When most people think about populating their customer information file (whether it is a data warehouse or operational data store), they look internally at their customer databases. These sources -- typically management systems, call center systems and sales systems -- contain information the organization has about each customer and each customer's "touchpoints" with the company.
While these sources of information are absolutely critical, they do have a major deficiency: they only contain information that the company has collected about its customers.
That doesn't mean it isn't useful information. With this information, for example, the company can analyze its sales and establish some trends. It could identify pairs of products that sell well together, then consider customers who only purchased one of these products and target those customers as prospects for a compatible product.
The company could be much more effective in its marketing efforts if it could augment or enhance its customer data. There are a variety of external data sources that can help here, and we will describe some of them in this article.
Personal demographic data
Personal demographic data is a set of characteristics about each customer or prospect of interest. These characteristics include age, household (and personal) income, marital status, number of children, credit card debt, home ownership and net worth Here are some critical business questions that could be answered using this data:
- What are the buying patterns of people in a specific income bracket?
- How do sales patterns change as people migrate from one age group to another?
- Which products sell better to homeowners and which sell better to renters?
- What are the characteristics of customers who buy certain products?
- Who are the other customers who share these characteristics and do not buy these products?
Each of these questions yields a group of prospects for which a directed sales campaign can be targeted. Companies that use such information in developing their direct mail (and other) campaigns are more likely to have a higher success rate than those that do not. The higher success rate occurs because the people who receive the mailing are those who are most likely to buy the promoted product. Additionally, by screening out people who don't fit the profile, the company is not wasting time and money and is not bothering customers with offers they are not interested in.
Geographic demographic data
Geographic demographic data provides similar information, but instead of providing it on individuals and households, the information is provided for geographic areas such as census tracts, postal codes and municipalities. By knowing where the existing customers live, the company can then extrapolate this data to obtain (likely) characteristics about its customers. Some caution is needed in this case, since individuals within the geographic area may be exceptions. For example, although the geographic demographics for an area indicate that it is primarily populated by young professional adults, some of the people living in that area may not fit that profile.
Geographic demographic data is also useful for supporting geographic analysis. With internal data, the company can identify distances. For example, it can discern that its customers travel to the retail outlets. Armed with this information, the companies can also perform an analysis to see if the travel distances differ based on the characteristics of the geographic areas. Retail companies analyzing sites for future stores cannot only estimate the cannibalism (existing customers migrating to their new stores), they can also estimate the compatibility of their offerings with people living in certain geographic areas.
Customers have opinions about the company's products and services. Often, these opinions go unnoticed except when the company receives a complimentary letter or a complaint. To better understand its customers, a company could conduct (or engage an external firm to conduct) a customer survey. A properly structured and administered customer survey can provide a wealth of information that can be used to adjust the product, its delivery, the associated services, the fees and so on.
Combining internal and external data is not always a straightforward process. At the customer level, the internal customer record must have attributes that are available for the customer in the external database. Without common information, the customers in the internal database cannot be reliably matched to people in an external data file. Examples of such attributes that can be used for matching include social security number, phone number and address.
Similarly, customer opinions are of limited value unless the opinions can be linked to customer clusters. Comments by new customers provide information that is helpful for customer retention, and comments by unprofitable customers provide information that could possibly be used to turn them into profitable customers. Hence, it is important to obtain matching data in the attitudinal survey information as well. Companies should customize the survey by target group or include questions that could be used to link the responses to customer segments in the survey.
Education is another key deployment consideration. Through the external databases, the company will be learning a lot about its customers. The people using this information need to be educated in what they can use, what they can reveal and to whom they can reveal it. For example, if we learn a customer's birth date through an external database, it may be tempting to acknowledge the customer's birthday. If the company feels this is advantageous (e.g., the person may be eligible for some benefits), then this may be appropriate. There are circumstances under which a customer may be offended by the use of the birth date and misuse of the information could cost a valuable customer relationship.
External data sources, in addition to the internal databases, can significantly enhance companies' marketing capabilities. Armed with more complete data about customers and prospects, companies can perform more comprehensive customer analytics and can be much more effective in their marketing efforts.
Lisa Loftis, Jonathan G. Geiger and Claudia Imhoff represent Intelligent Solutions Inc., a consultancy on CRM and business intelligence technologies and strategies in Boulder, Colo.