Retail chatbots serve as advanced AI-powered assistants that integrate online and in-store interactions. Modern chatbots utilize multimodal inputs, real-time data, and large language models to deliver personalized shopping experiences, streamline workflows, and enhance consumer satisfaction.
We examined retail chatbots powered by the latest trends, use cases, and technology, highlighting important success metrics while providing best practices for deployment and compliance.
Top 12 chatbot use cases in retail
1. Product search & discovery
Retail chatbots minimize friction in extensive catalogs by enabling customers to find products with conversational language and images. These bots effectively accelerate discovery by leveraging visual search and understanding user intent.
Real-life example
ASOS’s Style Match tool enhances search engagement by allowing customers to upload a photo to the chatbot, which subsequently offers visually similar items from an inventory of over 85,000 products.1
2. Recommendations
Recommendation engines integrated into chatbots can help retailers increase revenue and enable users to discover products that align with their preferences. This adaptation of chatbots, particularly on messaging apps like WhatsApp, is known as conversational commerce. Enhanced customer satisfaction from recommendations leads customers to spend more.
Real-life example
Stitch Fix’s Chatbot Assistant utilizes user profile data and stylist feedback within chats to provide personalized suggestions for clothing and accessories.2
3. Store locator & inventory checks
A customer may want to see or try on a product they found while browsing the website, in person. Through the chatbot, the customer can locate nearby stores, inquire about the product’s availability, and learn about opening hours.
By integrating with inventory management systems, chatbots provide real-time stock information and enable customers to reserve items at nearby stores before visiting, reducing disappointment and no-shows.
Real-life example
Burberry’s Facebook Messenger shop finder feature allows customers to share their location, enabling the chatbot to find the nearest store. To transport clients to Makers House and view the brand’s display, the bot also offers the option to schedule an Uber ride.3
Figure 1. Burberry’s chatbot.
4. Place orders and pre-orders
Customers can choose the products they wish to purchase, set up their address and contact information, and place their orders via the chatbot.
Additionally, the chatbot can provide information about upcoming products that are relevant to the customer’s preferences and allow them to pre-order new items before they start shipping.
Real-life example
Staples, an office supplies retail company, integrated IBM Watson’s chatbot into their Facebook Messenger, Slack, and texting services. The chatbot enables customers to place orders for current and upcoming products from Staples stores.4
5. Conversational checkout
Integrating payment gateways directly into chat allows users to make safe, one-click transactions without leaving the conversation.
Real-life example
Customers of 1-800-Flowers can browse through flowers, select arrangements, and complete purchases all within the chat interface, thanks to the Facebook Messenger chatbot. With the bot’s conversational guidance throughout the entire ordering process, customers can place an order in under a minute.5
6. Shipment tracking & proactive alerts
Chatbots provide automated updates on shipping, delivery estimates, and delay notifications, ensuring customers stay informed and minimizing inbound support inquiries.
Real-life example
1-800-Flowers’ chatbot informs consumers about shipping updates and delivery alerts via Facebook Messenger, keeping them updated on the entire fulfillment process without requiring them to check their email or visit the website.6
7. Live agent assist
Chatbots are connected to the company’s database, which contains all information about products, services, features, and locations. If customers prefer speaking to live agents, the agent can rely on the chatbot to quickly look up answers and prevent incorrect information from being communicated to customers.
Real-life example
Sephora’s Facebook Messenger chatbot offers instant access to live beauty consultants for consumers seeking personalized consultations. To ensure a good transition and service, the bot provides context about the client’s previous interactions.7
8. Self-service returns & exchanges
In-chat automation for returns helps customers with label printing, scheduling pickups, and tracking refunds, thereby simplifying the overall returns process.
Real-life example
H&M’s chatbots streamline returns by creating digital return labels and guiding customers through the process step by step. This system lessens the load on customer service teams while providing a stress-free experience for shoppers.8
9. Loyalty & subscription management
Users can utilize chatbots to check and redeem loyalty points, handle subscriptions, and receive personalized offers, promoting enduring engagement.
Real-life example
Nike’s membership program features AI-driven chatbots and virtual assistants to provide enhanced user support and assistance. Members can track their loyalty points, access exclusive products, enjoy birthday rewards, and receive personalized product recommendations based on their purchase history and interaction with Nike’s various apps.9
10. Feedback & sentiment analysis
Embedded surveys and sentiment analysis gather feedback after interactions, highlighting dissatisfied customers for prompt follow-up.
Real-life example
Covergirl’s influencer-driven chatbot campaign achieved remarkable engagement metrics, with an average of 17 messages per discussion and a 91% favorable sentiment score. The bot delivered product details and special offers while collecting comments.10
11. Omnichannel & customer support automation
Advanced bots respond to standard queries around the clock while preserving the context of conversations on the web, mobile devices, and social media platforms.
Real-life example
DECA, the conversational AI assistant from Decathlon UK, manages over 45,000 client interactions each quarter across various channels. By automating 65% of customer inquiries since its launch, the technology achieves 96% customer satisfaction while allowing human agents to focus on more complex issues problems.11
12. Fraud prevention & payment security
To safeguard transactions and consumer data, chatbots utilize identity verification, two-factor authentication, and secure payment processing.
Real-life example
Nike utilized Facebook Messenger to create secure social commerce, compelling users to enter specific emoji sequences sourced from social influencers to gain access to exclusive product drops. This strategy effectively combines exclusivity with security, as evidenced by the fact that Nike sold out of Kyrie 4 sneakers in less than an hour.12
Other technologies for retail
Chatbots are one of many technologies that the retail industry can benefit from. Other eCommerce technologies include AI, RPA, intelligent automation, web crawling, and analytics. Combining these technologies will enable:
- Brand endorsement by customizing shopping experiences.
- Data and prediction-driven merchandise and promotions.
- Managing a higher volume of orders at the same time.
- Reduction of cost-to-serve:
- Chatbots can serve as helpers in self-service cashier stands.
- Analytics can offer predictions about customer service workload.
- Analytics can also provide estimates of in-store replenishment workloads, such as shelving.
However, to reap the benefits of AI technologies, especially chatbots, a high level of training and testing is required to recognize the user’s intent and provide them with proper responses. Otherwise, chatbots may say unacceptable things or not take ‘no’ for an answer, which could drive customers away from the brand.
Success measures & best practices for retail chatbots
Key performance indicators are as follows:
- Containment rate: The percentage of chats resolved without the need for human intervention.
- Conversion lift: The increase in sales linked to interactions with the chatbot.
- Average order value (AOV): The growth resulting from in-chat recommendations.
- Cost-per-ticket: The decrease in support costs achieved through automation.
- Customer satisfaction (CSAT/NPS): Direct feedback gathered during the chat.
Best practices to implement
- Comprehensive intent coverage: Continuously review conversation logs to spot unaddressed scenarios.
- Fallback flows: Develop smooth transitions and apology messages for inquiries that are misunderstood.
- Brand-aligned persona: Develop a cohesive tone that embodies your brand’s voice and values.
- Privacy and compliance: Enforce data retention policies, obtain customer consent, and ensure secure storage of chat transcripts.
- Continuous training: Update models with current data to respond to new product offerings, seasonal trends, and changing customer language.
FAQ
How do retail chatbots improve customer experience and drive sales?
Retail chatbots enhance customer experience by providing instant, accurate responses to customer inquiries 24/7, eliminating wait times for human agents. These AI-powered virtual shopping assistants can guide customers through their shopping journey, offer personalized recommendations based on customer data, and assist with order tracking and checkout processes. By analyzing customer preferences and delivering exceptional customer service, retail bots help businesses engage customers more effectively, leading to increased sales and improved customer satisfaction.
What are the key benefits of implementing chatbots in the retail industry?
Chatbots for retail offer numerous advantages, including the ability to handle customer queries simultaneously, reduce costs compared to human customer service agents, and provide seamless integration across mobile apps and websites. AI chatbots powered by machine learning algorithms can analyze customer data to deliver personalized assistance throughout the buying process, helping customers find products faster. Many businesses see successful implementation results through enhanced customer engagement, valuable insights from customer interactions, and a competitive edge in the retail sector.
How do AI-powered retail chatbots handle complex customer questions and maintain brand identity?
Modern conversational AI retail chatbots use natural language processing and machine learning to understand complex customer inquiries and provide accurate answers that align with brand identity. When the chatbot’s ability reaches its limits, these virtual assistants can seamlessly hand off to human agents while providing context about the customer’s shopping experience. The best retail chatbots are designed through careful chatbot development to match business needs, ensuring they assist customers effectively while maintaining the brand’s voice and delivering the exceptional customer service that drives returning customers.
Further reading
External Links
- 1. Asos Just Launched Style Match On Their Mobile App And I'm Obsessed With It. BuzzFeed
- 2. Stitch Fix Taps AI to Redefine Its Business Model. PYMNTS.com
- 3. Chatbots: 5 Luxury Brand Examples.
- 4. Staples integrates IBM Watson intelligence into mobile ordering flow | Retail Dive.
- 5. Two months in: How the 1-800 Flowers Facebook bot is working out - Digiday. Digiday
- 6. 1-800-FLOWERS.COM® Takes Customer Experience to a New Level Through Facebook Messenger. Cision PR Newswire
- 7. Unveiling Sephora's Chatbot: A Virtual Beauty Advisor.
- 8. How H&M Uses AI-Powered Chatbots to Improve Customer Service. Redress Compliance LLC
- 9. Nike Loyalty Program Case Study: A Well-Worn Path to High Sales.
- 10. Famous Beauty Brands Using Chatbot Technology. Skin Match Technology
- 11. Decathlon UK improves its customer experience by combining human expertize with conversational AI. diginomica
- 12. Nike sells out of Facebook Messenger sneaker drop in less than an hour | Retail Dive.
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