The enormous amount of data being generated is hard for the human mind to grasp. According to tech researcher IDC, the amount of consumer data captured reached 2.8 zettabytes (billion terabytes) in 2012, and will double again by 2015. Think that's scary? IDC adds that 80 percent of customer data is wasted by businesses.
How can companies get on a path toward data excellence? Leading analytics expert, author, and professor Tom Davenport and TeleTech Analytics Partner Don Ryan shared insights with Customer Strategist about how analytics and customer experience can align to reveal undiscovered innovation in data strategy.
Don Ryan: In the last 10 years or so, customers have started to gain the upper hand in their interactions with brands. If businesses want to be customer-centric they have to know more about the customer. That's why I think it's a particularly propitious time for those in the customer experience field to be thinking about how Big Data can propel them forward. Today, companies want to know how consumers are going about their daily lives, and how they are using products and services. This whole new arena is calling on not only large volumes of data, but also different kinds of data and the immediacy of data to make the customer experience more relevant for them.
Companies see the promise of improvements in customer experience using Big Data, but I think they're looking for more proof that it will actually have an impact on their business before they invest heavily in Big Data and put it into practice.
Tom Davenport: The customer experience has always been something that has been revealed through data, but data in the past were fairly moderate in terms of size and structure. It was relatively easy to manage. Now if you talk about all the different types of interactions that customers have with organizations (in-person, web, voice, social, text), you're certainly entering the realm of Big Data. The challenge that every organization has is how to pull all of this together and make sense of the customer experience.
It's still a tough thing to do despite all the tools that we now have to make this possible. It's tough and expensive to pull all that information together, have a common customer identifier throughout, turn all the speech into text, analyze the text, and so on. You have to have a pretty strong level of commitment to the customer experience to make that kind of investment today. Not very many people are terribly far along in that process.
CS: Tom, you've been writing recently about the concept of Analytics 3.0, which combines Big Data and traditional analytical capabilities to meet modern business needs. What do you mean by that?
TD: There are two main components of Analytics 3.0. One is the development of products and services based on data that you offer to customers. The other is analytical decisions at speed and scale. Analytics has typically been a back-office phenomenon. People had some leisure with which to carry it out. If there was a big direct mailing being planned, it would take a while to identify the key variables and score the data, and so on before we did a big mailing. Clearly that's not going to be possible in the current environment, where we need to be much more real time and react very quickly to any behavior by our customers. It's raised the ante for what we do with analytics. We need to use more data, we need to change our processes for analyzing and acting on it, and increasingly we can now take the data that we have and monetize it by turning it into products and services.
CS: What other innovations do you see in the data world?
TD: There's a shift toward prescriptive analytics—analytics that tell you what to do in the moment. The analytics have to be highly understandable. In banks for example, if you're talking about trying to upsell or cross-sell a product during a conversation, you have to give customer-facing employees very straightforward indicators about the one or two products to mention. It can get confusing if you send complex information that historically has come out of analytics.
It means reducing the amount of detail, being very action-oriented, and embedding analytics into processes and systems where they were not embedded before. Analytical activity used to stand apart, and we would go into the data warehouse and work on it. Now it's in-database processing, it's in-database analytics, all typically done in memory, so it's a lot faster. It's a very different way of structuring the technology, as well as how we think about presenting information to workers.
Companies like JP Morgan Chase, Barclays and others in banking are beginning to work with this, creating entirely new business units to turn the data they have about customers, payment activities, assets, and more into real business value.
DR: There's an emerging category that I call "un-data," which is the absence of data about the customer. Essentially un-data occurs when customers stop interacting with you and leave a void in their behavioral activity. The lack of any customer interaction is itself important information that can be critical in understanding whether a customer is starting to move his business to a competitor, if you're losing favor with him, etc. For instance, if we see a customer interacting regularly by going to a website or retail store, and all of a sudden we don't see him as much or he disappears entirely, that's important data that's often overlooked by companies. And in speech analytics, extended pauses can be very telling and important predictors of certain negative behaviors, like churn. Many companies are not looking at the change in how people are interacting and thus they are missing an important signal.
CS: Do you recommend an executive-level position to oversee these new analytics areas?
TD: You are going to see a lot of new roles related to this sort of thing—chief data officers, chief analytics officers, etc. If this is going to be a key aspect of your strategy, it makes sense to put someone in charge of it. Ultimately, these types of activities will be embedded into the jobs of other senior managers. Every chief marketing officer will be highly conversant with big data and analytics. But let's face it, they aren't now. So it is necessary in the next decade or so to put some senior roles in place that advocate and build the capability, and then hopefully you can integrate it into the rest of the work of the senior management team.
CS: Where in the world do you see the most innovation involving data and analytics?
TD: Certainly the United States is the most aggressive in terms of using analytics and Big Data. It's spreading to other parts of the world, as well. But even in the United States, Silicon Valley was certainly an early adopter of Analytics 3.0 ideas because of its many online firms. Boston is probably second in part because there's a good amount of healthcare opportunities with Big Data and analytics. New York focuses on media and advertising in a big way, and in Washington you see government and intelligence applications. So depending on the industry and the focus, you'll see different locations that are really setting the tone.
And there are hotbeds of data and analytics elsewhere. For example in the U.K. companies are doing interesting work in financial services analytics, Singapore has a pretty strong math-oriented culture, and firms in Ireland are trying to build up analytics expertise rapidly.
DR: One of the things that might be a drag on the overseas application [of Analytics 3.0] is that there is more of a vanguard over privacy concerns outside the U.S., particularly in Europe. It will be curious to see how companies handle privacy concerns as they try to push forward on the Big Data platform and use more advanced kinds of personal information. The population is much more attuned to companies' use of data and tracking, so there might be a little bit of a backlash unless they can show that this is beneficial to consumers, especially in those countries with heightened levels of consumer privacy protection.
CS: What can executives do right now to make the most of data and analytics opportunities?
TD: A lot of executive teams simply don't have a deep understanding of analytics. Specific efforts to educate them are in order at many companies. It's a whole new world for executives these days. They weren't trained in data strategy, they don't know what is possible, they don't know what data sources are out there, they don't know what analytical methods might be used to analyze data, etc. There's some real education that needs to be done.
DR: Despite what everyone believes about the power of data and information, there are still a lot of barriers out there in terms of bringing information together, aggregating it, and taking action on it in a timely manner. I'm always amazed that even in leading customer experience organizations there are sources of data that haven't been integrated to form a more complete picture about who customers are, what they are doing, and how much they are worth. I recommend that companies conduct a data assessment to see how well they are taking advantage of their own data assets as well as other data assets that they might create or acquire.
The other side of that coin is that consumers are generating a really big data haystack in which it's hard to separate the signal from the noise. Companies have to be careful, especially when they bring a lot of information together for the first time, not to go overboard in trying to bring everything they know about the customer together at once. This could lead to astronomically large data investments. Companies have to decide what the really important information is that will guide their decision making, and focus on that.
