Artificial intelligence (AI) developed for customer service is not intended to replace agents, but to complement them. The emotional intelligence of agents combined with the automation provided by AI is the perfect combination to deliver a flawless customer experience (CX).

But its power does not end there. This technology also has a positive impact on the agent experience (AX), freeing them from managing simple enquiries and performing repetitive tasks where they do not add value.

Here are ten use cases that demonstrate the benefits of integrating AI to help agents be faster, more efficient and work on their continuous improvement.

1. INTENTIONAL QUALIFICATION

Bots are ready to ask an open-ended question like “How can I help you?”, interpret the customer’s response in the moment, and decidewhether to offer self-service (if within their capabilities) or transfer the conversation to the most qualified agent to handle the request.

As with IVR menus, you can use them to identify the user’s intention and intent and create transfer rules that take into account agent skills.

Unlike IVR, conversational and/or generative AI bots do not require you to configure the conversation diagram per channel and provide a better customer experience by understanding the customer’s query first.

2. USER IDENTIFICATION

Another task a bot can do before transferring the conversation to an agent is to identify the person it is talking to.

By querying the CRM, ERP or ticketing tool, it can search the database for the ID card, phone number or any other identifying information the user has provided.

This way, when the agent receives the call or chat, they can address the customer by name and quickly check their call/message history and customer record.

3. CONVERSATION SUMMARY WITH AI

When the customer indicates they want to speak to an agent, or the bot has determined it’s best to transfer the conversation to an operator, the agent receives the call or chat along with a summary of the previous conversation.

In addition to a transcript of the conversation and a record of all actions taken by the bot, the summary of the conversation identifies keywords and the customer’s sentiment.

With this information in advance, the agent can resolve the customer’s query more quickly and efficiently, because they know exactly what the customer needs, what the bot wasn’t able to provide, and what tone to use depending on the customer’s emotional state.

For example, if the bot has detected that a customer is angry because they have a problem with their phone line, the agent will know all this information before answering the call and will be much more effective in their verbal communication and problem solving.

4. RESOLVING COMMON QUERIES

According to a recent study we conducted, bots can handle up to 80% of simple queries in a contact centre.

Whether answering FAQs about a product or service, resolving a technical issue found in a user manual, resetting a password or recommending a credit line, bots free agents from these types of calls or messages, giving them more time to handle inquiries that require emotional intelligence or to assist those customers with special needs.

5. SENDING DOCUMENTS

In addition to resolving simple queries, bots can also provide self-service by sending documents to the customer, such as invoices, contracts, forms, etc.

Whether verbal or written, the bot can instantly deliver the document the customer needs through any platform.

For example, if a customer calls the call centre and asks for their latest electricity bill, the bot can send it via the customer’s preferred channel: WhatsApp, SMS or email.

6. TICKET GENERATION

As part of self-service, bots can also generate a ticket detailing the customer’s problem. By gathering all the relevant information from the conversation, they can create a new ticket, fill it in, and send the customer the incident tracking number.

Especially for queries that cannot be resolved in the first instance, bots can handle this part of the process, reducing human error and freeing agents from repetitive tasks.

7. INSTANT TRANSLATION

As well as being omnichannel, Artificial Intelligence is multilingual, meaning it speaks and understands all languages. This feature opens up a world of possibilities for contact centres, not only to better deal with requests that are not in our native language, but also to provide service in any language.

Whether it is a voice or chat conversation, we can ask the bot to transcribe the customer’s answers in the language we want, and also respond to the customer in their own language.

This functionality is ideal for when we have a spike in Spanish calls in the queue, as we can redirect these calls to agents with basic knowledge and have them assisted by the bot to help customers more smoothly and professionally.

8. KNOWLEDGE BASE

In addition to assisting during a call as a translator, the bot can also help agents when they have a question about a product/service or a clause in a contract.

If we share all our documentation with the AI: manuals, legal documents, blog posts, use cases, etc., we will end up with an internal ChatGPT that is knowledgeable about all our processes and products/services.

This will be the great ally of the agents, because if they don’t know how to answer a customer’s question, they can ask the internal AI Agent, and if it’s in the documentation we’ve provided, it can answer it.

This functionality not only avoids prolonging the conversation, but also allows any agent to provide support on a topic they are not an expert in.

9. ACW OPTIMISATION

The After Call Work (ACW), refers to all the tasks that agents must complete after each interaction before moving on to the next customer.

Artificial Intelligence can speed up this process by creating a real-time transcript of the conversation, analysing customer satisfaction by highlighting keywords that serve as typification, and linking this data to the details of the conversation.

The ability to automate AWC time as much as possible is essential to increasing agent efficiency.

10. AGENT EVALUATION

AI also has Speech Analytics capabilities, which means it can evaluate the performance of agents. This data helps contact centre managers to identify each agent’s areas for improvement and work on strengthening them.

We can train AI to generate a report after each interaction that includes: the ID number of the interaction, a title, a summary and, for example, an assessment of the agent’s presentation, friendliness and effectiveness.

This functionality contributes to the continuous improvement of agents and therefore to their speed.

FINAL THOUGHTS

As we have seen from these examples, AI provides automation across the entire customer service cycle: from the tasks that precede the interaction to the assistance it provides during and after the conversation.

Currently, there is no other technology that can optimise so many processes at once, taking into account both the customer and agent experience.

Want to meet the artificial intelligence that will revolutionise your customer service? Call us on +34 900 670 750 or fill in this form.

La entrada 10 examples of AI automation to help agents work faster se publicó primero en Enreach ES.

When artificial intelligence enters the equation – particularly with the use of chatbots for customer service – it is natural for customers to be concerned. In fact, PwC’s latest report shows that 80% of consumers are concerned about the future development of generative AI and its impact on data privacy.

Of the many myths surrounding this technology, we acknowledge that this is perhaps the most worrying of all. However, according to Termly research, 91.1% of businesses are willing to prioritise data privacy in order to increase customer trust and loyalty.

SECURITY PROTOCOLS IN CHAT CAN MATCH THOSE IN CALLS

When an IVR (Interactive Voice Response) system identifies a customer during a phone call, various security protocols are triggered to protect their data. Typically, the system encrypts the data as it is entered or dictated by the user, ensuring that it does not appear in the call recording or become visible to the agent.

Channel switching – where the customer interacts with a chatbot rather than an IVR – does not change the security measures in any way. AI-powered chatbots can apply the same privacy principles, ensuring encryption, masking and anonymisation of sensitive information.

HOW WE PROTECT NUMERIC DATA IN A CHATBOT

When interacting with a chatbot, numeric data remains protected by the same security standards. When a customer enters their insurance policy number, national ID or access code, the AI processes it securely using automatic masking and encryption.

User: I would like to check my insurance policy number 987654321.

Chatbot: Thank you, we have received your request. The policy ending in 4321 is active.

This ensures that data is never stored in clear text and can only be accessed through secure authentication and encryption processes.

HOW WE PROTECT SENSITIVE INFORMATION IN A CHATBOT

When a customer shares highly sensitive information, such as health details or religious beliefs, the AI needs to be trained to apply additional protocols that minimise risk. Chatbots can detect sensitive data and apply anonymisation, tokenisation and automatic deletion to ensure user privacy.

Here are six ways to protect sensitive data when customers interact with a chatbot.

6 METHODS TO PROTECT DATA IN AN AI-POWERED CHAT

1. DATA MINIMISATION PRINCIPLE

Customers should not be asked to provide sensitive information unless it is absolutely necessary.

For example, instead of asking “What is your disability?”, the chatbot could say “If you need specific assistance, please respond with ‘’ or ‘'”, ensuring that the user does not have to share personal information.

2. AUTOMATIC RECOGNITION AND USER WARNING

If a user types: “I have epilepsy, how do I manage my insurance?”, the chatbot can detect this and respond: “For security reasons, we don’t need you to share any medical information in this chat. Would you like to speak to a specialist agent?”

This prevents sensitive information from being stored unnecessarily in the chat.

3. REAL-TIME ANONYMISATION

When a chatbot collects sensitive data, it can be replaced with generic labels before storage.

For example, if a user types: “I’m a diabetic and want to know what coverage I have,” the database stores: “User inquiring about medical coverage”, ensuring that no specific medical details are recorded.

This method transforms the original message so that it contains no personally identifiable information. While the original structure is lost, the general intent of the query remains intact.

4. AUTOMATIC DELETION OF SENSITIVE DATA

A chatbot can be programmed not to store certain words or phrases that have been identified as sensitive data.

For example, if a user asks: “I have a chronic illness, how does this affect my life insurance?”, the chatbot will process the query without recording the specific medical condition and will only store: “User inquiring about life insurance terms”.

Unlike anonymisation, this method preserves the original message structure with minor adjustments. Instead of replacing sensitive data with generic labels, it simply removes them.

5. COMPLIANCE WITH THE GDPR AND THE EUROPEAN AI LAW

If sensitive data is essential (e.g. in health insurance cases), the chatbot must ask for explicit consent before storing or sharing the information.

For example, when a user starts a chat, the chatbot could greet them by saying: “To help you, we may need medical information. Do you agree to share it according to our privacy policy?”

6. ACCESS RESTRICTION AND TOKENISATION

If sensitive data needs to be stored, the best approach is to use a token instead of the actual data.

For example, if a user discloses that they have celiac disease, the database can store “medical condition #874632″, where only an authorised system can decode the actual condition. This prevents employees or unauthorised third parties from directly accessing sensitive information.

This method is particularly useful for organisations with multiple levels of data access, ensuring that no agent can view sensitive data without proper authorisation.

FINAL THOUGHTS

The same security protocols that protect data during a phone call apply when a customer interacts with a chatbot. AI in customer service does not change the rules of the game – it automates and strengthens the protection of personal data.

A well-configured chatbot can prevent users from sharing unnecessary information and ensure that sensitive data is not stored without consent.

DISCOVER THE SECURITY MEASURES WE IMPLEMENT AT ENREACH

La entrada How to protect customer data when using a chatbot se publicó primero en Enreach ES.

In fact, research shows that 35% of businesses fear that AI could make mistakes that negatively impact the customer experience (CX).

Here’s the uncomfortable truth: AI isn’t perfect. But then again, are humans? Just as you trust your best agents, knowing that they might occasionally make a mistake, AI works on the same principle. The mistakes it makes aren’t due to its nature, but rather to unforeseen scenarios or a lack of continuous improvement.

However, AI has one undeniable advantage over humans: it’s far more consistent. It sticks to the guidelines you give it, it doesn’t get tired, it doesn’t go off script, it doesn’t improvise.

So the key question isn’t whether AI can make mistakes, but rather: “What can I do to make sure it doesn’t make mistakes that my best agents would never make?” Read on, because we’ve got the answers you need.

3 KEYS TO GETTING AI RIGHT

1. KEEP YOUR KNOWLEDGE BASE ORGANISED AND UP-TO-DATE

The challenge:

A bot that relies on disorganised or out-of-date information will not only provide incorrect answers, but may even “hallucinate” by making up details that don’t exist.

How to fix it:

• Develop a clear strategy outlining who is responsible for keeping the knowledge base up to date and how often it should be reviewed.
  Organise your information by separating similar processes, such as the steps for subscribing to and unsubscribing from a service, to avoid the bot mixing things up and confusing customers.
  Use an AI solution that flags conversations by satisfaction level, and assign someone from your team to review the bot’s responses in interactions flagged as “unsatisfactory”.

2. TRAIN YOUR BOT TO HANDLE MALICIOUS QUERIES

The challenge:

A bot that isn’t prepared to recognise requests that have nothing to do with your business, or attempts by hackers to exploit it for free services or products, puts your brand at risk.

How to fix it:

• Configure the bot to respond only within its area of knowledge. Irrelevant queries should be redirected to a human agent or met with clear responses such as: “Sorry, I can’t help with that.”
  Protect your brand by limiting the bot’s capabilities: don’t allow it to process payments, close sales or access sensitive data without supervision.
  Implement AI tools that label conversations by query type. Regularly review interactions in the “other” category to monitor how the bot handles unknown topics.

3. USE A BOT THAT UNDERSTANDS EMOTIONS

The challenge:

A bot that can’t detect a customer’s emotional tone could escalate a tense situation or mismanage a complex request, ultimately damaging the CX.

How to fix it:

• Make sure your bot uses natural language processing (NLP) algorithms to detect emotions such as frustration, urgency, or dissatisfaction in customer messages.
  Train your bot to distinguish between simple queries, such as FAQs, and complex issues that require human attention.
  Design a clear process for the bot to automatically escalate emotional or critical queries to a rep, letting the customer know that a specialist will be handling their case.

IS AI READY TO REPRESENT YOUR BRAND?

The answer is yes – but only if you take the time to implement it properly and focus on continuous improvement.

Even generic AI solutions marketed as “easy to implement” require time and effort. It’s important to define the bot’s purpose from the outset, tailor its tone of voice to reflect your brand identity, and test it thoroughly with malicious queries before deploying it on your channels.

This initial phase is essential to correct up to 90% of the bot’s potential errors. Bear in mind, however, that the remaining 10% can’t be identified and resolved until the system is live, as it’s simply impossible to predict every future scenario.

Remember, well-configured AI can reduce errors by 40%, but poorly implemented AI can damage not only your reputation, but also consumer trust in the technology as a whole.

So if you decide to invest in a bot, make sure you do it right. The AI you choose and how you manage it will reflect your commitment to your business, your customers and the future of technology.

READY TO TALK AI?

La entrada 3 tips for implementing AI in customer service without compromising CX se publicó primero en Enreach ES.

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.

We had the pleasure of interviewing Jorge García, our Product Director, for several articles on artificial intelligence. During one of those informal meetings where we were discussing potential use cases in the contact centre sector, he told us:

“The philosophy behind self-service should be nothing more than automating customer service without losing the quality of experience you would get when talking to an agent.”

It’s true. The word “self-service” can have a certain negative connotation because it evokes a scenario where the customer is alone in the face of danger.

For example, we call the checkout machines in supermarkets self-service, but the machine does not help the consumer to scan the products, the consumer scans them alone.

So when we talk about an AI-powered chatbot that serves users who come to the website and provides answersto their questions or redirects them to the best available agent, we are talking about a system that automates responses.

We have been stressing this a lot lately, but we think it is very important to remind those companies that are thinking of investing in this technology that if it is not applied with certain criteria, the bot can end up truncating the customer experience.

WHAT ARE THE KEYS TO MAINTAINING QUALITY WHEN AUTOMATING RESPONSES?

1. PROCESS SIMPLE AND REPETITIVE ENQUIRIES

The first thing we can start automating are FAQs. Frequently Asked Questions are those that come up all the time, either before buying a product/service or after.

One of the questions a telecommunications company could automate would be “Why has my phone bill gone up this month?”.

If we connect the AI to our customer service agent, the bot can access the customer’s bill, analyse the document and inform the customer of any additional charges.

2. TAKING THE CUSTOMER’S EMOTIONAL STATE INTO ACCOUNT

The AI that powers the chatbot not only understands the user’s request, but also has the ability to extract their emotions. As a result, it knows from the outset the severity of the incident and the emotional state of the customer.

The bot is not a human, so no matter how much we train it to deal with serious problems, it will always lack emotional intelligence.

So if the bot is talking to a customer who has a power cut at home, for example, we need to train the AI to transfer the conversation to an agent.

3. TRANSFER THE CONVERSATION TO THE AGENT WHEN THE USER REQUESTS IT

There is nothing more frustrating for a user than talking to a bot when they want to talk to an agent. So it’s always a good idea to give the user instructions on how to get in touch with an agent.

In addition, it will soon be illegal to offer customer service based on voicemail or bots that do not allow you to speak to an operator. When the new Spanish customer service law comes into force, companies that do not comply will be fined up to €100,000.

4. AVOID BOT HALLUCINATIONS AT ALL COSTS

We don’t want our bot to give a customer the wrong answer. So we need to learn how to maintain the knowledge bases properly.

When we create a knowledge article, we must avoid explaining multiple processes in the same document.

WANT TO ADD A CHATBOT TO YOUR BUSINESS?

La entrada How not to lose quality when using a chatbot to automate responses se publicó primero en Enreach ES.

Yes, artificial intelligence enhances our customers’ experience by providing immediate responses. In fact, 68% of consumers cite this as their favourite feature, according to Userlike.

“68% of consumers cite this as their favourite feature”.

Userlike

However, a good experience can turn into a nightmare if the chatbot isn’t programmed correctly.

Here are four mistakes we see on some websites and how to fix them.

1. POORLY STRUCTURED FAQS

With a conversational AI chatbot, we’re obliged to create a menu of options (similar to a written IVR menu) for users to select the most relevant question.

But what if there isn’t a suitable option, or worse still, there’s no place to ask an open-ended question? This hampers the customer experience.

The philosophy behind self-service is to automate customer service without sacrificing the quality of the agent-assisted experience.

If we don’t embrace this premise, we’ll create a bot that won’t benefit the user and will result in a flood of phone complaints.

SOLUTION 1: OFFER A VARIETY OF FAQS

Especially at the beginning, when you are unsure of which FAQs to offer, we recommend offering a range of options, from pricing and product information to job applications or demos. Once the bot is up and running, it is important to continually improve these FAQs.

Using the bot’s configuration panel, you can track how many queries each topic receives and replace those that have no queries within a month.

SOLUTION 2: IMPLEMENT A HYBRID CHATBOT

Another approach is to enable a hybrid chatbot that combines a conversational AI tree menu with open-ended, generative AI queries. This reduces user frustration when they can’t ask for what they want.

While generative AI can be creative and susceptible to trolling, it is important to limit it to specific knowledge bases and test it before activating it on our website.

This option is particularly attractive for contact centres, which can use open-ended questions to incorporate the most common ones into FAQs.

2. INABILITY TO SPEAK TO AN AGENT

Customers who contact a chatbot are generally looking for a quick, out-of-hours response. However, some may have more complex queries and prefer not to call.

“The customer is always right” is a mantra when dealing with the public. Even if phone channels are reserved for incidents, if a user contacts the chatbot with a serious problem, it is necessary to allow the chatbot to transfer the request to an agent.

TRAINING THE BOT PROPERLY

We train our generative AI to automatically transfer the chat to an agent if it doesn’t understand the nature of the query, without the customer explicitly requesting it.

Firstly, because with the new Spanish Customer Service Act, we must always offer human support as an alternative to chatbots, or face fines of up to £100,000.

And secondly, it significantly improves consumer readiness: according to Simplr, 80% of consumers are more likely to engage with a chatbot if they know the conversation will be transferred to an agent.

“80% of consumers are more likely to engage with a chatbot if they know the conversation will be transferred to an agent”.

Simplr

3. NOT ASKING FOR FEEDBACK

If we’ve recently activated the chatbot on our website, we’re missing a significant opportunity to measure customer satisfaction by not requesting feedback after each interaction.

The AI can analyse and identify user sentiment during conversations, and send us a rating for each one. However, it’s also beneficial to contrast the bot’s automated assessment with direct user feedback.

Consumers like to be heard, so while we can measure their sentiments independently, it is always valuable to involve them in the bot’s improvement process.

4. 4. ARTIFICIAL INTELLIGENCE HALLUCINATIONS

AI hallucinations occur when the bot confidently gives a wrong answer because it thinks it is the right answer.The purpose of AI in customer service is to free agents from routine queries, not to give us headaches.

To avoid this, we recommend that knowledge bases are well structured. This means avoiding mixing concepts in the same article.

For example, if a document explains how to activate a service, don’t include the steps to deactivate it.

READY TO IMPLEMENT A CHATBOT FOR YOUR BUSINESS?

La entrada 4 common chatbot implementation mistakes and how to avoid them se publicó primero en Enreach ES.

Generative Artificial Intelligence, such as ChatGPT, is the best ally for customer service. Not only is it known for its ability to generate personalised responses, but it also doesn’t need a content tree to work, which makes it easier to implement.

Both qualities stem from its ability to learn and reason independently, taking into account the context of the conversation and the previous interaction history with other users.

ChatGPT for customer service has hundreds of applications. In this article, we explain the most common ones used by our customers to automate tasks, gather more information and ultimately be more effective with customers.

6 USE CASES FOR CHATGPT IN A CONTACT CENTRE

1. CHATGPT TO SUMMARISE A CUSTOMER INTERACTIO

AI is known for its ability to analyse large amounts of data and extract behavioural patterns, so we can use it to summarise any type of interaction (emails, phone calls, video calls and chats).

Once the agent has finished the conversation, we can use ChatGPT to process the tone of voice, keywords and the evolution of the dialogue and generate a summary.

2. AUTOMATIC CATEGORISATION OF A CONVERSATION WITH CHATGPT

Another administrative task that can be delegated to artificial intelligence for customer service is categorisation. Not only do we free agents from a routine task where they don’t add value, but we can also standardise a specific tagging protocol.

For example, if ChatGPT detects that a user has revealed their religious orientation in a conversation, it can tag the conversation so that the supervisor knows to remove sensitive information that should not be stored.

3. CHATGPT TO EVALUATE AGENTS

At the end of the conversation, we can also use ChatGPT to generate a report on the agent’s performance. This will allow us to evaluate all conversations, create more objective reports and provide written evidence.

When building our generative AI, we should specify the particular aspects we want it to analyse, be it the agent’s problem solving skills, the way they present themselves, their tone of voice, etc.

4. USING CHATGPT TO TRAIN CALL CENTRE AGENTS

Just as we use ChatGPT on a personal level to search the Internet for us, we can also create an AI that is fed only with internal company information (manuals, instructions, return policies, etc.).

To make agents more autonomous and faster at resolving queries, they can ask the bot a question and, after consulting the knowledge base, it will provide a concise and immediate answer.

5. CHATGPT AS A SUPERVISOR TO PROACTIVELY HELP AGENTS

ChatGPT can also provide proactive assistance to agents by eliminating the need for manual information searches. Like a ubiquitous entity, it integrates with contact centre software, analysing nuances and helping each agent in a personalised way.

It can even suggest text to speed up an email response, detect and correct spelling mistakes and indicate when to offer a discount, based on the history of interactions.

6. PROACTIVELY HELP CUSTOMERS WITH CHAT

Anticipating customers’ problems before they need to contact you is key to delivering a great customer experience.

In the automotive sector, one brand created a bot that automatically scheduled a service appointment every time the vehicle displayed an oil change warning.

7. CHATBOT FOR AUTOMATIC PERSONALISED RESPONSES

Yes, generative AI is known for its great ability to generate personalised responses because, as mentioned above, it “gets to know” the user through previous interactions with other consumers.

Recently, we analysed several success stories in the retail sector and showed how some brands could use a chatbot with ChatGPT to offer personal shopper services to users visiting their website.

HOW TO USE CHATGPT TO SERVE YOUR CUSTOMERS

To use ChatGPT in your contact centre, you need to hire a virtual customer service assistant that integrates with ChatGPT, such as EVA (Enreach Virtual Assistant). This AI assistant can be used on all your channels, whether phone or written (chat, email, WhatsApp, etc.) and can be more easily connected to third party systems.

Discover all you can do with EVA, the customer service AI integrated with ChatGPT.

La entrada How to use ChatGPT for customer service se publicó primero en Enreach ES.

Artificial intelligence applied to e-commerce does everything from automating conversations, to providing 24/7 support, to extracting data to optimise the customer experience.

Here are ten ways AI can transform the service your online store provides:

1. INTENT CLASSIFICATION

If we want to prioritise questions about problems or address unhappy customers first, the quickest and most efficient way is to use artificial intelligence.

Conversational AI can analyse the words used by the customer to determine their needs and feelings, and act accordingly, either by automating the service through a virtual assistant or transferring the conversation to an agent.

2. EXPERT ROUTING

In addition to routing based on skills and prioritising queues like an advanced routing system (ACD), contact centre AI can take into account the customer’s interaction history when routing the call to an agent.

This allows us to route the call to the same agent who opened the ticket if a customer has an open case. This small change can have a significant impact on our NPS and provide a better customer experience.

3. CUSTOMER IDENTIFICATION

To provide a personalised experience, we can use AI to identify the customer. When they contact us with an issue, as in the previous cases, AI can request the customer’s ID or phone number, access our database and check any open tickets.

4. FREQUENTLY ASKED QUESTIONS

AI excels at providing specific answers, so for all those customers who ask us about the process for returning a package or the customer service phone number, we can use a bot to automate the answers, freeing up agents for more complex questions.

In addition, using a bot with generative AI, you can help users with their post-sale queries, such as providing tips on how to remove stains from clothes or restore furniture.

5. PERSONALISED RECOMMENDATIONS

As we have seen, AI can analyse historical data to identify consumer buying patterns. This information can help you recommend products and improve your up-selling and cross-selling.

It is now very trendy to select clothes based on skin tone, so you can use a bot with generative AI to act as a personal shopper for your online users.

6. SHIPMENT TRACKING

With the rise of phishing emails and text messages posing as couriers, we can build trust with our customers by allowing them to track their package from our website.

A chatbot can provide automatic updates on the status of the shipment, informing customers of any delays or changes to the delivery.

7. RETURNS MANAGEMENT

This is probably the most common reason for a call to your contact centre. The good news is that you can streamline this process with a conversational AI bot.

When the bot detects this type of query, it can send the necessary instructions, open a ticket to process the return, and even send the label to the customer.

8. SATISFACTION SURVEYS

With AI, we can automate the sending of NPS or CSAT surveys. If we only want to send a survey when a case is closed, we can train it to send an email/SMS (if by call) or a link (if by chat) to increase the number of responses.

We can also use it to help us analyse responses and identify areas for improvement.

9. REAL-TIME ANALYSIS

Since its inception, Artificial Intelligence has been used to identify patterns, allowing us to answer the most common questions asked by supervisors, such as the main reason for calls in the last three hours.

This allows us to gauge the sentiment of conversations, identify common issues and provide better responses to customers.

10. PROACTIVE SUPPORT

One of the most remarkable qualities of artificial intelligence is its proactivity. Externally, with customers, it can detect if a user is spending a lot of time on a product page and offer a pop-up message with additional help or a discount.

Internally, with agents, it can help manage a complaint or defuse a problematic situation.

WANT TO INTEGRATE ARTIFICIAL INTELLIGENCE INTO YOUR CONTACT CENTRE?

A minimum of artificial intelligence maximises human intelligence. Harness the talent of your agents by allowing them to focus on tasks where they can add real value, and use AI to automate the processes where they cannot. Contact our team of experts for a personalised consultation.

La entrada 10 examples of AI automation in e-commerce contact centres se publicó primero en Enreach ES.

THE IMPACT OF AI

The day began with a welcome from our CEO, Alfred Nesweda, who highlighted the importance of artificial intelligence in the sector, noting that there are more and more AI solutions for the contact centre.

“Every week I discover a new company offering AI solutions for the contact centre.”

Alfred Nesweda

Alfred Nesweda, CEO of Enreach ES, welcomes attendees to the CX Brunch by Enreach.

This technology, which is now gaining significant momentum, could become a new business model and “new economy” in the future, as it is expected to contribute $15.7 trillion to the US economy by 2023, according to PwC.

SPANISH LEGAL FRAMEWORK FOR AI

Our CEO then handed over to Daniel Vidal, a lawyer and legal director of Webpilots España, who began his presentation by explaining the nature of AI and its rapid development.

“The train is leaving, and those who don’t catch it now may not be able to get on at the next station.”

Daniel Vidal

After this metaphor, he moved on to the topic of AI regulation, making it clear that Spain is a global leader in the regulatory field, highlighting that a Royal Decree was issued as early as 2023 to oversee artificial intelligence on Spanish territory, long before the AI Act was approved at the European level last month.

Daniel Vidal, Legal Director of Webpilots España, during his presentation at Enreach’s CX Brunch.

Another piece of Spanish legislation that will regulate the use of AI in customer service is popularly known as the ‘3-Minute Law’, which, once passed, will, among other things, limit phone hold times to three minutes and prohibit the exclusive use of auto-attendants or conversational bots to assist consumers, among others.

As a result of users’ bad experiences when calling customer service and wanting to speak to an agent, or at least a more advanced bot that understands their queries, this bill is the perfect opportunity to adopt technological solutions that enable quality self-service while reducing waiting times to zero.

THE ROLE OF AI IN THE CONTACT CENTRE

The third and final presentation of the CX Brunch was a live demonstration of our virtual agent: EVA (Enreach Virtual Assistant).

Led by Marc Brunés, an AI specialist, and Jorge García, Product Director, they were able to demonstrate how our virtual assistant works and is configured through a user case.

Our AI solution for customer service was able to engage in a chat conversation with a fictional customer, recognise their problem, check the customer’s record in Salesforce, log the issue raised by the user and return the tracking number in a matter of seconds.

On the other side of the spectrum, Enreach’s virtual assistant is programmed to leave a note for the agent with the incident alert, the ticket and the history of the conversation it had with the customer.

On the left, Jorge García, Product Director, and on the right, Marc Brunés, AI Specialist, during the demonstration of Enreach AI.

EVA has been programmed to follow the steps that any other agent would take, but in a fully autonomous manner, freeing operators from tasks where they do not add value, and allowing them to focus on providing a more personalised and humanised service.

We have no doubt that consumers who still prefer human contact will appreciate being assisted by a person who takes the time to listen to them, while those looking for a quick solution will marvel at the efficiency and immediacy of the bot.

EAGER TO KNOW MORE?

Together with Webpilots España, we are preparing a whitepaper with the aim of sharing, free of charge, the main contents discussed during the two sessions of the CX Brunch.

Subscribe to the blog and you will be the first to receive our report on the latest trends in AI in the contact centre.

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La entrada Challenges of AI in customer service: Summary of the second CX Brunch se publicó primero en Enreach ES.

Improving customer service means adapting to the needs of the modern consumer.

A recent Gartner study shows that 55% of Millennials and Generation Z will use the product/service less if their issue could not be resolved in self-service.

55% of consumers between the ages of 43 and 11 will use the product/service less if their issue could not be resolved in self-service.

– Gartner

This data highlights two undeniable realities: first, consumers are more demanding than ever, and second, their consumption habits are very different from previous generations.

The key question we need to ask ourselves now is: how do we adapt our customer service to avoid losing customers and, as a result, deliver an experience that exceeds their expectations? As the title of the post suggests, by using a chatbot for customer service.

1. PROVIDE INSTANT ANSWERS

The most common friction point in our customers’ experience is often the lack of immediate responses, which can be addressed by incorporating digital channels such as WhatsApp, social media or an online chat managed by a chatbot that provides instant responses.

Unlike agents, the bot is capable of handling millions of concurrent conversations without compromising the quality and effectiveness of its responses.

Following a pre-defined conversation pattern, it responds instantly to customers with the primary goal of determining their needs and emotional state, and if it has sufficient knowledge, it can solve the problem or, alternatively, transfer the conversation to the best available agent.

2. OFFER SELF-SERVICE

In addition to providing immediate answers, the chatbot can also provide instructions on how to return a package or resolve a problem that is reflected in a user manual.

If we feed the bot with all the documentation for our products/services, it will be able to answer all questions related to this information.

In addition, as it is able to analyse large amounts of data in a matter of seconds, its response will be instantaneous.

3. DELIVER PERSONALISED EXPERIENCES

The most fascinating aspect of the chatbot is its ability to track a ticket or recommend a product based on the customer’s profile.

If we give it access to our ERP or CRM system, where this information is stored, the bot can request the customer’s ID, identify them in the database and, for example, provide the status of an issue.

In addition, generative AI relies on an algorithm that can “create itself”, allowing it to cross-reference data to recommend products/services.

4. HELP AGENTS

Through an internal chat, agents can ask it any question: What is our return policy? What discounts has this customer taken? What orders have they placed before?

Without agents having to manually search for this information, the bot provides it instantly.

We need to remember that the chatbot is another member of the customer service team, so we can also use it to help agents provide faster and better service.

FINAL THOUGHTS

Collaboration between artificial intelligence and agents is essential to improving customer service. Not only because we can offer immediate responses 24 hours a day, but also because we can consider adding more touch points with our audience.

Remember that once the Spanish Customer Service Act is passed, it will be strictly forbidden to use chatbots exclusively, so the conversation must be transferred to a human agent if the customer specifically requests it.

Thinking about implementing a chatbot for your customer service? Get in touch with our team of experts by calling +34 900 670 750 or filling out this form.

La entrada How to take customer service to the next level with a chatbot se publicó primero en Enreach ES.