The Road to a More Efficient Retail Contact Center Starts Here

Forecasting and scheduling are vital components in the success of every call center, particularly in the retail industry where a contact center may receive thousands of orders, returns, questions and complaints from customers every day. It is essential to provide outstanding customer service – or many of your callers will look elsewhere.

 

Achieving this goal is impossible without reliable data. For decades, that data was gathered through spreadsheets and would take hours to compile. Even then the results were not always accurate, or flexible enough to accommodate last minute changes or other staffing issues.

 

As this is a topic that strikes at the very heart of contact center efficiency, Monet has created a whitepaper that describes how our retail call center software can help a retail contact center achieve best practices in forecasting, scheduling, adherence tracking and agent productivity.

 

Our new whitepaper for the Retail Industry is just one resource available free from Monet. We have also written informative blogs on a wide range of topics and issues related to WFM for the Retail Industry. You can also check out short video presentations on the benefits of a WFM solution, or schedule an online demo to become more familiar with how Monet solutions can make your contact center more productive.

 

Download Call Center Forecasting and Scheduling Best Practices for the Retail Industry

 

Perhaps this is the year your contact center will make the investment in a workforce optimization (WFO) system like Monet Software for the retail industry. But how do you know which system is right for you?

 

Don’t invest in a solution without downloading our whitepaper Forecasting and Scheduling Best Practices for the Retail Industry. It’s free!

“After having Monet, we can never go back.”

View Case Study

“Our quality and service levels are averaging in the top 97% tier.“

View Case Study

“We are already abandoning almost 2,000 less calls than a year ago.”

View Case Study
Close