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.

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

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

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

1. FAQ RESOLUTION

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

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

Use case:

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

2. SELF-SERVICE

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

Use case:

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

3. TICKET MANAGEMENT

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

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

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

Use case:

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

4. SPEECH ANALYTICS

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

Use case:

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

5. FRICTION POINT ANALYSIS

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

Use case:

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

6. CALL TRANSCRIPTION

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

Use case:

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

7. AGENT SUPPORT

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

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

Use case:

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

8. EXPERIENCE PERSONALISATION

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

Use case:

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

FINAL THOUGHTS

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

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

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

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

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

When organizations empower customers to resolve their queries on their own with self-service technology, they can improve CX and increase sales.

Voice channels will continue to be present and, according to Metrigy, they will continue to be the second channel of interaction after email. However, many users prefer to manage their queries by themselves or with the help of virtual assistants.

To improve CX, contact centre leaders can implement customer self-service best practices, such as updating knowledge bases, using virtual assistants, and embracing omnichannel capabilities. In fact, self-service knowledge bases can improve user ratings, increase sales, and boost agent productivity, according to Metrigy’s “Customer Engagement Transformation: 2022-23” study.

How Can CX Leaders Optimize Customer Self-Service Performance?

1) Updating the Knowledge Base

All self-service strategies rely on a knowledge base. However, most organizations do not continually update the content, search, and media types in these databases.

Before customer service leaders implement or expand their self-service strategies, they need to consolidate, modernize, and update their knowledge bases to provide a solid foundation for customer service.

2) Identifying Common Customer Queries

Certain inquiries, such as return policy questions, lend themselves to self-service more effectively than others. CX, IT and business leaders need to discuss the types of inquiries and questions customers may have and decide which channel of interaction would be best for each point.

For example, if customers regularly “pepper” an organization’s contact centre with questions about the company’s return policy, the CX team could create an FAQ page that clearly explains the policy and then direct them to that page when ask the question.

3) Using Virtual Assistants to Direct Customers

When customers access a self-service channel (an organization’s website, mobile app, or FAQ portal), they want to obtain information, resolve a query, or perform an issue. In these cases, a pop-up virtual assistant can ask a few simple questions to guide customers down the right path to self-service or route the conversation to a live agent. In any case, virtual assistants can offer them what they need at all times and keep satisfaction rates and technology functionality high.

4) Offering Omnichannel Capabilities

Often, when customers access self-service channels, they must move to other channels to resolve their issues. For example, users can start their self-service journeys on a Frequently Asked Questions (FAQs) page, and then move to a screen-sharing live agent chat.

If customer service leaders don’t integrate self-service channels, this lack of cohesion can frustrate customers, as they have to repeat their inquiry over and over again. This fact also creates frustration for agents who don’t have a full view of customer interactions, making it difficult to resolve queries.

Only 21.4% of call centres have omnichannel capabilities, according to the Metrigy study. However, omnichannel improves customer (CX) and employee experiences by allowing agents to track agent interactions across channels.

5) Embracing Artificial Intelligence (AI) Technologies

Customer service leaders need to regularly update their knowledge bases to provide customers with the most up-to-date information about the company, products, and services. As a result, many organizations use AI and machine learning (ML) to identify areas that need to automate regular content updates.

6) Measuring Success

After customer service leaders have implemented a self-service strategy, they need to measure its success to determine if objectives have been met. Customer satisfaction scores and revenue increases can indicate the success or failure of the self-service strategy. If results don’t meet CX leaders’ expectations, the team can work to improve performance and create a better customer self-service strategy.

La entrada Top 6 Customer Self-Service Best Practices se publicó primero en Enreach ES.