The Top 5 (ok 6) Data Mistakes that Marketers Make

Top 5 Data Mistakes

The Top 5 (ok 6) Data Mistakes that Marketers Make

Marketing databases allow sales and marketing to reach customers and nurture relationships more effectively (and efficiently). If they are designed properly and used correctly they are the “secret sauce”. Unfortunately, this is often not the case. Here are some of the most common mistakes made by direct marketers as they build a data-driven marketing engine:

1. No method or procedures established for monitoring the vitality of the customer base over time. Statistics such as retention, reactivation, conversion and percent new-to-file will allow a direct marketer to more easily determine the success of various marketing strategies.
2. Lack of standards or process in place regarding data hygiene including householding the file prior the delivery of promotions, etc. The result being mailing inefficiencies and potential customer service problems. Avoid this and scrub that data!
3. All response models are not created equal. Many managers don’t realize that roughly 75% of analysts’ time should be spent becoming intimate with the customer data through data manipulation and review to ensure it’s predictive power is exploited to its fullest potential.
4. Lack of basic knowledge regarding database architecture, hardware and software. Without some basic database knowledge, a marketer is not well suited to establish marketing specifications for good database development which are reasonable and will maximize effectiveness.
5. Little knowledge of the rules that must be followed when establishing promotional or list tests to ensure results are readable, reliable and projectable and/or a lack of understanding of how to read test results once final.

Bonus mistake:

6. Purging customer records after 24 months of inactivity (or less). Most marketers don’t understand the implications of doing this. At a minimum, a direct marketer should roll up key data for inactives including all promotional data and make available for future analysis purposes for at least 4 years.

Bottom line, sometimes what you don’t know can hurt you more than you think.

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