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.

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.

With the approval of the Spanish Customer Service Bill, customer service organisations must be prepared to serve their customers in less than three minutes.

Hiring more agents and using advanced contact centre software will be essential to meet the upcoming regulations.

1. DISPLAY KEY WAITING TIME KPIS

You can’t manage what you can’t measure, including customer wait times. That’s why supervisors need to be able to check the percentage of calls answered in less than three minutes.

With a real-time control panel, they can see if there’s an overloaded queue, for example, or if an agent is taking too long to deal with a customer.

They can then make decisions and act immediately, whether it is adding more agents to a queue or entering the call to give instructions to the agent without the customer hearing them.

2. SPEED UP THE INTENT QUALIFICATION PROCESS

To reduce customer hold times, it’s necessary to streamline the process of identifying the reason for the call.

In the past, the only way to do this was through IVR menus, where we programmed a series of prompts to route the call depending on the number the customer pressed (DTMF) or the word spoken (ASR).

For several years now, call centres have been using open-ended questions. Instead of giving several options to choose from, they ask directly: “Please state the reason for your call”.

Call qualification can be done using an advanced IVR or a bot. Both systems can recognise keywords in a sentence and route the call to the most appropriate queue.

But remember! If you want a ‘human’ conversation before the call is routed to the queue, an advanced IVR won’t do.

3. ADDING ARTIFICIAL INTELLIGENCE TO SELF-SERVICE

Self-service is an effective tool for reducing customer waiting times by providing quick and automated responses to recurring and simple queries.

Without the need for supervision, callbots are able to speak, understand and respond like humans thanks to conversational and/or generative artificial intelligence (such as ChatGPT).

Although they require prior training to learn how to identify and resolve these types of queries, they can relieve agents of the need to: identify a customer, answer FAQs, read an instruction manual, manage a medical appointment, recommend a pension plan, etc.

4. CONFIGURING QUEUES USING ADVANCED METHODS

Of all the queue configurations available, there are three that are specifically designed to effectively manage customer waiting times: skill-based routing, queue prioritisation and maximum queue capacity.

4.1 ADVANCED ROUTING

Specifying the skills of the agents working in the contact centre is essential for routing calls more efficiently and thus reducing waiting times.

The programme you use to handle calls should allow you to specify the skills of each agent, from general knowledge to the most specific skills and even soft skills.

For example, we can have an agent with technical support skills, based in Barcelona, who specialises in fixing routers and also has empathy skills.

This agent will be assigned to the technical support queue with a maximum score (100 points), but will also be able to handle some calls from the customer service queue (20 points). They will be able to handle calls in Catalan (100 points), Spanish (100 points) and English (60 points), they will be able to resolve queries about the Internet (100 points), and they will also have knowledge of computers with the Windows operating system (80 points), in addition to being empathetic (100 points).

This system allows us to list all the operators’ skills and assign them a value from 0 to 100. They will therefore deal first with requests that require their strongest skills, and then those with lower scores (if the agents with the highest scores are busy).

4.2 QUEUE PRIORITISATION

Queue prioritisation is used to further refine routing and consists of assigning a value from 0 to 100 to the queues themselves.

For example, if we have three queues: one for customer service (50 points), one for technical support (100 points) and one for sales (20 points), we can tell the system which queue has the highest priority.

In this way, agents trained to receive technical support and customer service calls will deal with technical queries first.

This configuration is very useful if we find that the volume of calls is higher for one type of query. By prioritising them, we will deal with them before the rest.

4.3 QUEUE SIZE

Limiting the size of a queue can help us to prevent overflows, as we not only specify the number of calls that queue can handle, but also specify an alternative queue for when it fills up.

Following the previous example, if we create several technical support queues by language, we can make the Spanish queue only take up to 5 calls and use the Catalan queue as a backup, since it’s made up of agents who also speak that language.

5. CONFIGURE A BOT TO QUEUE YOUR CUSTOMERS

The definitive way to end phone queues is to have an artificial intelligence queue for your customers when they call the contact centre.

As soon as the customer enters the queue, the AI informs them that they can end the call and receive a callback when an agent is available.

Despite hanging up, the bot will continue to advance positions in the queue, and when it reaches the first position, it will automatically call the customer and the agent to connect them.

The main difference between this AI and a traditional callback is that when the customer calls back, the bot informs them of their current position without losing their turn.

6. ADDING ALTERNATIVE CHANNELS

Another way to meet the new customer service SLA is to reduce the volume of incoming calls by enabling digital channels, whether it’s WhatsApp or a chat on the website.

These channels do not have minimum attention times and can also be managed by chatbots. Just as AI can detect intent and intentionality in calls, it can also do so through written channels.

So, the process would be the same: present the user with a menu of query options, or an open-ended question.And once the bot understands the customer’s need, it can offer self-service or transfer the call to an agent.

The best thing about omnichannel contact centre software is that both the chatbot and the agents can seamlessly switch from a WhatsApp conversation to a phone call.

FINAL THOUGHTS

The technology available on the market today can help you serve all of your customers in less than three minutes.

Depending on the volume of calls your contact centre receives, you’ll need to assess whether you only need an advanced customer service programme, or if you need to add alternative channels and/or artificial intelligence.

Will your company’s customer service be affected by the new by the future law? Get in touch with our team of experts on +34 900 670 750 or chat to us. We’re here to help!

La entrada How contact centre software can help you manage customer waiting times se publicó primero en Enreach ES.

Looking at the most popular questions we ask ChatGPT, we’ve realised that in our personal lives we use this new technology for almost everything. 

It’s almost anecdotal to discover that the vast majority of us ask it to tell us a joke, recommend TV series or help us with our maths homework.

The applications are so varied that they make us reflect on how much our lives have changed by having a chatbot ready to answer our questions 24 hours a day. 

While we’re using artificial intelligence in our personal lives to turn on light bulbs, we’re not there yet in the business world. 

This may be because there are still many unknowns to be resolved: the price, the level of data security and, above all, how to use it correctly to add value.

Artificial intelligence in contact centres has already taken its first steps with intelligent IVR menus: those that can connect us to customer service by simply saying “I want to unsubscribe”. 

But now AI is ready to go one step further.

We see our artificial intelligence as part of the customer service team. Broadly speaking, it takes on tasks where humans don’t add value. 

It also provides us with data that helps us to identify improvements to the content of our website or even a feature of our service. Seriously, we’re not exaggerating.

Without any programming knowledge, but with a good understanding of question structure, you can program a bot in less than thirty minutes to work wonders, as we’ll describe below.

CASE 1: MANAGING RESERVATIONS

If your customer service department spends 40% of its time managing reservations or product deliveries, you’ll love this use case.

You’ve probably heard a thousand times that these processes no longer need human intervention. 

Until now, there was no AI on the market that could successfully handle this task. Now things have changed.

The most advanced AI can identify the customer, check a reference number, show alternative dates and recordchanges in both written and voice channels. 

But it doesn’t stop there; you can also have it send the reservation/order update via SMS or email for written confirmation. 

As you can see, AI is not only answering queries during office hours, it is also querying databases and modifying records. Amazing!

CASE 2: GATHERING QUALITATIVE DATA

Contact centres typically work with quantitative data: how many calls were handled, how many users left a queue, how many seconds a call took… 

The only way to get qualitative information was to conduct satisfaction surveys after the call. We’re happy to use the past tense to say that artificial intelligence, while not a replacement for NPS or CSAT surveys, provides very valuable qualitative data on a daily basis.

If you’ve ever interacted with a bot, you’ll know that it usually starts by asking how it can help, and then presents a series of options to choose from. Every time a user selects one of these options, their choice is recorded in the statistics panel. 

This information is very useful for identifying the most common reasons for contact and acting accordingly – making changes to our website, improving a service functionality, etc.

CASE 3: BREAKING DOWN INFORMATION SILOS

In addition to managing reservations and collecting data, AI can also be used as an internal source of knowledge. 

Just as ChatGPT was trained to analyse and store millions of pieces of web data, there are chatbots for internal use that can be trained with product manuals, company presentations or legal documents.

Once it has all the knowledge it needs, employees can chat with it and ask any question, from product configuration to delivery times or contract clauses. 

Whether it’s for customer service or any other area, the internal ChatGPT streamlines the onboarding process and promotes a workspace where knowledge is accessible to everyone and there are no silos of information.

FINAL THOUGHTS

With a mission to put artificial intelligence in the hands of contact centres, we help millions of professionals integrate this technology into their daily lives. 

Tell us about the processes you want to streamline by calling +34 900 670 750 or using the chat below. Our team of experts will be happy to help.

La entrada 3 use cases of artificial intelligence in the contact centre se publicó primero en Enreach ES.