Set-top-box data, loyalty card data, cookie data, and more have created vast databases of customer data. While this gives marketers great opportunities to reach receptive customers, making sense of big data can be a challenge. We caught up with John LaRocca, Vice president, Strategic Partnerships at dunnhumbyUSA to get his expert opinion on making business sense of massive data sets.
ARF: What are the most exciting new data sources you've been able to use in the last decade?
John LaRocca: As someone who has spent a career analyzing and gleaning insights from syndicated panel and point-of-sale databases and shopper loyalty card data, today is a tremendous opportunity to better understand customers in ways we never thought possible. Although much of the data is anonymous, this type of data allows us to understand the behavior and motivations of a larger universe of customers. It gives us the resources to personalize an experience and deliver value through relevance in areas like assortment, pricing and promotions, for example. It gives us the tools identify customer growth and attrition and empowers brands and retailers to develop better relationships with their best customers.
There is an effort today to associate these databases with customer data from other sources, to develop a better understanding of the customer on the path to purchase. These efforts are revolutionizing the advertising industry, for example, where ROI and effectiveness has traditionally been difficult to measure and targeting has largely been based on panel-based demographics. At dunnhyumbyUSA, I am responsible for building collaborative partnerships with other data and solutions providers to create new solutions and enable our clients to deliver more value to customers in impactful ways. We have a collaborative agreement with comScore to measure the effectiveness of online advertising, pairing online and in-store behavior. We have a similar agreement with TRA, which measures the television viewing habits of two million set-top box households, to develop a similar understanding of the effectiveness of television advertising planning that is also based on actual purchases.
Integrating a customer-driven approach across channels, connecting in-store with online, for example, begins to shed light on a shopper's decision process throughout the path to purchase. And I think we are only beginning to understand its potential.
ARF: What have we learned about customers from big data?
JL: We have certainly learned a lot of new things about the customer, but the data and resulting insights have also confirmed, quantified, and expanded some existing beliefs:
· Each customer is unique and, therefore, brands need to treat them as such, including adopting a personalized approach in how they communicate with them, rather than at them.
· Customers behave differently in different categories. A "price sensitive" customer is not equally price sensitive in every category. A "discriminating" customer is not consistently discriminating across the store.
· People don't behave as they claim, and that actual behavior is a better starting point for understanding WHY customers do what they do than merely asking them. And when you link actual behavior with attitudes, you create insights that are game changing.
· Demographics, psychographics and attitudinal data are poor tools for targeting and interpreting customer preferences because they drive generalizations, reinforce the myth of the "average" shopper and do not correlate with behavior.
· Focusing on customer acquisition can be an expensive choice. At dunnhumby, we've found that it takes between 12 to 20 new customers to make up for the loss of just one committed customer. I think we will find out that this number increases dramatically as we gain a greater understanding of social networks.
ARF: What are some of the nagging problems you don't think we can address because they're too time-consuming, costly, or require too much processing power? Do you see the industry ever solving these problems?
JL: Many of the challenges companies face are due to both the quantity and complexity of the data available. Most companies collect data but struggle to understand how to use the data they collect effectively to drive business results and leverage data-driven insights throughout their organizations. Data itself is in danger of becoming a commodity. Any company can collect data, but the real value comes from leveraging that data to create even more valuable data (e.g., segments, dimensions), develop the right insights and discover the "customer /DNA."
As databases continue to grow, real-time reporting is a challenge. There are more data dimensions to measure across multiple products, geographies, time periods, and customer segments. For example, online shopping has created this long tail of UPCs because there is no limit to the number that can be "shelved" in the digital store. Thus, the number of items in the report has grown significantly and will continue to do so.
It isn't only the explosion of raw data, but the complexity it drives in the data cube that creates the reporting challenge. There are more disparate sources of customer behavioral data -- from loyalty cards, search/browsing, social, mobile, and set-top-box data, among others – each with aggregation and manipulation challenges, that have created opportunities to generate an increasing number of behavior-based customer segments to report, analyze, and model. This complexity creates an issue with the timeliness of insights. People have become accustomed to simplified, instantaneous results and so, when they have to wait an hour to two, or overnight, it creates a lot of frustration.
To put it simply, we have the database management and business intelligence tools to find the needle in the haystack but the haystack is now bigger. I don't believe that these issues are too great to solve. The industry typically finds a creative way to solve its problems.
John LaRocca is Vice President, Strategic Partnerships at dunnhumbyUSA responsible for developing the company's partnership strategy within media and analytics, identifying and leveraging collaborative partnerships with companies that have complementary data assets and capabilities in order to develop innovative, customer-driven solutions for dunnhumby clients. He plays a critical role in enhancing dunnhumby's media measurement solutions and works to expand the company's capabilities in this area.