Taking into account all the input data,

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we proposed using the AI ​​Contact Center platform, which operates on the basis of a Virtual PBX.

The organizer's office already used IP telephony, but for the event we connected a separate virtual city number, which we connected to the PBX. Then we connected the progressive dialing function so that the robot automatically connected the sales manager with the subscribers who answered the call, and loaded the dialing database into the Virtual PBX.

After that, we connected the widget on the landing page and created a simple chatbot in a no-code constructor for qualifying leads. For a channel in a popular messenger, we created another chatbot with a similar scenario.

Then we activated the Contact Center option in the long legacy of chinese migration abroad our client’s personal account, connecting to it a PBX scenario associated with a contact number for calling about an event, and chatbots for a widget on the website and a messenger.

Thus, operators were given the opportunity to track requests in text channels, connect to client dialogues with the chatbot, and, if necessary, make calls directly from the Contact Center interface.

Initially, it was planned to use the Speech Analytics function to save and analyze transcribed conversation records directly in the Contact Center interface, for which purpose the PBX scenario was added there. Subsequently, the customer decided to reduce costs, and the Speech Analytics function was not activated.


Results
As the customer wanted, we managed to prepare and launch a comprehensive solution for organizing a contact center within one working week.

Operators could see all text dialogues in a single interface, which allowed them to promptly answer questions and promptly connect to customer dialogues with the chatbot to maintain service quality.

As for the database calling, the progressive calling function helped to significantly reduce the operators' downtime due to waiting for the client's response, so the task was completed as quickly as possible.

After the event, data on users who clicked on the landing page and interacted in the widget was analyzed to optimize subsequent advertising campaigns.