The consumer packaged goods leader had a large sales force covering multiple areas in a city. The company was managing various sales territories within the city and needed a system to assign an equal workload between field sales representatives. It wanted to ensure optimum utilization of resources with balanced workload on sales reps for efficient meetings.
The company created sales territories based on predefined geographic boundaries like state and city borders and zip codes. This led to uneven distribution of deals and inefficient utilization of sales executives’ time. It also wanted to avoid overlapping of field sales reps in the same territories. They needed a system to quickly redesign territories depending on the varying number of sales reps.
The sales team were spending the majority of the time traveling longer distances to meet customers. The company wanted to reduce the travel time and focus on meeting more customers. Managers also lacked high-level visibility on the sales team.
- Dista Insight, our spatial analytics software, clustered customers based on location. This offered a better understanding of handling customers and helped improve the distribution of the sales team.
- Our platform defined sales territories for field sales personnel and divided accounts between them for better coverage.
- It enabled managers to visualize sales executives that were mapped to territories. Depending on the sales volume, they could create, merge, and split clusters.
- Dista Insight offered managers a bird’s eye view of the sales force team and the territories assigned to them. It enabled them to cover areas and merge with existing clusters depending on multiple reasons like sales revenue, location, proximity, and more.
- The system offered a quick summary of clusters with details like total customers, area size, total revenue, etc.
- Quick summary helped understand geographies in detail to improve and remap sales coverage.
- Managers could visualize data with customizable filters as per different values like sales revenue range for each territory.
- They also got crucial insights like types of customers (high importance/low importance) by using filters like revenue, customer type, and more.
- Reports with customized fields like city type, revenue, customer type, latitude, longitude, and address helped with crucial insights for business expansion.