Advanced causality analytics helped evaluate a broad set of variables - based on historical performance – to uncover each of its impact on future sales. The system incorporated algorithms that helped business users visualize cause-and-effect relationships of several parameters. For instance, category managers could explore price elasticity and determine the price point for sales promotions based on the threshold of pricing in the past. Similarly, business users could forecast the sales volume based on usage during summer and winter.
The solution leveraged machine learning for statistical analysis of diverse and complex datasets. The entire repository of past and current data was leveraged to determine outcomes against every individual user request - accurately, and within seconds - without the intervention of a data scientist.
The solution could be easily oriented to suit the requirements of a specific business user or group within the enterprise. The solution was hosted on Alteryx Analytics Gallery, an analytics cloud platform that delivered business intelligence on the go. The solution also incorporated self-service functionalities to help users visualize business outcomes based on their unique needs and market dynamics. Pre-built and easy to maneuver screens and interactive visuals made the user experience both convenient and intuitive.