Analyzing CRM Data

How much do you know about your customers? After spending thousands upon thousands of dollars on a sophisticated customer relationship management (CRM) system, you probably think you know quite a bit. You've seen numbers about your customers' purchasing habits, their zip codes, their income and some other demographic characteristics. But location affects customer behavior as much, if not more than demographic characteristics. So to fully understand your customers you must understand where they live and what their location means. Customers are more than just a series of numbers. They exist in the real world, in a physical environment that defines who they are, what they need, what they like, what they buy and where they buy it. Understanding where they are is key to understanding how to better serve them. Often CRM analysts find that by including location technology and demographic data, they gain a deeper understanding of customer behavior and purchasing preferences. That understanding can then be leveraged to increase profitability and realize greater ROI from CRM initiatives. They can also use this information to establish more effective growth and expansion strategies. Take the example of a wireless carrier that is trying to understand the difference between those customers who have terminated their service--or churned--and those who remain loyal. The carrier has two data sets (most likely within one database) filled with demographic information: one of the churned customers and another of those who are loyal. When you look at all the data points associated with churn, such as length of contract, age, income, gender, and monthly usage, it turns out that whether people live in an area with poor signal strength is the most predictive variable. If people are not able to use their phones, they will leave the service. Gaining new insights is one thing, but the real power comes from using that insight throughout the organization for better, more profitable decision making. Knowing that the signal strength affects customer loyalty may determine where the carrier puts new towers. It may also have an impact on where it focuses direct marketing and sales campaigns, and by giving a sales office this information--with the real-time ability to utilize it--the organization can be sure to offer a mix of products and services that will enable the company to be competitive even in the face of a high churn risk. To get the most out of a CRM system, the company must first collect at least the most basic geographic information about its customers, such as a zip code. Preferably the information includes a name and address, the products they purchased and the costs of the products. This information can then be linked with geodemographic information to bring new and meaningful insight into these customers. Understanding the geodemographics is critical to the ability to share and analyze data. In all, the United States can be broken in to 65 geodemographic clusters. These clusters are dependent on location, but also take demographic characteristics into play. Understanding the geodemographic profiles enables CRM analysts to begin building intelligent profiles of customers and markets that can help companies spot new opportunities. Brookstone, a nationwide specialty retailer offering an assortment of distinctive consumer products, collects basic customer information plus more. While Brookstone is well known for its high-end retail store outlets, another large part of the company's yearly revenue comes from catalog and Internet sales. This is where the company focused its analytical CRM efforts to improve per-catalog ROI. During the development of Brookstone's current marketing plan, it needed to know what specific groups of customers were the most profitable and therefore deserve a stronger focus and higher marketing budget. Brookstone was already using customer-modeling applications to a significant degree, but wanted to apply geodemographic data to tie its existing information to the real world and to supplement its study with neighborhood demographic data. This would enable the company to gain a better understanding of where the most profitable Brookstone customers were located and what they were like in terms of demographics and lifestyle habits. While Brookstone Director of Customer Analytics Steve August already knew that the typical Brookstone catalog customer was an affluent individual, he needed to confirm this belief and determine the location of specific customer clusters fitting this description. August began by extracting demographic data and geodemographic data at a census block group level. He then linked this data with Brookstone's current customer database, which maintains the historical promotion history of all of its customers. By combining the two databases, August could determine which demographic clusters gave Brookstone the highest catalog ROI during the past three years. August could look for similar customer profiles in each cluster. Once high-ROI clusters were identified, August could determine the penetration Brookstone had in each specific cluster. That is, he was able to determine how many customers Brookstone had by cluster and to compare this number to the total, national population of each cluster. This allowed him to identify the clusters with high catalog ROI and then to determine what percentage of the market Brookstone had not yet penetrated. By locating high ROI clusters with untapped potential, August was helping the company realize the full potential of its catalog mailing. For instance, if a certain cluster of customers provides Brookstone with a high ROI, August can determine the Brookstone cluster penetration percentage and expand marketing promotions and catalog orders to get the fullest potential out of a specific market. According to August, being able to access this information proved powerful in analyzing specific product purchasing trends. It enabled Brookstone to identify where certain product transactions were taking place, which helps them better stock stores and target selected products to specific geographic areas. For instance, if Brookstone is interested in promoting a certain high-end product with an expensive marketing campaign, August can determine where they have sold the product in the past and track purchasing patterns from a geodemographic standpoint. By analyzing this type of information, August can suggest the type of customer clusters that should be targeted with marketing dollars for the high-end product, and he can point out geographic areas with a high concentration of these target clusters. In one recent case August applied this data to understand the seasonality of certain products. In the case of a summer outdoor product that Brookstone planned to promote heavily, August could analyze where customers responded most favorably to the product, and then tailor the marketing accordingly. As future products come up with similar characteristics, August can draw on this information to best focus Brookstone's marketing dollars. Collecting customer information in a CRM database is a necessary step, but you can only begin to reap profits from that data by analyzing it. The numbers themselves can only go so far, the best way to truly analyze the data is through location-based intelligence. Only then can you understand your customers in a real-world context, giving you the information you need to better serve them. Serving your customers what they want keeps them happy, and keeping them happy keeps the revenue flowing. *************** More than Income: Using geodemographics to truly understand your customers When it comes to neighborhoods, they are, in fact, the sum of their parts. Some neighborhoods are defined by income, others by environment, and still others by race and ethnicity. The trick to effective marketing is to look at all of these factors to come up with a strong definition of what a neighborhood is and the buying habits of the people who live there. One method comes from combining the spatial information of a given neighborhood with its demographic data; this is called geodemographics. People with similar cultural backgrounds, means, and perspectives naturally gravitate toward one another or "cluster" to form a community. Once settled in, people naturally tend to emulate their neighbors, adopt similar social values, tastes, and expectations. Most important, to marketers and CRM analysts, these neighbors share similar patters of consumer behavior. That's not to say that everyone is like his or her neighbor, nothing could be farther from the truth. But geodemograhpic clusters define people who live in a given area who share similar lifestyles and predictable consumer behavior. The characteristics can be broken down into eight key categories: urbanization, race and ethnicity, household composition, mean household incomes, education levels, occupation levels, age groups, and dwelling types. Each of those categories can then be broken down further into a variety of areas. For example, most populated (urbanized) areas in the United States can fit into one of several categories including: urban downtowns, urban fringes, greenbelt suburbs, towns, rural farm areas, and exceptions. Once you start matching the characteristics, certain patterns begin to emerge. Picture Madison Avenue in New York City, with ritzy brownstones and coop apartments, each guarded by a doorman. Walking in and out of the art galleries and fancy food shops are top executives wearing the finest clothing. This is the Ivory Tower of America, a place that is defined by its urban downtown environment, white population that is in the 25- to 34-year-old age bracket, have college or graduate degrees, and are employed as executives and managers. They are single or couples--few have children--live in multiunit or high-rise buildings, and have an income in the top 20 percent of the country. The Ivory Tower demographic can be found not only in New York City, but also in areas such as Chicago, Los Angeles, and Florida. Compare this neighborhood to one that is filled with people at a Cross Roads. Like the Ivory Tower, those at the Cross Roads live in an urban downtown environment in multiunit or high-rise buildings and are mostly singles and couples. The age range is slightly broader, stretching from 18 to 34, but they typically have just a high school education. Their jobs are much different too, with a concentration of white collar and service sector jobs and an ethic makeup of mixed minorities, mostly recent immigrants. The big disparity is in income. While those in the Ivory Tower earn incomes at the top, those at the Cross Roads are in the lower 40 percent of income. While these two clusters have some similar characteristics, no one would confuse the two and marketers would never target the audience with similar products and services. Each community has certain homogeneity when it comes to purchasing. People in the Ivory Tower, for example, tend to drive expensive European cars, while those at the Cross Roads look toward smaller Asian cars and SUVs. In all, the United States can be broken into 65 distinct geodemographic clusters. Once you understand who your customers are in relationship to these clusters, you can find similar clusters in other communities and market to them. This information can help increase the effectiveness of site selection, direct mail, advertising and promotions. Knowing your customer is more than just knowing their income bracket. It's about knowing where they live, how they live, and whom they live near. With geodemographics, you can create far more effective marketing programs that attract the customers you want. About the Author Jon Winslow, market director of CRM at MapInfo Corp., is a leader in the innovation of technology solutions that integrate location into targeted market analysis. Throughout his tenure with MapInfo, Winslow has played an integral role in developing applications for and maintaining relationships with key customers such as Pepsi, Sprint, and Meineke Mufflers for targeted marketing and demographic analysis.