Auto Repair Shop Dashboards
Executive Metric Dashboards
The goal of our two dashboards is to answer questions that we believe will allow the local repair shop manager to make decisions more efficiently. The questions answered include: which months tend to be the busiest, which repair types contribute most to overall revenue, and how and when to reach out to customers.
When creating our executive metric dashboard, we identified four key performance indicators (KPI’s) including number of customers and transactions, the revenue earned, and the labor cost of repairs. Not only can the manager filter the entire dashboard in terms of one of the four KPI’s but also time in order to observe repair shop’s performance to his or her liking. We also incorporated trends discovered during our analysis of customer segmentation. For example, contact type filter customers based on how their business was generated. To further elaborate, we broke down each type in terms of customer loyalty. We consider customers who have more than five transactions as high on loyalty scale, two to five transactions as medium and one transaction having low loyalty. This view not only enables the manager to view how each contact type performs in terms of KPI’s, but also investigate contributions based off customer loyalty groups within those contact types. The local manager will also be able to view, and compare, the most lucrative months over the past 2 years based off their choice of KPI. This enables the manager to uncover that March and August being the busiest months based off number of transactions. Finally, the executive dashboard illustrates the top 5 customers and suppliers based off parameters chosen by the manager. When hovering over each customer’s name, the manager will be able to view a tooltip indicating where that customer is located (if included in the data set).
Expanding on the executive metric dashboard, we decided to further elaborate on the shop’s repairs in the Repair Types Analysis dashboard in order to uncover areas where the shop can improve. For instance, the local manager will be able to easily identify the revenue contribution for each type of repair. Taking the top five: Tires, No, Oil, Alignment and Brakes, we generated which months have the highest number of transactions. This will also allow the manager to better optimize their inventory for the shop and possible utilization of discounts during the less busy months. To further analyze each of the top repair types, we incorporated KPI parameters. Finally, our last visualization features the top five repair types in order to measure to number of transactions based off vehicle year. From the visual the manager can see that vehicles that are between 11–14 years old have a higher amount of transactions throughout the five repair types. Having this information, the local manager can optimize the best time to reach out to customers to come into the shop in order to generate more revenue for the company.
Thanks to my teammates: Haeun Park, Yanqing Shen, Gaurav Singh, and Alina Yang.
