Designing and implementing a system that uses data to improve your marketing requires a large amount of planning, preparation, understanding, and time. It’s not something that can be bought from a software vendor, then easily implemented. That would be nice, but it’s just not possible.
Recently, a couple of smaller business entities have asked me the same questions. To help them, and you, I’ve outlined the basic steps they must follow to begin to improve their system. These steps apply to small and large businesses alike.
The following steps and resources help a marketer improve data for marketing programs:
- Assign, train, or hire a “data guru.” An obstacle to the successful development and deployment of any type of measurement system is the complexity of the data to be used. Combining data from multiple sources, producing reports, recommending, and taking appropriate marketing action all require knowledge of all the data. This won’t be possible unless a point person is familiar with all the data. You must have a marketing data guru on staff to help with all aspects of your program.
- Assess your data. To understand what’s possible to accomplish with your data, you must understand what’s in it. If you want to conduct a telemarketing campaign to existing customers, do you know how many of them supplied you with their phone numbers, for example? If you want to measure catalog results in combination with Internet results, do you have a way to connect purchase data from the same customer who makes purchases in different channels?
- Decide on your goals. What, exactly, do you plan to measure and how will you do that? Are those things actually possible? Will you measure response? Sales? Lifetime sales? In one channel? In multiple channels?
It’s unrealistic to think you can develop and deliver a useful, automated wall-to-wall marketing analysis program in one step. You must first determine some first steps (e.g., can you already measure everything you want within one channel?).
- Create a road map based on your determined goals. This really isn’t a data insight but a management insight. You need to know what you’re going to do and how long it’s going to take. Plan this ahead of time, so you can measure and communicate your progress. Often, the final results of a major analytical initiative aren’t seen for some time (months or a year), so interim check-in points must be monitored.
- Implement your project. If your initiatives start with a relatively achievable first step (which is recommended), a team can complete that specific project. This may require a careful design and execution of a special campaign or just some thoughtful analysis of existing data. Regardless, it’s during the first implementations that you’ll find hidden problems (with data and processes) and begin to hardwire a data and analytical solution.
- Test the data. If you’ve never actually tried to analyze tens of thousands of records (or more) in a data file, you might become frustrated by how much time a data project can take. It’s a fact: strange data can exist in computer files. These data must be found, and the reasons for them must be traced. This is a time-consuming process. However, it’s imperative this level of detail be examined. Without it, campaign results can be completely erroneous. I saw a data set recently that showed customers of a certain company ranged in age from 30 to 72. If the data aren’t checked and examined, the resulting reports will be of little or no value.
- Automate the process. Once a process can be manually executed successfully, it’s time to think about automating the process. At this stage, many packaged software products come into play. In actuality, implementing these products is just the final step of a longer, more thought-out process.
On the inexpensive end of the spectrum, automated systems can involve handwritten code and products such as Microsoft Excel and Microsoft Access. On the more expensive end, companies such as Business Objects, Cognos, Siebel, Oracle, and SAS will be happy to bill you for products and services ranging from $5,000 to millions of dollars.
Marketers need to know what’s in their data and trim out the filler to provide continuous, data-driven ROI for their brands.
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