Routine, statistics, analytics: what an AI assistant can help a technical support or hotline department manager with

There is a protocol for every scenario: how to respond, where to look for information, how to avoid emotional remarks. Also: what to fill in and how in the customer, product (if the complaint is about the product), and employee records...

But are you 100% sure that all rules are being followed?

There are two ways to verify this: listen to the call and compare it with the protocols, or choose “senior managers” who do almost nothing except supervise their colleagues.

It is almost impossible to learn about all the mistakes and shortcomings at this pace. A manager's time is not unlimited.

To evaluate every call, you would need to hire as many experts as you have frontline employees, which is not cost-effective. At best, you can afford a small random sample, and that may miss the most problematic calls or employees.

Meanwhile, every incorrect answer or emotional outburst is a threat to the company's reputation and metrics.

If a manager makes such mistakes regularly, it is better to find out as early as possible. An assistant that evaluates calls objectively and provides you with department statistics Now imagine receiving data for every call from every manager.

And these data are analyzed by an unbiased expert who can easily match calls against existing procedures, assess compliance accuracy, and break down results by project or manager.

To make decisions, you only need to look at a simple table like this: and you will see that in half of the cases managers suggest solutions that do not match the script.

Why does this happen: because the script is outdated, or because managers do not know it well?

You can figure that out if you have reliable analytics data at hand.

The manager themselves will receive emails with a detailed analysis of how their calls comply with procedures, along with recommendations on what needs improvement! What will the result be?

Within a month, the quality of procedure compliance will improve by 50%-95%, and the procedures themselves will improve too - because you will now receive data about their obsolescence much faster.

Undervaluing employees, like overvaluing them, are twin problems that cost you money.

But how do you get objective data showing that someone is not yet at the required level, while someone else is already truly overqualified for their role?

Ask their colleagues, ask the senior manager, and yes, again, listen to the calls where this employee speaks with the client.

You cannot rely entirely on subordinates' opinions: human judgment always depends on personal relationships.

A convenient visualization format and multiple criteria for evaluating incoming information

Aggregated and visualized employee data Now imagine that all of an employee's calls have already been listened to, transcribed, and evaluated for the criteria that matter to you.

You see a detailed table in front of you that you can sort by call type and date. And you can tell at a glance how the employee's score changes over time: whether performance has improved or stayed the same, whether they make mistakes, whether they are polite to customers, and whether they respond quickly to complex requests.

Imagine a client you have worked with for a long time refers, in a new request, to a problem that happened one or two years ago.

Meanwhile, the client is getting anxious, time is passing, other tickets are being pushed back... Get an answer in seconds

All it takes is changing the tool, and the situation changes too.

Instead of searching through the CRM or another system you use, your manager writes to the chatbot: “give me the link to the ticket for this situation with this customer.”

In 15 seconds, they will get the link, a summary of the issue, the solution, a link to the call recording, and a screencast... Everything they need. What is even better: there is no need to repeat the exact wording that may have been used to describe the problem. The AI assistant understands both professional slang and natural speech.