Today, I’ll share the results of an informal survey I just completed. The object was to determine what topics marketers are most and least interested in, what areas they feel they already have the most knowledge of, and where they feel the biggest gaps lie between their level of interest and their knowledge.
The survey covered a variety of topics regarding the data and analytics world. The topics related to how to use data to improve marketing to consumers and businesses. Among the survey topics were data sources, data warehousing, reporting, surveys, predictive modeling, segmentation, Google AdWords, profiling, and testing.
Respondents are attendees of a seminar I’m conducting this week in Sydney. They’re from medium-sized to large companies in various industries, including telecomm, financial, and retail. Respondents filled out the survey in a Word document, then forwarded completed surveys to me.
Survey results show three clusters of interest level: low, medium, and high.
Google AdWords had the least amount of interest. Whether this is because the program isn’t viewed as useful to medium-sized to large companies or for some other reason, the topic ranked dead last in interest level. Perhaps AdWords is of no value to major marketers. Maybe the program hasn’t caught on yet. We’ll have to wait to see how useful the tool becomes.
Of medium interest is data warehousing. Though this topic is certainly not the most interesting and intriguing of the many areas marketers deal with, it’s actually one of the most critical for marketers looking to use data and analysis to improve their programs.
Trust me on this one. If you’re in marketing and don’t know how to read a table schema for a data warehouse, you may be missing opportunities for significant improvement in your marketing programs. Your data warehouse staff doesn’t know what your marketing plans are, so they’re not going to read through their warehouse and tell you how to make improvements.
Some high interest topics were reporting, profiling, and marketing mix modeling. All these topics are useful for marketers.
I consider reporting the bread-and-butter of analysis. Unfortunately, it’s still sometimes a complicated issue for marketers. Data warehouse problems cause delays (or worse) in reporting. Bad data cause problems. Antiquated systems that haven’t been updated cause problems.
Marketing mix modeling is a topic that’s garnered increased attention in the last few years, but most people still don’t know what it is. In short, it’s the application of predictive modeling to aggregated sales and marketing data. It’s used to determine the effect of various marketing mix components.
If you want more information on marketing mix modeling, a decent paper on the topic is available from my personal site.
Respondents said they have the most knowledge of segmentation. They have the least knowledge about predictive modeling and advanced analysis of customer surveys.
Though the low level of knowledge regarding predictive modeling isn’t surprising, a low level of knowledge regarding surveys is. Considering how easy it is to develop and execute a survey these days, it’s a bit shocking marketers don’t know how to get the most from them.
The Biggest Gap
The survey confirms marketers are very interested in predictive modeling. Predictive modeling is an extremely useful tool and should be used in customer retention, acquisition, and up-sell programs.
Getting the most from predictive modeling requires statistical models developed using what some consider to be advanced statistical techniques. It may be no surprise, then, the topic of predictive modeling also had one of the lowest scores for marketers’ level of knowledge.
The result is predictive modeling has the biggest gap between level of knowledge and interest. Considering how useful predictive modeling can be, this gap is surprising.
Are these earth-shattering findings? No, but some of the results are eye-opening. They show marketers are looking for information about or could better use the tools of predictive modeling and advanced survey analysis.
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