Making use of big data to create value for the customer

Interview with Professor Philippe Baecke (Vlerick Business School)
Statements reported by Marion Dupire (Vlerick Centre for Financial Services)

Philippe, you have been working on the use of technology in the banking sector. In your view, what is the bank of the future?

“Indeed I have been working on incorporating new technologies, and more specifically on the incorporation of data sources within banks.  What I see is an evolution from product-oriented to customer-centric banks. Banks are now selling advice, in addition to financial products. As a result, nowadays the banks that perform better are the ones that know their customers better from the data they have. In terms of big data analytics, the financial sector is one of the leading sectors, together with tech, mobile and retail industries.

However, it is uncertain to me that banks should create all new business models. In order to improve customer experience, I rather think that banks need to collaborate more with other companies. It can be small start-up companies that create mobile payment applications for example. But it can also be larger companies like retailers or telecom providers for the development of online payment technologies. As an illustration, digital ecosystems with banks, telecom providers and retailers are created, such as “Sixdots” in Belgium or “CurrentC” in the US. It is a big change for banks which were used to work independently and are now forced to collaborate more with other companies.

We might even go one step further: banks might start collaborating not only for mobile banking but also to get a better view of their customers. At this moment, this is not at an operational level but you might think that banks and telecom providers start to collaborate with each other in order to understand their customers better. The big challenge then becomes privacy: the opt-in of the customer is needed before doing things like that. One example outside the banking sector is “Weve”, which is a joint venture of 3 telecom operators in the UK to offer new services for personalized marketing communication by mobile phones.”

There is a view that tech companies like Google or Amazon may become banks because of their technological advantage. This would constitute a threat for existing banks. What is your view on this?

“The threat is certainly over there. Some of these companies have already obtained a banking license and can start immediately. From a customer-centric point of view, they would probably perform very well because they have more customer data available than banks. However, customer centricity is not only about front office but also about back-office, where Google or Amazon lack a bit of experience. It is true that banks are not as agile as the technology industries but tech companies would probably lie behind on the operational aspects of banking activities. And a weak back-office will make it difficult to offer good front-office. Again, this is a reason why collaboration might be a better option than competition. I think that would be the big challenge for tech companies. However if they would be able to overcome this, then for sure they are a big threat for banks because they have more data and can become more relevant to their customers.”

How should banks deal with the issue of privacy given the current context where rebuilding trust in financial institutions is a key challenge?

“Privacy is indeed the biggest hurdle in the strategy of using big data to know customers better. It is not really about legislation, but more about ethics: the customer first needs to accept that the bank implements such a strategy. If the customer finds it too intrusive, it will not work in the end. During the implementation of this customer-centric data strategy, consumer ethics should always be taken into account. There is a very thin limit between being intrusive and being relevant, and this is something very important to consider.

However, companies that are relevant to the customer typically have a high satisfaction score. It is possible to make use of the data, be relevant and get a good customer satisfaction. One example is Amazon which extensively uses customer data and has a net promoter score  of about 70%. The reason of this good performance is that they make use of the data in a way that is relevant for the customer. As long as the benefits to the customers are perceived higher than the costs, they will be willing to give away the data.

You need to be relevant but you cannot be intrusive. In the end, the goal should always be to create value for the customer, which is the definition of a customer-centric strategy. In this way, the customer will be eager to give away the data because he gets back value from doing it.

Banks should see the privacy issue in this way: how can we make use of the data to give value to the customer? For example, KBC has created an app that keeps track of customer expenses and enables to visualize them in a structured way. For KBC itself, there is not a lot of value in this, except that it creates a lot of value to the customer who therefore becomes more willing to give away data. Making use of data only for the bank’s own goods will run it quickly into troubles.

It is true that banks already lack trust because of the crisis. But on the other hand, banks can gain trust again by being relevant. They have to be more cautious about privacy than telecom operators for example, because banking is simply a more sensitive issue than communication nowadays.

Privacy will even become a bigger challenge in the future because of legislation. In 2017, the European Commission will implement a new legislation in which the customer agreement to use his/her data will have to be more explicit. In the US, privacy is less of a big issue; Wells Fargo was for instance allowed to implement third-party promotion services, while ING could not implement such a strategy because of the reticence of their customers. Again ethics is even more important than legislation.

I would also like to emphasize on the fact that a lot of predictions are made at banks. It can be predictions about whether a customer will purchase a product or whether a customer will leave the company. Banks should now start making predictions on whether the customer is privacy-sensitive. They can then make use of this information to evaluate whether making highly personalized offers will be properly valued by the customer.”

The focus of your research is mainly on the use of big data in the banking sector; do you also see applications for insurance companies?

“In insurance companies, a new strength is to make use of sensor data. One typical example is putting a sensor in the customer’s car in order to get data on the customer’s driving behaviour in return for a discount to the insurance contract. In the US, there are already companies implementing it while in Belgium it is still in a testing phase, at least on my knowledge.

With this kind of sensor, new data is coming in, which can be used to better estimate the price of an insurance policy, an immediate financial benefit for the company and the customer. Each of these technology innovation should trigger insurance companies to ask themselves: ‘how can I increase customer experience with this?’. Insurance companies may think of new services to be provided based on these data. They may therefore create new business models based on these new data available. But they should always think about how to create value for the customer. With the sensor example it could be predicting when the customer needs to replace his/her tires. This service would directly create value to the customer who, as a result, would be more willing to give away his data. It has to be a win-win situation.”

Technology seems to move at a very fast pace, while banks are very complex and heavy organizations. This can be seen as an important disadvantage to evolve quickly in response to new technologies. What is your view on this?

“I totally agree that the big challenge for banks and insurance companies is that they do not have the agility that the tech companies have. One of the solutions is to create a start-up within your own big company. This is known as the sandbox model where a sandbox is created within the big castle. People in the sandbox have more flexibility than in the big castle, they have less restrictions, more freedom, they can try new business models. The big advantage is that it allows to fail fast in case a new innovation does not work.

If the new business model works, then there is a challenge to bring it back to the castle. My recommendation there would be to build the sandbox with people from inside the organization instead of delegating the full task to external consultants, because internal people would be more able to bring the new business model back to the castle.”

At Vlerick you recently launched an Executive Masterclass on big data, can you briefly describe the content of the programme and give feedback on the first session?

“There are different ways to make use of data. It can be in a descriptive way, or to produce predictive statistics and translate predictions into immediate actions with prescriptive analytics. What we see is an evolution from descriptive statistics to predictive and prescriptive analytics. Now that more and more new types of information are now coming in, e.g. with phone calls, social media, emails, location, sensors…, the challenge for banks and other companies is to incorporate the big data to improve their predictive models.

The Executive Master Class Creating Business Value with Big Data is mainly targeted at executives from tech companies, banks, telecom and retail, where the use of big data is particularly relevant. It is organized around four main dimensions that companies should take into account in making use of big data. These dimensions are based on an academic publication of my collegue Prof. dr. Stijn Viaene, who is also involved in this programme. The programme also includes visits of Microsoft, HP, Oracle in Seattle and Silicon Valley.
1. Modeling part: it is crucial to break down the walls that typically exist between business domains, and notably between IT people and data analysts who need to optimally collaborate with each other. Implementing a good big data strategy requires a good understanding about data and technique, but also on the business. Without this synergy, the value from the data can easily be lost. This is also the reason why this programme aims to bring together these different profiles. Which is very challenging for the faculty, but a great learning experience for the participants.

2. Discovery: this part focuses on making use of new technologies in order to transform the huge amount of data into useful information. We want to give the people an insight on how this works.

3. Operationalizing: how to incorporate all this in your organization? How do you bring this into the big castle? A huge challenge that we see within banks is that they want to become more customer-centric but they are not able to create a single-customer view, connecting all the dots of data they have. Data are collected in different data marts (sales, marketing…), but banks do not have a single view on all the data. That is a big challenge from an IT point of view: banks need to re-architect their IT infrastructure in a way that brings the data in a single system.

4. Cultivating: How can we transpose a no-data-driven culture to a data-driven one? In this fourth part we investigate the models that make predictions from the data and see how to make sure that people actually make use of these models once implemented.

A first wave already took place, with people from different backgrounds including business, IT and data scientists. Overall there were very nice synergies between the participants and the feedback was in general very good !”

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