Issue-type distribution, top parts consumption, engineer efficiency rankings, customer satisfaction trends, regional fault heatmaps and product failure rates. Visualize data across every after-sales dimension, so management decisions are evidence-based.
Not just counting numbers — seeing the essence of problems through data
Pie charts of types like faults / installation / inquiries / complaints, identifying high-frequency issues for focused investment
Which parts have the highest consumption and unit cost, reverse-informing inventory and procurement
Compare each engineer's monthly visit count, average handling time and customer satisfaction
A trend curve of customer satisfaction scores, with drill-down into the details of single-star-rated tickets
Count failures / in-warranty quantities by product model to find problem models and feed back to R&D
Density maps of faults at the city / district level, pinpointing weak service areas
Failure Rate by Model
Which model has a high failure rate? Which batch reports faults in clusters? Which part has a design defect? After-sales data holds the answers. When the after-sales department hands data to R&D, product quality naturally improves.
Engineer KPIs are no longer based on impressions. A composite score across four dimensions — monthly visit count, average handling time, customer satisfaction, parts consumption — that's fair, transparent and comparable.
Pull data across all dimensions at month-end to pinpoint problem areas and drive targeted improvements next month.
R&D uses after-sales data to reverse-engineer product quality, deciding on recalls / redesigns / parts upgrades.
Review the top-consumption parts list to negotiate bulk discount pricing / safety stock levels.
Identify low-satisfaction stages and optimize specifically (e.g. add engineers if average visit time is too long).
Upgrade your after-sales team from an execution unit into a data-driven optimization unit