Real-Time Quote Optimization for Route-Based Services

Background

One of our clients in the snow removal industry faced challenges as their business expanded. The manual process of generating quotes for new prospects was not only time-consuming but also prone to overpricing or underpricing — hindering their ability to generate optimal prices that were both profitable and competitive. Additionally, they needed a way to optimize routes across multiple fleets. To address these issues, our client partnered with us to develop a custom software solution capable of optimizing quotes by pricing on the margin and assessing, in real time, the additional time each new prospect would add to the company's total route.

Challenges

The technical problem we needed to solve is a variant of the well-studied Vehicle Routing Problem (VRP), which involves determining the most efficient routes for a fleet of vehicles to service a set of clients. While many software solutions address basic routing, they fall short in dynamically quoting on the margin — they don’t accurately assess the additional time and cost implications of adding new prospects to existing routes in real-time. As a result, the client experienced inefficient scheduling and increased operational costs. Moreover, they needed a method to stay competitive by providing accurate and timely quotes that reflected the true cost and time implications of adding new clients to their routes.

Imagine this scenario: you can offer a discount to the neighbor of a current customer because servicing their property requires minimal additional drive time. But… how much of a discount is reasonable? Conversely, how do you quote a prospect who lives far out of town? How do you handle someone in between? These questions highlight the need for gradations of optimal pricing.

Solution

Our solution was to develop a custom real-time quote optimization software that seamlessly integrated with the client’s existing systems. The software was designed to perform real-time route analysis, determining the additional time required to service a new prospect based on their location relative to existing routes. It also incorporated margin-based pricing to calculate quotes accurately, ensuring competitive and fair pricing. The automated quote generation feature allowed the client’s sales team to provide instant, accurate quotes, significantly reducing the time and effort required. We utilized technologies such as Python for backend processing, Django for web application development, 3rd party APIs for real-time route analysis, and PostgreSQL for data storage.

Given the complexity and computational intensity of solving the Vehicle Routing Problem (VRP), especially in real-time, we employed specific heuristics to achieve optimal calculations quickly. Traditional methods for solving the VRP can be time-consuming and computationally expensive, making them impractical for real-time applications. By using heuristics, we were able to streamline the process, ensuring rapid and efficient quote generation without compromising accuracy.

Implementation

A crucial aspect of the implementation was the seamless integration of the new software with the client's existing sales system. We ensured that once a quote was generated and accepted, a contract would be automatically created and delivered directly to the client's inbox. This automation allowed new clients to sign up and make payments within seconds, significantly streamlining the onboarding process.

We integrated the software with the client’s CRM and scheduling systems to ensure seamless data flow and real-time updates. This integration was vital for maintaining accurate records and ensuring that the new system worked harmoniously with existing workflows. We conducted thorough testing to guarantee the accuracy of quote generation and the reliability of route optimization before successfully deploying the software.

Comprehensive training was provided to the client’s sales team to ensure a smooth transition and effective use of the new system. By the end of the implementation phase, the client had a fully functional, integrated solution that enhanced their operational efficiency and customer experience.

Results

The implementation of our solution yielded significant benefits for the client. The time required to generate quotes was reduced by 75%, enabling the sales team to handle more inquiries and close deals faster. The accuracy of the quotes improved, leading to more competitive pricing and higher win rates. Feedback from prospects was overwhelmingly positive, with faster response times and more accurate quotes contributing to higher satisfaction levels.

Future Considerations

Looking ahead, there are several opportunities to further optimize our client’s operations and profitability. One significant feature to consider is the implementation of shared discounts through a referral system, whereby a group of people can be optimally priced (not just one new customer). Customers who refer neighbors are optimally discounted.

This referral system would not only drive new business but also increase route density, which is the second biggest factor in profitability after pricing — meaning more efficient scheduling, reduced operational costs, and increased profit margins. Implementing this referral system would attract more customers and enhance customer satisfaction through cost savings, creating a win-win situation for both the company and its clients. 🤝

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