How Wells Fargo Identifies nuggets of opportunity with Predictive Modeling?

As enterprises strive to attain actionable insights out of large chunks of data being generated all around them, every nanosecond, the data officers have a lot riding on them. Being at the helm of things, the chief data officer is one important person in today’s data-driven world. We get into conversation with A Charles Thomas, Chief Data Officer, Technology and Operations Group, Wells Fargo & Company to understand the complexities of this role, data governance strategies for large organizations, and how all this works to create value for customers, and in turn new opportunities for the company.

How important and challenging is the role of a chief data officer in a digital enterprise of today?

The amount and complexity of data companies collect and analyze has risen dramatically over the past few years and will only increase in the future. It’s important to have a CDO (or similar role) whose full-time job is to oversee and harness the power of the data and bring all the insights together for a common purpose. Companies need someone to help in the development of customer strategy, leverage data to help customers achieve their goals, and mitigate risk.

Companies of our size (and even those that aren’t our size) have data in many places, and if they want to receive the benefits and mitigate the risks of having all this data, then they have to appoint someone accountable for the data. Chief data officers represent this singular point of accountability.

What are the typical challenges you face in your role as a CDO of a global banking institution like Wells Fargo?

At Wells Fargo, there are between 80 and 90 businesses that range from home lending, auto loans, college loans to mortgages and insurance. We need to highlight opportunities to deliver a better, more common, and more consistent customer experience by channel and across products owned. We’re still on that journey, but we have been able to lay some solid groundwork about why we want to do that and we have some good commitments from our partners. In addition, there are so many different data elements we could be focused on.

Prioritization of efforts is a challenge because each of the individual businesses and corporate functions have different priorities and approaches to doing their business. Yes, we all want to put the customer first, and we want to mitigate risk and ensure that we adhere to regulatory guidelines, but the businesses go about doing these things differently and priorities may need to converge a bit to ensure we’re working on the right data projects.

How does the use of advanced analytics help banks to turn customer insights into profitable business strategies?

A great deal of the work we do to help optimize the customer experience and find new business is focused on helping our partners see the power of looking at customers across businesses in order to find new opportunities.

Therefore, merging together data for a given customer across business lines and across interaction channels, despite the data being owned by different groups in the company is a priority. After using big data approaches to aggregate the data, we’re leveraging predictive modeling and innovations in analytical techniques to identify nuggets of opportunity to, for example, enhance cross-sell.

Finally, we have to present the results in business terms ($) in order to convince our business partners to take action on the findings. Though these aren’t new activities (the banks have been using data for years to drive decisions), we’re trying to bring new approaches to aggregate more and different types of data, expedite the process of going from data collection to action, and take advantage of new developments in analytics to find greater nuance in customer interactions than before for the purpose of being more relevant and timely in our communications with our customers.

What kind of data governance strategy do you have in place to ensure uniformity across diverse business areas?

We started by establishing an enterprise data governance program. An important part of that is the creation of the enterprise data council, which includes data leads from each of our lines of business, to talk about the challenges and arrangement of data and how to solve those challenges. There is also an increased focus on people who do data and analytics and ensure that they have the ideal skills and appropriate tools. My enterprise team helps orchestrate this by acting as a ‘hub’ and collaborating with line of business data teams ‘spoke’ to ensure we have common data, tools, and governance. This ‘hub and spoke’ model allows our team to partner with different areas of the company based on their needs and capabilities.

Can you cite some specific examples where big data/ analytics helped in improving customer experience or profitability in your business?

We’ve recently launched a program designed to aggregate all our customer leads, meaning cases where customers ‘raise their hands’ and show interest in purchasing a product, across channels. From this, we model and score each customer’s likelihood of purchase, and then use this aggregated, prioritized list for lead nurturing and follow-up. So some may ask—why is this different, hasn’t the bank always followed up on leads? The response is— of course, and we’ve done that very well. But historically, each channel or business may have prioritized their own leads, and if a customer showed up in different channels or for different products, they may have received multiple, potentially confusing offers.

This new, rather simple approach, enables us to see our customers more holistically, and then generate a follow-up conversation that is more relevant, timely, and accurate. Customers, in turn, see us as one company that understands them, has listened to them, and is making it easy to do business with them. In many cases, for this reason, they reward us with more business. Results have been very promising.