The Ultimate Guide to Delivering E-Commerce Customer Service with Generative AI

Modern customers are demanding — they want personalized responses, near-zero response times, and an omnichannel experience. The problem? It’s nearly impossible to deliver exceptional ecommerce customer service without an army of support agents or using AI. That’s one of the reasons why 85% of execs expect generative AI to interact directly with customers by 2025.

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The question here is not whether you should invest in generative AI, it’s how to best use generative AI to meet customer expectations. Because many of your competitors are already jumping aboard the AI train. If you sleep on it, you’ll be left behind.

“One in 10 agent interactions will be automated by 2026, an increase from an estimated 1.6% of interactions today that are automated using AI.” - Gartner (2022)

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Since 2022, generative AI has grown exponentially and is likely to automate over half of all customer interactions. At Yuma, we’re tapping into the power of generative AI and predict that 5 to 6 interactions out of 10 will be automated by 2026. The game has totally changed.

In this extensive guide, we explain various ways to use generative AI and how it’s revolutionizing ecommerce customer service as we know it. Let’s dive in.

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What is generative AI in ecommerce customer service?

Generative AI in ecommerce customer service refers to the use of advanced AI models like GPT that can generate instant, accurate, and personalized responses to customer queries.

Picture this. A customer reaches out via chat, asking a question related to order tracking. The AI agent springs into action, pulls the customer’s order history, tracks the order, and provides the information within seconds — no manual effort necessary.

That’s one example of what generative AI can do for your ecommerce business. There are plenty of other use cases for generative AI.

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4 use cases of generative AI in ecommerce customer service

Here are examples of how an AI agent can empower your ecommerce brand:

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Automating repetitive and complex queries

AI agents with generative AI also come with other capabilities like natural language processing (NLP). AI agents use a combination of these technologies to analyze the context of a customer’s questions, past interactions, and data to generate personalized responses not just to basic WISMO (Where is my order) queries but also more complex requests.

Let’s say John, a disgruntled customer, needs help resolving multiple problems. He ordered a set of products but some were damaged during shipping. One item is on backorder, and he wants to redeem a store credit for a future purchase.

If you use traditional systems, John will likely be stuck playing email tennis with your support agents for at least a few days. On the other hand, if John explains his problems to the AI agent, here’s how it might respond:

“Hi John,

I see that you ordered Products A, B, and C. I apologize for the inconvenience caused by the damage during shipping. I’m arranging for a replacement for the damaged Product A, which will be delivered by September 20, 2024.

Additionally, Product C is on backorder, but we’re expecting new stock by October 5, 2024. I’ve made sure that you’ll receive it as soon as it’s available, with free expedited shipping.

Regarding your store credit of $20, you can apply it to your next order at checkout, or let me know if you’d prefer I apply it to your current order.

Please feel free to reach out if you have any further questions. I’m here to help!”

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Personalized shopping assistance and support

Imagine a guided experience where, as customers browse products on your site, the AI agent generates personalized recommendations to help them find a product. Think of it as a virtual shopping assistant you see in brick and mortar.

If you sell apparel, the AI agent could guide the customer toward the new arrivals in the jeans section once they settle on a sweatshirt. This elevates customer experience and increases average order values.

When customers need help, the AI agent is ready to step in 24/7. This keeps support traffic requiring human intervention to a minimum. It also enables you to deliver highly personalized responses at scale and saves your company plenty of money.

Take CABAIA for example. The French company that sells leather goods and accessories across Europe struggled with support during peak season. In 2023, the after-sales service team handled over 132,000 tickets. Needless to say, the team was overwhelmed and needed a solution to automate support tasks without compromising on service quality.

CABAIA started using Yuma AI in 2024 to automate handling routine ticket types like order cancellations, modifications, guarantee claims, and more. They integrated their knowledge base into Yuma to help Yuma access detailed product information like dimensions, weight, and more. Six months in, CABAIA had successfully automated many major cases and achieved cost savings per ticket of €2.75 and €9,675 in total over the next six months.

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Dynamic emails

AI agents can be configured to send dynamic emails to customers. This allows ecommerce brands to solve one of the most pressing challenges — abandoned carts. The average cart abandonment is over 70% but an AI agent can help keep your abandonment rates well below that mark.

Here’s how: Suppose you’re the customer. You’re commuting home from work and you’re browsing through the hoodie collection but before you find that perfect hoodie, you reach home and abandon the purchase.

A few hours later, you receive an email asking if you’d like to continue browsing through hoodies with a few personalized recommendations based on the products you browsed. You like one of the hoodies, click through, and check out.

That’s how one email from your AI agent can lower abandonment rates with zero manual effort and transform your customer’s experience.

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Multilingual support

Multilingual support is non-negotiable, and not only if you sell outside English-speaking countries. Even in the US, residents speak over 350 languages.

If you think your customers don’t care about the language as long as they understand it, here’s a fact check: 71% of respondents believe it’s extremely important that a brand promotes and supports its products and services in their native language according to a study.

Riddle me this. Let’s say you ship products to over 50 countries in North and South America and Europe. How do you ensure customers with native languages other than English receive the same level of exceptional customer service you aim to deliver to customers in your home country?

You have two options. One, you can hire dozens of support agents for every language spoken in countries where you sell. Two, use a multilingual AI agent that can field queries in a wide range of languages and costs $0 extra.

You do the math.

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Why ecommerce needs generative AI for customer service

So far, we’ve talked about how generative AI can elevate ecommerce customer service. The truth, though, is that ecommerce brands will find it progressively difficult to deliver decent experiences without generative AI. Let’s talk about why you need generative AI for customer service.

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Response time

Customers expect fast responses across all support channels. Let’s put this in context with some research-backed numbers. Customers expect instant responses over chat. 38.3% of customers expect you to respond over email within an hour or less. 39% of customers expect you to respond over social media in 2 hours or less.

If you’re currently not responding fast enough, you might lose business to competitors who beat you with their lightning-fast response times. But it’s not just about retaining your customers. It’s about delivering a top-notch experience and increasing average order values.

Using an AI agent allows you to meet this need for speed with real-time responses and connecting customers to human agents only when necessary.

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Personalization

The verdict is almost unanimous—89% of leaders believe personalization is critical to business success in the next three years. They’re not wrong. Almost every other study shows customers are on the same page. In fact, 81% of customers prefer companies with a personalized experience. That’s almost all of your customers.

Imagine you’ve grown your customer base and receive over 200 support queries a day via chat, email, and phone. Each customer wants you to know their name, past purchases, and buying patterns. An AI agent can access all of this information via your CRM in real time, allowing you to deliver personalized responses at scale.

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Managing multiple support channels

Even when you’ve consolidated communication rolling in through multiple channels, managing them simultaneously, continuing conversations when users switch channels, and responding instantly is difficult.

It’s not impossible. Your support agents can manually sift through data in internal systems and deliver a personalized response. But these responses likely won’t be instant. As the volume of support queries grows with business, you will need a powerful AI agent that supports your scaling business while ensuring an excellent customer service experience.

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Handling international customers

We discussed why multilingual support is important in a previous section. But there are more reasons to use an AI agent if you have international customers.

Cultural sensitivity is one of the key aspects—you want to be mindful of cultural nuances when interacting with international customers to ensure responses aren’t just accurate but also appropriate. You can train AI models to understand the cultural nuances of hundreds of countries, but training humans for the same reason requires a ton of time and money.

There’s also the question of 24/7 availability. You need to be available to respond during business hours in countries you ship to. Instead of hiring more agents to cover the night shift, invest in an AI agent—they work 24/7 and never take coffee breaks.

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How to use generative AI to deliver exceptional ecommerce customer service

Using generative AI in customer service isn’t as complex when you follow a structured implementation process and have access to the right tools. Let’s go over how you can use generative AI on your ecommerce site to deliver exceptional customer service.

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Identify your most pressing customer service challenges

There’s a wide gap in what businesses and customers believe when it comes to customer service. According to a Bain & Company report, 80% of surveyed companies believed they delivered a “superior experience,” while only 8% of their customers said the companies were really delivering on their expectations.

The reason? Many companies fail to understand what their customers want. Your first step before going the generative AI route should be to identify your customers’ most pressing challenges. Old support tickets are a good place to look to identify these problems. You can also survey customers to see how they feel about your customer service experience.

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Understand how generative AI can address your challenges

This is the reason we created this guide. No matter what your challenges are, you’ll find a solution in this guide. Here are some common challenges and how generative AI solves them:

• Slow response times: AI agents are available 24/7 and able to respond instantly to queries rolling in via chat, email, and phone.
• Busy customer service desk: AI agents eliminate the need for support agents to deal with every little query by automating the process. Support agents can then focus on managing the AI agent and working on more strategic tasks like monitoring customer satisfaction scores (CSAT) and finding ways to further optimize CX.
• Scalability: No matter how much your business grows, you only need one powerful AI agent to field customer queries. AI agents help you scale personalized customer service without investing a ton in human resources.
• Multilingual support: AI agents typically support multiple languages, enabling you to deliver support in multiple languages based on client preferences. On the flip side, hiring support agents who understand languages your clients prefer requires you to take on a ton of operating leverage.
• Human error: Customer service automated through an AI agent isn’t prone to error like human-led customer service processes. This reduces the chances of poor customer experiences and potential liabilities that could occur from committing errors.

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When you identify your challenges and know how the AI agent can alleviate them:

• You’ll be able to find an AI agent that’s powerful enough to actually solve your problems — check out our guide on choosing an AI tool for ecommerce customer service.
• You’ll focus on high-impact areas and be able to monitor progress by tracking relevant KPIs.

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Pay attention to pricing

The ROI on automating your customer service is directly tied to the cost of the systems you use for automation.

Chasing to the bottom of the barrel is a bad idea when it comes to pricing—instead of looking at price as a standalone, think about the value it offers given the price.

If possible, calculate the number of interactions you expect the software to automate and the costs this automation will lower. Compare that with the cost of the AI agent and calculate your ROI.

The problem? Not all AI agents have a transparent pricing model. Users often can’t determine the money they’ll spend to use the AI agent until they actually start using it. That’s not how it works with Yuma, though.

The price you pay to use Yuma depends on the tickets it automatically and fully resolves. This results-driven model ensures you pay only for the value you get. Click here to check out Yuma’s results-driven pricing model.

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Integrate AI with existing systems

Once you’ve chosen and setup your AI agent, integrate the AI agent with other systems in your tech stack for seamless data exchange. Here are examples of tools you can integrate an AI agent with:

• Ecommerce platform: Integrating the AI agent with your ecommerce platform, whether that’s Shopify, BigComments, or Magento, allows it to manage orders, provide real-time stock updates, and assist customers with checkout, among other things.
• Marketing tools: If you use third-party email, VoIP, and social media tools, integrate the AI agent with them so it can automatically take care of customer queries coming in through those channels.
• CRM: CRMs are home to customer data. The AI agent needs access to CRM to access customer profiles, previous support tickets, and account changes.
• Order management system: OMS-AI agent integration enables instantaneous order status updates, shipment tracking, and return handling.
• Knowledge base software: Connecting the AI agent with your knowledge base software makes it easier for the AI agent to pull information from knowledge base content.

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Train your models

Before you let your AI agent lose, teach it some manners and hand it some information about your company, product, and customers. Here are some great resources you can use to train an AI agent:

• Knowledge base: Knowledge base gives the AI agent a comprehensive overview of your product or service. Ideally, your knowledge base should be comprehensive and cover every question your customer may potentially have, from return policies to troubleshooting guides.
• Historical customer data: Past customer service data (like emails, chat transcripts, or social media interactions) are a great way to help the AI agent understand common questions and identify patterns in these questions. It also helps the AI agent understand how customers might phrase questions.
• Scenario-based training: The AI agent needs to be trained for those occasional bizarre questions and edge cases. For example, when a customer is angry, the AI agent needs to respond with empathy. Stress test the AI agent under these scenarios before you take it live.
• Feedback loops: Feedback is food for AI agents. The more feedback they receive, the sharper they’ll become. Set up feedback loops where customers and agents can rate AI’s responses and use them to retrain and refine the AI.

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Configure an option for smooth handoff to human agents

On occasion, your customers will throw a curveball that the AI agent might not be able to handle. For example, when a customer complains that their account was used to fraudulently purchase an item using their credit card, a human agent needs to jump in.

Dealing with overly sensitive cases requires human intervention, so configure the AI agent to seamlessly hand off conversations to human agents when customers:

• Use specific keywords: Configure the AI agent to hand the conversation off to a human agent when a customer uses keywords like “fraud,” “dispute,” or “urgent.”
• Indicate dissatisfaction, anger, or frustration: AI analytics are capable of analyzing sentiments. If it detects the customer is dissatisfied with the response, angry, or frustrated and the AI agent has already made one attempt to offer a satisfactory response, the conversation should be routed to a human agent.
• Have had multiple failed attempts at resolution: If the AI agent has made multiple failed attempts to resolve an issue, it should hand that conversation off to a human agent.
• Need help with sensitive issues: High-stakes issues like canceling a large order or sensitive issues like billing disputes and account fraud should involve a human agent.
• Request an escalation: The AI agent should route customers to a human agent when they explicitly request to speak to a human agent.

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Don’t forget the ethical considerations

This is the final step before you take your AI agent live. To make the best use of your AI agent, here are some ethical considerations to be mindful of:

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Data privacy and security

AI agents handle sensitive data like personal details and order histories. Mishandling this data can lead to privacy breaches and identity theft. To prevent this, make sure you choose an AI agent that’s compliant with regulations like GDPR and CCPA and has an SOC certification at the bare minimum.

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Bias and fairness

AI agents can inadvertently develop bias based on training data. This can lead to unequal treatment of customers and manifest as unfair prioritization of customer service issues or inaccurate interpretation of requests from diverse cultural or linguistic backgrounds.

It’s your duty to use a diverse dataset that includes various languages, customer demographics, and problem scenarios to train the AI agent. Regular audits to test AI’s decision-making also go a long way.

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Hallucinations

Generative AI might generate responses that are incorrect, nonsensical, or fabricated. AI hallucinations are usually caused by the limitations of the language model, where AI tries to fill gaps in its understanding or generates creative responses based on patterns even when the information doesn’t exist or make sense in a given context.

It’s your responsibility to deploy an AI tool that uses a reliable language model. But remember, even the best AI model (GPT-4o) generates incorrect information 1.5% of the time. That’s why additional steps are necessary, such as linking the AI agent to structured data sources and implementing confidence thresholds to prevent hallucinations.

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How generative AI elevates customer service outcomes

Here’s how generative AI elevates customer service outcomes:

• Instant, personalized responses: Personalization at scale and instant responses are the most valuable benefits of using generative AI for customer service. Countless studies show customers want quick and personalized responses, and currently, the only practical and cost-effective way to offer them is generative AI.
• Cost efficiency: Automating basic queries and enabling the AI agent to take action based on the customer’s instructions is a game-changer. It frees up support agents to work on more complex tasks and lowers your total cost of delivering good customer service.
• Dynamic context retention: Generative AI retains and dynamically adapts to the conversation’s context across multiple interactions, not just a single session. This helps deliver a more personalized and seamless experience.
• AI agents are always learning: AI agents learn in real time from customer interactions. They rapidly adapt to new products, emerging trends, and changing customer behavior. This allows the AI agent to optimize its tone and response delivery as it interacts more with a customer.
• Sentiment-aware responses: Advanced generative AI models can detect and adapt their tone based on customer sentiment. They deliver empathetic responses when they sense frustration or disappointment among customers. This makes the conversation more human-like and improves customer satisfaction.

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How to assess the impact of generative AI on customer service

When generative AI works well, you should see your customer service experience KPIs improve. Here are some KPIs you can track:

• CSAT: Measures how satisfied customers are with your customer service by asking for feedback after the issue is resolved.
• Customer Effort Score: Tracks how easily customers are able to get their issues resolved by surveying them.
• First Response Time: Measures how quickly the AI agent responds to customer inquiries. It’s calculated by taking the total of first response times during a specific time frame (such as a day) and dividing it by the total cases resolved during that time frame.
• First Contact Resolution: Tracks the percentage of issues resolved in a single interaction by the AI agent. It’s calculated by taking the total cases resolved on first contact during a specific time frame and dividing it by the total cases.
• Escalation Rate: Monitors how frequently the AI agent had to escalate queries to human agents. This number should drop as you train your AI agent over time. It’s calculated by taking the total cases escalated to humans during a specific time frame and dividing it by the total cases.

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Transform CX with Yuma

Ecommerce leaders have a full plate. Throw in the support desk traffic that comes with exponential growth, and you’ve got a recipe for poor customer experiences. Generative AI can change that — it can take care of a major share of customer service traffic so you and your team can focus on things that require human attention.

All new technologies come with a price tag, but with Yuma, you can try it for free. Take Yuma’s AI agent for a spit to see firsthand how it can transform your customer experience. Contact us today to learn more about how Yuma can help.

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