How AI Agents Drive Higher Cart Value and Lower Drop-Offs

Conversational GenAI experiences turn passive browsing into guided discovery—enabling higher-value orders, lower bounce rates, and integrated post-sale support without overhauling the tech stack.

Conversational

Context-aware product discovery, Q&A, and support

Bundled

Agent-generated collections that match trip or gift goals

Performance-Tuned

Systems optimized for AOV, conversion, and CSAT

IndustryRetail
Services usedGenAI

Overview

Conversational commerce powered by GenAI transforms static shopping into guided, personalized journeys. With Turing Intelligence, retailers capture intent, recommend bundles, and streamline checkout in real time—reducing bounce rates, raising average order value, and boosting customer satisfaction. By embedding agents directly into PDPs, homepages, or quiz flows, brands turn browsers into buyers while routing post-sale support through the same interface for a seamless experience.

Challenges

Conventional e-commerce UX—filter, browse, add to cart—is built for inventory, not customers. PDP bounce rates exceed 40%, cart abandonment remains above 70%, and personalization is limited to past clicks, not current goals. Recommendation widgets underperform, with <10% engagement.

Customers don’t want catalogs—they want guided, helpful, conversational journeys.

The Turing Approach

Turing Intelligence enables conversational commerce by integrating multimodal agents into the shopping journey. These agents capture user intent, surface relevant SKUs, and dynamically build bundles based on context. Post-sale flows—returns, reorders, FAQs—route through the same interface, creating a loop that improves CSAT and reduces support tickets.

What This Enables

  • Launch conversational flows from PDP, homepage, or quiz modules
  • Surface personalized SKUs based on stated trip, intent, or constraints
  • Auto-build and cart bundles that match budget or event needs
  • Handle exchanges or returns through the same agent system
  • Track conversion, AOV, and user satisfaction across each touchpoint

Business Impact

  • Product discovery time: -40% with contextual Q&A
  • AOV uplift: +15–25% via bundling and intent-aware recs
  • Bounce rate: -20% from guided entry points
  • CSAT: +30% through seamless assistance

Agent Configuration: Turning Browsers into Buyers

Turing’s conversational commerce agents each serve a specific retail function:

  • Intent Capture Agent: Parses shopper goals via natural language
  • Discovery Agent: Maps intent to real-time SKU availability
  • Bundle Builder Agent: Curates shoppable collections from catalog
  • Transaction Agent: Optimizes budget, checkout flow, and promo logic
  • Post-Sale Agent: Manages return, reorder, and product Q&A

Why Turing

Turing’s commerce AI isn’t a chatbot overlay. It’s a full-stack interaction model—CRM-aware, composable, and evaluated on KPIs that matter: order value, retention, and satisfaction. Retailers can start with one agent and scale across the funnel.

How a Footwear Retailer Increased AOV by 22% With a Trip Planner Bot

A global footwear brand piloted Turing’s conversational agents for their “Summer Travel Essentials” campaign. The goal was to help shoppers planning vacations or events (e.g., weddings, festivals, beach trips) find relevant gear—fast.

Using Turing’s Intent Capture Agent, users answered simple questions: “Where are you going?”, “What kind of events?”, and “Any color or price preferences?”

The Discovery and Bundle Builder Agents parsed responses, filtered catalog SKUs in real time, and presented complete bundles:

  • For a “beach weekend with friends,” the system recommended sandals, a light sneaker, matching socks, and a waterproof bag
  • Carting logic grouped discounts for bundles, increasing conversion
  • Post-sale, the Support Agent managed size exchanges and suggested alternate picks if inventory shifted

The pilot group saw:

  • 40% faster product discovery vs. static search
  • +22% AOV from bundle suggestions
  • 17% drop in support tickets for order changes
  • 28% lift in CSAT from survey feedback


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