Transform Your Customer Strategy Through Data Innovation

We sat down with Jasmine de Gaia, an Executive at Wells Fargo & Co and Head of Customer Data Strategy, to discuss how organizations can drive innovation and customer experience transformation through the effective use of data. Below is an excerpt from our conversation.

Jasmine de Gaia The sheer amount of data being generated today – through virtually every transaction and interaction – is staggering. Whether we realize it or not, we leave data bread crumbs via every digital process, every sensor, and every smart device we interact with throughout the course of our days. This isn’t going away, it’s only going to become more so. And now, unlike any time before in our history, we have the ability to manage, store and analyze this vast amount of data with incredible speed and efficiency. Organizations also have a responsibility and an opportunity to accelerate innovation by examining ethical and responsible uses of data to provide enhanced customer experiences.

How can data be used to drive innovation?

We think of technology as a force that changes the world, and it is. But oftentimes, data is the underlying driver behind many of these innovations. When we begin to understand precisely how much data there is, both within and outside our organizations, and how it’s generated, collected, stored, and managed, and ultimately how to analyze and leverage it, we can start to unlock the power it has to help us provide better customer experiences.

The amount of data that organizations now have access to can feel overwhelming. When thinking about data and innovation, where should people start?

There are a number of ways that we can tap into the power of data to help us transform and innovate our customer and operational strategies. A good place to start is by seeking to deeply understand the end-to-end customer journey - map out all the touch points you have with your customers, including their engagement with your products and services, even before they become customers, as well as ongoing, long-term customer support. What data is being generated at each stage of the process, where and how are you collecting it, and what’s being done with it today.

Analyzing this data will show you where your opportunities are, and guide you on a path to solving them. As you identify potential solutions, start small, and try things out on a small scale. Test and learn, iterate, and then scale your approach. Utilizing a customer-centric lens to this exercise will also help you think critically about the experience from your customer’s vantage point.

For example, if you’ve been a customer with an institution for years, what are your expectations for that relationship? What data have you provided to that institution as part of your relationship, and how do you expect the organization to use that information? As a company, are you fulfilling those expectations or treating your customers like a one-size-fits-all? An ideal customer experience that respects both the data and the relationship will ensure that you don’t ask customers for information you already have or can infer. Instead, the data can be used to provide a more personally relevant and customized experience.

How can organizations accelerate innovation using data?

Many data scientists will tell you that they spend about 80% of their time looking for the data they need, getting approvals, cleansing it, formatting it, and a number of other tasks all BEFORE they can get to actually learn from it and derive insights. In designing your organization’s data strategy, think about how your data is actually managed and made available to internal data analysts and users. What are the steps they go through, and how can you streamline so your organization can derive VALUE from the data faster.

Another opportunity for innovation acceleration is to bring all three dimensions together from the start: Business Product Owners, Data Analysts, and Technology Partners. We often see these teams operate in silos, and largely independently, which unfortunately leads to gaps in communication, lack of understanding of the true problem, and much longer timelines. Instead, set up small, nimble teams that encompass all three key roles from the beginning, working together to solve a common problem.

What’s one overlooked aspect when people think about how data fits into their business strategy?

Data Creativity. Many times when we think of our strategy for managing the data in our organizations, it’s about just that - managing the data IN our organizations, typically in silos. Where I think things start to get really exciting, is when you merge disparate data sets together. It might be data from different silos within your organization, or with data from outside your firm.

For example, think of how helpful it would be if your grocery store merged your pick-up times with local weather or traffic data, and proactively gave you alternative options based on that information. Consider the value you can deliver to your customers when you bring a level of creativity together with the data that will help them as they engage with your product or service. How can you use data to make it inherently easier for customers to do business with you - make the experience faster, more proactive, and with less friction.

In closing, what are you most excited about when you think about how data can drive innovation?

Data Humanity. Big data is inherently unemotional. By definition, it’s all about lots and lots of objective data. And leveraging Artificial Intelligence and Machine Learning to harness the power of that data. Which is amazing. But we can’t lose sight of the human side of it. To THINK, about what the data is telling us, to SEE the clues embedded in the patterns, to QUESTION what we’re seeing and try different things, and learn and continually course correct. Big data can never replace the insights we gain from engaging directly with our customers, observing them, talking to them, and learning from them. The data you get from those individual and human interactions is rich, deep data, based on our humanity and emotions. Pairing data with our humanity is what ultimately drives innovation.