In today’s marketing world, data are multisourced, pouring in from e-mail delivery system, workflow application, point-of-sale system, and Web application, not to mention myriad other places. Yet increased data often means increased complexity and confusion, not increased insight. As a quantitative marketer, I frequently meet marketing professionals who are working with systems and fundamental data strategies that are simply not set up to handle the complexities of marketing in the 21st century.
But there is a better approach. By developing and using frameworks, models, and methodologies that greatly reduce the complexity inherent in multichannel marketing, you can begin to make sense of the chaos.
To start, turn to a five-stage process. The first stage — and the focus of this article — is data aggregation. In this step, you essentially take inventory of the data that you have, map data to your information requirements, collect the relevant data, and integrate the most appropriate data from different sources into a single location. In aggregating your data, you can realize the following benefits:
- Provide a more universal data language and understanding of what data are available and how the data may be used to increase customer intelligence;
- Avoid redundancy and increase efficiency by examining what other groups within your company are doing to effectively process and analyze data;
- Provide insights that will help you to develop improved reporting and analysis systems and enhance campaign design and knowledge;
- Bring to light analytical opportunities based on your business’s specific information requirements.
From an informational (analysis and reporting) perspective, you should use four dimensions of data to define customers and prospects:
- Profile data—These are used to describe customers and products. Profile data that should be tracked include the attributes and activities of customers and prospects. Attribute data are relatively stable descriptions of the customer and include segments, account types, open dates, and other descriptive information. Activity data include transactional, e-mail engagement, and Web tracking behavior.
- Contact data—these address how you have reached out to customer or prospects in terms of promotions, campaigns and communications, and why. Data available on customer contacts range in sophistication from simple lists of contacts to precise individualized data on the nature of the contact.
- Response data—These describe the possible responses of customers and prospects to various types of promotional contacts. Customer responses to a contact can take several forms (nonresponse, positive response, negative response) and changes rapidly over time.
- Profitability data—These provide information on the value of a customer response to a contact. Profitability data are not considered pure response, because such data are influenced by the initiatives and priorities placed on a customer response by your company.
A fifth data dimension, operations data, may also be included for its relevance to the e-mail channel. Operations data are concerned with the efficiency and validity of systems that send e-mail contacts. These data are typically analyzed only in the context of how it directly impacts customer behavior or product performance.
By collecting data that are relevant to current marketing and business objectives, you will be better able to develop targeted, effective marketing programs that help meet business objectives, demonstrate results. and increase the bottom line.
Katie Cole, Ph..D, is vice president of research and analytics for Merkle|Quris, the e-mail marketing agency of Merkle. Contact Katie Cole at firstname.lastname@example.org