Speech Analytics, or assessing the customer service we are providing at any given moment, can be automatedthanks to artificial intelligence for the contact centre.

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

The four steps we need to follow to get the best results are:

1. DEFINING GOALS AND METRICS

First, we need to decide which elements of the conversation we want the bot to consider; for example, politeness, problem-solving skills, protocol adherence, etc.

Once we know what we want to measure, we need to establish metrics for each aspect, such as using a 0-10 rating or qualitative analysis.

2. ACQUIRING A BOT

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

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, which means it should include an ASR (automatic speech recognition) model.

3. ACQUIRING POWER BI

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

4. DEVELOPING THE WORKFLOW

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

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

Step 1 – User request

The process begins when we ask: “Which agent and which call?

Step 2 – Call identification

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

Step 3 – Audio transcription

Once it has found the audio file, it will transcribe the file and convert it to text.

Step 4 – Conversation analysis and summary

The bot will analyse the conversation to assess the aspects we defined in the first step.

If one of the aspects is ‘agent courtesy’, the model will look for indicators of courtesy in the conversation, such as the use of polite language, respectful tone, etc.

It will then assign ratings or tags to the specific aspects according to the metrics we have defined (for example, a score from 1 to 10).

To create a summary of the conversation, it will look for key points such as the reason for the call, the solution provided by the agent and any relevant issues.

Step 5 – Generating the full report IN POWER BI

The bot will compile the transcript, summary and scores and send them to Power BI, and the tool will sync the information every hour.

4. CONFIGURING THE APIs

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

We also need to configure the API to ensure that the bot and Power BI are integrated and can automate report generation.

FINAL THOUGHTS

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

All we need to do now is test and tune the model, deploy the system and, as always, monitor its performance.

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 regulations to comply with GDPR.

If this process is too complex 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 to help.

WANT TO OPTIMISE YOUR CONTACT CENTRE MONITORING?

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AI in customer service has many facets, and one of them is using AI Agents to help team supervisors or managers train and support agents on a daily basis, whether they are new to the team or have been with it for years.

Keeping the department well trained will always help to improve their performance, as it is impossible to provide the best customer experience without the relevant knowledge, which can result in the user being put on hold to seek information.

Perhaps the most important aspect for the industry is improving agent engagement. It’s no secret that the contact centre faces a significant talent retention challenge, as the constant interaction with customers takes a lot of energy.

Therefore, providing agents with a tool that can instantly resolve all their queries is essential to avoid the stress and tension of not being able to address a customer’s concern, thereby contributing to their well-being.

4 USE CASES OF AI FOR AGENT TRAINING

1. AI AGENT AS AN INTERNAL KNOWLEDGE BASE

Imagine a ChatGPT for internal use, integrated into the call centre programme and ready to handle queries.

Both new agents and those handling queries outside their area of expertise can quickly access procedures,policies, product information or solutions to recurring problems during calls or messages.

This helps to broaden perspectives and share knowledge across the team, eliminating information silos.

2. AI AGENT AS A CUSTOMER INTERACTION SIMULATOR

In a completely private environment, a simulator can be programmed with a AI Agent to emulate customers, allowing agents to practice their responses.

The AI Agent can also evaluate agents, highlighting areas for improvement and acknowledging successes, thereby supporting their training process.

3. PROACTIVE INTERVENTION AI AGENT

Similar to copilots in search engines or CRMs, contact centre AI vendors are developing proactive AI Agents that suggest responses or provide tips during conversations.

These AI Agents can suggest next steps or offer solutions to problems.

By constantly analysing interactions and evaluating them against pre-defined parameters, they proactively offer suggestions that agents can choose to apply.

4. SPEECH ANALYTICS AS AI AGENTS

Supervisors or managers can now evaluate all agent conversations.

The bot can be programmed to send reports on conversations categorised as negative experiences, summarise interactions, rate aspects such as empathy and problem-solving skills, and provide full transcripts if required.

Reviews can generate objective ratings from the AI, helping to provide evidence and quickly identify training needs for each agent.

INTERESTED IN INTEGRATING A AI AGENT INTO YOUR CUSTOMER SERVICE TEAM?

La entrada How to use an AI Agent to train call centre agents se publicó primero en Enreach ES.

Implementing automation systems in the contact centre is synonymous with improving service levels, reducing waiting times, improving both the customer and agent experience, better resource allocation and increased efficiency.

Using AI in the contact centre, in addition to all the above benefits, we can make strategic decisions based on data we couldn’t measure before, such as how many customers called us for ‘X’ reason, or how agents performed in each interaction.

The recent democratisation of AI means that the contact centre sector is still in a phase of discovery and testing, so today we’ll demonstrate its potential through eight use cases.

1. FAQ RESOLUTION

AI Agents can handle the most common customer queries. When training the bot to recognise these types of queries, we can either specify the answer it should give, or alternatively let it automatically generate one after reviewing manuals, web pages or conversation histories.

Note: The AI itself will help us identify and add new FAQs in the future.

Use case:

A customer visits our website to check the return policy. After not finding the section, instead of calling customer service, the user can access the online chat on our website and get instructions from the AI Agent.

2. SELF-SERVICE

In addition to answering simple questions, bots can perform more complex tasks that may require accessing a database or sending documentation.

Use case:

A customer calls customer service to reschedule their doctor’s appointment, and our call bot qualifies their intent. After understanding the request, the bot identifies the user by asking for their ID, accesses the appointment system and suggests a new date and time. Once the new appointment is agreed, the AI records the change in the system.

3. TICKET MANAGEMENT

AI-powered bots can automate ticket creation and updates to reduce After Call Work (ACW).

If the bot recognises during the intent qualification that it can’t help the customer, it will transfer the call to the best available agent and simultaneously create a ticket in the system.

Note: If the customer calls to follow up on an incident that has already been registered, we can also train the bot to update the associated ticket.

Use case:

A customer calls the contact centre because they have no electricity at home and our call bot qualifies their intent. After identifying the customer, it accesses the ticketing system and creates a new ticket, filling in all the necessary fields.

4. SPEECH ANALYTICS

AI can also benefit contact centre supervisors or managers, as its ability to analyse a large amount of data can help them generate detailed reports to evaluate agents.

Use case:

The customer service manager can create a report for a team member detailing their performance based on service level, tone and efficiency.

5. FRICTION POINT ANALYSIS

Customer experience (CX) managers will also benefit from AI’s analytical capabilities. As the bot collects data after each interaction, it’s easy to see what friction points customers encounter when contacting customer service.

Use case:

An airline’s CX manager can quickly see how many people have called to complain about lost luggage.

6. CALL TRANSCRIPTION

This powerful technology can also transcribe all calls in real time. In addition to creating a document that separates the dialogue between the customer and the agent, we can also program the bot to summarise and/or label the conversation.

Use case:

An agent is talking to a customer; instead of taking notes, the agent can focus on solving the problem. Then they don’t have to typify the conversation and can move on to the next person in the queue.

7. AGENT SUPPORT

As well as freeing agents from many administrative tasks, AI can also be programmed to assist operators during customer interactions.

Whether it’s through an internal ChatGPT where they can type during a call to resolve a product/service query, or as a proactive bot that analyses the conversation in real time and suggests responses.

Use case:

A customer service agent is on the phone with a customer who has a technical problem. Instead of transferring the call to the technical department, the agent can enter the internal ChatGPT. The bot will check the manuals and provide an immediate answer.

8. EXPERIENCE PERSONALISATION

Generative AI can recommend products/services based on user responses. To do this, we need to feed the bot with our portfolio of products/services and their buyer personas.

Use case:

A user arrives at a bank’s website because they need to apply for a credit card. They turn to the AI Agent to ask about the types of cards available and which one is best for them. The bot asks about their spending habits and recommends the most suitable card based on the answers.

FINAL THOUGHTS

There is still a lot of uncertainty about how artificial intelligence can be applied in the contact centre. Contrary to what many may think, this technology doesn’t have the ability to replace humans, but it can replicate the way we communicate and the methodology we use to perform administrative tasks.

AI solutions for customer service have a positive impact on many aspects that are vital to businesses, such as customer experience and, in recent years, agent experience.

So it’s no wonder that after implementation, our NPS increases, our cross-sales increase and our churn rate decreases.

Want to discover the power of Artificial Intelligence for your contact centre? Call us on +34 900 670 750 or fill in this form.

La entrada 8 of the most common AI automations used in customer service se publicó primero en Enreach ES.

In this article, you’ll find the essential software to help you carry out the day-to-day tasks of running a contact centre and, most importantly, to drive continuous improvement.

1. CONTACT CENTRE SOFTWARE: COMMUNICATIONS

It’s impossible to imagine a customer service team without contact centre software to communicate with customers by phone, chat, email, WhatsApp… Or without a dashboard for supervisors to measure service levels, or an agent panel to respond to customers.

Contact centre software is essential.

2. CUSTOMER MANAGEMENT PROGRAMME: CRM, ERP OR TICKETING

It’s essential to have a programme for managing customers, from their contact details to contracted services, communications, call notes, etc. This solution can be a CRM, an ERP or a ticketing tool, depending on the needs of the call centre.

3. BUSINESS INTELLIGENCE TOOLS: DATA-DRIVEN DECISIONS

Using data extracted from contact centre software, these programs allow you to create dynamic reports in real timeand visually track and analyse large amounts of data.

4. SPECH ANALYTICS PROGRAMME: CONTINUOUS IMPROVEMENT

With a focus on continuous process improvement, quality management tools use technologies such as artificial intelligence and conversational analysis to analyse all interactions that take place in the customer service centre. They are used to streamline audits and work on continuous improvement.

5. SPEECH ANALYTICS SOFTWARE: CONVERSATION ANALYSIS

Using recordings stored in your contact centre programme, these tools make it easy to listen to and analyse millions of calls and chats.

6. CAMPAIGN MANAGER: AUTOMATIC DIALLER

Important: this programme is only necessary if a contact centre makes outbound calls. 

Autodialers free agents from having to manually dial each digit of a phone number, increasing call time and the volume of calls they handle each day.

Request a demo by calling our team of experts on +34 900 670 750 or using the chat box below.

La entrada 6 software every contact centre should have se publicó primero en Enreach ES.