As a leading European provider of smart contact solutions, has acquired Numintec from Telavox in Spain. This transaction strengthens Enreach’s market position and its ability to serve customers and partners in Spain with tailored communications solutions, local market expertise and the benefits of an international European organisation.

Strategically located in Barcelona, where Enreach already has important contact centre operations, Numintec adds 20 years of experience in CCaaS and a team of 60 experts to Enreach’s contact centre business unit.

“We welcome our new colleagues from Numintec. We are very pleased to be joined by such a team of professionals with a proven track record and relevant expertise. This acquisition will advance Enreach’s position in Spain, where Numintec brings specialist capabilities and a complementary solution portfolio in the CCaaS sector”.

Alfred Nesweda, CEO Enreach for Contact Centers

The transaction in Spain reflects Enreach’s strategic focus on key customer segments, including Contact Centre, Small Business, Midsize and Service Providers.

“This acquisition is an important step in the evolution of Enreach. We continue to build stronger presence in key markets and drive more scale and capability to deliver the best offer for our Contact Centre customers. Numintec adds valuable expertise and capabilities that will help us further strengthen our Contact Centre proposition”.

Stijn Nijhuis, CEO of Enreach

CONTINUITY AND LONG-TERM CUSTOMER VALUE

For customers, partners and employees of Numintec, continuity remains a key priority. Numintec will become part of Enreach and will continue to operate from its current locations, supported by its experienced local team.

“Joining Enreach creates new opportunities for Numintec and its customers. As part of a strong European group and working alongside Enreach’s Contact Centre experts with a clear focus on customer needs, we look forward to further developing our business and creating even more value for customers”.

Albino Campo, CEO at Numintec

La entrada Enreach strengthens contact centre position through acquisition of Numintec se publicó primero en Enreach ES.

In many companies, a large share of inbound calls tends to be repeated again and again: simple queries, requests for information, basic validations or processes that do not always require a human agent. When all of that volume goes through the team, waiting times increase, operational pressure rises and it becomes harder to dedicate time to the cases that really need personalised attention.

This is where the voicebot for customer service comes in: a solution that uses artificial intelligence to talk to the customer, understand their intent and resolve or route the call automatically.

For supervisors, managers and operations leaders, this has a direct impact on three very specific areas:

• Service efficiency
• The ability to absorb higher volumes
• The customer experience in simple or repetitive calls

In this article, you will see what a voicebot is, how it works, what benefits it can provide and in which situations it is especially worth implementing.

• 1. What a voicebot for customer service is
• 2. Why it can be important in a contact centre
• 3. How a voicebot works
• 4. What tasks it can handle
• 5. Operational benefits for supervisors and managers
• 6. In which cases it is especially worth implementing
• 7. Common mistakes when planning it.
• 8. Practical example.
• 9. Conclusion.

1. WHAT A VOICEBOT FOR CUSTOMER SERVICE IS

A voicebot for customer service is a system that uses artificial intelligence to hold a voice conversation with the customer, understand what they need and try to resolve the call or route it correctly.

Put simply, it works in a similar way to a chatbot, but instead of operating through written conversations, it works in voice channels.

A voicebot can, for example:

• Answer frequently asked questions
• Identify the reason for the call
• Guide the customer through simple processes
• Collect information before passing the call to an agent
• Route the interaction to the most suitable team

Within Enreach’s current offering, AI Agents for customer service can operate as voicebots in phone calls or click&talk sessions, as well as functioning as chatbots in other channels. :contentReference[oaicite:0]{index=0}

2. WHY IT CAN BE IMPORTANT IN A CONTACT CENTRE

In many contact centres, part of the incoming volume is made up of repetitive calls, low-value queries or requests that follow a fairly predictable pattern.

When all of those interactions always depend on a human agent, several problems tend to appear:

• Waiting times increase
• The team’s workload rises
• Capacity to handle more complex cases is reduced
• The customer experience suffers during peak periods

That is why a voicebot should not be seen as just another automation. In many cases, it is a way to absorb simple calls, improve first response and free up agents for higher-value work.

Enreach presents this exact logic within its AI offering, where the AI Agent can handle reasons for calling and resolve queries in voice, while the wider omnichannel and automation ecosystem helps scale the service. :contentReference[oaicite:1]{index=1}

3. HOW A VOICEBOT WORKS

Although each solution may vary, the overall logic is usually quite similar.

– IT LISTENS AND TURNS VOICE INTO TEXT

The first step is to interpret what the customer is saying. To do this, the technology converts speech into text and analyses the information contained in the call.

– IT IDENTIFIES THE CUSTOMER’S INTENT

The artificial intelligence then detects what the customer is asking for, what their need is and what kind of response is required.

– IT RESPONDS OR GUIDES THE PROCESS

If the request can be resolved automatically, the voicebot responds, provides information or guides the customer through the next steps.

– IT ROUTES THE CALL WHEN NEEDED

If the case requires human assistance, the voicebot can transfer the call to the most suitable agent or team, ideally with the context already collected.

– IT RECORDS USEFUL INFORMATION

When the platform is properly integrated, the interaction can leave useful traceability for CRM, reporting, classification or later analysis.

This type of setup works especially well when the environment is supported by AI for customer service and an Omnichannel Contact Centre platform that brings together context, channels and contact distribution. :contentReference[oaicite:2]{index=2}

4. WHAT TASKS IT CAN HANDLE

The value of a voicebot depends heavily on the type of calls assigned to it. It works especially well when the tasks are clear, repetitive and follow a relatively predictable flow.

For example, it can help to:

• Handle frequently asked questions
• Provide information about opening hours, status or basic conditions
• Collect data before transferring the call to an agent
• Classify the reason for contact
• Support simple processes
• Filter calls that do not need human intervention

On Enreach’s website, the voice AI Agent is presented as a solution capable of handling calls, identifying customer intent, resolving common questions and guiding customers through simple processes. :contentReference[oaicite:3]{index=3}

5. OPERATIONAL BENEFITS FOR SUPERVISORS AND MANAGERS

This is where the voicebot starts to prove its real value in contact centre management.

– IT REDUCES THE TEAM’S WORKLOAD

When a share of repetitive calls is handled automatically, agents can focus more effectively on cases that genuinely require judgement, empathy or more complex resolution.

– IT IMPROVES RESPONSE CAPACITY

During high-volume periods, a voicebot helps absorb more interactions without requiring the team to grow at the same pace.

– IT PROVIDES CONTEXT BEFORE TRANSFER

If the case needs to be escalated to an agent, arriving with the intent already qualified and some information already collected makes the following conversation much more efficient.

– IT HELPS YOU SCALE WITHOUT LOSING CONTROL

When it forms part of a broader platform, the voicebot does not just automate. It also helps organise operations more effectively and gives the business better traceability.

– IT IMPROVES THE EXPERIENCE IN SIMPLE CASES

There are many calls where the customer does not want to wait or speak to several people. They simply want a quick answer. In those cases, a voicebot can clearly improve the experience.

When the operation is also connected with other channels and tools, a cloud omnichannel contact centre solution helps ensure that automation is not isolated from the rest of the service. :contentReference[oaicite:4]{index=4}

6. IN WHICH CASES IT IS ESPECIALLY WORTH IMPLEMENTING

Not every operation needs the same thing. But there are situations where implementing a voicebot usually makes a great deal of sense.

– WHEN THERE ARE MANY REPETITIVE CALLS

If a significant share of the incoming volume follows the same pattern again and again, automating that layer can take a great deal of pressure off the team.

– WHEN WAITING TIMES ARE TOO HIGH

If the customer is waiting too long to resolve something simple, a voicebot can significantly improve the initial response.

– WHEN YOU WANT TO FILTER BETTER BEFORE PASSING TO AN AGENT

Qualifying intent properly before transfer helps improve routing, context and the likelihood of successful resolution.

– WHEN THERE ARE PEAKS IN DEMAND

During campaigns, large-scale incidents or other high-volume moments, the voicebot can absorb part of the traffic without sharply increasing pressure on the team.

– WHEN THE COMPANY WANTS TO ADVANCE SELF-SERVICE

If the goal is to offer customers more autonomy and reduce friction in simple queries, this type of solution is a very good fit.

7. COMMON MISTAKES WHEN PLANNING IT

For a voicebot to deliver real value, it is worth avoiding some very common mistakes.

– TRYING TO AUTOMATE EVERYTHING

Not every call should go through a bot. The key is to choose carefully which processes to automate and which ones need human attention from the start.

– NOT DEFINING THE USE CASES PROPERLY

If it is not clear what the voicebot should resolve and what it should transfer, the result is usually a more confusing experience.

– FORGETTING THE AGENT’S CONTEXT

When the bot transfers a call without properly passing on the information it has already gathered, a large part of its value is lost.

– NOT LOOKING AFTER THE KNOWLEDGE BASE

The quality of the responses depends heavily on the information used to train and feed the solution.

– MEASURING ONLY SAVINGS AND NOT EXPERIENCE

Reducing workload matters, but you also need to measure whether the customer is receiving an experience that is genuinely useful and smooth.

8. PRACTICAL EXAMPLE

Imagine a contact centre receiving many calls related to appointments, basic billing questions and simple request follow-up.

Until now, all of that volume goes directly to the team. Over time, several problems appear:

• Waiting times rise too much
• Agents spend too much time on simple tasks
• Complex cases compete for capacity with repetitive calls
• During peaks in demand, the operation becomes strained very quickly

After implementing a voicebot to filter, inform and collect context in the most predictable calls, the team is able to free up capacity, improve first response and scale much better during high-volume moments.

The result is not only an operational improvement. It is also a faster and more organised service for the customer.

9. CONCLUSION

A voicebot for customer service can deliver a great deal of value when it is used to automate simple calls, qualify customer intent more effectively and relieve the team of repetitive tasks.

It is not about replacing agents, but about building an operation that is more scalable, more efficient and better prepared to handle volume without losing quality.

If you want to move towards more automated voice service, with more context and stronger response capacity, this is one of the most useful ways to begin.

REQUEST INFORMATION HERE TO AUTOMATE VOICE CUSTOMER SERVICE WITH VOICEBOTS

La entrada Voicebot for Customer Service: What it is and when it makes sense se publicó primero en Enreach ES.

The 17th edition of the Platinum CX Awards was much more than just an awards gala; it was the ultimate gathering for over 400 professionals across the customer experience industry, coming together once again to celebrate the talent, innovation, and commitment of end-user companies, contact centres, BPOs, and tech providers.

BEST IT PROJECT IN BPO & BACK OFFICE SOLUTIONS AWARD

In the “Technological Innovation” category, we were recognised with the Best IT Project in BPO & Back Office Solutions award for our collaboration on the 010 helpline for Sevilla City Council; a benchmark project within the public sector.

Jose Luís Gil, our Customer Success Manager, and Alvaro Figueira, Director of SBU Industries & Public Administration Solutions at Servinform, stepped up to collect the accolade, which was presented by Juan Carlos Alcaide, CX Ambassador and expert in empathy, CX, marketing, and the Silver Economy.

Jose Luís Gil, Customer Success Manager at Enreach, Juan Carlos Alcaide, CX Ambassador and expert in empathy, CX, marketing, and the Silver Economy, and Alvaro Figueira, Director of SBU Industries & Public Administration Solutions at Servinform.

THE SEVILLA 010 PROJECT

THE CHALLENGE: MAKING CITIZEN SUPPORT MORE HUMAN AND STREAMLINED

The lack of digitalisation in local government procedures has always been a major friction point for citizens. In fact, studies confirm that 43% of Spaniards are dissatisfied with administrative services.

With a large percentage of the population now digitally active, the 010 service needed to evolve to meet the demands of younger citizens without losing the quality of face-to-face support.

The challenge, therefore, was to scale up the workforce both in branch offices and over the phone, interconnect touchpoints to deliver seamless experiences, and automate high-impact tasks to enhance both the customer and agent experience.

THE SOLUTION: OMNICHANNEL & ARTIFICIAL INTELLIGENCE

On a technological level, our solutions are delivering three key benefits:

• A 360° view for agents: Our contact centre software centralises all channels into a single dashboard. This ensures that no matter which touchpoint citizens choose, agents can assist them with full visibility of their history, without needing to jump between different tools.
• A 360° view for supervisors: To ensure total operational control, supervisors have access to two dashboards (one for voice channels and another for digital channels) to monitor key KPIs, services, queues, overflows, and agent status.
• Smart automation with AI Studio: Our AI orchestrator has enabled the deployment of voice and chat virtual agents. These are capable of fully understanding a citizen’s intent and handling appointment bookings, bill payments, and reporting council incidents 100% autonomously.

Whenever a citizen needs something else, the system is smart enough to transfer the conversation to the best available human agent, providing full context so the customer never has to repeat themselves.

THE RESULTS SPEAK FOR THEMSELVES

The combination of conversational AI and human talent achieved the following in 2025:

• Automated responses for over 465,000 calls
• Automatically resolved 50% of 120,000+ WhatsApp conversations
• A 20% increase in First Call Resolution (FCR)
• A 15% reduction in Average Handling Time (AHT)

This new approach not only frees agents from repetitive tasks so they can focus on cases that truly require human empathy, but it also provides citizens with local authority support available 24 hours a day, 7 days a week.

We would like to say a massive thank you to the entire Servinform team who made this possible: Álvaro Figueira, Flor Hernández, Sara Hernández, Héctor Rodríguez, Sebastián Pacheco, and Jesús Pacheco. Thank you for trusting us to transform the experience of millions of citizens!

WANT TO FIND OUT HOW CONVERSATIONAL AI CAN TRANSFORM CUSTOMER SUPPORT WITHIN YOUR ORGANISATION?

La entrada We have won the 2026 Platinum CX Award for Best IT Project in BPO & Back Office Solutions se publicó primero en Enreach ES.

Customer satisfaction is shaped by every interaction and depends on many factors: whether support is available across all channels, how long customers have to wait, how quickly their queries are resolved, how empathetic the service is and how well the interaction is personalised.

That’s why measuring customer satisfaction goes far beyond a simple CSAT survey. Here are nine key KPIs that will give you a 360-degree view of your customers’ satisfaction:

TRADITIONAL METRICS FOR MEASURING CUSTOMER SATISFACTION

1. CSAT

Customer Satisfaction Score (CSAT) is derived from direct surveys where customers rate their satisfaction after interacting with an agent.

Optimal score: 80% or higher.

Tips for improving this KPI

• Use chatbots to handle frequently asked questions (FAQs).
• Automate email notifications when a ticket is created, under review or resolved.
• Provide empathy and active listening training to agents who need it.

2. NPS

The Net Promoter Score (NPS) measures how likely customers are to recommend your brand to others.

Optimal score: 50 or above.

Tips for improving this KPI

• Evaluate the quality of service provided for the most common requests.
• Ensure customers don’t have to call back for the same issue by implementing proactive solutions.

3. FCR

First Call Resolution (FCR) measures how effectively customer issues are resolved on the first call, reducing the need for follow-up calls.

Optimal score: 70% or higher.

Tips for improving this KPI

• Train each agent with the knowledge they need to resolve issues independently.
• Develop a knowledge base so that all agents have access to accurate information.

4. AVERAGE QUEUE TIME

This metric calculates how long customers wait in a queue before being served.

Optimal score: Less than 30 seconds.

Tips for improving this KPI

• Hire additional staff to handle peak times.
• Provide a comprehensive FAQ section on your website.
• Implement a call bot to assess customer intent and route them to the best available agent.

5. AHT

Average Handle Time (AHT) measures the average duration of a call, including waiting time and post-call processes.

Optimal value: between 4 and 6 minutes.

Tips for improving this KPI

• Simplify internal processes that need to be completed during calls.
• Integrate all possible tools with your customer service software (CRM, ticketing system, ERP, collaboration tools, etc.).
• Automate administrative tasks to speed up After Work Call (AWC) processes.

COMPLEMENTARY METRICS TO MEASURE CUSTOMER SATISFACTION

6. CES

The Customer Effort Score (CES) is a measure of the effort required by a customer to resolve a problem. The lower the effort, the higher the satisfaction.

Optimal score: 3 or less (on a scale of 1 to 7).

Tips for improving this KPI

• Implement self-service strategies to address common questions. For example, use an AI chatbot on your website to handle FAQs and reduce the need for customers to call support.

7. CALL TRANSFER RATE

This metric shows how many times a call is transferred before it is resolved. Frequent transfers may indicate inefficiency.

Optimal score: Less than 10%.

Tips for improving this KPI

• Route calls based on agent skills to ensure customers are connected to the right person.
• Build a knowledge base so that every agent can handle level 1 queries.

8. ABANDONMENT RATE

This KPI measures the percentage of customers who hang up before being served, often due to long waiting times or inefficient processes.

Optimal score: Less than 5%.

Tips for improving this KPI

• Implement a callback system to reduce the frustration of waiting.
• Replace outdated IVR menus with a generative AI call bot to manage complex queries more effectively.

9. CUSTOMER RETENTION RATE

This metric measures the ability to retain customers and prevent churn.

Optimal score: 90% or higher.

Tips for improving this KPI

• Conduct NPS and CSAT surveys to identify service friction points.
• Monitor customer sentiment using sentiment analysis tools.
• Work with sales and marketing teams to develop loyalty campaigns.

CUSTOMER SENTIMENT ANALYSIS IS ESSENTIAL

Sentiment analysis is becoming increasingly popular thanks to the integration of AI into contact centres. Put simply, this technology uses natural language processing (NLP) algorithms to interpret human communication, summarise, categorise and assess customer satisfaction at the end of a call. It then classifies interactions as satisfied, neutral or dissatisfied.

PROS

Provides more natural and spontaneous feedback compared to NPS and CSAT surveys, as it analyses real-time interactions without relying on customer memory.

When integrated with Business Intelligence (BI) software, it helps identify trends and patterns to pinpoint the types of queries that cause dissatisfaction.

Facilitates the identification of targeted training needs by filtering interactions flagged as ‘dissatisfied’ and linking them to specific agents.

CONS

Struggles to detect sarcasm and irony, leading to potential misinterpretations. Human supervisors remain essential to review and validate bot interpretations.

Requires investment in AI technology and tools, although this technology is becoming increasingly accessible, even for SMEs.

OUR AI EXPERTS ARE AT YOUR SERVICE

La entrada 9 call centre metrics to measure customer satisfaction se publicó primero en Enreach ES.

When FCR is low, repeat calls increase, customer friction rises, and team efficiency suffers. If a customer has to call back to resolve the same issue, something is going wrong.

That is exactly the idea behind FCR, or First Contact Resolution: measuring how many queries are resolved during the first interaction, without the need for further calls, emails, or unnecessary follow-up.

For supervisors, managers, and operations leaders, this metric is especially useful because it clearly connects with three critical areas of the contact centre:

• The customer experience.
• The efficiency of customer service.
• The team’s workload.

On the Enreach blog, FCR is already highlighted as one of the most important service metrics and as an indicator directly linked to customer satisfaction.

In this article, you will see what FCR is, how it is calculated, why it matters so much, and which actions can help you improve it.

• 1. What FCR is.
• 2. Why FCR is such an important metric.
• 3. How FCR is calculated.
• 4. What good FCR indicates.
• 5. Which factors affect FCR.
• 6. How to improve FCR in a contact centre.
• 7. How FCR relates to other metrics.
• 8. Common mistakes when trying to improve it.
• 9. Practical example.
• 10. Conclusion.

1. WHAT FCR IS

FCR (First Contact Resolution), also referred to as resolution at first contact or first call resolution, measures a contact centre’s ability to resolve a customer query during the first interaction, without the need for further contact about the same issue.

Put simply: the higher the FCR, the fewer times the customer has to get back in touch to resolve the same issue.

And that has very direct consequences for both the customer and the profitability of the customer service operation.

2. WHY FCR IS SUCH AN IMPORTANT METRIC

FCR is not just another KPI. It is an especially valuable metric because it gives a very clear indication of whether the service is genuinely useful for the customer.

When FCR is low, this is what usually happens:

• Customer dissatisfaction increases with the company or the customer service team.
• The volume of repeat contacts grows.
• The team has to spend more time on issues that should have been closed earlier.

In addition, resolving issues during the first contact makes the service more efficient and reduces the operational cost of dealing with the same case again.

3. HOW FCR IS CALCULATED

The most common formula is:

FCR = (Number of cases resolved on first contact / Total number of cases) x 100.

For example, if a team handles 1,000 enquiries and resolves 760 during the first interaction, the FCR would be:

(760 / 1,000) x 100 = 76%.

As a general benchmark, an FCR of 70% or higher is usually seen as positive, although the right figure always depends on the type of service, the complexity of the queries, and the channel.

4. WHAT GOOD FCR INDICATES

A high FCR usually indicates that:

• Agents have the information they need to resolve requests, either with AI Agents supporting them in real time or through internal documentation.
• Internal processes are well designed and make first-contact resolution possible.
• The customer receives a solution first time, without having to get in touch several times, and ends up happy with the service received.

A low FCR, on the other hand, may point to problems such as:

• Unnecessary transfers.
• Lack of knowledge.
• Unclear processes.
• Poor visibility of the customer’s history.

That is why FCR is often closely linked to other operational indicators such as ASA, AHT, repeat calls, or satisfaction levels.

5. WHICH FACTORS AFFECT FCR

Many different elements can push this metric up or down. These are some of the most important.

– AGENT KNOWLEDGE

If the agent does not have the necessary information or does not fully understand the process, it will be much harder to close the case during the first interaction.

– ACCESS TO CUSTOMER CONTEXT

When there is no access to previous contact history or the real status of the case, the likelihood of the customer needing to get back in touch increases.

– ROUTING AND CALL DISTRIBUTION

If the query reaches the wrong agent or goes through too many transfers, FCR suffers.

– INTERNAL PROCESSES

Sometimes the issue is not the service itself, but rather slow processes, unnecessary approvals, or a lack of coordination between teams.

– AVAILABLE TOOLS

Technology also has an impact. Supporting agents with the right processes and tools is key to improving first-contact resolution.

When the software can integrate channels, open customer records automatically, classify contact reasons, summarise conversations, and assist the agent in real time, it becomes much easier to resolve issues without escalating or repeating steps.

For example, using AI to support customer service agents can help.

6. HOW TO IMPROVE FCR IN A CONTACT CENTRE

This is the most important part for a supervisor or manager: what to do to raise this metric without damaging the customer experience or overloading the team.

– GIVE AGENTS BETTER ACCESS TO INFORMATION

The easier it is to access all relevant information, such as customer history, the reason for contact, and previous actions, the more likely it is that the issue will be resolved during the first interaction.

What matters here is not only having the data, but having it available at the right moment. When customer service software is integrated with the CRM, ERP, or ticketing tool, the agent can see the customer context as soon as the interaction begins and avoid repeated questions or unnecessary transfers.

In addition, if the platform includes AI for customer service, it can also provide the agent with a summary of the previous conversation, the detected intent, and even a transcript of what the customer has already explained. This speeds up handling and increases the chances of resolving the issue first time.

– REDUCE UNNECESSARY TRANSFERS

The more transfers there are from one agent to another, the harder it becomes to close the case quickly and properly.

– IMPROVE ROUTING

Assigning each query to the most appropriate team from the outset is one of the most effective ways to improve FCR.

It is not just about distributing calls, but about directing them better. If a complex technical issue reaches an agent without the right knowledge, the likelihood of a second call increases. By contrast, when routing takes skills, query type, or even the last agent the customer spoke to into account, first-contact resolution improves significantly.

In more complex operations, a cloud contact centre with smart routing helps distribute calls and conversations across channels more intelligently and with more context.

– STRENGTHEN CONTINUOUS TRAINING

Training each agent well and maintaining a clear knowledge base helps them respond better and close more cases during the first interaction.

But today, training no longer depends only on periodic sessions or manuals. It can also be reinforced with internal AI-based assistants that support the agent live, checking procedures, policies, or recommended responses while they are speaking to the customer.

When the team has this real-time support, doubts are reduced, unnecessary escalations fall, and it becomes much easier to resolve the issue correctly from the very first conversation.

– QUALIFY INTENT BETTER BEFORE PASSING THE INTERACTION TO AN AGENT

In many contact centres, the problem starts before the agent even answers. If the customer’s need is not identified properly from the beginning, transfers increase, context is lost, and the likelihood of resolving the issue first time drops.

That is why one of the most useful levers for improving FCR is to qualify intent better from the very first seconds, whether in voice or digital channels. When technology can summarise the reason for contact, detect emotions, and pass that context to the agent, the conversation begins with a major advantage. Using intelligent IVRs is very helpful for qualifying intent more effectively.

– USE TECHNOLOGY TO DETECT ROOT CAUSES

When repeat contacts are analysed properly, it becomes much easier to identify which types of issue are preventing first-contact resolution.

Here, tools such as Speech Analytics for call centres or AI for customer service can help detect patterns, classify contact reasons, and find friction points more quickly.

In addition, it is worth monitoring FCR in real time, both at individual and team level, to detect which processes are failing or which types of query are being resolved less effectively. And when this is combined with satisfaction surveys such as CSAT or NPS, it becomes much easier to understand not only whether the case was closed, but also how the customer perceived the resolution.

7. HOW FCR RELATES TO OTHER METRICS

FCR should not be analysed in isolation. It has a direct relationship with other contact centre metrics.

– FCR AND CUSTOMER SATISFACTION

When the issue is resolved first time, the experience usually improves significantly.

– FCR AND CONTACT VOLUME

A low FCR generates more repeat contacts and more pressure on the team.

– FCR AND PRODUCTIVITY

Improving this metric helps free up capacity because it avoids handling the same cases again and again.

– FCR AND AGENT EXPERIENCE

It also affects the team. When the service does not resolve issues first time, operational pressure increases and the day-to-day perception of the work gets worse.

8. COMMON MISTAKES WHEN TRYING TO IMPROVE IT

– OBSESSING OVER THE NUMBER ALONE

Improving FCR matters, but not at the cost of closing cases artificially or forcing weak resolutions.

– NOT ANALYSING THE REASONS FOR REPEAT CONTACTS

If you do not know why the customer is getting back in touch, it is very difficult to improve in any meaningful way.

– FAILING TO CONNECT QUALITY, OPERATIONS, AND TRAINING

FCR improves far more when quality, supervision, and operations work from the same findings.

– MEASURING IT WITHOUT CONTEXT

Not all services have the same level of complexity. Comparing FCR values without taking the type of operation into account can lead to misleading conclusions.

9. PRACTICAL EXAMPLE

Imagine a contact centre receiving large numbers of queries about billing and technical support.

The team notices that many customers are calling back about the same issue within 48 hours. When they review the problem, they discover that:

• Some agents do not have access to all the information.
• Many calls are being transferred unnecessarily.
• Certain incidents are not being classified properly.

After improving access to history, reviewing routing, and strengthening the knowledge base, the team manages to resolve more cases during the first interaction.

The effect is not only an increase in FCR. Repeat calls also fall, the customer experience improves, and operational pressure is reduced.

10. CONCLUSION

FCR in a contact centre measures something very simple but very important: whether the customer gets a solution during their first contact or has to keep coming back.

That is why it is such a valuable metric for supervisors, managers, and operations leaders. It helps show whether the service is truly effective, how much extra effort it generates, and where action is needed to improve it.

If you want to improve FCR, there are usually three levers that make the biggest difference:

• Better access to information.
• Better distribution of contacts.
• Better analysis of what is going wrong.

And that is where a well-connected strategy combining omnichannel service, conversation analysis, and intelligence applied to the contact centre can deliver real value.

LET US HELP YOU IMPROVE YOUR FCR WITH ENREACH OMNICHANNEL

La entrada What FCR Is in a Contact Centre and How to Improve It se publicó primero en Enreach ES.

If you’re looking for a solution specifically designed for this type of analysis, Speech Analytics for call centres allows you to transcribe, classify and evaluate conversations automatically, without relying on manual call listening across a limited sample.

In this article, you’ll discover how automated call evaluation works, the benefits it offers contact centres and why it can help improve quality, productivity and supervisory capabilities.

• 1. What automated call evaluation is.
• 2. Why manual monitoring falls short.
• 3. How AI-powered call evaluation works.
• 4. What can be analysed using this system.
• 5. Operational benefits for supervisors and managers.
• 6. When it makes particular sense to implement it.
• 7. Common mistakes when rolling it out.
• 8. Practical example.
• 9. Conclusion.

1. WHAT IS AUTOMATED CALL EVALUATION

Automated call evaluation is a way of reviewing service quality using artificial intelligence to analyse conversations at scale, consistently and far more quickly than traditional manual monitoring.

Instead of listening to a handful of random calls, the system can review a much larger volume of interactions and automatically identify useful signals for quality assurance, operations and continuous improvement.

This makes it possible to move from monitoring based on small samples to a much broader understanding of what’s really happening across the contact centre.

In practice, this type of evaluation can help assess aspects such as:

• Protocol compliance.
• Agent courtesy and tone.
• Resolution capability.
• Reason for contact.
• Signs of customer dissatisfaction.
• Overall conversation quality.

2. WHY MANUAL MONITORING FALLS SHORT

Manual call listening still has value, but as call volumes grow, its limitations become increasingly obvious.

IT ONLY PROVIDES A PARTIAL VIEW

When quality teams only review a small sample of calls, many important conversations are never analysed. That makes it much harder to identify patterns, recurring issues or genuine performance gaps.

IT CONSUMES TOO MUCH TIME

Listening to calls one by one takes up a huge amount of time. That reduces the time available for the work that actually drives value: identifying root causes, prioritising improvements and supporting the team.

IT’S HARD TO MAINTAIN CONSISTENT CRITERIA

It’s common for different reviewers to apply different standards when assessing calls. Automation helps create a much more consistent evaluation framework.

IMPROVEMENTS ARRIVE TOO LATE

When reviews take too long, improvements are delayed as well. That means problems can affect far more customers before action is taken.

3. HOW AI-POWERED CALL EVALUATION WORKS

Although each solution may differ slightly, the overall logic is usually very similar.

IT TRANSCRIBES THE CONVERSATION

The first step is usually turning the call into text. This makes it possible to analyse conversations in a structured way and work with much greater depth.

IT INTERPRETS WHAT HAPPENED

The AI then processes the conversation to identify topics, intent, tone and other relevant patterns. This is one of the biggest leaps forward compared to manual reviews: the system can analyse interactions continuously and at scale.

IT CLASSIFIES AND ORGANISES INFORMATION

The tool can tag calls according to the reason for contact, summarise them and organise the information to simplify further analysis.

If you’re looking for a solution built specifically for this kind of work, Speech Analytics for call centres is designed to transcribe, categorise and evaluate conversations without relying on manual reviews one call at a time.

IT SCORES OR EVALUATES QUALITY CRITERIA

Businesses can define exactly what they want to measure, such as protocol compliance, resolution capability, customer satisfaction or signs of poor customer experience.

IT GENERATES ACTIONABLE INSIGHTS

The end goal isn’t just data. It’s having a useful information base to improve training, review processes, identify risks and make decisions based on objective insights.

When this analysis is integrated into a broader AI-powered customer service automation strategy, its value increases even further by connecting quality, efficiency and operational improvement.

4. WHAT CAN BE ANALYSED USING THIS SYSTEM

One of the key advantages of this approach is that it goes far beyond a single overall score. It allows different aspects of the conversation to be analysed.

• Protocol compliance.
• Use of the appropriate tone.
• Ability to resolve or manage the issue effectively.
• Reason for contact and call type.
• Signals of customer satisfaction or frustration.
• Conversation summaries and key points.

This allows supervisors and managers to work with far greater precision and identify not only what’s going wrong, but also where, how often and with what impact.

5. OPERATIONAL BENEFITS FOR SUPERVISORS AND MANAGERS

This is where the technology starts to demonstrate its real day-to-day value within a contact centre.

GREATER VISIBILITY ACROSS OPERATIONS

Instead of relying on a small sample, you can review a much larger share of conversations and gain a clearer understanding of what’s genuinely happening.

LESS MANUAL WORKLOAD

Teams spend less time listening to calls individually and can focus more on analysis, coaching and continuous improvement.

FASTER ISSUE DETECTION

When the system analyses conversations continuously, it becomes much easier to identify negative trends, recurring issues or agents who may need support before problems escalate.

A STRONGER FOUNDATION FOR TRAINING

With more complete and consistent data, coaching is no longer based on isolated impressions but on real behavioural patterns.

BETTER INFORMED DECISION-MAKING

Automation helps organisations move from intuition-led management to data-driven management. And that’s critical when justifying changes, prioritising actions or demonstrating improvement.

If you also want to connect this analysis with the rest of your operational ecosystem, contact centre integrations help information flow much more smoothly between systems.

6. WHEN IT MAKES PARTICULAR SENSE TO IMPLEMENT IT

Automated call evaluation can deliver value in many situations, but there are certain scenarios where the return is especially clear.

WHEN CALL VOLUMES GROW

The more calls you handle, the less practical it becomes to rely solely on manual reviews.

WHEN THERE ARE MANY AGENTS OR TEAMS

Maintaining consistency in monitoring becomes significantly more difficult as operations scale.

WHEN CUSTOMER EXPERIENCE NEEDS IMPROVING

If you need a clearer understanding of what’s causing dissatisfaction or where conversations are breaking down, this type of analysis provides much greater visibility.

WHEN THE QUALITY TEAM IS OVERSTRETCHED

If the team spends too many hours on manual tasks, automating part of the review process frees up time for higher-value activities.

WHEN YOU WANT TO SCALE MORE EFFICIENTLY

For operations aiming to grow without multiplying internal friction, this type of automation can make a huge difference. In this context, solutions such as AI Agents can also complement the strategy by handling interactions and improving operational efficiency.

7. COMMON MISTAKES WHEN ROLLING IT OUT

To make sure the system delivers genuine value, it’s important to avoid some common pitfalls.

ASSUMING IT REPLACES ALL HUMAN JUDGEMENT

The goal isn’t to eliminate human supervision, but to increase capacity, review more conversations and work more consistently. A quality lead will still be needed to review insights, define evaluation criteria, make decisions and drive continuous improvement.

NOT DEFINING WHAT YOU WANT TO MEASURE

Without clear criteria, the system may generate data, but not necessarily useful insights for decision-making.

FAILING TO LINK EVALUATION TO OPERATIONAL IMPROVEMENT

If the results aren’t reflected in coaching, processes or follow-up actions, much of the value is lost.

OVERCOMPLICATING THE SCORECARD OR CATEGORIES

If the taxonomy is confusing or impractical, the analysis becomes less actionable and harder to use effectively.

8. PRACTICAL EXAMPLE

Imagine a contact centre with 80 agents and thousands of calls every month.

Until now, the quality team has only been able to review a small proportion of conversations. This creates several problems:

• Many calls are never audited.
• Recurring patterns are difficult to detect.
• Coaching arrives too late.
• The overall picture depends too heavily on a limited sample.

With an automated evaluation system, the business can:

• Transcribe calls automatically.
• Classify the reason for contact.
• Score interactions.
• Detect satisfaction levels.
• Generate summaries.
• Cross-reference that information with other service data.

Using these insights, the company can identify:

• Reasons for contact that consistently generate very low scores. And propose solutions.
• Agents with higher and lower satisfaction levels. And provide targeted coaching where needed, whether in soft skills or product knowledge.

The result isn’t just greater control. It’s greater ability to improve quality with less manual effort.

9. CONCLUSION

Automating call evaluation in a contact centre makes it possible to review more conversations, save time and make decisions based on far stronger data.

It doesn’t replace all human supervision, but it does help expand capacity, detect issues earlier and improve quality more consistently.

If the goal is to review conversations without relying on manual listening across limited samples, this approach makes a great deal of sense.

And if you’d also like a specialist solution to make it easier, you can do it with Speech Analytics, as part of a broader omnichannel contact centre management strategy.

La entrada How to automate call evaluation in a contact centre using AI se publicó primero en Enreach ES.

Because there’s a big difference between simply having a customer service team and actually knowing, with real figures, whether calls are being answered on time, how long customers are waiting in queues and what happens when volumes increase.

What’s more, accurate measurement isn’t just useful for meeting internal targets. It also helps you identify bottlenecks, justify improvements and gain a much clearer picture of the real customer experience.

If you need a simple way to do this, Enreach Omnichannel allows you to monitor queues, waiting times and service levels from a single environment, without relying on manual processes or scattered reports.

In this article, you’ll discover what you should be measuring, how to check whether you’re meeting your response time targets and why doing it with Enreach is far simpler than it may seem.

• 1. What meeting customer service waiting time targets actually means
• 2. The data you should be reviewing
• 3. How to check it in practice
• 4. The most common mistakes when trying to verify compliance
• 5. How to do it easily with Enreach
• 6. It’s not just about complying: it’s about knowing in time
• 7. Conclusions

1. WHAT MEETING CUSTOMER SERVICE WAITING TIME TARGETS ACTUALLY MEANS

In customer service, compliance isn’t just about answering calls or messages. It’s about doing so within reasonable timeframes and being able to prove it with reports.

Put simply: if you can’t measure it, you can’t verify it.

To do this properly, you need to review queue waiting times, configured service levels and the percentage of interactions answered within the defined target. The same approach can be applied to both voice and digital channels.

2. THE DATA YOU SHOULD BE REVIEWING

To understand whether your service is performing well, there are several particularly useful metrics to monitor.

QUEUE WAITING TIME

This is the time between a call entering the queue and an agent answering it. It’s the foundation for checking whether the service is responding with the expected level of speed.

PERCENTAGE OF CALLS ANSWERED WITHIN SERVICE LEVEL TARGETS

This is one of the most important metrics. It shows what proportion of interactions are answered within the maximum acceptable timeframe you’ve defined.

ABANDONMENT RATE

It’s also important to review how many calls are abandoned before being answered. This metric helps identify issues related to overload, workforce planning or queue management.

AVERAGE WAITING TIME

This is a very useful metric for understanding the real customer experience, even when overall service levels appear to be acceptable. Sometimes averages reveal friction points that other data doesn’t show as clearly.

3. HOW TO CHECK IT IN PRACTICE

In practical terms, the process looks like this:

1. Set the maximum response time to 3 minutes
2. Review the percentage of calls answered within that timeframe
3. Analyse abandonment rates, overflow calls and average waiting times
4. Identify whether the issue lies with demand, planning or agent availability
5. Make decisions based on data, not assumptions

When this monitoring is done properly, businesses stop managing customer service “by feel” and start working from a much stronger operational foundation.

4. THE MOST COMMON MISTAKES WHEN TRYING TO VERIFY COMPLIANCE

This is where many businesses get stuck.

ONLY MEASURING PART OF THE SERVICE

If you only review phone calls and leave out other channels, you’re only seeing part of the picture.

NOT DEFINING SERVICE LEVELS PROPERLY

If you don’t clearly define what maximum waiting time is considered acceptable, interpreting results accurately becomes much harder later on.

IGNORING ABANDONED OR OVERFLOW CALLS

If these situations aren’t reviewed, you miss a key part of the problem. And very often, that’s exactly where a significant part of the negative customer experience lies.

ANALYSING DATA WITHOUT CONTEXT

Waiting times don’t depend solely on incoming call volumes. Team activity, availability and occupancy levels also play a role. Without operational context, data on its own can easily lead to misleading conclusions.

5. HOW TO DO IT EASILY WITH ENREACH

This is the part that matters most to businesses that don’t want to waste time building complicated reports.

With Enreach Omnichannel, this process is far more straightforward because the platform already allows you to:

• Review queue reports by channel
• Adjust service level settings
• Measure waiting times
• Check the percentage answered within target
• Analyse abandonment and overflow rates
• Export results for internal monitoring or audits

That means there’s no need to build parallel systems or rely on manual Excel calculations to understand whether the service is performing as it should.

And if the business operates across multiple channels, being able to apply the same measurement logic to both voice and digital interactions makes monitoring far more valuable.

If you’re looking for more advanced queue management, automation and operational analytics, an AI-powered customer service solution can also help you gain more context, automate processes and improve service efficiency.

6. IT’S NOT JUST ABOUT COMPLYING: IT’S ABOUT KNOWING IN TIME

There’s a big difference between discovering too late that you’re failing to comply and spotting the issue early enough to fix it.

When a business can easily monitor:

• • Waiting times
  • Service levels
  • Abandonment rates
  • Channel performance

Start managing customer service with far greater precision.

And that’s where the real value lies: not just proving compliance, but improving operations before problems impact the customer.

7. CONCLUSIONS

Checking whether your customer service operation meets response time requirements shouldn’t be a complex process.

The key is understanding what to measure, reviewing the correct service level targets and having reports that provide a genuine picture of what’s happening across both voice and digital channels.

With Enreach, this becomes much simpler because you can centralise queue monitoring, waiting times and channel performance within a single solution. And that allows you to move from intuition to data, and from data to improvement.

Put simply: if you need to prove your customer service operation is compliant, the most important thing is having a tool that makes it easy.

La entrada How to check whether your customer service meets legally required response times se publicó primero en Enreach ES.

In addition, this type of automation fits within a broader strategy of intelligent customer service automation, where the goal is not only to save time, but to improve productivity, reduce manual tasks and gain visibility over every interaction.

The problem arises when this work is done manually. Each agent may use different criteria, take longer than necessary or leave inconsistent records. Over time, this complicates operations, makes analysis harder and slows down continuous improvement.

This is where automatic call classification comes into play.

It is not just about saving time. It is about achieving more consistent data, better understanding why customers get in touch and reducing administrative workload without losing control.

In this article, you will learn what automatic call classification is, how it works, its key benefits and when it makes sense to implement it.

• 1. What automatic call classification is
• 2. What automatic classification is for
• 3. How automatic call classification works
• 4. Difference between manual and automatic classification
• 5. Key benefits of automatic call classification
• 6. When it makes sense to implement it
• 7. How it improves contact centre productivity
• 8. Common mistakes when implementing it
• 9. Practical example
• 10. Conclusion

1. WHAT IS AUTOMATIC CALL CLASSIFICATION

Automatic call classification is the process by which a system identifies the main reason for a conversation and assigns it a category or label without the agent having to do it manually.

Put simply: it turns conversations into data. And in a high-volume operation, that makes a real difference.

For example, a call may be automatically classified as:

• Technical issue
• Billing query
• Appointment change
• Cancellation request
• Complaint
• Sales follow-up

The goal is to transform conversations into structured data that can then be used for monitoring, reporting, operational improvement and process automation.

This capability becomes even more valuable when it forms part of a broader conversational analytics and automation solution.

2. WHAT AUTOMATIC CLASSIFICATION IS FOR

Automatic classification does more than organise information and save time. It also helps drive better decision-making. It is used to:

• Reduce the time agents spend classifying calls
• Standardise recording criteria
• Identify the most common reasons for contact
• Improve reporting quality
• Spot peaks in incidents or emerging trends
• Trigger automation based on the type of query

It also enables teams to work with a more reliable data set. This is critical when making decisions around staffing, quality, processes and customer experience.

3. HOW AUTOMATIC CALL CLASSIFICATION WORKS

Although it can be implemented in different ways, the process generally follows the same logic:

ANALYSES THE CONTENT OF THE CONVERSATION

The system analyses what was said during the call, usually based on a transcript or processed audio.

This makes it possible to turn an unstructured conversation into useful information for classification, monitoring and reporting. When this analysis is part of an omnichannel customer service strategy, the information can be leveraged more effectively across channels and teams.

IDENTIFIES THE INTENT OR REASON FOR CONTACT

Once the conversation has been analysed, the system looks for patterns, keywords, context and intent to determine what the call is actually about.

This is one of its main advantages over manual tagging: it does not rely solely on the agent remembering to select the correct category at the end.

ASSIGNS A CATEGORY

The call is then classified within a taxonomy defined by the company.

For example:

• Billing
• Technical support
• Cancellations
• Appointments
• Complaints
• Sales enquiries

TRIGGERS REPORTING OR FOLLOW-UP ACTIONS

Once classified, this information can be used for reports, dashboards, alerts, workflows or follow-up automation.

That is why classification does more than bring structure. It also helps connect the conversation with real business operations.

4. DIFFERENCE BETWEEN MANUAL AND AUTOMATIC CLASSIFICATION

The main difference lies in time, consistency and scalability.

MANUAL CLASSIFICATION

In the manual model, the agent selects a category after the call.

This may work in smaller operations, but it has several limitations:

• It consumes post-call time
• It depends on each agent’s individual judgement
• Errors or incorrect categorisation can occur
• It is difficult to maintain consistency across multiple teams or campaigns

AUTOMATIC CLASSIFICATION

In the automatic model, the system suggests or directly assigns a category based on the conversation content.

This allows you to:

• Save administrative time
• Standardise criteria
• Obtain more consistent data
• Analyse large volumes of calls more accurately

If you want to take this analysis a step further, this call analytics tool helps transcribe, categorise and evaluate conversations for a more complete view of operations.

5. KEY BENEFITS OF AUTOMATIC CALL CLASSIFICATION

This is where the real impact becomes clear.

REDUCES POST-CALL WORK

Every second an agent spends manually classifying a call is time not spent handling another interaction or focusing on higher-value tasks.

Automating this helps reduce administrative workload and streamline operations.

IMPROVES DATA QUALITY

When classification depends on different individuals, inconsistencies are inevitable.

Automatic classification helps standardise records and work with more consistent data.

HELPS YOU BETTER UNDERSTAND WHY CUSTOMERS GET IN TOUCH

It is not enough to know how many calls come in. What really matters is knowing why they come in.

When every conversation is classified, it becomes easier to identify:

• Recurring issues
• Process errors
• Frequent queries
• Reasons for cancellation
• Opportunities to improve service and sales

ENABLES BETTER PRIORITISATION

If you know which types of contact are increasing, you can act sooner.

For example, you can reinforce teams, review internal processes, adjust self-service messaging or identify an issue before it escalates.

ENABLES AUTOMATION

Automatic classification can also act as a trigger for follow-up actions.

For example:

• Create a task in the CRM
• Route a case to a specific team
• Send a survey
• Trigger a follow-up
• Flag conversations for review

When these automations are connected to internal tools and workflows, contact centre integrations help ensure information flows more effectively across systems and teams.

6. WHEN IT MAKES SENSE TO IMPLEMENT IT

Not every operation has the same needs. However, there are scenarios where this functionality delivers value very quickly.

HIGH CALL VOLUME

The higher the volume, the harder it is to maintain consistent manual classification.

MULTIPLE REASONS FOR CONTACT

When an operation handles incidents, sales, support, appointments, complaints and queries, manual tagging becomes more complex and less reliable.

NEED FOR MORE DETAILED REPORTING

If the business wants deeper insight into its conversations, automatic classification helps structure information more effectively.

GOAL OF REDUCING ADMINISTRATIVE TIME

It is particularly useful in teams where post-call work is too heavy and there is a need to reduce it without compromising data quality.

OPERATIONS LOOKING TO SCALE

When a business wants to grow in volume or complexity, automating these tasks prevents operational workload from increasing at the same rate.

In these scenarios, solutions such as AI Agents can also help absorb interactions, capture context and scale service without increasing friction.

7. HOW IT IMPROVES CONTACT CENTRE PRODUCTIVITY

Productivity is not just about handling interactions faster. It is also about reducing friction.

Automatic classification improves productivity because it:

• Frees up agent time
• Reduces classification errors
• Improves traceability
• Facilitates analysis
• Better connects conversations with business processes

And the better it integrates with other customer service tools, the greater its impact on overall contact centre efficiency.

8. COMMON MISTAKES WHEN IMPLEMENTING IT

Before implementing it, it is worth avoiding some common pitfalls.

THINKING ONLY IN TERMS OF LABELS

Classification should not be seen as just a list of categories. It delivers more value when connected to reporting, quality and automation.

USING AN OVERLY COMPLEX TAXONOMY

Too many categories or overly similar ones make the system less useful and analysis more confusing.

NOT REVIEWING RESULTS

Automation does not mean disengagement. It is important to regularly review whether classifications reflect operational reality.

NOT INTEGRATING WITH OTHER TOOLS

Its value increases significantly when connected to CRM, dashboards, automation or monitoring tools.

9. PRACTICAL EXAMPLE

Let’s look at a simple scenario.

Imagine a contact centre receiving hundreds of calls per day related to:

• Breakdowns
• Billing queries
• Appointment changes
• Cancellations
• Sales enquiries

Previously, each agent had to manually select the reason for contact at the end of the call.

Over time, several issues emerge:

• Incorrect category selection
• Incomplete records
• Inconsistent data
• Too much time spent on administrative tasks

After implementing an automatic classification system, the operation achieves:

• More consistent call classification
• Reduced post-call time
• Faster identification of growing contact reasons
• Targeted actions based on conversation type

The change is not just about saving seconds. It is about gaining operational clarity, improving analysis and acting sooner.

10. CONCLUSION

Automatic call classification is a highly valuable capability for any contact centre looking to reduce manual tasks, improve data quality and better understand what is happening in its conversations.

On its own, it does not replace a broader operational improvement strategy, but it provides a much stronger foundation for analysis, automation and scalability.

And the greater the volume, number of channels and operational complexity, the more value it tends to deliver.

If you want to move towards more efficient customer service, with less manual workload and greater visibility over every interaction, you can rely on an AI-powered customer service solution and capabilities such as this specialised call categorisation and evaluation platform to analyse conversations more intelligently.

La entrada Automatic call typing: what is it and how does it contribute to improving productivity in the contact centre se publicó primero en Enreach ES.

Although the two are sometimes used interchangeably, not quite.

A customer service chatbot is usually designed to answer frequently asked questions, guide users through simple queries, or route them to the right team when human intervention is needed.

A virtual agent, on the other hand, goes a step further. It can understand context more effectively, hold more natural conversations, gather useful information, and carry out actions within other tools to help the customer.

1. A SIMPLE WAY TO LOOK AT IT

Put simply:

• A chatbot is usually focused on responding.
• A virtual agent is better equipped to resolve, guide and carry out tasks with greater context.

That is why, as a company needs more advanced automation, more closely connected to its channels and more useful for day-to-day operations, the virtual agent becomes increasingly important.

2. WHEN A CHATBOT MAY BE ENOUGH

A chatbot may be enough when:

• Queries are repetitive.
• Support flows are simple.
• The goal is to take pressure off FAQs.
• The company wants to begin with basic automation.

3. WHEN A VIRTUAL AGENT USUALLY MAKES MORE SENSE

A virtual agent is usually a better fit when:

• Conversations are more complex.
• You want to operate across multiple channels.
• There is a need to better understand customer intent.
• Automation needs to be integrated with internal processes and tools.
• You want the agent to carry out actions in other tools to help the customer.

4. IT IS NOT JUST ABOUT AUTOMATING

The real difference lies not only in the technology, but in the outcome.

It is not simply about automating responses, but about providing customer service that is more useful and faster, so the customer feels they are being supported just as well as or even better than by a human agent, while also helping the business save time on certain tasks that can now be automated thanks to this technology.

If your goal is to move towards customer service that is more efficient and context-aware, it may make sense to rely on AI solutions for customer service, explore how AI Agents work, or review an omnichannel strategy that brings channels together and improves the overall experience.

5. CONCLUSION

Chatbots and virtual agents share a common foundation, but they do not offer the same level of capability, although it is very common to refer to AI agents as chatbots, both among providers and users, as that term is far more widely used.

A chatbot can be a good starting point. A virtual agent, however, is better suited when a business needs automation that is more advanced, more flexible and better prepared to scale.

La entrada Virtual agent vs chatbot: what’s the difference se publicó primero en Enreach ES.

When ACW spikes, agents have less availability, queues begin to suffer, productivity drops, and the operation becomes harder to scale. But here’s the key point: reducing it isn’t about putting more pressure on the team, it’s about removing friction.

Because yes, lowering ACW is entirely possible. And no, you don’t need to sacrifice service quality to achieve it. In fact, when a business has a solid call and contact centre software in place, it becomes much easier to reduce administrative time, gain better context, and wrap up each interaction more efficiently.

In this article, you’ll learn what ACW is, how it’s calculated, why it increases, and, most importantly, what you can do to reduce it without harming the customer experience.

1. WHAT IS ACW

After Call Work is the time between when a call ends and when the agent becomes available again to handle the next interaction.

Even though the customer has already hung up, the work doesn’t always end there. In many teams, this time is used to properly close the case and document what happened.

During this period, agents typically carry out tasks such as:

• Recording the reason for contact.
• Updating customer details.
• Adding notes from the conversation.
• Categorising the interaction.
• Sending a follow-up email.
• Scheduling a next action.
• Completing mandatory fields in the CRM or contact centre software.

In short, ACW measures all the administrative or wrap-up work that takes place after speaking with the customer.

2. WHAT ACW MEANS IN A CALL CENTRE

In a call centre, ACW is a key metric because it shows how much operational time is spent outside the actual conversation.

It’s important to make a clear distinction: speaking to the customer is not the same as properly closing a case. Both take time, but they don’t impact the operation in the same way.

In environments with multiple channels, agents and workflows, having a cloud omnichannel solution also helps reduce friction and keeps everything far more connected.

It shouldn’t be confused with other common metrics:

• AHT: measures the total average handling time of an interaction.
• Talk time: measures the actual time spent speaking.
• Hold time: measures time spent on hold.
• ACW: measures post-call work.

ACW is part of the overall handling time. That’s why, when it increases significantly, metrics like AHT tend to rise as well.

3. WHY MEASURING ACW MATTERS

Measuring ACW isn’t just about tracking time. Above all, it helps identify where efficiency is being lost.

When analysed properly, it reveals issues that often remain hidden in day-to-day operations: unnecessary steps, slow tools, duplicated records, or poorly designed processes.

Monitoring it helps to:

• Improve team productivity.
• Reduce unproductive time.
• Optimise workforce planning.
• Identify poorly designed processes.
• Spot friction in CRM systems, forms or tools.
• Maintain data quality without compromising service.

It also helps answer key business questions:

• Are agents spending too long documenting each call?
• Are there repetitive steps that could be automated?
• Does the software require too many clicks?
• Are post-call tasks properly designed?

4. HOW ACW IS CALCULATED

The most common formula is:

Average ACW = total post-call work time / total number of calls handled.

For example, if a team spends 1,800 minutes on post-call work and handles 600 calls:

1,800 / 600 = 3 minutes per call.

That said, looking only at the overall average isn’t always enough. To uncover real improvement opportunities, ACW should also be analysed by:

• Agent.
• Team.
• Campaign.
• Call type.
• Channel.
• Time slot.
• Reason for contact.

This is where meaningful optimisation opportunities tend to appear.

5. WHAT IS A GOOD ACW

This is one of the most common questions. The honest answer is: it depends.

There’s no universal ACW benchmark that fits every contact centre. A reasonable level in a simple service may be inefficient in a more automated one… or even too low in a complex operation.

Context matters. A lot.

Factors that influence ACW:

• Process complexity.
• Industry.
• Level of regulation.
• Number of systems involved.
• Need for follow-up actions.
• Customer type.
• Level of automation.

A complex technical support case is not the same as a simple customer query or a short commercial interaction.

That’s why the goal shouldn’t be to achieve the lowest possible ACW, but to maintain a level that is efficient, sustainable and aligned with high-quality service.

Reducing it should never compromise:

• Data quality.
• Process compliance.
• Traceability.
• Case resolution.
• Customer experience.

6. WHY ACW INCREASES

When ACW rises, the issue rarely lies with a single individual. More often, it’s a combination of operational friction, poorly optimised processes and technology that doesn’t quite support the workflow.

These are the most common causes.

POORLY INTEGRATED TOOLS

If agents have to jump between multiple platforms, manually copy information or duplicate records across systems, post-call time increases almost inevitably.

That’s why it’s crucial to have a connected environment where telephony, customer service and CRM work in sync. A solution such as contact centre integrations helps reduce duplicated tasks and speeds up case closure.

TOO MANY MANUAL TASKS

Entering notes, classifying calls, sending emails, creating tickets and updating the CRM manually may seem manageable in a single interaction. The problem arises when this is repeated hundreds or thousands of times a day.

What looks small individually becomes a major time drain operationally.

COMPLEX OR UNCLEAR PROCESSES

When it’s not clear what needs to be recorded, which fields are mandatory or how each case should be closed, every agent ends up handling it differently.

This not only increases ACW, but also creates inconsistencies and makes analysis more difficult.

INSUFFICIENT TRAINING

An agent who doesn’t fully understand the tools or the closing workflow will need more time to complete each task.

Often, the issue isn’t lack of effort, but lack of operational clarity.

QUALITY OF THE PREVIOUS CALL

The more complex, unclear or incomplete the interaction, the greater the post-call effort.

Multiple transfers, conflicting information or lack of context make closing the case slower and more cumbersome.

SLOW OR NON-INTUITIVE SYSTEMS

Sometimes the bottleneck isn’t the process, but the tool itself: slow screens, confusing navigation, too many clicks or overly complex forms.

In those cases, asking agents to work faster doesn’t solve the issue it only worsens the internal experience.

7. RISKS OF REDUCING ACW TOO AGGRESSIVELY

Reducing ACW is a good idea. Reducing it poorly is not.

When this metric becomes an isolated obsession, without operational context or quality considerations, it can produce the opposite of the intended effect.

INCOMPLETE RECORDS

If agents don’t have enough time to properly document the interaction, valuable information is lost.

And when context is missing, the customer usually pays the price on the next call.

LOWER OPERATIONAL QUALITY

Excessive pressure to close quickly can lead to errors, incorrect classifications or poorly executed follow-ups.

What you gain in seconds may be lost later through rework.

MORE REPEAT CONTACTS

When post-call work is incomplete, issues resurface — and so does the customer.

This increases workload and damages service perception.

WORSE AGENT EXPERIENCE

If ACW is used purely as a pressure tool, it creates stress, a sense of constant monitoring and reduced control over the work.

The result is rarely a better operation, more often, it leads to burnout.

8. HOW TO REDUCE ACW WITHOUT COMPROMISING SERVICE

This is the key point. The smartest way to reduce ACW isn’t to push agents to do more in less time, but to design an operation where closing a case requires less effort.

These are the levers that tend to have the greatest impact.

AUTOMATE INTERACTION LOGGING

The more tasks that are handled automatically once the call ends, the lower the administrative burden on the agent.

For example:

• Automatic call summaries.
• Automatic classification by reason for contact.
• Suggested field completion.
• Automatic ticket creation.
• Automated follow-up emails or tasks.

Automation and AI can make a significant difference here. If you want to reduce post-call time without losing context, a solution such as AI for contact centres can help summarise conversations, suggest classifications and speed up repetitive tasks.

INTEGRATE TELEPHONY, CRM AND CUSTOMER SERVICE TOOLS

When agents have all the information in a single interface, closing interactions becomes much smoother.

Good integration eliminates duplication, reduces errors and saves time on every interaction. In this context, having a call and contact centre software connected to the rest of your operational stack makes a clear difference.

REDUCE UNNECESSARY FIELDS AND STEPS

Over time, processes tend to accumulate layers: more fields, more validations, more categories and more “just in case” steps.

The result is predictable: longer closure times.

It’s worth reviewing:

• Which fields are truly mandatory.
• What information can be auto-filled.
• Which steps add no real value.

STANDARDISE CALL CLOSURE

When all agents follow a consistent approach to documenting and closing cases, the process becomes faster and more reliable.

You can support this with:

• Note templates.
• Clearly defined contact reasons.
• Closure workflows by case type.
• Short checklists for logging.

USE REAL-TIME AGENT ASSISTANCE

Agent assistance tools don’t just help during the call, they also reduce post-call work.

For example:

• Automatic suggestions.
• Quick access to history.
• Call reason detection.
• Post-call summaries.
• Automatic extraction of key data.

The less agents have to reconstruct afterwards, the lower the ACW.

IMPROVE CALL ROUTING

Part of high ACW originates before the call even ends. If the call reaches the wrong agent, lacks context or goes through multiple transfers, closing it becomes more complex.

Improving smart routing helps reduce post-call work by connecting customers with the right team sooner and reducing the need to rebuild information.

ANALYSE WHICH CALL TYPES DRIVE HIGHER ACW

Not all interactions require the same post-call effort.

It’s important to identify:

• Which contact reasons increase ACW.
• Which campaigns generate more admin work.
• Which teams or processes have more friction.
• Which agents need more support or training.

Often, significant improvements come from fixing a few specific cases.

IMPROVE OPERATIONAL TRAINING

Training agents isn’t just about product knowledge or customer service skills. It also means teaching them how to close each case efficiently and correctly.

For example:

• How to document interactions.
• What needs to be recorded.
• When to escalate.
• How to classify correctly.
• Which automations to use.

9. ACW AND OTHER CONTACT CENTRE METRICS

ACW shouldn’t be analysed in isolation. It is directly linked to other key performance indicators and therefore to overall operational efficiency.

ACW AND AHT

If ACW increases, AHT usually increases as well.

ACW AND PRODUCTIVITY

High ACW reduces the number of interactions an agent can handle during their shift.

ACW AND FCR

If post-call work is cut too aggressively and records are incomplete, first contact resolution may suffer.

ACW AND CUSTOMER SATISFACTION

Although customers don’t see ACW directly, they feel its impact through poor follow-ups, lack of context or having to repeat information.

10. BEST PRACTICES TO IMPROVE ACW

As a quick reference, these actions tend to deliver good results:

• Consolidate tools into a single interface.
• Automate repetitive tasks.
• Review processes regularly.
• Remove unnecessary fields.
• Use smart templates.
• Leverage AI for summaries and classification.
• Analyse ACW by contact reason.
• Train agents on operational closure.
• Avoid setting ACW targets without context.
• Balance efficiency and quality.

11. PRACTICAL EXAMPLE

Imagine a customer service team that spends an average of 2 minutes and 40 seconds on post-call work.

At first glance, it may not seem excessive. But when multiplied across hundreds of daily interactions, the operational impact is significant.

After reviewing the process, the team identifies that:

• Agents write notes manually.
• Cases are classified in two systems.
• Follow-up emails are sent manually.
• Time is spent searching for history in another tool.

After implementing:

• CRM integration.
• Closure templates.
• Automatic call summaries.
• AI-based classification suggestions.

ACW drops to 1 minute and 25 seconds.

The improvement doesn’t come from pushing agents to work faster, but from removing operational friction and simplifying post-call tasks.

12. CONCLUSION

ACW (After Call Work) is a key metric for understanding how much time is spent on post-call work. When managed well, it improves contact centre efficiency and agent availability. When mismanaged, it becomes a source of overload, errors and poor experience.

The key isn’t to cut it at any cost, but to simplify processes, integrate tools and automate repetitive tasks so agents spend less time on admin and more time delivering value to customers.

Ultimately, reducing ACW isn’t just about saving seconds. It’s about building a more agile, scalable and effective operation for both your team and your customers.

La entrada What is ACW (After Call Work) and how to reduce it without compromising customer service se publicó primero en Enreach ES.