Conversational artificial intelligence (AI) presents tremendous opportunities to shape the future of retail. As consumers increasingly use natural language to interact with technology, conversational interfaces like chatbots and voice assistants are poised to revolutionize retail operations, customer engagement, and business growth.
This 3500+ word expert guide will explore the key capabilities of conversational AI and its multitude of use cases that can transform retail businesses. We’ll analyze leading examples, provide predictive insights, and equip retail leaders with actionable recommendations to harness conversational AI based on their business context and AI maturity.
The Rise of Conversational Commerce and AI
The retail landscape continues to rapidly evolve. As consumers flock to ecommerce, competition intensifies while customer expectations of highly personalized, seamless shopping experiences continue to rise. Legacy retailers and emerging D2C brands alike feel increasing pressure to digitally transform to enable frictionless omnichannel commerce.
Conversational user interfaces present a timely solution to many rising complexities in retail, evidenced by their surging adoption. Gartner predicts that by 2023, 25% of employee interactions will occur via voice assistants, chatbots, or mobile apps, up from less than 2% in 2018. And by 2029, IDC forecasts that over 50% of consumer-facing companies will have adopted conversational platforms and AI.
What’s fueling this growth? AI-powered conversational agents like chatbots and virtual assistants enable more intuitive, personalized interactions that simplify complex retail processes for shoppers and staff alike. They provide instant access to information, facilitate transactions, and tailor services to individual needs and preferences. As AI continually advances, so will the capabilities of conversational platforms to deliver game-changing value.
Key Capabilities and Benefits of Conversational AI in Retail
Before exploring use cases, let’s examine some key enabling capabilities of conversational AI that unlock major opportunities for retailers:
Natural Language Processing
Using advanced NLP, conversational AI can understand context and intent within customer messages across languages and channels to determine appropriate responses. Instead of rigid menus, customers can engage in flexible, free-form conversations.
Sentiment Analysis
By detecting sentiment, tone, and emotion, conversational AI can gauge how customers feel about products, services, and the brand itself. These insights inform business strategy and experience optimization.
Deep Learning
Continuous learning from conversational data and interactions with humans make AI agents increasingly intelligent over time. They become better at anticipating needs, personalizing interactions, and even making recommendations.
Omnichannel Engagement
Leading solutions integrate across service channels like web, mobile apps, messaging, voice assistants, and phone for continuous conversations across touchpoints.
Process Automation
Conversational AI can integrate with backend systems to automatically execute tasks like checking inventory, processing payments, scheduling deliveries etc based on messaging conversations.
With these core capabilities, conversational AI delivers major benefits including:
Superior Customer Experiences
- Personalized, efficient engagements that reduce effort and delight shoppers
- Instant access to helpful information for informed purchasing
- Proactive notifications and recommendations
Operational Efficiency
- Automation of high volume repetitive tasks to lower costs
- Optimized business processes across sales, marketing, supply chain etc
- Increased productivity by empowering staff to focus on complex assignments
Revenue Growth
- Higher conversion rates and average order value
- Improved customer acquisition and retention
- New conversational commerce sales channels
Enhanced Analytics
- With every interaction, conversational AI solutions gather data to derive customer and product insights to inform strategic decisions and experience optimization.
Let’s now explore some of the most impactful applications of conversational AI across the retail enterprise.
9 Key Use Cases of Conversational AI for Retailers
1. Customer Service and Support
Consumer expectations for retail customer service continue to rise. Not only do shoppers demand 24/7 omnichannel support, 52% expect the same personalized level of service across channels.
Conversational AI is critical for cost-efficiently meeting these needs at scale while delighting customers. Chatbots and virtual assistants from leading vendors like Salesforce, Ada, Yellow.ai, and Hubspot are driving massive improvements:
- Query handling – AI assistants can answer millions of repetitive questions around the clock without wait times, freeing human agents for complex issues.
- Conversational self-service – Customers can complete tasks like track orders, print receipts, request refunds etc through natural dialog without needing staff assistance.
- Sentiment analysis – Detecting customer emotion and effort during service interactions enables quality assurance and experience personalization. Agents can be proactively notified to intervene if a chatbot detects disgruntled customers.
- Agent assist – Virtual co-pilots analyze customer questions and transaction history to suggest responses that human agents can modify or instantly send, boosting productivity.
For example, Sephora’s chatbot handles ~70% of repetitive customer queries. The beauty retailer can deliver instant, personalized service at scale while optimizing human staffing.
2. Social Messaging Commerce
Messaging apps like WhatsApp and Facebook Messenger have become a preferred way for consumers to communicate. Retailers that meet shoppers in these contexts enable conversational commerce.
With over 2 billion monthly active users, Facebook Messenger alone represents a massive opportunity for retail sales. China’s WeChat began testing conversational commerce years ago. Over 200 million users now transact through its chatbots annually.
AI is essential for managing messaging at scale while providing personalized shopping assistance. Requested products can be dropped into carts or checked out directly within message threads using integrated payments.
The luggage brand Away increased sales 69% by launching a Messenger chatbot. It provides luggage recommendations based on travel needs and enables in-channel transactions.
We‘ll likely see more immersive shopping experiences embedded into messaging apps in the future.
3. Intelligent Recommendations
Generating highly targeted, customized product recommendations is critical for optimizing retail revenue growth. According to a Salesforce study, 63% of online shoppers are more likely to purchase when AI recommendations align with personal preferences and purchase history.
Powered by deep learning algorithms that determine customer interests based on data intelligence, conversational platforms like chatbots can deliver personalized recommendations in the moment across endless aisles of inventory.
Amazon’s shopping assistant Alexa highlights top products daily per customer based on individual data. Users have bought over 100 million Alexa-recommended products. scarIA, an AI stylist chatbot from Tommy Hilfiger provides outfit ideas based on preferred styles and contexts.
To maximize relevance, some solutions dynamically assemble customer micro-segments using intent and behavioral data. Coterie uses conversational AI to categorize visitors and modify site content accordingly. Conversion rates have increased 300% for somemerchant segments as a result.
4. Shopper Assistance
Conversational shopper assistants enhance self-service while mimicking the interactive elements of brick-and-mortar retail service.
Sephora’s chatbot helps shoppers identify products from images, check personalized item availability across stores, and book salon appointments. Luxury brand Burberry created a conversational bot that makes outfit suggestions based on weather data and enables seamless reservations of personalized 3D printed handbags.
Inside physical stores, conversational interfaces are also transforming experiences. Walmart recently patented AI-driven shopping carts equipped with voice commands, visual displays, sensors, and navigation assistance to simplify in-store shopping.
Lowe‘s is testing in-store robots that customers can summon via text messages. The robots navigate aisles autonomously using computer vision to find requested items and answer customer questions.
5. Intelligent Payment Processing
Payment friction costs retailers billions in abandoned carts annually. Integrating conversational platforms with payment systems enables seamless secure transactions with minimal effort for customers.
Tommy Hilfiger’s chat commerce bot accepts payments natively within Messenger, leveraging saved customer credentials and Loyalty IDs to enable completion of purchases with a click rather than reentering data.
Voice AI takes convenience even further with voice-activated purchases. Alibaba unveiled “AliPay fish”, a new voice assistant that lets car drivers complete fuel purchases and payments through conversational interactions powered by blockchain technology.
Payments security does warrant careful evaluation when expanding retail conversational commerce. Biometrics like voice recognition and AI-driven behavior analysis help ensure ethical use.
6. Customer Data Analysis
With every customer inquiry, purchase, service review and returned item, retail conversational AI solutions ingest data that holds invaluable insights. Natural language and speech processing reveal voice-of-the-customer sentiment, perceived product performance issues, dynamic category demand signals across regions and much more.
By analyzing and correlating this data, retailers gain a granular understanding of KPIs like customer satisfaction drivers and metrics to inform management decisions. Merchandisers receive real-time feedback to optimize assortments. Automated replenishment prevents stockouts. Strategists identify areas needing process improvements or additional customer service focus comparing performance across channels and touchpoints.
7. Internal Training
At leading retailers like Walmart, conversational AI powers training for over 2 million associates to onboard staff faster while enabling experienced employees to upskill on the fly.
L‘Oreal utilizes conversational lesson bots to enforce product knowledge across 5,000 stylists at 30,000 salons annually. Automated training drives upselling.
Home Depot’s mobile app lets over 400,0000 store associates chat existing knowledge bases or pose questions to access operating procedures, technical specifications etc. Lowes deploys AI-assisted wearable devices called LETAssist to guide warehouse pickers.
Conversational user experiences deliver on-demand access to institutional knowledge via natural interactions to solve pain points faster. Reinforcing skills through microlearning vs lengthy manuals also boosts retention. AI tutors nurture progress, boost confidence in workers to provide better service.
8. Predictive Analytics for Demand Forecasting
AI is transforming legacy retail planning processes to be predictive rather than reactive. By applying predictive models, optimization algorithms and sentiment analytics to data on past sales, shopper behaviors, weather, events and more, retailers can much more accurately anticipate consumer demand across seasons to optimize supply chain operations.
Machine learning further sharpens demand sensing and automatically incorporates signals to generate probabilistic forecasts for production volume, inventory needs, merchandising etc. to minimize waste. AI can also capture real-time demand shifts from customer conversations and pinpoint emerging trends to pivot faster.
According to Boston Consulting Group, a midsize retailer leveraging AI for demand forecasting achieved 20%+ increase in forecast accuracy, driving 5% revenue lift.
9. Conversational Commerce Innovation
While retail organizations at various stages of conversational AI adoption can derive value from the applications above, newly emerging use cases present untapped potential.
AI voice assistants for self-checkout, in-vehicle commerce, smart appliance reordering, and cashierless stores like Amazon Go will likely soon emerge as retail‘s next frontier of conversational transformation.
Immersive 3D virtual shopping simulations can enable more experiential product exploration online. AI avatars with emotion simulation can influence attitudes and purchase decisions through empathetic conversations.
As language models continue to achieve human parity, possibilities abound at the intersection of creativity and retail for pioneering brands to invent category-defining shopping experiences centered around natural dialogs and emotional connections with customers.
Choosing the Right Conversational AI Provider
The conversational AI market has ballooned with platforms tailored for virtually every industry and use case. Hundreds of vendors provide chatbots, virtual assistants, voice interfaces and enabling technologies like NLP, dialogue management tools and custom skills development capabilities.
Here are key evaluation criteria for retailers assessing solutions:
Functionality – Ensure conversational capabilities align with your current and future application roadmap needs around customer service, shopper assistants, internal communications, analytics etc.
Ease of use – Seek platforms permitting non-technical teams to manage content, build conversations, analyze metrics. Vendor provided professional services can accelerate rollout.
Customer experience – Test tools hands-on to evaluate conversation quality, personalization, emotion simulation if it meets brand voice expectations.
AI advancements – Favor platforms investing in continuous platform enhancements like predictive modeling for more intelligent, autonomous deployments over time.
Scalability – Serverless architecture supports high volume interactions without performance lags especially during peak seasons.
Security – Verify data encryption, access controls, and compliance with standards like ISO and HIPAA depending on data sensitivity.
Ecosystem integrations – APIs should flexibly connect with existing martech and databases to unify data for a 360-customer-view and trigger cross-stack workflows.
Pricing – Opt for monthly models over upfront licenses to control costs and maximize ROI.
8-Step Roadmap for Implementing Conversational Commerce
Once clear on objectives and solution options, following a structured approach for planning and executing retail conversational AI projects helps ensure realizing rapid returns on investment.
1. Set goals – Define metrics for success aligned to KPIs around revenue growth, cost savings, customer satisfaction, cycle times etc.
2. Map conversations – Storyboard front to back user dialog flows addressing priority personas and use cases.
3. Load knowledge bases – Structure data assets like FAQs, product specs and policies to power conversations.
4. Configure integrations – Connect commerce platforms, ERPs, CRMs for unified data and workflows.
5. Test and tweak – Gather user feedback, monitoring tools to fix experience gaps.
6. Promote adoption – Market bots internally and externally to drive utilization.
7. Analyze performance – Review analytical dashboards to optimize systems.
8. Expand use – Pursue new applications, channels and user segments for maximized value.
The optimal roadmap and timeline varies across retailers based on in-house capabilities around strategy, design, integration and conversation management. While testing minimum viable products makes sense for early pilots, taking an enterprise approach to scale on robust architecture enables maximizing disruptive potential.
The Future of AI in Retail – 2030 Outlook
What’s on the horizon for further evolution of conversational commerce experiences? Here are five predictions for the coming decade:
- Rise of voice commerce – As smart speakers and surfaces grow ubiquitous in homes and vehicles, voice will increasingly become shoppers preferred interface for frictionless transactions.
- Wearable augmented shopping – Retail worker use of AI-powered glasses and sensors will expand to assist customers in stores with vision, voice, and gesture commands.
- Lifelike avatars – Human-like 3D retail assistants simulated using generative AI will create emotional connections driving purchases in virtual environments.
- Seamless biometrically powered payments – Voice recognition, gait analysis and other integrated sensors will enable cashierless invisible payments reducing physical checkout friction.
- Predictive product experiences – Retailers will increasingly anticipate customer needs through data patterns to proactively ship products aligned to preferences while providing conversational guidance on use personalized to individuals leveraging sensors, IoT and cloud connectivity.
In the end while conversational interfaces enable retailers to operate with greater speed and efficiency at scale, the most disruptive implementations artfully balance both cutting edge technology and human sensibilities to drive meaningful relationships between brands and the people they serve.
