Data Driven Marketing For Dummies
Data driven marketing, or database marketing, as it’s often called, is successful to the extent that the data itself is good and that you use it to its fullest extent. The basis for all your campaigns is your customer contact database. Sorting those customers into groups for various marketing purposes can help your campaigns in several ways. And once you have that data at your fingertips, you need to know what you can do with it.
Managing Customer Contact Information
Database marketing depends fundamentally on being able to communicate directly with your customers. That means that you need your customer address book to be as clean as possible. The following are some key steps you can take to achieve this goal. In most cases, third-party service providers can provide these services at reasonable costs. These considerations apply to not only to physical addresses, but to e-mails and mobile device numbers as well:
Standardize names and addresses into a common format. This is a prerequisite for doing both data cleansing and householding.
Validate that addresses are deliverable. Sending a piece of mail or e-mail that can’t be delivered is inefficient.
Update addresses to account for moves. Mail (physical or e-mail) doesn’t always get forwarded.
Keep your opt-out file up to date. It’s very important that you do not send marketing messages to people who don’t want to hear from you.
Remove duplicate addresses from your mail files. You don’t want to be sending the same communication multiple times to the same address.
Segmenting Customers into Groups
Marketers group customers together into segments in several ways. These various approaches are characterized by the type of data they use. Here are some common customer segmentation schemes:
Demographic segmentation: Demographic segments are developed by looking at age, income, marital status, presence of children, and other similar traits. Understanding yours customers’ financial means and lifestages helps you offer relevant products and services.
Geographic segmentation: Customers’ needs and attitudes vary according to where they live. Weather drives different product needs in different regions. And one has to look no farther than the red state/blue state divide of American politics to see that attitudes vary dramatically by geography.
Behavioral segmentation: Past purchase behavior and web browsing behavior yield powerful insights about your customers. This data can show you which customers are price sensitive versus premium benefit oriented. It also shows which customers are most loyal to your brand.
Customer profitability: Grouping your customers according to how much they contribute to your bottom line allows you to prioritize target audiences for your campaigns.
Psychographic segmentation: Based largely on survey research, psychographic segmentation is an attempt to understand the needs and attitudes of different customers. This understanding is very useful in crafting messages and offers that will resonate with customers.
Statistical Data Used in Data Driven Marketing
Anybody who’s ever used a spreadsheet is familiar with the idea of data types. Data comes in two basic flavors: numerical and character — numbers and text. Character data isn’t involved in statistical analysis. Numerical data breaks down into integer data and decimal data and can be formatted in various ways.
But when it comes to performing statistical analysis of data, some differences are important to keep in mind. Not all data is created equal when it comes to calculating statistics.
Following are the basic data types along with a brief description of the kinds of statistics you can meaningfully perform with them. Note that each data type in this list supports the calculations described in all the preceding types:
Categorical data: This is data that is, from a statistical point of view, is non-numeric. It simply classifies records by categories. The numbers on football jerseys are an example. With this type of data, the only meaningful statistic is the number of records in each category.
Ordinal data: This type of data simply indicates some sort of order in which records fall. A typical example is a survey question that asks responders to rank something on a scale of 1 to 10. This sort of data supports the calculation of percentiles. The notion of median is also meaningful here. It is important to note that averages are not meaningful with ordinal data.
Interval data: Interval data supports comparisons of intervals. Dollar amounts, age, and temperature all have this property. For example, the difference between 1 dollar and 2 dollars is exactly the same as the difference between $100 and $101. This type of data supports most common statistical calculations such as means and standard deviations.
Ratio data: Ratio data is the most robust data type. It’s characterized by allowing comparisons of ratios. Ten years is twice as long as five years, for example. This type of data supports virtually every statistical calculation imaginable, including the coefficient of variation as well as more esoteric means like the geometric mean.