If you are like most marketers and business owners, there have likely be times when you have wished for a crystal ball to see how your prospects would react to your marketing campaigns. While there are no known crystal balls, there is something that can help: predictive analytics.
Predictive analytics was once a practice only open to Fortune 500 companies equipped with huge data mining and marketing budgets. However, today entrepreneurs and small businesses alike are leveraging predictive analytics in order to maximize their marketing budgets.
Predictive analytics is a loosely-defined set of database marketing and statistical techniques used to create models that can exploit patterns found in historical data. A company's transactional data is scanned for underlying relationships and patterns that can then be used to predict customer behavior in the future. This type of analysis provides business owners with information we can use to make better decisions about our marketing strategies.
What all of this means for businesses is this: you can use the transactional data you have been collecting from your online store or brick and mortar storefront and combine it with externally-available data in order to predict future customer or prospect behaviors. As the business owner or marketing manager, you become empowered to categorize customers based on their various characteristics and behaviors.
There are essentially two types of customer data: characteristics and behaviors. Characteristics relate to who your customers are, such as age, income, race, and location are all characteristics. Meanwhile, behaviors are actions that your customers' take with respect to your products, services, offerings and promotions. For example, a customer's e-mail response rate, whether they take company surveys, how often they shop for the company's product can all be cross-tabulated against whether the customer does or doesn't make a purchase.
This type of information gives you insights into who your customers are. It also allows you to predict what they will do in response to certain marketing stimuli. For example, suppose you are able to meaningfully categorize your customers based upon their total purchase amount within the past year as being greater or less than $300. Using predictive analytics based upon your historical customer and sales order data, you can determine what other data, such as age, race, income, or even the time of day, week, month, or year, can be used to predict which buying group a given customer likely belongs to.
There are thousands of possible variables that can be used to better predict a customer or prospective customer's behavior. Some of these variables will turn out to be irrelevant (or, as we say, "not predictive"), of future actions. However, other variables will exhibit a high degree of correlation with purchase behavior. You can use these highly-relevant variables to construct a predictive model and apply it to those prospects who have not yet purchased from you.
In this way, you can focus on your best, most profitable customers, targeting them with carefully-designed advertising and special offerings. With predictive analytics, you know who these customers are and what they will respond to. This information can help you better design ads, campaigns, websites, or even your business model.
This sophisticated level of data collection and analytics was once only possible for large companies with deep pockets who could afford the technology and human resources required to draw predictive patterns from databases. Luckily for marketers everywhere, data today is more available and more easily accessed than ever before. Extremely large databases can be stored on personal computers. And, analytics software and techniques become more accessible to small and midsize businesses. These trends allow just about any company, regardless of size, to use predictive analytics to maximize their marketing budget.
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