Business process optimization

Optimizing the After-Sales Process

Our customer operates in the telecommunications business. After a contract has been signed, customers are re-contacted to provide the opportunity for feedback and to take advantage of up-selling opportunities. Based on available customer data and booked products, customers are automatically assigned to the different service partners of our customer, who handle the after-sales process.

Replace Manual Processes with Automated Ones

Our customer’s various service partners only have limited resources and each agency can therefore only contact a limited number of customers. In addition, there are restrictions regarding the customers a service partner is able to contact (e.g. specific products, customer regions, contract age etc.). Until now, the assignment of customers to service partners has been a multi-step process which had to be done manually via Excel. This costs a trained administrator several hours every day.

A data scientist optimizes a complex and inefficient business process.

Automatic Allocation of Potential Leads

Since there are defined rules on how to allocate potential customer leads (tickets) among service partners, it made sense to automate the process. The problem was solved in two steps:

  1. Potential candidates: Based on the assignment rules, each ticket was assigned all potential service partners who could contact the corresponding customer. These rules can be easily adapted by our customer at any time. An optional GUI allows people with no programming experience to edit the rules dynamically.
  2. Optimized allocation: In the optimization, the optimal allocation to a corresponding service partner is accomplished. Each ticket may only be assigned once, subject to fulfillment of all existing constraints, such as the maximum number of tickets per partner, region, etc. In order to optimize the allocation from a business point of view, an expansion stage is planned which will allow certain service partners to be selected on the basis of their performance criteria, such as service quality, cancellation rate, up-selling rate, handling costs, and many more. This is particularly relevant for cost optimization if the partners have different cost models.
From a mathematical point of view, the tickets are optimally assigned by linear optimization, whereby the final assignment can be further controlled by any number of constraints. Finally, the tickets for each service partner are made available in a central ticket system.

In Which Ways Could cognify Support This Project?

  • Concept

    Design of a stable and scalable application solution

  • Automation

    Automated data processing and pre-processing

  • Cost Reduction

    Increased efficiency in terms of financial and human resources

  • Process Optimization

    Improving existing business processes

  • Flexibility

    Allocation of potential leads according to customer-defined optimization criteria

How Our Customers Benefit

Through the optimization of the after-sales process, our customer can save important financial and human resources. Monotonous routine work is now automated, so employees can turn to other activities to directly increase daily efficiency. In addition, the business process remains flexible and transparent despite automation, as the implemented rules can also be viewed and changed independently by employees without IT knowledge via an accessible user interface.