{"id":50784,"date":"2024-08-22T09:42:07","date_gmt":"2024-08-22T07:42:07","guid":{"rendered":"https:\/\/enreach.es\/?p=50784"},"modified":"2025-11-27T09:17:16","modified_gmt":"2025-11-27T08:17:16","slug":"how-to-use-ai-to-monitor-conversations-in-the-contact-centre","status":"publish","type":"post","link":"https:\/\/enreach.es\/en\/blog\/how-to-use-ai-to-monitor-conversations-in-the-contact-centre\/","title":{"rendered":"How to use AI to monitor conversations in the contact centre"},"content":{"rendered":"
Level: <\/strong>Intermediate<\/h5>\n

Speech Analytics<\/strong>, or assessing the customer service we are providing at any given moment, can be automated<\/strong>thanks to artificial intelligence for the contact centre<\/a><\/strong>.<\/p>\n

This technology is capable of analysing millions of pieces of data<\/strong>, classifying them and presenting an assessment in a matter of seconds, which means we can evaluate every single conversation<\/strong> that has taken place with just one query.<\/p>\n

The four steps<\/strong> we need to follow to get the best results<\/strong> are:<\/p>\n

1. DEFINING GOALS AND METRICS<\/strong><\/h2>\n

First, we need to decide which elements of the conversation we want the bot to consider; for example, politeness<\/strong>, problem-solving skills<\/strong>, protocol adherence<\/strong>, etc.<\/p>\n

Once we know what we want to measure, we need to establish metrics<\/strong> for each aspect, such as using a 0-10 rating or qualitative analysis.<\/p>\n

2. ACQUIRING A BOT<\/strong><\/h2>\n

To achieve high Speech Analytics, the bot must be able to generate summaries<\/strong>. This means we need generative AI with an NLP (Natural Language Processing) model<\/strong> to process the language and evaluate the content.<\/p>\n

Important! If we want to monitor the calls in our contact centre, we also need to ensure that the bot is able to transcribe speech to text<\/strong>, which means it should include an ASR (automatic speech recognition) model.<\/strong><\/p>\n

3. ACQUIRING POWER BI<\/strong><\/h2>\n

This tool, developed by Microsoft<\/a>, allows us to have customised charts<\/strong> with the information that the AI extracts from all conversations, as well as tables that record the analysis and summary of each call.<\/p>\n

4. DEVELOPING THE WORKFLOW<\/strong><\/h2>\n

Once we’ve got the bot and Power BI in place, we need to create the workflow that the AI will follow to collect<\/strong>, analyse<\/strong> and send<\/strong> the information so that it’s synchronised within Power BI.<\/p>\n

For example, if we want the bot to provide an assessment of a call that includes a summary<\/strong>, the agent’s courtesy rating<\/strong>, and the original audio<\/strong> of the call, we need to create the following workflow:<\/p>\n

Step 1 – User request<\/strong><\/h3>\n

The process begins when we ask: “Which agent and which call?<\/p>\n

Step 2 – Call identification<\/strong><\/h3>\n

The bot searches the database for the specific call using the information we have provided (agent name<\/strong>, date and time<\/strong> of the call, or any other unique identifier<\/strong>).<\/p>\n

Step 3 – Audio transcription<\/strong><\/h3>\n

Once it has found the audio file, it will transcribe<\/strong> the file and convert it to text.<\/p>\n

Step 4 – Conversation analysis and summary<\/strong><\/h3>\n

The bot will analyse the conversation to assess the aspects we defined in the first step.<\/p>\n

If one of the aspects is ‘agent courtesy’<\/strong>, the model will look for indicators of courtesy in the conversation, such as the use of polite language, respectful tone, etc.<\/p>\n

It will then assign ratings<\/strong> or tags<\/strong> to the specific aspects according to the metrics we have defined (for example, a score from 1 to 10).<\/p>\n

To create a summary of the conversation<\/strong>, it will look for key points such as the reason for the call, the solution provided by the agent and any relevant issues.<\/p>\n

Step 5 – Generating the full report IN POWER BI<\/strong><\/h3>\n

The bot will compile the transcript, summary and scores and send them to Power BI, and the tool will sync the information every hour.<\/p>\n

4. CONFIGURING THE APIs<\/strong><\/h2>\n

Once the process is defined, there are two things to consider. Firstly, if we are using a third party tool for transcription and analysis<\/strong>, we need to ensure that the API<\/strong> works with the bot.<\/p>\n

We also need to configure the API<\/strong> to ensure that the bot and Power BI are integrated and can automate report generation.<\/p>\n

FINAL THOUGHTS<\/strong><\/h2>\n

After following these steps, our bot will be ready to provide us with scores for every agent and every call in our contact centre.<\/p>\n

All we need to do now is test and tune<\/strong> the model, deploy<\/strong> the system and, as always, monitor<\/strong> its performance.<\/p>\n

Of course, we’ll also need to ensure internally that staff know how to use the bot, and technically that the AI meets privacy and security<\/strong> regulations to comply with GDPR<\/strong>.<\/p>\n

If this process is too complex<\/strong> for your team or you don’t have someone with the skills to set up a bot, don’t hesitate to contact our technical team<\/strong> to help.<\/p>\n

WANT TO OPTIMISE<\/span> YOUR CONTACT CENTRE MONITORING?<\/strong><\/h2>\n