The Challenge

Today’s customer no longer bases their loyalty on brands, products, or prices. Their loyalty is dependent upon the service they receive, their experience of a business and their level of satisfaction. To thrive and succeed in a highly competitive world, the business needs to put in a serious effort to delight their customers. At that end, two key areas of the company must work in sync to achieve the desired results: Customer Service and Service Operations.

So, two initiatives were determined as the first step to support management goals: 

  • Use Analytics to understand Customer Sentiments and if not meeting expectations, then identify why (KPI’s) and where the process can be more efficient
  • Perform analytics on operation services to understand if deployment and coverage work is being performed in an effective manner, or being altered from plan

How It Was Solved

The company looked for a partner to consolidate in a data store a set of critical indicators relevant to customer satisfaction, service response and track these metrics regularly. These analytics results enable them to scale up to customer expectations. Examples of such key indicators include download speeds, feedback score, Average Revenue per User (ARPU), response time from the service provider, and more.

Understanding how to analyze the data related to customer representative conversations regularly and actions by their service team, to find out if the levels of service, strategies and tactics work as planned. They try to analyze customer interactions at various touchpoints and offer personalized service to the extent possible to improve customer satisfaction.

The Results

Improved customer satisfaction levels by processing IoT data from sensors and transmission data and comparing to customer service calls, the analytics results show multiple points of improvement in the process. The improvements came by identifying agents that were not following the established workflow and created additional touchpoint that overloaded the service center and services team with work that was not necessary. Operations analysis showed that some of the work created by the additional steps was scheduled as emergency but already existed on a plan and created additional expenses.

The analytics were run from a data warehouse that included supplementary key indicators for operations control, as well as more detailed insight on the service control application by adding data at a more granular level to allow drill down. The key for the success of the project was driven by identifying clearly the KPI’s and the data flow on the preferred workflow that was established for the services team.