Queueme

Scheduling | Workflow | Analytics





About Queueme

Queueme is a business SaaS platform for enterprises which require scheduling and analytics. Queueme spans across products like warehouse scheduling software and employee scheduling software which help businesses manage their workflows in a digital and collaborative way. Queueme is created with the latest technology and runs seamlessly on desktop, tablet and mobile thereby creating a great experience for our customers. Our vision is to replace papers and spreadsheets and bring more life and data to businesses and their operations.

Generalized Scheduling

Configurable Scheduling is at the core of Queueme's platform. Businesses can benefit from schedule appointments or tasks along with maintaining and managing a live queue. There is a need for mixing queueing and scheduling in the right ways which is appropriate for the business environment. Queueme helps solve this.

Time Mesurement

Queueme software enables easy measurement of time taken between various stages of the flow in a business operation. Queueme can also learn statistically and estimate the time it takes for the entries to move to the next stage. Giving reasonably correct estimates in an automated way boosts NPS of the business. Repeated manual estimation may be a significantly challenging task and error prone for the human mind. Not only because its repetitive in nature, but there could be multiple scenarios, like types of services, and availability of staff, equipment and resources that would influence the time of accomplishing the tasks by the business.

Analytics

Every business needs to tap into its data in a quick and convenient way. Analytics of historical data with the right visualizations techniques provide insights into the problems in the business that may otherwise be ignored. Queueme's platform covers analytics and reporting of the most useful aspects of business's data. Moreover, Queueme's team can offer customized data science and analytics for the businesses which may need deeper analysis of their data.

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