Beyond Prediction: Why Adaptive Analytics Matter

Predictive analytics were once considered the cutting-edge way to interact with consumers. The data sets were built on history – what a consumer purchased before, how many times a consumer visited a site before buying, and at what point in the buying journey consumers were getting stuck. Those data points offered a wealth of information to companies – and sometimes some surprisingly obvious revelations – that directly impacted how the consumer journey was approached in the future.

Predictive analytics are still a valuable driving force behind business marketing decisions but contemporary technology calls for even more. Companies should not just look at the past when planning marketing campaigns; adaptive analytics in real time should play a major role, too. Interacting with consumers during the buying process gives greater depth of connection and ensures that what a company is offering to a specific consumer is relevant.

A “GPS” for Companies

The best predictive analytics have an adaptable component that makes them even more relevant. In a piece written on Pega.com, smart adaptive analytics platforms are compared to GPS systems.

“GPS leverages historical data (predictive) with real-time data (adaptive) to guide me to make better decisions. Earlier generation GPS systems would provide sub-par results due to insufficient data, much like most (insurance) carriers are using predictive analytics without adaptive capabilities.”

Pegasystems is referencing insurance companies specifically, but the point rings true across industries. It’s not enough to base decisions on what to offer consumers based on historical data, like past purchases, alone. There has to be an adaptive portion that can intuitively predict what should be presented based on how a consumer is behaving in real-time.

How to Use Adaptive Analytics

So how can companies go beyond historical data to put adaptive analytics in place? Here are some examples of using a predictive analytics base with adaptive analytics for the best outcome:

Upselling. When a consumer is using a site to research a particular product or service, make sure the most relevant complements are suggested. You should not just throw everything you’ve got at people shopping your site; you have a better chance of a relevant sale if you target a small set of what you offer based on consumer behavior in that moment.

Retention. Believe it or not, there are ways to “sense” frustration when a consumer is on your site. When that happens, and an adaptive analytics algorithm senses it, you want to take a different approach to meeting your consumer’s needs before he or she abandons the sale. This could mean a live chat option prompt or even a phone number pop up that a consumer can call for immediate assistance. If a consumer gets frustrated, he or she will quickly jump to a competitor’s site.

If you’re still working with a predictive analytics model, it’s time to upgrade. Incorporate adaptive analytics and you’ll see a better return on consumer engagement – and you’ll build a stronger data set, too.