Our Users (new)

Our Users can be categorized as managers of in-house, in-bound only, small contact centers.

 

Case study: Worldline

Worldline The Netherlands  is pioneering the future of payments by offering products and services in the retail business area. To service the needs of the users of these systems a small contact center is operating in-house in their office in The Netherlands.

Because they are operating in the payment service provider space it i obvious that service levels are very important.

It is a challenge to forecast the expected number of calls during the weeks, days, and quarters of the day. And as seen in many contact centers the flexibility of the workforce is not optimal. Because the contact center is of a faily small size they prefer to sent money on hiring people instead of spending a lot on an expensive workforce management system.

As Agenses offers a lot of the functionality of the expensive wfm systems it was decided to try Agenses for free for 30 days.

 

Agenses simplified the forecasting process by directly importing data from the phone-system of Worldline. Copying data into Microsoft Excel was no longer necessary. As amtetter of fact once the data was being atomatically imported each day, it was proven that Agneses Insights gave clear and actionable insights in how to improve service level on a daily and weekly basis.

 

 

 

Timeline
– employee turnover high with new manager coming in
– out of date tools (mainly excel and manually copying data needed)
– looking into when and what has caused a low SL took a lot of time and more than one tool
– need for better results ordered by customer contacts and wishes upper management (no extra budget or change in employee contracts available)
– request for Agenses Forecast PILOT user agreement
– Agenses Insights and Forecasting PILOT started
– Daily check-in performed or one month
*better understanding of variations and call center dynamics
– First forecast created in Agenses
*better forecast than previous tools
*manually adjusted with new insights to focus SL efforts highest at times when most outages occur
*daily service levels above desired level
*outages have less impact on average service level

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