A variety of data can be used for targeted marketing:
■ Demographics
■ Psychographics
■ Transactional data
In order to be customer-focused, an organization's data segmentation strategy must include customer communication preferences and can include individual customer satisfaction rankings.
Demographics
Demographic data helps identify groups based on information like age, gender, geographic location and marital status. Demographic data is core to understanding what groups may be likely to perform in what way.
Acquiring demographic data about customers may or may not be easy to do. Some associations require that date of birth and other demographic information be provided. Other associations may be bound by privacy rules that may make it harder to capture this information.
Demographic data can be acquired from outside sources. For instance, you can recruit the services of a vendor who has access to income tax data and other filed information.
Psychographics
Psychographics refers to how someone behaves: their likes, dislikes, interests, and key values. Although demographic data generally remains consistent, psychographics change. A key measurement of psychographics is customer mood. Customer moods change based upon how an organization interacts with a customer.
Psychographic data is usually gathered through polls and surveys.
Transactional Data
Analyzing transactional data is critical to measuring customer satisfaction and loyalty.
RFM (Recency, Frequency, Monetary Value) is a common way of scoring transactions and determining loyalty and value. By establishing a matrix of values, you can measure where a customer ranks within the matrix and the direction they are migrating.
RFM is the science of developing this matrix of scores based on values we associate with the scores. In the end, records are grouped together based on their rankings. They share their support in common. Because the rankings are standardized, it is a very statistically valid way of building a standardized segmentation model. The RFM scoring process inherently converts flat data files into three dimensional data files.
Other ways of looking at transactional data are available but they are one dimensional in nature:
■ Last Year But Unfortunately Not This (LYBUNT)
■ Second-to-Last Year But Unfortunately Not This (SYBUNT)
■ Third-to-Last Year But Unfortunately Not This (TYBUNT)
This data measures only one RFM characteristic: Recency. You cannot use a LYBUNT, SYBUNT, TYBUNT report for identifying your best customers; its purpose is to identify the erratic donor.
High-value customer reports are also common for measuring value, but judgments made solely upon value can be misleading in determining customer strategies. For example, should you spend time building a relationship with a customer who has given you a lot of money but not for the past five years? Or, should you spend time building a relationship with a customer who has given less financial support but given it very recently? Or, very loyally every year for the past ten years?
Reports and analytics that focus on only one element of the transaction can be misleading. The RFM approach is preferred because of its ability to slice data three ways.
RFM is also ideal because it can be implemented easily and regularly within a database. Ideally, data should be reviewed with a vendor/partner who specializes in understanding marketing statistics and segmentation, but this is not always necessary. Other more advanced data analytic services are also available, but can be expensive and difficult to implement and are best used to develop a predictive model.
A predictive model is used to consolidate all of the demographic, psychographic, and transactional data available to the organization into a data algorithm that can be used to target records that appear to be becoming a target market. For example, a "lapsing customer" model can identify customers whose loyalty is waning before they disappear. Or, a "planned giving" model can identify the point at which we've learned the two or three key criteria that we need to gather to qualify a planned giving prospect.
Predictive models generate leads for a different marketing strategy. Marketing campaigns/appeals follow through on those leads. Data underlies the success of these marketing efforts.