In the 1990’s, as databases became more sophisticated, marketers started to practice what became known as “segment marketing” – breaking customer audiences up into distinct market segments based on demographics and other commonalities. It was a huge step forward from the days of mass marketing, when marketers attempted to appeal to customers with a one-size-fits-all message.

Individual relevancy was still a problem however; not all people with the same demographics have the same interests. Online shopping and subsequent research have enabled companies to easily collect behavioral data, which has proven to be a much better predictor of future buying behavior. Examples of behavioral data include whether a person clicked on a link, visited certain web pages, or requested information on a specific vehicle.

Predictive analytics leverages both behavioral and segment data to predict what customers will do next – such as what vehicle the customer is most likely to buy, and in what timeframe.

The results of these analytic models can become the basis for micro-targeted, personally relevant marketing communications that drive superior sales results across email, SMS, mobile, chat, social, and direct mail. A leader in this advanced marketing approach is Outsell.

Outsell’s predictive analytics engine is called NeuroMotics^®. This machine-learning software enables automotive marketers to send timelier, more relevant campaigns without the cost and complexity associated with standalone predictive analytics tools – freeing them up to acquire and engage customers instead of wrestling with data and technology.