Vooban developed a sales prediction algorithm based on machine learning techniques. The model was deployed on the Microsoft Azure cloud platform and runs automatically every week. It makes two types of predictions simultaneously: the number of upcoming orders and the number of transactions that will be completed successfully.
Each time the code runs, the algorithm retrieves the most recent data related to the products being analyzed from various internal sources using a database. The sales forecasting model is also trained on a range of external data, such as from Statistics Canada, that has a significant impact on fireplace sales (e.g., weather). In addition, SBI has access to an interactive dashboard that allows it to visualize the sales forecasts issued by the algorithm, which it can then compare with its sales team’s forecasts.
To ensure the reliability of the AI model, we performed a series of retrospective tests. During these tests, we first trained the model on data from previous years and then tested it with another year for which actual sales data was available. This allowed a comparison between the model’s predictions and actual sales for a large selection of products while ensuring that the model’s predictions were reliable.