Qlean Case: How to Create Case of the Qlean company. In our new article, we tell how a cleaning service managed to reduce the load on operators by 7% and increase the average response speed by 20 times using a chatbot.
- About the company
- Tasks
- What was done
- Summary
About the company
Qlean is a service for ordering various household services. With Qlean, customers can delegate cleaning, order dry cleaning and laundry, and arrange water delivery.
Tasks Qlean Case: How to Create
It was important for the company to build luxembourg whatsapp data support for all users. Most often, clients write about changing or transferring an order, and performers – when they cannot contact the client or need information on the regulations. Such issues require a quick solution, and more than 1,000 requests are received per day.
What was done Qlean Case: How to Create
They connected messengers — FB* Messenger, VKontakte, Telegram, Viber, WA. This allowed them to systematize the entire volume of messages and combine 5 communication channels in one service. Clients and performers write in messengers convenient for them, and support specialists respond in one window.
The chat system proved to be very convenient a long time and there is much to learn from operators, reducing the average response time from 20 minutes to 60 seconds. Interaction with colleagues from other departments on a specific case was also simplified by transferring the chat to the responsible specialist with one action in the interface.
In each chat, there is an option to leave a link to the CRM and a phone number so that a specialist can easily find any user in the system without asking for contact information each time. This helps to clarify all the information as quickly as possible and return to the user with a ready-made solution.
Qlean values live communication, so most messages slovakia business directory sent without automation. But to relieve the load, a self-service menu. In it, cleaners can find answers to typical questions about payments, warehouse and office work, and solving possible problems with the application.
This reduced the load on operators by 7%. This value was obtained from aggregated statistics: the total number of chats per month and. Compared with the number of requests resolved using the chatbot. It turned out that on average, the chatbot handles about 70 messages per day.
