Next Gen System
Case 01 Toronto RETAIL · CHATBOT DEVELOPMENT
Retail Sales Chatbot That Closes Carts
Product-aware chatbot that lifts checkout conversion without adding headcount.
01 — Context
The Client’s Story
A multi-brand specialty retailer was losing shoppers at the exact moment they needed confidence—size, stock, materials, and promo eligibility. Those questions bounced to email or live-chat queues that answered after the shopper had already left.
Catalog truth lived in three systems. Agents often gave conflicting answers depending on which tool they opened first. Peak campaigns made the gap worse: traffic spiked, wait times grew, and assisted conversion stalled even when demand was strong.
Leadership wanted containment on the site itself—fast, grounded answers that protected brand voice—without hiring a seasonal army of agents that would sit idle after the rush.
02 — Ambition
Objectives
The brief was clear: answer product, policy, and promo questions in under three seconds inside on-site chat, with every reply grounded in live catalog and inventory truth—not a stale FAQ dump.
Assisted sessions needed to convert at a higher rate without adding headcount. That meant the bot had to resolve the common path cleanly and hand off only when confidence dropped or the shopper asked for a human.
Ops also needed weekly visibility: containment rate, unanswered intents, and conversion on assisted sessions—so the team could improve prompts and content without guessing.
03 — Friction
Challenges
Product feeds and promo calendars were fragmented across brands. A single SKU could carry different return rules depending on the banner, and shipping cutoffs changed by warehouse.
Hallucination risk was real on returns and shipping policy. A confident but wrong answer would create chargebacks and support tickets that cost more than the sale.
Peak-season volume historically broke human-only coverage. Any design that assumed a steady agent queue would fail the first flash sale.
04 — Approach
Solution
We shipped a retrieval-backed sales chatbot wired to catalog, inventory, and policy documents. The model answers from retrieved facts; guardrails block unsupported claims before they reach the shopper.
When confidence drops—or the shopper asks for a person—the handoff includes transcript context so agents do not restart the conversation. Analytics track containment, conversion, and unanswered intents every week.
Content and retrieval packs are versioned with the same discipline as code. Campaign teams can update promo windows without redeploying the whole stack, and failed retrievals surface as ops alerts instead of silent wrong answers.
05 — Outcomes
Results
Within the first two campaign cycles the assisted path started carrying more of the conversion load while support volume on stock and policy intents fell.
- Assisted chat conversion rose meaningfully across the first two campaign cycles.
- Average first-response time moved from minutes to seconds on covered intents.
- Support volume for “where is / is it in stock” intents dropped sharply.
06 — Forward
What’s Next
The next wave adds Voice AI for store associates who need the same catalog truth on the floor, plus recommendation-driven bundles pulled from purchase history so the chatbot can suggest adjacent products without leaving the grounded retrieval path.
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