Predictive Modelling is …
Predictive modelling, on the other hand, focuses on accurately anticipating visitors’ future actions. Predictive modelling engines commonly run on an advanced machine-learning algorithm, tracking actions of visitors across all touchpoints. One visitor’s behaviour is compared to other visitors’ journeys and historical data to make predictions, enabling marketers to act on ready-to-use segments based on future behaviours of their visitors.
How does predictive modelling work?
Insider’s predictive modelling engine runs on an advanced machine-learning algorithm, tracking actions of visitors across all touchpoints. The algorithm for predictive segmentation uses thousands of signals within thousands of users’ behaviours to carve out predictive models. In predictive modelling one visitor’s behaviour is compared to other visitors’ journeys and historical data to make predictions, creating ready to use predictive segments based on future behaviour of visitors.
How are predictive segments leveraged by marketers?
Predictive segments help marketers take actions based on variables like likelihood to purchase, customer lifetime value, interest-based segmentation, income prediction and customer life cycle status prediction. For instance, most commonly used predictive model within Insider’s platform is the likelihood to purchase algorithm where a visitor’s likelihood to achieve a predefined goal in the next 7 days is predicted and visitors are grouped into segments based on their likelihood to purchase. At Insider’s another predictive model, customer lifetime value is predicted as standard, high and VIP based on a visitor’s realization of a predefined goal such as completing a purchase or reading a specific number of articles. Based on these predictive variables marketers can make data-backed decisions and deliver truly personalized experiences to each visitor.
Other than being a highly effective personalization solution, at Insider we use predictive marketing technologies to help marketers increase the effectiveness of their ad spend, with its capability to be used in third party paid ad channels. Using predictive segments in ad channels like Google Adwords, Facebook, etc, marketers deliver ads to audiences that will respond to them most effectively, thus increasing the efficiency of their spend.
Overall difference in a nutshell
Delivering thoroughly personalized experiences requires a 360º visitor profile and creating a highly detailed customer profile requires robust data and the right technologies to make the best use of your data ocean. Recommendation and predictive modelling engines are two different technologies helping marketers achieve this ultimate goal of delivering unique and favorable experiences to each and every user at different respects. Recommendation engines feed on historical data to deliver relevant products/content to visitors. Predictive modelling makes predictions about future behaviour of customers, carrying customer experiences to the next level. Predictive marketing technologies help marketers completely transform their digital marketing operations, from online conversions to increasing ad spend efficiency. Recommendation engines work as a quick solution to increase conversions.