03/21/2013 (press release: MiklinSEO) // Greenwood Village, CO, USA // Alexander Miklin
On the surface, mathematics and marketing seem to be two entirely different disciplines; one deals with equations while the other is concerned with sales. In reality, they are as correlated with one another as cheese and crackers. The purpose of “Mathematical Ideas for Marketers”, a blog post written by an author who calls himself willcritchlow, highlights a number of ways (and provides examples) in which relatively basic statistical concepts can be used to drastically improve the effectiveness of ones marketing ability. At no point does the author claim to be an expert, but he does provide insightful links further describing each idea; some prove to be quite tough to understand upon first read.
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He begins by talking about averages, which is typically a quite elementary subject. Further, he notes that averages, as simple as they seem, are often misreported because of different factors that are ignored. You have to pay close attention in order for the numbers to tell you the correct story. Next comes correlation coefficients; these coefficients show how closely linked two variables are. For example, income is usually highly correlated with education. In designing predictive models, we want our X variables to be highly correlated with our Y variable, and we want to know the extent to which they move together. Now becoming a little bit more complicated, the author talks about the Bayesian approach to web analytics which helps determine if discrepancies in web analytics data is meaningful. The last thing he mentions is perhaps the concept most closely associated with marketing: the Markov chain. A Markov chain is a sequence of random variables that only depends on the last variable. In terms of marketing, it has to do with the probability of moving from one page to another; moving to page B is only dependent on having been on page A in the first place.
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There is no such thing as a perfect predictive model in statistics; there will always be an error term. The goal, in the end, is to minimize this error term and come up with a model that can maximize the effectiveness of these predictive statistical models. Every day, as the database full of numbers and records grows, our ability to minimize this error term dramatically improves. In other words, more data equates to more predictability; imagine where we’ll be in 20 years in terms of forecasting ability. It is important that we constantly utilize this ever-growing database to improve our marketing effectiveness. While these concepts are complicated, it is easy to see how with practice, they can vastly improve ones marketing prowess.
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