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Advertising investments are as important to make and they are challenging to evaluate and monetize. All the options, specialties, and partners involved complicate things. The work on the ground is a nonstop interaction among strategists, media planners, buyers, content developers, and IT types. The line between choreography of efforts and a cacophony of results can be thin. And a successfully managed campaign is no guarantee of a successful campaign.

That's where the marketing science comes in. Marketing science is the application of scientific and statistical methods and social science (psychology, sociology, and economics) to the business endeavor we love and know as marketing. This approach (or paradigm) starts with questions and challenges and then assesses the data available to address them. Business questions and constructs are translated into data models (variables), hypotheses, tests, and models. The deliverables range from reports, to processed data output, to insights (descriptive models) to predictions, forecasts, simulations, and optimizations. These products can then become inputs to assist strategy and planning work and also serve as direct inputs to other models and decision support processes.

I've worked in marketing science with major agencies and diverse clients for (almost) twenty years and have done a lot of applied analytics work. In database marketing, CRM, sales & demand forecasting, customer segmentation, purchase funnels, association analysis, media mix models, market opportunity comparisons, digital campaign evaluation, and other areas. It's a really diverse and stimulating field to work in. My site shows some successful applications, big and small as well as some of planning and execution details. I'll be adding to this over time and I'll stick to what I know. All of the examples are based on my own experience either as the sole/main analyst or leading the effort. Frequently I was figuring it out as I went, as is SOP in data mining and modeling, so let me know what you think. Maybe you'll get an idea and I'd love to hear about that too.

My Best, Dan Shea

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Planning, measurement, modeling

Welcome: Services
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Analysts and analytic services can be costly but the investment will absolutely pay off in results when well planned and executed. Conversely, unconnected and ad hoc analytics can be valuable but they have a limited shelf-life and may not be informative to other situations. From point-of-truth data to ETL hell to analytics tech stacks to dashboarding, planning and vision are critical to effectively developing the analytics capabilities to optimize your marketing strategy and media delivery.

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Targeted testing, analysis, and reporting are the foundation of cross-media optimization and also provide a measure of quality assurance and clarity around those investments. On the strategy side analytics can provide customer, competitive, and market insights using all variety of data and methods. These are some examples of smaller projects that straddle analytics and data science and can add real value.



My roots are in predictive modeling, data science, and machine learning and I have developed many models and forecasts using behavioral and econometric statistical methods. Website valuation, digital demand forecasting, customer segmentation and profiling, customer preference and timing models, media investment modeling, and structural preference analysis are among my favorites. These are examples of bigger projects meant to have broader, long-term, applications.

Click around to see more and happy to discuss projects and share ideas!

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