Increasing Recommendation Accuracy and Customer Conversion via Machine Learning

Our data science team assisted a customer in the sports events industry by creating a recommendation engine that suggests events based on individual preferences. The dispersed data posed a significant challenge in accurately mapping preferences to events and ensuring relevance of suggestions. Our team overcame this issue by utilizing anonymous user pageview data and various tools and technologies, including Azure Machine Learning, Google Analytics, Google Big Query, and Python.

Our solution uses matrix factorization to provide successful recommendations to end users. It is now actively helping individuals find sports events that they are likely to be interested in, while aiding the business in learning more about their target customers and users. The system continuously learns through customer behaviour, allowing it to continually improve its recommendations over time.