Although the traditional model has served us well, customers now have exceptionally high expectations for service in an eCommerce experience. Despite the complexity of building product catalogs, developing a headless frontend with top-tier UX, promotion engines, and segment-based personalization, customers still expect high-quality information delivered instantly, in a way that minimizes the time required to make a purchase.
Conversational Commerce is a revolutionary solution that combines artificial intelligence with natural language processing to guide users to the products they seek as efficiently as possible.
The Problem and the Opportunity
Traditional online shopping often feels like navigating a maze blindfolded. Customers face overwhelming product catalogs, complex filtering systems, and frustrating searches for exactly what they need. Meanwhile, businesses struggle with high cart abandonment rates (averaging 70% across industries), low conversion rates, and an inability to capture valuable customer insights during the shopping journey.
The opportunity is significant: Conversational Commerce bridges the gap between digital convenience and human-like assistance, creating a shopping experience that is both scalable and deeply personal. Companies implementing conversational commerce solutions report up to a 35% increase in conversion rates and a 25% improvement in customer satisfaction scores.
Context
Traditional B2C eCommerce follows a standard customer journey: Home Page → PLP (Product Listing Page) → PDP (Product Detail Page) → Shopping Cart → Checkout → Order Confirmation. The challenge has always been designing the best experience for each stage. This requires properly defining the product catalog and identifying all relevant attributes for the PDP, including technical specifications, tags, and labels (e.g., sport, family-friendly, business-casual). Customers should be able to find all necessary product details on a single page rather than searching elsewhere.
Additionally, defining the product hierarchy is crucial. A configurable product (as known in Adobe Commerce) must be associated with simple products for color and size. This allows customers to filter products on the PLP using these attributes (referred to as variants in SAP Commerce Cloud). Even after structuring the catalog correctly, the challenge remains: exposing this information effectively to a headless frontend for an intuitive product discovery experience.
The Problem
Even when all relevant product information is available, customers often have unresolved questions, such as:
- “Is this cotton clothing comfortable for someone who sweats a lot?”
- “Does it fade?”
- “I live in a cold area but want to use it for running—will it work?”
A salesperson in a physical store could easily address these concerns, but digital channels often fail to provide similar guidance. In-store experiences are also limited by staff capacity.
Other Use Case: Simplifying Product Searches
Filtering product attributes no longer needs to be complicated. Previously, customers navigated endless menus—“Category > Subcategory > Color > Size > Price.” Now, they can simply describe what they’re looking for: “I need a good laptop for video editing, with a great graphics card, under $2,000.”
Another Use Case: Streamlining B2B Purchases
Business transactions often involve complex CPQ (Configure, Price, Quote) products with numerous options. Traditional configurators, filled with technical details, can be overwhelming. Instead, users should be able to describe their needs without having to be experts in the field.
Solution
The goal is NOT to replace human interaction with chatbots, but to empower chatbots to scale advisory capabilities, supporting multiple customers simultaneously. These chatbots are deeply knowledgeable about the product catalog and can also assist in-store staff by generating better questions for clients, monitoring system responses, and continuously improving chatbot learning. Additionally, chatbots serve as a valuable staff training tool—a topic for another discussion.
Moreover, AI assistants seamlessly integrate with marketing automation workflows, enabling real-time personalization. This article will explain how we implement Conversational Commerce at Perficient and share best practices for your adoption.
Technology Foundation: Conversational Commerce OpenAI Assistants
OpenAI’s Assistant API (beta) provides a powerful framework for sophisticated conversational commerce integrations. With this tool, businesses can:
- Configure the assistant with a single purpose.
- Connect the assistant with their commerce backend using functions.
- Enhance the assistant’s knowledge of commerce specifics using vector files.
- Maintain isolated context for each customer through separate conversation threads.
- Preserve context throughout long interactions.
- Handle complex queries requiring multi-step reasoning.
- Ensure a consistent brand voice in all interactions.
Function Calling: Integrating Conversations with Commerce Data
The function-calling feature allows assistants to retrieve relevant information from existing commerce infrastructures when necessary.
First, it is critical to categorize products correctly and apply appropriate tagging (e.g., family-friendly, portable, cost-effective). Without proper tagging, customers may experience confusion or frustration when interacting with the chatbot. A poorly tagged catalog is like chatting with ChatGPT in a generic way—it lacks precision.
Common Functions Used:
- Product search and filtering based on natural language queries.
- Inventory checking and availability confirmation.
- Price calculations, including discounts and promotions.
- Product recommendations based on tagging.
Businesses don’t need to expose real inventory numbers but can simply provide availability flags based on customer location and promotional strategy.
Vector Files
Not all commerce data needs to be retrieved via functions. While function calls can provide specific customer-requested information, they can sometimes slow response times due to execution delays.
For static commerce-related details—such as return policies, delivery times, warranties, brand preferences, and category systems—vectorized files offer a more efficient approach. These files become part of the assistant’s context, eliminating the need to provide such information within each conversation thread.
MACH Architecture Integration
Modern commerce implementations following MACH principles (Microservices, API-first, Cloud-native, Headless) are naturally suited for conversational commerce integrations. Even legacy platforms like SAP Commerce, Adobe Commerce, and HCL can leverage this feature, as all modern platforms expose APIs.
Adopting a MACH approach significantly simplifies ecosystem development, performance monitoring, multi-channel integrations, and global scalability.
Conversation Development Challenges
Implementing conversational experiences involves several challenges:
- Concurrency: Scaling workers up or down efficiently to handle thousands of conversations.
- Thread Lock: Ensuring only one worker manages a conversation at a time to avoid flooding customers with overlapping messages.
- Conversation Pace: Supporting fast-typing users without missing incoming messages during the assistant’s response time.
Cost Optimization
Conversational Commerce involves several cost factors, including OpenAI’s Assistant usage, which is billed per token processed. Conversations typically cost between $0.05 and $0.10, depending on complexity and length.
To reduce costs, businesses can:
- Optimize their assistant strategy with prompt engineering.
- Minimize function call payloads.
- Provide essential information through vectorized files instead of frequent function calls.
While OpenAI’s Assistant offers one of the best user experiences, alternatives include ElevenLabs AI Voice Tools, Azure OpenAI, AWS Bedrock, and Google Vertex AI. Pricing varies slightly across providers.
Once businesses validate the improved user experience, they can explore training custom models and hosting AI solutions in the cloud for better control and cost efficiency. Models like Mistral or LLaMA can be set up using Ollama for further optimization.
Return on Investment
Beyond enhancing customer experience, businesses can train AI models to capture customer emails and integrate them into automated marketing campaigns.
Monitoring Best Practices
Performance Metrics:
- Conversation completion rates
- Time to resolution
- Customer satisfaction scores
- Conversion rate (email collection)
- Cost per successful interaction
The Future of Commerce Is Conversational
Just a few years ago, this concept was merely a dream—something out of a futuristic car commercial. Today, the technology is here, and customers expect clear, educational answers about how to use a product and which option best suits their specific needs.
Personalization is no longer just a nice-to-have enhancement—it’s a mandatory feature, just like the “Add to Cart” button.
At Perficient, we believe that how we connect with our customers, how we learn about their needs, and how we solve their problems form the foundation of any successful commerce channel.
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