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No matter where it comes from, data falls into three basic types: demographic, behavioral, and psychographic or attitudinal. Each type has its strengths and weaknesses.
Demographic data:
Demographic data generally describes personal or household characteristics. It includes characteristics such as gender, age, marital status, income, home ownership, dwelling type, education level, ethnicity, and presence of children. Demographic data has a number of strengths. It is very stable, which makes it appealing for use in predictive modeling. Characteristics like marital status, home ownership, education level, and dwelling type aren't subject to change as frequently as behavioral data such as bank balances or attitudinal characteristics like favorite political candidate. And demographic data is usually less expensive than attitudinal and behavioral data, especially when purchased on a group level. One of the weaknesses of demographic data is that it is difficult to get on an individual basis with a high degree of accuracy. Unless it is required in return for a product or service, many people resist sharing this type of information or supply false information.
Behavioral data:
Behavioral data is a measurement of an action or behavior. Behavioral data is typically the most predictive type of data. Depending on the industry, this type of data may include elements like sales amounts, types and dates of purchase, payment dates and amounts, customer service activities, insurance claims or bankruptcy behavior, and more. Web site activity is another type of behavioral data. A Web site can be designed to capture sales as well as click stream behavior or the exact path of each Web site visitor. Behavioral data usually does a better job of predicting future behavior than the other types of data. It is, however, generally the most difficult and expensive data to get from an outside source.
Psychographic or attitudinal data:
Psychographic or attitudinal data is characterized by opinions, lifestyle characteristics, or personal values. Traditionally associated with market research, this type of data is mainly collected through surveys, opinion polls, and focus groups. It can also be inferred through magazine and purchase behavior. Due to increased competition, this type of data is being integrated into customer and prospect databases for improved target modeling and analysis.
Psychographic data brings an added dimension to predictive modeling. For companies that have squeezed all the predictive power out of their demographic and behavioral data, psychographic data can offer some improvement. It is also useful for determining the life stage of a customer or prospect. This creates many opportunities for developing products and services around life events such as marriage, childbirth, college, and retirement.
The biggest drawback to psychographic data is that it denotes intended behavior that may be highly, partly, or marginally correlated with actual behavior. Data may be collected through surveys or focus groups and then applied to a larger group of names using segmentation or another statistical technique. If data is applied using these methods, it is recommended that a test be constructed to validate the correlation.
Comparison of the three main types of data.
| PREDICTIVE POWER | STABILITY | COST | |
| Demographic | Medium | High | Low |
| Behavioral | High | Low | High |
| Psychographic | Medium | Medium | High |
Sources of Data
Data for modeling can be generated from a number of sources. Those sources fall into one of two categories: internal or external. Internal sources are those that are generated through company activity such as customer records, Web site, mail tapes from mail or phone campaigns, or databases and/or data warehouses that are specifically designed to house company data. External sources of data include companies such as the credit bureaus, list brokers and compilers, and corporations with large customer databases like publishers and catalogers.
Internal Sources
Internal sources are data sources that are housed within a company or establishment. They are often the most predictive data for modeling because they represent information that is specific to the company's product or service.Some typical sources are the customer database, transaction database, offer history database, solicitation tapes, and data warehouses.
External Sources
The pressure is on for many companies to increase profits either through acquiring new customers or by increasing sales
to existing customers. Both of these initiatives can be enhanced through the use of external sources.
External sources consist mainly of list sellers and compilers. As you would expect, list sellers are companies that sell lists. Few companies, however, have the sale of lists as their sole business. Many companies have a main business like magazine sales or catalog sales, with list sales as a secondary business. Depending on the type of business, they usually collect and sell names, addresses, and phone numbers, along with demographic, behavioral, and/or psychographic information. Sometimes they perform list "hygiene" or clean-up to improve the value of the list. Many of them sell their
lists through list compilers and/or list brokers.
List compilers are companies that sell a variety of single and compiled lists. Some companies begin with a base like the phone book or driver's license registration data. Then they purchase lists, merge them together, and impute missing values. Many list compliers use survey research to enhance and validate their lists.
There are many companies that sell lists of names along with contact information and personal characteristics. Some specialize in certain types of data. The credit bureaus are well known for selling credit behavior data. They serve financial institutions by gathering and sharing credit behavior data among their members. There are literally hundreds of companies selling lists from very specific to nationwide coverage.
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