Dynamic experimentation for customer recommendations in real-time is an innovative approach to providing customers with tailored recommendations based on their individual preferences.

ecosystem.Ai uses a combination of machine learning and computational social science to predict customer behavior and provide personalized recommendations. Using the ecosystem.Ai Platform, this approach allows for quick and easy identification of which products and services best fit each customer’s individual needs.

Recommendations are then served up in real-time, allowing you to engage with your customers in the moment they need it most. Experimentation can be used to enhance the human experience at various touch points in a customer’s journey with you. Alongside product offers, you can also run experiments on web offers, interface colour schemes, engagement message options, logged-out vs. logged-in experiences, and more. Ultimately, by using experimentation, you can increase customer satisfaction and drive long-term success for any business.

This presentation will discuss how dynamic experimentation can be used to improve customer recommendations in real-time. In the previous meetup, we discussed the advantages of dynamic experimentation and the potential benefits to customer engagement, and the potential for dynamic experimentation to help businesses better understand their customers and deliver improved customer experiences. This session will explore experimentation from the capabilities available in The ecosystem.Ai Workbench. We will explore a demo of the ecosystem.Ai Platform and a practical visualisation of what experimentation could be for your business.