these people also applied these new skills to have an impact within the organization. So it was not only about doing analytics but also, for instance, about things like: how do you visualize what you found? or how do you communicate? how do you tell your story to the management? That was an important aspect of the program. In doing so, this company was, at that point, able to build a successful analytics team.
However, many of these companies face problems about how to retain these employees. So building up a successful analytical capability is one thing, the next step is to figure out how you retain your people and how you keep these people happy and satisfied and ensure that they still find challenges in what they do. Especially, given that the people you trained are very, very attractive for other companies as well.
Darima Fotheringham: And in conclusion, what advice would you give to organizations’ leaders as they are navigating the complexities of big data?
Professor Verhoef: I think maybe the most important advice is that you should not consider big data as some kind of revolution, as “the big new thing”. We actually think it’s more of an evolution and, by acknowledging that, it’s very important that you start with small projects which can immediately create value for your firm. For example, we have an example in our book, where we describe a case of an online retailer that wanted to improve their recommendations systems. They started very small and that proved to be successful. Then, they invested strongly in building up these recommendations systems to a much higher level. So start small, and then scale up.
Darima Fotheringham: Thank you again for your interview today. We talked to Professor Peter C. Verhoef, one of the authors of the book Creating Value with Big Data Analytics: Making Smarter Marketing Decisions. Peter, thank you for talking to us.
Professor Verhoef: Thank you.
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