How Predictive Analytics Anticipate Future Behavior And Impact Your Bottom Line - Lift361

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How Predictive Analytics Anticipate Future Behavior And Impact Your Bottom Line

By Ed Higdon. Posted in Uncategorized

April 22, 2015

Historically, marketing has been a bit of a guessing game. No matter how much companies think they know about their customers, it has always been difficult to predict customer behavior accurately. Thanks to predictive analytics, this “holy grail” of marketing is quickly becoming a reality.

According to a survey published by Accenture, the use of predictive analytics has tripled since 2009. The rapid adoption of a forward-looking approach to analytics has been driven by the desire to know not just what happened yesterday, but to anticipate what will happen tomorrow.

What Is Predictive Analytics?

Predictive analytics isn’t necessarily about finding new information. Instead, it utilizes historic data collected over time and organizes it in new, actionable ways. Predictive analytics mines data to identify reliable patterns of customer behavior, and those patterns are then used to predict future events.

There is an endless number of use cases for predictive analytics. For example, financial institutions can use data to calculate the probability that a loan applicant will default. With the right tools in place, companies can predict customer behavior to a high degree of accuracy based upon historical data models.

Improving Targeted Customer Communications

Predictive analytics can be a powerful tool for strengthening customer relationships and improving the bottom line. Without analytics in place, retailers have to make intuitive assumptions about their customer segments. Communications are broad, and take on a bit of a shotgun approach. Mass communications that lack strategic targeting are often a waste of time and resources.

With predictive analytics in place, however, retailers can make data-driven assessments of customer segments. If a predictive model identifies a subset of customers that are three times more likely to leave than the average customer, the retailer can formulate retention offers to target that specific, at-risk segment, rather than committing valuable retention-based resources to customers who aren’t likely to attrite.

Actionable Analytics Are Within Everyone’s Grasp

At one time, advanced analytics that offered a forward view were reserved for Fortune 100 companies with deep pockets. Now, thanks to the accessibility of big data technology, companies of all sizes can utilize predictive analytics models to improve decision making both online and offline.

Putting this type of data to work involves strategic planning. What does your company want to accomplish? Do you want to improve retention, increase your direct mail response rate, or make personalized product recommendations? Once you know the problems you want to solve, actionable solutions are within grasp using analytics.

Predictive models can be applied to a number of business challenges, but the key is to know what type of behavior you want to predict. Predicting direct-mail response is vastly different from predicting one-and-done shoppers, for example. The information gathered from predictive analytics is most valuable when it is utilized. Analytics can help develop models that assess current customer needs, anticipate future customer needs and isolate risk, which in turn, reduces churn, strengthens relationships and has a positive impact on the bottom line.