How an AI call transcription assistant saves a sales manager 10 hours a week and helps improve sales metrics

Sales managers spend about half their working time on calls and several more hours entering data into the CRM system and writing follow-ups. The head of sales spends a significant share of their time listening to calls to give managers feedback and improve call guidelines.

In this article we explain how to cut these costs while speeding up the training of sales managers and increasing customer satisfaction.

According to Bain & Company researchers, senior executives spend an average of two days a week in meetings.

Other data show that calls take up half of all working time: up to 23 hours a week.

That's exactly how a sales department works: it calls clients, then analyzes those calls and uses them to improve processes and boost conversion.

But let's imagine that call processing can take 2 hours a week instead of

On all these meeting notes, entering data into CRM, analyzing script usage and reviewing individual employees.

And those 2 hours will deliver the same (or even greater) efficiency and data reliability!

Rank-and-file sales staff spend up to 35% of their time in meetings.

Picture your typical manager - let's call her Maria.

Every day she talks with several clients.

But her work on a call doesn't end the moment she hangs up.

She spends about another hour a day writing call minutes and summaries to send to the client, save the data in the CRM and produce a report for you.

Maria gets tired or has a headache, and she makes a mistake or forgets to include important details in the minutes, such as the client's steel grade requirements.

Maria communicates excellently with clients and sells well.

She is most effective precisely on a call.

Maria devoted more attention and time to minutes, listening to calls twice over.

You could hire additional managers so the targets are met and the minutes are still perfect.

Maria to work on minutes in her own time.

But then the best people on your team will quickly find new jobs, where their manager will respect their time.

Maria sees the call summary and can compile a recap quickly, accurately and without forgetting details. Even better: the call recap goes out to all participants automatically. And

Maria gains +1 hour a day for extra calls to new clients.

Of course, you already have a whole system for storing agreements.

There are emails, there are call notes (filled in by reps), there are chats. And finally there's the CRM that aggregates and stores all this information. But…

First, are you one hundred percent sure that your managers never forget to include anything in meeting minutes?

People tend to forget some details and never "carry" them through to a written record.

You get distracted for a second during a call by a pinging notification, and an entire layer of information is forgotten. And second, even meticulously saved information is far from always convenient to use.

How many emails, minutes and chats you have to dig through to recall what you agreed on with a manager

? Now imagine this: any information about agreements with clients is, first, saved automatically without human error, and second, easily accessible.

You type in the chat: "Remind me of this information for this period."

- and 15 seconds later you get a detailed summary: what was agreed, why the deal was not closed at that moment, and what can be done to restart the partnership. As a result, you can make more personalized offers, quickly resume conversations, and identify recurring questions and requests. According to a McKinsey study, using AI in customer service can increase customer satisfaction while reducing service costs.

Collecting data and presenting it neatly in a single document is a big task.

There are many ways to solve it, from complex integrations to forcing senior managers to prepare reports.

But imagine this: a week ago you changed the script, and sales did not change - why?

Or you launched a new product line, but it is not selling - what's going on?

As soon as you have new data cross-sections, you and your analysts have to figure out how to collect them, how to present them in reports, and which figures count as the norm.

Or ask the chatbot "In how many calls this week did reps offer customers this product line".

Or, a couple of days after the training, check the stats: who started applying the new knowledge and how.

The key is to choose the right criteria and questions to evaluate the parameters you care about. And that, you have to admit, is faster than getting the IT team to make time for a new integration or competing with the procurement department for a business analyst.

A regular AI tool requires many extra steps.

To get a transcript, you either let an AI bot into the meeting or upload the meeting recording through a separate interface and then collect the finished files.

The ideal way the AI assistant works, the way we deploy it, looks like this: you or your reps simply press the "record call" button. That's it.

To do this, we integrate the AI assistant with your CRM or any other systems that hold the data you need.

We will build a chatbot or add an extra branch to an existing one.

It pulls recordings from the system you usually hold calls in and customer data from the spreadsheets you want to use to improve the quality of responses.

We will tune the language model to transcribe your specific slang and terms flawlessly. The meeting minutes will contain no interjections, meaningless phrases or accidental slips of the tongue.