5 Things to Know About Predictive Analysis

Predictive analysis is a hot topic in the trade fund and advertising realm. But because it’s very mathematical and data-driven, it can be hard to grasp exactly what it is or how it works.  Here are 5 common predictive analysis FAQs that explain exactly what you need to know.

1. What is predictive analysis? Predictive analysis tools create data models of existing qualitative and quantitative consumer data, and then use those models to anticipate future customer buying patterns. These data models rely heavily on complex statistical components, such as advanced regression or time-series models.

   The system of credit scores is a common example of a predictive analysis model, where a company predicts the service-payment patterns of their consumers based on prior credit history.

2. How can it increase ROI? American Airlines, for one, increased their ROI approximately 1200% within a two-month period upon implementing their predictive tools. Their strategy focused on pinpointing the service areas that customers responded positively to, and eliminate / restructure those that customers found little value in. Other companies claiming successes with predictive analysis include Amazon, Procter & Gamble, and the Boston Red Sox.

   When applied to marketing efforts, predictive analyses can uncover hidden triggers to consumer behavior. Identifying those can drive changes to marketing campaigns to present offers or levels of customization that data indicates have traditionally brought increases in sales. As deals become more targeted and campaigns drive greater response, ROI for each campaign can increase.

3. What role do predictive models play? Predictive models can be presented in various shapes and sizes and often serve as a physical representation in graphical format or a mathematical equation to output predictive data. For instance, models can assess how likely a consumer / prospect is to purchase a product or how likely he / she is to churn. Because these models have such large amounts of information, data mining is a prime component in the predictive modeling process used to identify any potential trends.

   Predictive models are generated through various methods and strategies. Linear regression lends itself to specific data types, time series, and panels. Logit / Probit regressions provide a direct outcome based on data variables, providing a “yes” or “no” answer based on consumer behaviors and characteristics.

4. What competitive marketing advantages can predictive analysis provide? Using predictive analysis can provide firms with a better understanding of current and future behaviors of their customers and prospects that their competitors may not be able to access with traditional data tools.

   Many companies using predictive analysis have updated their business processes and information systems so that customer insight is provided throughout the enterprise on a continued basis. In these cases, all business functions in the enterprise study the behavior of their consumers and provide appropriate initiatives to them.

5. What pitfalls should I be aware of? The most common problem that firms have with predictive analysis is building models with data that are redundant or inconsistent. Inaccurate results can lead to business decisions that are more detrimental than helpful. Any considerable changes in data parameters can also result in an inaccurate model.

   In addition, implementing a new system often results in organizational resistance, and predictive models are no exception. Because they are heavily analytical and rely on numbers rather than instinct, users may be hesitant to learn about them.

If you have additional questions about implementing predictive tools for your business and would like to learn more, contact Michelle Collins at (800) 937-2667, or at mcollins@sharedmarketing.com.