If you are immersed in the marketing world, you may have heard the phrases "big data," "predictive analytics," and "microtargeting" tossed around. As we all know, knowing a buzzword doesn't necessarily mean you truly understand the concept. So, let's break down the concept of predictive analytics a little further and explore how it applies to marketing.
Predictive analytics and statistical analysis are based on the concept of relationships between observed actions or occurrences and future actions or occurrences. You choose a dependent variable based on observed data of what you want to predict and use a software program like Azure Machine Learning,SAS, or Alpine to compare that variable to other variables that may be related at various levels of statistical likelihood.
With regard to analyzing people, you look at a small sample of data on people who have bought your product-or taken whatever action it is you want more people to take-and build a predictive model to identify a number of shared traits they have. These traits can be income, region, marital status, or number of children. Additional variables may be as broad as interest in winter sports, creative writing, software magazines, or anything uniquely tied to this group of people. In some cases, you may have some of this data in-house, such as purchase records and survey responses. In the case of consumer and demographic data, you likely need to purchase this data from one or more vendors who specialize in collecting, organizing, and selling massive lists of data.
Within the umbrella of predictive analytics-finding relationships among variables to identify what traits your target audience share-is the idea of microtargeting. This allows you to not only identify the right people, but then to take the set of traits you found statistically significant, and create a subset of people who have those same traits. This group of people is then recognized to have traits that make them most likely to be receptive to your campaign. This is because we analyzed a small sample of people who had already taken some action that demonstrated they were open to buying our product. Think of it like this: "Who are my existing customers? What do they have in common? Which other people have the same traits as these existing customers, and therefore, represent opportunities for me to expand my customer base?"
This type of extremely targeted marketing is expected to grow over the next few years. According to David Raab, principal analyst for Raab Associates, 2015 was the year when predictive analytics in the marketing sector took off, with at least $242 million in new funding for startups that pioneer this technology, compared with $376 million in all prior years combined. Companies will continue to refine their predictive analytics capabilities to ensure they're reaching the right people with the right message, and using funds effectively. By understanding and embracing the principles behind predictive analytics now, you can apply these concepts and strategies to your current marketing, sales, or content creation efforts in various ways, depending on your needs and your budget.
Now let's dig into how you can combine the power of predictive analytics and micro targeting to predict purchasing behavior and design a campaign to expand your customer base:
Having a data-based justification for whom to target can save a lot of money and guesswork. Also through targeting people who are statistically much more likely to buy your cloud solution, attend your webcast, or agree to be contacted can offer valuable insights for organizations.
Predictive analytics is, at its core, an art and a science, melding the fields of data science, statistical analysis, and software development, in order to form correlations between observed behavior or occurrences, and future behaviors or occurrences. Working side-by-side, marketers and data scientists can create better, cheaper, and higher-return campaigns, bypassing guesswork and one-size-fits all approaches in favor of demonstrated probability.