A convenience store chain with over 20 stores faced a challenge in managing inventory, maintaining stores, handling customers, and dealing with competition. These issues were interrelated and caused unsatisfied customers to switch to competitors, resulting in loss of revenue.
SmartCat team analyzed and proposed changes to the current data model. We delivered value through smart reporting that incorporates pattern analysis, anomaly detection, smart suggestions, and insights as well as forecasting. This enabled anyone to understand the data and make impactful decisions.
SmartCat’s Data Analytics team also utilized the Smart Narrative feature of Power BI, which auto-generates insights based on what users would like to understand from the data and dashboards. This made it much easier for users to read insights than to look at many data points on charts. We found this to be a simple but valuable feature that made a ton of difference in providing operational insights for online retail owners. It is a great example of how product design should focus on finding small but valuable features.
The solution was delivered in the form of an interactive report. Metrics tracked were the number of customers, active and returning customers, popular products, purchasing trends, number of orders, preferable days for shopping, popular products per day, and hour of the day. The client managed their inventory better and predicted customer behavior, resulting in improved customer satisfaction and increased revenue.
About the Clients
The convenience store chain had over 20 stores in the US.
Python and Pandas for data analysis, Power BI for report visualization, and Power Apps and Power Automate for including smart capabilities such as automatic insights and ChatGPT.