The guide to building an AI persona
The guide to building an AI persona: Retail edition AI chatbots have changed how retail businesses support customers. But not all AI chatbots are created equal. Some AI chatbots feel smart, helpful, and easy to talk to. Others come across as flat, robotic, and hard to connect with. So, what makes the difference? It often comes down to their personality. In this blog, we’ll help you build an AI persona that fits your retail brand and makes customers want to come back. Let’s begin! What is an AI chatbot persona? An AI chatbot persona is the personality your smart digital assistant shows when it talks to people. It’s the way it speaks, including the tone, words, and style it uses in conversations. This persona should match your brand, whether that’s friendly and casual or professional and polite. It helps your AI chatbot feel more human and less like a machine. When done right, it makes customers feel understood and more comfortable. Instead of giving cold, robotic replies, your chatbot sounds like a helpful team member. For example, Converso’s AI chatbot customisation options allow retailers to adjust tone and personality settings, ensuring it aligns with your brand voice. READ: Conversational AI Benefits and Use Cases What is an AI chatbot persona? Your AI chatbot persona might be the first interaction a customer has with your brand, so how it sounds really matters. A strong, well-defined persona turns a basic bot into something a guide, a helper, maybe even a brand ambassador. It makes conversations feel natural, not scripted. And when people feel like they’re talking to something that “gets” them, they’re more likely to stick around. Fact: 74% of users prefer when an AI chatbot introduces itself at the start of a conversation, this simple act increases trust and sets the right tone. Trust that lasts: People respond better to bots that feel real. When your AI chatbot has a consistent voice and introduces itself clearly, it builds instant credibility. Brand you can recognize: Whether your tone is playful, professional, or somewhere in between, a clear persona helps create a smooth, on-brand experience across every platform. Conversations people remember: No one enjoys boring, robotic replies. An AI chatbot with personality can surprise, delight, and hold attention. Better outcomes: When users feel at ease, they communicate more quickly. That means fewer misunderstandings, quicker resolutions, and smoother support. Fact: A study conducted at University of Twente suggests that a casual tone of voice leads to higher levels of trust and customer satisfaction compared to formal or overly enthusiastic tones. How to create a perfect AI chatbot persona Designing the right AI chatbot personality can make a big difference in how users interact with your business. An AI chatbot that feels human, helpful, and in tune with your brand will keep people engaged and improve their experience. Here’s how to do it step by step: Get to know your users first Before shaping your AI chatbot’s tone or style, take time to understand who you’re talking to. Think about the people visiting your site or using your products or services: Fact: 74% of users prefer when an AI chatbot introduces itself at the start of a conversation, this simple act increases trust and sets the right tone. Are they after fast answers or step-by-step help? Do they prefer straight-to-the-point replies or a more relaxed tone? What questions come up most often? Use what you already know, including past enquiries, chat logs, customer reviews, to spot common needs. This helps you personalise the AI chatbot’s language, tone, and functions so it genuinely supports your users. For example, a gardening supply store might see lots of questions about delivery times and seasonal planting tips. In that case, the AI chatbot should be helpful, clear, and perhaps a little friendly, like a helpful neighbour who knows their plants. Reflect your brand’s personality Your AI chatbot should feel like a natural part of your brand, and to achieve that: Fact: 74% of users prefer when an AI chatbot introduces itself at the start of a conversation, this simple act increases trust and sets the right tone. Decide what tone fits best (e.g. warm, casual, no-nonsense, polite). Create a simple guide for how the bot should “speak”, including which phrases to use and avoid. Keep language consistent with how your team talks to customers elsewhere, whether on social media, email or in person. For example, a trendy streetwear brand might go for a casual, upbeat tone with a bit of attitude to match its youthful audience. On the other hand, a high-end furniture store would likely use a more refined, polished tone to reflect its premium feel and help customers feel confident in their purchase. Build a personality outline Give your AI chatbot some character but keep it realistic and on-brand. You don’t need to make it feel human, but you do want it to be relatable and consistent. Think about: A brief intro: Give it a name and a purpose (e.g. “Hi, I’m Ava! Here to help you find the perfect gift.”) Type of responses: Should it be short and clear, or more detailed and guiding? Do’s and don’ts: Decide on things like whether to use emojis, humour, or casual phrases. Having this personality outline in place helps keep the AI chatbot’s voice steady, no matter the situation. Check how it sounds in real chats Once the AI chatbot is set up, test it in action. Don’t just check for errors—listen to how it comes across: Does it make sense for your audience? Does the tone match your brand? Are the answers helpful, or too vague? Try a few example chats or ask your team to give feedback. Adjust replies as needed until it sounds just right. This step is great for spotting anything that might feel off or unclear before users experience it. Collect feedback and keep improving No AI chatbot is perfect from
AI Agents vs. Human Agents: Which Option is Better?
AI Agents vs. Human Agents: Which Option is Better? Sarah purchased a laptop from an ecommerce website. She was really excited about it but the order didn’t arrive on the promised date. She wanted to check her order’s status. The moment she clicked “Live Chat” on the company’s website, she expected the usual frustration, a conventional chatbot that would trap her in an endless loop of unhelpful responses. But this time, something felt different. Chat agent: “Hi Sarah! I see your order was scheduled for delivery yesterday. Let me check on that for you.” The response was instant, but more importantly, it felt… natural. As Sarah explained her issue, the chat agent didn’t just spit out generic replies, it asked follow-up questions, clarified details, and even acknowledged her frustration. Chat agent: “I completely understand how frustrating that must be. Let me contact the courier and see what’s going on.” Before Sarah could even ask, the Conversational AI-powered chat agent pulled up real-time shipping updates and provided a clear resolution. No long wait times, no being put on hold. Just fast, smart, and empathetic service. As her issue was resolved in a matter of minutes, she realised that AI-powered customer service isn’t too bad. In fact, quite the opposite. This is just one example! Conversational AI-powered solutions are proving that chatbots doesn’t have to be just script-following machines. They can be engaging, problem-solving, and smart agents that support customers But does this mean that we can take human agents out of the equation? Or should there be a mix of AI and human agents? Let’s find out! But first things first… what are AI agents? Why retailer should invest in AI-driven customer support What are AI agents? AI agents as autonomous intelligent systems performing specific tasks without human intervention. In the customer service context, AI agents are like always-on smart digital shopping assistants powered by advanced AI technologies, including machine learning and natural language processing. They can engage with customers, answering everything from basic questions to more complex requests, while continuously learning and improving to provide better service each time. They can interpret the meaning behind customer queries, considering context and intent, to deliver accurate and relevant responses. Suffice to say, they are not your regular chatbots. But what’s the difference? Well, traditional chatbots used to follow fixed scripts, meaning they could only respond in certain ways based on pre-set answers. If a customer asked something outside of these set responses, the chatbot would often get stuck or give irrelevant answers. This made it frustrating for customers, especially when they had more complicated issues. Now, conversational AI has changed all of this. Instead of just following a script, AI agents use LLM technology to understand language better. This means they can handle complex questions and understand the customer’s needs. For example, if a customer asks, “Can I return an item I bought online?”, a chatbot might give a generic response like, “Check the return policy,” but an AI agent can understand that the customer might need specific instructions, details about return windows, or assistance with creating a return label. AI Agents vs. Human Agents: Which Option is Better? When considering whether to use AI agents or human agents for customer service, it’s important to look at several factors, including costs, efficiency, performance, and scalability. Costs Human agents in the UK typically earn, on average, £24,183 per year, depending on their experience and the region. So, a team of four human agents would cost around £100,000. This salary does not include the costs of benefits, training, workplace amenities, or the need for regular breaks. A customer service team also requires ongoing management, training, and recruitment efforts, all of which add to the cost. In comparison to the cost of hiring and a human workforce, AI agents offer significant savings over time, especially for businesses handling a high number of customer interactions. They don’t require salaries, benefits, or breaks. AI agents can run 24/7 without additional costs, making it particularly cost-efficient for handling high volumes of routine inquiries. Efficiency Human agents can only attend to one customer at a time, which may result in longer wait times during peak periods. Even with the best training and resources, human agents are inherently limited when it comes to handling large volumes efficiently. AI agents, on the other hand, are great in efficiency by providing immediate responses to customer inquiries, with minimal wait times. AI systems are designed to handle multiple tasks simultaneously, which significantly reduces customer wait times compared to human agents. An IBM study show that AI-powered chatbots can handle up to 80% of routine customer inquiries without human intervention. This ability to process numerous interactions at once means that customers receive faster resolutions. Performance Human agents offer a level of adaptability and emotional intelligence that AI agents can’t fully replicate. They are ideal in situations that require a mix of empathy, creativity, and problem-solving, making them ideal for handling complex or sensitive customer inquiries. For example, in cases where customers are experiencing frustration, human agents can respond with empathy and draft their responses accordingly. This human touch can lead to stronger customer relationships and higher satisfaction levels. But human agents are also limited by factors such as fatigue, mood, and workload. Their performance can vary depending on their energy levels and the time of day, which may impact the consistency and quality of service. Scalability Human agents face significant challenges when it comes to scalability. As demand for customer service increases, businesses must hire and train additional agents, which can be both costly and time-consuming. Human agents also require ongoing support, such as supervisors and trainers, to ensure quality and consistency across teams. This makes scaling a human workforce more resource-intensive, particularly for e-commerce businesses experiencing rapid growth. AI agents, conversely, can scale effortlessly. They can handle a large number of customer interactions simultaneously and can be deployed across multiple channels, including websites and messaging apps, without requiring additional
Generative AI in Retail: Use Cases and Statistics
Generative AI in Retail: Use Cases and Statistics E-commerce managers are always searching for the next big thing to give them a competitive advantage, and Generative AI is perhaps that very thing. It’s not just allowing retailers to improve operational efficiency or save costs, it’s helping them transform the entire customer experience. GenAI is designed to learn, adapt, and think, creating smarter, faster, and more personalised shopping journeys. In this blog, we’ll dive deep into the world of Generative AI. From useful statistics and practical use cases to the key benefits of integrating AI, you’ll see how it’s helping retailers get better results. The future of retail is here, and it’s powered by Generative AI. Let’s explore how it can reshape your business for 2025 and beyond. Generative AI in retail statistics Improvements in customer satisfaction through AI Integration NatWest’s AI chatbot:NatWest’s integration of OpenAI’s technology into their customer chatbot, Cora, led to a 150% increase in customer satisfaction, demonstrating the effectiveness of AI in enhancing customer service. AI chatbots in customer service: The use of AI chatbots in customer service has led to a 15% improvement in customer satisfaction scores, showcasing their role in providing efficient and responsive support. Consumer expectations: A report by Verint reveals that 80% of customers anticipate improved experiences using bots and AI in customer service interactions. Enhancements in customer engagement and experience Personalised recommendations: Major retailers like Amazon have implemented AI assistants, such as Rufus, to provide personalised recommendations, improving customer engagement and satisfaction. Hybrid customer journeys: Approximately 70% of customer experience leaders are reevaluating the entire customer journey, moving towards a hybrid model that combines human and AI interactions. Generative AI in online engagement:During the recent holiday season, traffic to retail websites via generative AI chatbots increased by 1,300%, highlighting the growing role of AI in driving online engagement. Enhanced online shopping experience:Over half (55%) of retail shoppers feel that generative AI has enhanced their online shopping experience, indicating widespread consumer approval of AI-driven enhancements. Financial impact and operational efficiency AI in retail revenue and cost reduction:The integration of AI styling has transformed retail by offering personalised fashion suggestions, enhancing both B2B and B2C interactions. A 2024 Nvidia study found that 69% of retailers experienced higher annual revenue and 72% saw reduced operating costs after adopting AI. Global AI retail market growth:The global AI retail market is predicted to reach $85.07 billion by 2032, reflecting significant investment and growth in AI technologies within retail. AI-driven personalisation benefits:Companies implementing AI-driven personalisation have observed a 67% increase in customer engagement rates compared to traditional marketing approaches. Reduction in customer churn:AI-driven personalised interactions have resulted in a 14% reduction in customer churn rates, highlighting the role of personalisation in retaining customers. Conversion rate improvements:AI-driven personalisation has led to a 49% improvement in conversion rates, demonstrating its effectiveness in driving sales. Operational Efficiency Gains:The implementation of AI in customer service has led to a 52% reduction in resolution times, improving operational efficiency. Improved Sentiment Analysis: Modern AI systems have demonstrated an 86% accuracy rate in detecting customer emotions and preferences through sentiment analysis, informing customer service approaches. Consumer trust and adoption of AI in shopping AI-assisted purchases:Approximately 34% of consumers are open to allowing AI tools to make purchases on their behalf, indicating a growing trust in AI-driven shopping experiences. 78% of e-commerce brands have either implemented AI in their online stores or plan to do so, highlighting the growing adoption of AI in online retail. These statistics collectively show the impact of AI in enhancing customer service, engagement, and operational efficiency within the retail industry. Generative AI in retail use cases Use Case 1: Product description generation Most of shoppers rely on product descriptions to make buying decisions. Generative AI enables retailers to create product descriptions quickly and easily. Instead of spending hours writing descriptions for each item, AI can generate them in seconds. This saves time and effort for the marketing team, allowing them to focus on other important tasks. In fact, 87% of shoppers say accurate, rich, and complete product content is very important when deciding what to buy. AI also helps make sure all product descriptions are consistent and accurate, which builds trust with customers. For retailers, using AI to create product descriptions is a smart way to save time, improve the shopping experience, and increase sales. Use Case 2: AI-driven email campaigns AI-driven email campaigns allow retailers connect with their customers on a more personal level. Instead of sending out generic emails or ads, retailers can analyse customer data and preferences to use Generative AI to deliver personalised content that’s more likely to convert. In a nutshell, retailers can make their email campaigns more effective without needing to manually tweak every detail. Generative AI can even improve website’s visibility on search engines by creating SEO-friendly content. Better-optimized product pages can bring in more traffic. Use Case 3: Product image generation and enhancement In Etsy’s buyer surveys, 90% of shoppers stated that the quality of photos was either “extremely important” or “very important” when making a purchase decision. AI-powered product image generation and enhancement capabilities offer a more dynamic and cost-effective solution than traditional photoshoots. Retailers can use Generative AI to generate various angles, colour variations, or even 3D models of their products, allowing customers to explore items in detail from the comfort of their own homes. This enhances the online shopping experience and eliminates the need for multiple physical samples, making the process quicker and more efficient. High-quality visuals have been proven to significantly increase customer engagement and conversions. Use Case 4: Improved customer support Generative AI helps retailers improve their customer support by providing faster and more personalised responses. It learns from previous customer interactions, making future responses more relevant and personalised to the customer’s needs. Also, AI smart shopping companions can handle common queries, such as product details or shipping updates, without needing human intervention, freeing up customer support representatives for more complex tasks. With an AI-powered smart shopping assistant, retailers can
Conversational AI vs. Chatbots: Understanding the Differences
Conversational AI vs. Chatbots: Understanding the Differences The terms “chatbots” and “conversational AI” are often used as equivalent terms, but they’re worlds apart. Get the difference right, and you’re not just improving customer service, you’re setting yourself up for the success. UK retailers are constantly looking out for opportunities to deliver exceptional customer experiences while keeping operational costs in check. By 2025, it’s predicted that the UK retail sector’s use of AI-driven customer service will grow by 35%, helping them deliver better support. In their search for solutions, many are turning to automation technologies like chatbots and conversational AI. But here’s the catch: automation isn’t the same as AI. Traditional customer support automation relies on fixed, rule-based responses, while AI-driven support offers context-aware, flexible solutions. Therefore, understanding the difference between chatbots and conversational AI is crucial to providing a great customer service experience. Understanding the differences between chatbots and conversational AI Chatbots are computer programs that mimic conversations with customers. Some chatbots follow fixed scripts, meaning they can only respond to specific questions in a set way. These chatbots are suitable for handling simple, repetitive tasks like answering frequently asked questions or providing basic information. Conversational AI is a broader technology that includes chatbots and more advanced systems like virtual assistants. These platforms use AI, machine learning (ML), and NLP to understand and interpret written and spoken language. It can process complex customer inputs, learn from interactions, and engage in more natural, context-aware conversations, offering a more sophisticated level of interaction than basic chatbots. Moreover, the ROI from AI-driven customer support goes beyond just cost savings. AI-driven customer support boosts sales by offering personalised recommendations and support, enhancing the overall shopping experience. When customers feel well taken care of, they are more likely to return and make additional purchases. This combination of cost savings and increased sales creates strong and measurable returns for retailers. ” TL;DR: Chatbots follow simple scripts, while conversational AI thinks, learns, and adapts. “ What are chatbots? Chatbots have been around since the 1960s, starting with early examples like ELIZA, a program designed to mimic a therapist. Over time, chatbots evolved with the internet and became widely adopted in the 2000s for handling basic customer service tasks like answering FAQs and assisting with orders. Their simplicity, based on rule-based scripts, made them easy to implement, and they’ve remained popular for businesses looking to automate repetitive processes. According to Grand View Research, the chatbot market was valued at $2.6 billion in 2020 and is expected to grow rapidly, reflecting their continued use in customer support across industries like retail, banking, and travel. What is Conversational AI? Conversational AI emerged in the 2010s as AI technologies like machine learning, NLP, and deep learning advanced. Unlike traditional chatbots, conversational AI systems can understand context, learn from interactions, and adapt in real-time. Virtual assistants like Siri, Alexa, and Google Assistant popularised this shift, allowing for more sophisticated interactions that feel natural and intuitive. As adoption grows across industries, mainly in retail, companies are leveraging conversational AI to provide more personalised customer experiences, driving efficiency and improving satisfaction. By 2025, it’s expected that conversational AI will power 75% of all customer service interactions, as predicted by Gartner. The conversational AI market is also forecast to reach $13.9 billion by 2027, growing at a 21% CAGR, as reported by Statista. Chatbots and Conversational AI: A head-to-head comparison Here’s a comparison table that outlines the differences between Chatbots and Conversational AI: Feature Conversational AI Chatbots Overview Advanced AI-driven systems that enable dynamic, human-like interactions. Basic programs that simulate simple conversations. Technology Uses AI, Machine Learning, and Natural Language Processing. Primarily rule-based or predefined scripts. Learning Ability Learns from interactions, improves over time with ML and data analysis. No learning capabilities, follows fixed responses. Response type Context-aware, dynamic responses that adapt to the conversation. Fixed, scripted responses to specific queries. Complexity Handles dynamic, personalised conversations across various topics. Handles basic tasks and repetitive queries. Use cases Customer service, virtual assistants, personalised shopping assistance, complex problem-solving. Customer service for FAQ, basic inquiries, and appointment scheduling. Human-like Interaction More natural, intuitive, and context-aware, providing a human-like experience. Limited to preset patterns, can feel robotic. Integration Can be integrated into multiple channels (web, voice assistants, smart devices, etc.) for broader interactions. Often integrated into websites, apps, or social media for specific tasks. Scalability Scalable for handling complex, nuanced interactions, offering broader support. Good for handling high volumes of simple tasks. As we discussed earlier, chatbots are great for handling simple, repetitive tasks. Think of them as robots following a script, which works well for simple inquiries or scheduling. But they don’t get smarter with time. Conversational AI, on the other hand, is the real deal. It uses advanced AI technology to understand context, adapt to conversations, and learn from interactions. This allows for much deeper, more personalised interactions, handling everything from complex customer issues to anticipating their needs. And this is important! Why? Because the modern customer service isn’t about static responses, but it’s about intelligent systems that evolve and get better at helping your customers. Chatbots and Conversational AI: Handling customer inquiries Chatbots and Conversational AI handle customer interactions in different ways. Let’s put them into test. Here are common scenarios showcasing how each type responds. Scenario 1: Product availability In a scenario, when a user asks about product availability, the chatbot will simply respond with a “yes” or “no” depending on the stock status. If the item is available, it confirms the availability; if not, it suggests buying something else. However, the chatbot lacks the ability to engage in a deeper conversation, offer alternatives, or provide personalised recommendations. On the other hand, Conversational AI can guide users through the shopping process by seamlessly integrating with the cart. In this scenario, once the product is confirmed to be in stock, the AI offers the option to add the Nike Air Max to the user’s cart, creating a more interactive and convenient
Why Retailer Should Invest in AI-driven Customer Support
Why retailer should invest in AI-driven customer support Buyer satisfaction often hangs on the subtle art of customer support. Gone are the days when it was limited to nine-to-five hours. The modern-day consumers demand round-the-clock support. With AI-driven technologies, retailers can engage customers 24/7, offering tailored recommendations and excellent support. In retail, one of the most common issues customers faces is slow and unhelpful customer service. They must either wait on hold for long periods or receive generic responses from outdated chatbots that don’t solve their problems. Poor support can quickly drive customers away. In fact, PwC’s Future of Customer Experience Report shows that 43% of consumers would stop buying from a retailer after just one bad experience. The problem is clear: when customer service fails to meet expectations, retailers risk losing their most valuable asset, i.e., loyal customers who could give regular business. That’s where AI-driven customer support comes in. AI-driven customer support involves using advanced technologies such as machine learning (ML), natural language processing (NLP), and automation to deliver personalised, efficient, and scalable assistance. These AI agents analyse customer queries, understand context, and provide accurate responses in real-time. Table of Contents Why retailer should invest in AI-driven customer support AI-powered customer support solutions offer retailers a clear path to improving their return on investment (ROI). By automating routine customer service tasks, retailers can cut down on staffing costs while providing instant, round-the-clock support. This reduces the need for large customer support teams (especially during busy times) and eliminates the need for costly overtime. The result is a significant reduction in operational costs, directly improving the bottom line. Also, AI agents improve efficiency by handling multiple customer inquiries simultaneously; something human agents simply can’t do. With AI managing the repetitive tasks, human agents can focus on more complex issues that require their expertise. This leads to faster resolutions, fewer customer complaints, and ultimately, higher customer satisfaction – which directly impacts sales and customer loyalty. Moreover, the ROI from AI-driven customer support goes beyond just cost savings. AI-driven customer support boosts sales by offering personalised recommendations and support, enhancing the overall shopping experience. When customers feel well taken care of, they are more likely to return and make additional purchases. This combination of cost savings and increased sales creates strong and measurable returns for retailers. Understanding the technology behind AI-driven customer support AI-driven customer support, primarily through conversational AI route, combines ML and NLP to make interactions with customers more natural and effective. ML allows AI to learn from past interactions and improve over time by recognising patterns in data, adapting to new information, and providing better responses. NLP helps the AI understand human language, including nuances like tone, context, and intent, enabling it to respond more accurately to customer queries. Deep Learning (DL) takes this a step further by allowing the AI to process vast amounts of data and mimic how human understand and interpret language. Together, these technologies help create smarter, more proactive AI agent that can not only answer questions but anticipate customer needs. AI-driven customer support applications The applicability and use case of AI agent in retail is great. It’s more than simply a chatbot to answer general queries. Here are some interesting AI-driven customer support applications for retailers: Personalised responses: AI-powered smart shopping assistant can help retailers provide personalised experiences by analysing past customer interactions and purchase history. For example, they can suggest products based on what a customer has bought before or offer special discounts tailored to their preferences. Simplified Order Tracking: AI-driven customer support can provide instant updates on order status, shipment tracking, and delivery dates without the need for human intervention. This reduces customer anxiety and call volume for support teams. Salesforce reports that 70% of consumers expect real-time communication, and chatbots can ensure customers are always in the loop about their purchases. Reduced Cart Abandonment: AI agents can help reduce cart abandonment by engaging customers in real time as they’re browsing or about to leave a site. For instance, the AI smart shopping companion can remind customers of their abandoned items, offer discounts, or answer any last-minute questions to encourage a purchase. AI Agents as Brand Ambassadors AI agents are not just about providing support. They can also be a powerful way to build and reinforce your brand. Retailers can customise an AI agent’s tone and personality to match their brand’s voice to ensure a consistent experience for customers every time they interact with it. Whether a brand is friendly and casual or professional and formal, the AI agent can reflect that in its responses. This will help customers recognise and connect with their brand, building trust and loyalty. Beyond just chatting, AI agents can also drive engagement by promoting loyalty programmes, special offers, and personalised content. For example, the AI agent can remind customers about reward points, offer discounts based on their preferences, or even suggest products that fit their style. Reaching out and keeping customers informed help create a more personalised shopping experience. In-house vs off-the-shelf AI agents: Which option is better? Building AI-powered customer support in-house can be technically challenging for retailers. It requires expertise in machine learning, natural language processing, and system integration, which can take a lot of time and resources to develop. Plus, ensuring data security and privacy compliance like GDPR adds another layer of complexity. Retailers would need to continuously maintain and update the system, which can be costly and require specialised knowledge. Instead, using an off-the-shelf AI solution designed specifically for retail is a smarter choice. These ready-made solutions are built with the right tools and features for retail needs, saving time and development costs. They also come with built-in security and compliance measures, so retailers don’t have to worry about technical details. Choosing a specialised AI system allows retailers to get started right away and start seeing results without the hassle of building and maintaining a system from scratch. Conclusion The future of customer service is here, and it’s powered by
conversational ai benefits and use cases
Conversational AI in Retail: Benefits and Use Cases Modern retail is all about smarter, faster, and more personal service. Today’s customers want instant answers, personalised recommendations, and a hassle-free shopping experience. Artificial Intelligence (AI) is stepping in to meet those needs—particularly conversational AI, which serves as a smart shopping assistant to customers. It helps retailers connect with shoppers in smarter, faster ways. From reducing wait times to driving sales, conversational AI has numerous applications that makes it a highly popular choice for retailers. But what is it exactly? How does it work? And what are its benefits and use cases in retail? Let’s find out! Table of Contents New CommBox survey reveals that 43% of UK consumers are dissatisfied by long wait times, mainly due to no access to human agents and chatbots that can’t resolve issues What is conversational AI in retail? Conversational AI is like a smart, digital helper that can chat with customers, answer questions, and even assist with tasks. It goes beyond rule-based chatbots by using technology like natural language processing (NLP) and machine learning to understand what they say and respond in a way that feels natural, just like talking to a human. In retail, conversational AI works as an online shopping companion. It can answer product questions, recommend items based on your preferences, and guide customers through their purchase—all without them having to wait for a human. The benefits of conversational AI in retail According to a Salesforce Connected Shoppers Report, consumers use the chat channel for 20% of their goods purchase (31% for purchasing services). This number is increasing as consumers find it easier and faster to get responses over the chat channel. According to a 2024 Statista survey, 82% of consumers preferred using the chat channel for quick communication with brands over waiting for a customer representative. Here are some key benefits of Conversational AI for retail: Personalised customer engagement Conversational AI tools analyse customer preferences to offer personalised product suggestions. This personal touch enhances the shopping experience, making customers feel understood and valued. Round-the-clock customer support AI-powered smart shopping assistants are always available, providing 24/7 support to customers. This means shoppers can get answers to their questions at any time, improving convenience and trust. Many advanced conversational AI platforms also support voice search. The AI voice assistant allow customers to shop with ease. Simplified shopping experience Conversational AI simplifies shopping by guiding customers through product discovery and purchase processes. This assistance reduces friction, making it easier for customers to find and buy what they need. As per the SAP Emarsys 2024 survey, 35% of UK consumers have discovered new products through AI, an increase from 26% the previous year. Efficient order management AI-powered smart shopping companions help customers track orders, answer policy questions, and handle post-purchase inquiries. This streamlines order management, reducing the workload on human staff and speeding up response times. Operational efficiency Conversational AI frees up human employees to handle more complex issues. It automates routine tasks like answering common questions and processing simple orders, supporting better resource allocation and operational workflows. Top 5 conversational AI in retail use cases According to Shopify 2024 statistics report, over 80% of retail executives anticipate that their companies will implement AI automation by 2025. Interestingly, conversational AI is one of the most popular applications of AI in retail. Here are some common use cases of conversational AI in retail: Customer support automation: Conversational AI can handle common customer queries instantly, like product details, store hours, or return policies, without involving human agents. These customer service AI use cases save time for both customers and retailers, allowing human agents to focus on more complex issues. Personalised shopping assistance: AI-powered smart shopping companions can act like personal shopping assistants, recommending products based on a customer’s browsing history or preferences. AI answering questions based on personalised data and specific requirements creates a personalised shopping experience, helping customers find what they need faster. Order tracking and management: Once a customer has made a purchase, Conversational AI can keep them updated with real-time order tracking and delivery information. Instead of waiting on hold or sending an email, customers can simply ask the AI about their order status, making the process smoother and faster. Easier checkout process: An AI-powered smart shopping assistant can assist customers through the checkout process by answering any last-minute questions or issues they might face. It serves as an AI concierge for online shoppers. Whether it’s about payment options, shipping methods, or applying a promo code, it’s there to guide them through the final steps. Feedback and reviews collection: After a purchase, conversational AI can automatically request feedback or reviews from customers, making it easier to gather valuable insights. A personalised virtual assistant for customer service encourages more people to share their opinions. Conversational AI in retail: best practices Conversational AI has delivered promising results for retailers, simplifying how they interact with customers. When integrated correctly, it can enhance efficiency, improve customer satisfaction, and boost sales. Here are some best practices to maximise its potential and get the best possible outcomes. Keep it simple and clear While conversational AI can handle complex tasks, it’s important to keep the interactions straightforward and user-friendly. Avoid overwhelming customers with too many options or complicated language. The AI should guide customers step-by-step, ensuring they never feel lost. Clear, simple conversations lead to faster resolutions, making customers more likely to use the service again. Personalise interactions Conversational AI should offer personalised experiences based on each customer’s browsing history, preferences, and previous interactions. This makes customers feel valued and increases their likelihood of making a purchase. Ensure easy handover to human agents While AI can handle most basic tasks, sometimes a customer may need a human touch. It’s essential to have a smooth transition from AI to a live agent when necessary. This can happen when the AI identifies that it can’t resolve an issue or when a customer requests more detailed support. The future of conversational AI in retail
Comparing intelligent AI chat solutions for your e-commerce business
Comparing intelligent AI chat solutions for your e-commerce business Comparing intelligent AI chat solutions for your e-commerce, as e-commerce continues to evolve, conversational AI has become vital for engaging customers and optimising customer support. Conversational AI solutions offer personalised answers to the queries of customers with the help of advanced machine learning abilities. In this article, we will compare various conversational chat assistants and discuss which of these AI-powered assistants all e-commerce platforms should prefer. Essential considerations in selecting a conversational AI platform When evaluating conversational AI solutions, businesses should consider several critical factors, including ease of integration, scalability, natural language processing (NLP) capabilities, and analytics. Here are the essential aspects to assess in each solution. Adaptability This measures the platform’s ability to cater to a range of customer needs, from simple FAQs to complex inquiries. An adaptable platform should seamlessly adjust responses based on customer behaviour, industry-specific jargon, and preferences. NLP Precision Natural Language Processing (NLP) accuracy is crucial for understanding and accurately responding to user queries. High precision in NLP ensures that even complex or nuanced questions receive relevant, human-like responses, enhancing customer satisfaction. Ease of Setup For rapid implementation, the platform should offer straightforward integration with existing software, CRMs, or databases. A user-friendly setup process reduces downtime and resource strain. Scalability As customer interactions grow, the platform’s ability to expand and handle increased traffic is essential. Scalable solutions should support growing user bases, additional channels, and higher volumes of inquiries, adapting to evolving business demands without compromising performance. Comparisons of top conversational AI platform Converso AI Converso is a conversational AI for retail that offers a smart shopping experience to customers using advanced machine learning tactics to understand customer needs and recommend best-suited products. Your retail businesses get complete control over customer support with Converso, your 24/7 working sales agent powered by AI. Converso empowers retail businesses to elevate customer satisfaction. Agent Intelligence With the strength of Large Language Models (LLMs), Converso generates human-like responses, helping e-commerce platforms improve their sales support. Plus, enhanced data analysis of user data results in preparing customer-first responses, making communication more personalised than ever. With Converso, retail businesses get access to: Personalised product recommendations Support capability Improved customer engagement Integrations Converso easily integrates with your e-commerce business. Follow simple steps and begin a journey towards personalised customer support. Plus, you can connect Converso across all your omnichannel platforms to give your customers an unforgettable experience! With Converso, you get: Simplified CRM integration Advanced API support Omni-channel support Highlighted features Customisable chat settings for a personalized experience Enhanced data analytics for end-to-end sales journey tracking Scalable and adaptable to different customer engagement scenarios Voice assistance enabled chat Real-time product comparison and add-to-cart features Omni channel integration across different platforms Better understanding of language through NLP practices Onebot OneBot provides conversational AI solutions focusing on improving customer engagement. Known for its smooth integration with social media and e-commerce platforms, OneBot enables businesses to manage inquiries effectively. The platform includes a user-friendly dashboard centralising customer data, allowing companies to adjust responses to evolving customer expectations. Agent Intelligence Onebot uses NLP and machine learning models to understand natural language and engage in a human-like manner. This makes communication fluent and user-friendly. Its AI-driven algorithms analyse users’ decision-making which helps in generating personalised experiences. With Onebot, you get: Real-time answers to queries Intent-based conversations Improved customer relationship Integrations Onebot integrates with various tools and platforms, making it adaptable to various business infrastructures. The core integration benefits of Onebot include: Multi-platform integration API integration Highly compatible Highlighted features AI-powered chatbot with advanced conversational abilities Multilingual support for better communications Real-time collaboration and task-management tools Built-in analytics to track user data Voxly Digital Voxly Digital is a versatile AI solution offering a unique mix of customer support and sales tools. This platform provides detailed NLP capabilities and supports various languages, making it a suitable option for businesses targeting global audiences. Voxly Digital also offers high-quality analytics, allowing businesses to measure engagement and refine conversational strategies. Agent Intelligence Voxly Digital designs AI models to understand user intent and provide personalised responses across diverse conversational scenarios. Leveraging voice assistance and chatbot support, Voxly Digital develops personalised chat experiences and ensures fluent conversations. With Voxly Digital, you get: Real-time answers to queries Intent-based conversations Improved customer relationship Integrations Voxly Digital excels in integrating AI solutions with existing business ecosystems. These integrations simplify workflows and offer data-driven insights, ensuring improved consumer experiences. Voxly Digital enables: Multi-lingual conversational capabilities Data-driven marketing solutions Custom CRM integrations Highlighted features AI-driven brand-consumer matchmaking Interactive product demonstrations Voice-enabled assistance Senseforth AI Senseforth AI specialises in developing conversational AI for large-scale businesses, offering features like intent recognition and industry-specific templates. It integrates well with CRM systems, ensuring efficient customer interaction tracking. Senseforth AI’s chatbot framework allows businesses to automate FAQs and manage complex queries with accuracy. Agent Intelligence Senseforth AI’s chatbots leverage advanced NLP and machine learning to deliver seamless, human-like conversations. They ensure accurate responses and enhanced personalization, improving engagement across diverse business scenarios. With Senseforth AI, you get: Personalised customer support Automated troubleshooting Recommendations based on customer behaviours Integrations Senseforth AI supports CRM synchronisation, omnichannel communication, and API-based workflows, simplifying operations while enhancing efficiency and customer experience. Senseforth offers: Synching of customer data for targeted interactions Effortless integration of AI solutions into existing workflows, enabling end-to-end automation Engagement on WhatsApp, Google Business Messaging and web chat Highlighted features Industry-specific AI templates for rapid deployment Easy adaptability to different industry needs and customer bases CRM integration for a seamless data management experience Intent recognition for effective customer support automation Verint Conversational AI Verint’s solution is tailored for customer-centric businesses, focusing on scaling support operations through automated responses. Verint’s platform is suitable for enterprises seeking a balance between automation and human support. The tool’s analytics suite provides deep insights into customer interactions, making it easier to refine engagement strategies. Agent Intelligence Verint’s conversational AI is powered by advanced natural language processing (NLP) and machine