How did we save 2352 working hours

in Customer Support?

 

Technology industry

Case Study

How did we save 600 man-days in Customer Service?

Technology industry

Case Study

Clients

Number of requests yearly

Saved working hours

%

Automated tasks

Clients

Number of requests yearly

Saved working hours

%

Automated tasks

Overview

What’s the story?

This case study will show you how we managed to save almost 2500 working hours for customer service employees in one of the largest telecommunications organizations in Poland, eliminating the need to handle 30% of requests coming in from almost 14 million clients.

Overview

What’s the story?

This case study will show you how we managed to save almost 2500 working hours for customer service employees in one of the largest telecommunications organizations in Poland, eliminating the need to handle 30% of requests coming in from almost 14 million clients.

Context & Challenge

Project background and description

In the case of a telecom client, the project was a challenge for us both because of its scale and rigorous SLA by which we were bound. The scale of the project concerned working on requests coming in from over a dozen million clients.

The problem

The client’s biggest problem was constant increase of employment due to the volume of customers’ requests. The turnover at these working positions translated into fluctuations in customer request solving times, which led directly to a decrease in their satisfaction with the service.

Project goals and objectives

The main goal of the project was to decrease the workforce in the customer service team and the operational costs related to maintaining and recruiting it. The second goal was to decrease the incoming request solving time, which would influence SLA indicators.

Context & Challenge

Project background and description

In the case of a telecom client, the project was a challenge for us both because of its scale and rigorous SLA by which we were bound. The scale of the project concerned working on requests coming in from over a dozen million clients.

The problem

The client’s biggest problem was constant increase of employment due to the volume of customers’ requests. The turnover at these working positions translated into fluctuations in customer request solving times, which led directly to a decrease in their satisfaction with the service.

Project goals and objectives

The main goal of the project was to decrease the workforce in the customer service team and the operational costs related to maintaining and recruiting it. The second goal was to decrease the incoming request solving time, which would influence SLA indicators.

The process and insight

How we found a solution?

For carrying out this project, we gathered a team of 34 people who had to rise up to the rigorous SLA benchmarks.

In order to answer the question: how to do it? we started browsing through tens of thousands of requests, looking for repeating topics and categories. We identified over a dozen most popular ones and started to implement them in a tool which, after several months, we called Mr Wolf, referring to the Pulp Fiction character.

The process and insight

How we found a solution?

For carrying out this project, we gathered a team of 34 people who had to rise up to the rigorous SLA benchmarks.

In order to answer the question: how to do it? we started browsing through tens of thousands of requests, looking for repeating topics and categories. We identified over a dozen most popular ones and started to implement them in a tool which, after several months, we called Mr Wolf, referring to the Pulp Fiction character.

The solution

How we made it all happen?

The implementation process consisted in first making a very precise list of all steps performed manually for a certain category of requests, and then turning these steps into script(s) which could be carried out automatically.

The solution

How we made it all happen?

The implementation process consisted in first making a very precise list of all steps performed manually for a certain category of requests, and then turning these steps into script(s) which could be carried out automatically.

The results

What we achieved?

This way, we indicated a total of over 35 business processes, or in other words request categories, which are currently 100% automated.

They make up for ca. 30% of all requests coming into our expert team.

Automation enabled us to save ca. 2450 hours of our consultants’ work.

The results

What we achieved?

This way, we indicated a total of over 35 business processes, or in other words request categories, which are currently 100% automated.

They make up for ca. 30% of all requests coming into our expert team.

Automation enabled us to save ca. 2450 hours of our consultants’ work.

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