E-commerce
June 28, 2026
"Hello, I need a quote for 2,000 units, delivery by late September, with a logo." The message arrives at 9:00 PM via email, without an exact volume or specific requirements. On Monday, the sales team follows up three times to obtain the missing information. The prospect has already signed elsewhere.
Heeya estimates that manual RFQ qualification costs 20 to 30 minutes per lead before drafting the quote, compared to zero minutes if a chatbot captures 8 structured fields (Heeya, B2B quote qualification 2026).
This guide #176 covers the AI chatbot for e-commerce quote requests: qualification, volume, deadlines. Distinct from lead qualification (#99) (B2B profile detection) and the quote glossary: here is the complete conversational RFQ flow.
Summary
Why automate RFQs with a chatbot?
An e-commerce quotation outside standard checkout requires precise data: quantity, specs, lead time, budget. Static forms or unstructured emails generate incomplete files.
Cost of an incomplete quote
Virtina notes that "clarification" loops by email extend response times by 2 to 3× (Virtina, B2B Quote Chatbot 2026). Every back-and-forth = risk of losing the deal to a more responsive competitor.
What the chatbot brings
Ordered questions: volume before lead time, specs before price
Mandatory fields: impossible to submit without a quantity
24/7: RFQ captured in the evening, CRM brief ready in the morning
MOQ Filtering: below-minimum redirected before sales touch
Structured brief: sales rep doesn't call back blind
Typical use cases
Configurable products, high MOQ, logo customization, light B2B on DTC stores, corporate volume gift sets, custom-made equipment.
How does it differ from lead qualification and quote support?
Three neighboring contents, three moments of the quote journey.
#99 B2B Lead Qualification
Qualification (#99) detects reseller vs. private individual profile and scores the lead. The #176 builds the complete quote flow once the RFQ intent is confirmed.
#50 Human B2B Support
B2B Support (#50) handles existing accounts, Net terms, reorders. Here: automated first quote request before human intervention.
#158 Corporate gifts
Corporate gifts (#158) covers the multi-address human support process. The #176 automates the initial capture of volume + deadline + specs via bot.
Quote Glossary
Defines the concept. This guide #176 operationalizes the chatbot use case.
Which fields should the chatbot capture for a quote?
Eight RFQ quote qualification fields cover 90% of B2B and corporate e-commerce needs (Heeya, 8 RFQ fields).
Required fields (MVP)
Volume / quantity: units, boxes, weight, area
Delivery lead time: deadline or desired window
Product / reference: SKU, range, or described need
Professional contact details: email, company, telephone
Recommended fields (V2)
Specifications: color, size, material, certification
Budget / range: order budget excl. tax
Customization: logo, packaging, message
Frequency: one-off vs recurring
Advanced fields (configurable / industrial)
File upload (PDF logo, CAD drawing), technical constraints, Net 30 payment terms, geographical delivery zone. NimbleBiz recommends routing to the right sales engineer upon submission (NimbleBiz, RFQ manufacturing 2026).
How to structure the quote conversational flow?
The quote chatbot flow follows a commercial logic, not the alphabetical order of a form.
Typical sequence (6 to 10 steps)
Greeting: "Quote request" vs "Product question" (branching)
Product or need: catalog selection or free description
Quantity: with MOQ reminder if below threshold
Customization yes/no → details if yes
Desired delivery date: calendar or text
Delivery address or area (country, region)
Company + pro email + phone
Indicative budget (optional, slider or range)
Editable summary + confirmation
Request number + promised response time
UX Rules
One question per chat screen. Quick buttons when possible (quantities, yes/no). Progress bar "Step 3/8". Ability to resume later via email magic link. Completes guided selling (#150) for tree logic.
How to manage volume, MOQ, and lead times in the bot?
Volume and lead times are the two filters that prevent 40% of unprofitable quotes.
Volume and MOQ
Sync MOQ rules from Shopify metafields or internal table. If quantity < MOQ: message "Minimum [X] units for custom quote. For [Y] units, standard checkout here: [link]." First test order exception: offer a "request MOQ exception" form with lead escalation, not a hard block.
Delivery Lead Times
Three horizons to distinguish (aligned with wholesale (#144)): warehouse stock (D+X), production (W+Y), customization (+Z weeks). Bot calculates feasibility: if requested date < minimum product lead time, alert "Tight deadline, our team will confirm within 4 hours if feasible as a rush order."
Lead Time Follow-up Questions
"Is the date firm (event) or flexible?" Firm date = urgent flag + senior routing. Flexible date = standard queue.
How to connect the bot to the CRM and the sales team?
A quote bot without CRM quote sync recreates manual copy-pasting.
Minimum webhook payload
quote_request_id, timestamp, 8 section 3 fields, qualification score, summary transcript, origin page URL, Shopify customer_id if logged in.
CRM Mapping
HubSpot / Pipedrive: custom properties `rfq_volume`, `rfq_deadline`, `rfq_specs`, `rfq_score`. BotHero recommends a Slack notification within 2 min with name, question, and source page (BotHero, CRM integration 2026). Hyperleap warns: do not create a Deal per conversation, only if there is an explicit qualification signal (Hyperleap, CRM chatbot 2026).
Sales Routing
Score > 80: Senior AE, 4-hour SLA
Score 50-79: Inside sales, 24-hour SLA
Score < 50: Email nurturing, no call
Vertical: Route by product category or geography
When to escalate to a human and with what brief?
The chatbot quote handoff must arrive with full context, not a simple "customer wants to talk".
Immediate escalation triggers
Specs outside of standard catalog
Volume > auto-quote ceiling
Delivery date impossible without paid rush
Net 60 request or custom payment terms
2nd message of frustration or "no one is responding"
Handoff brief (template)
RFQ #[id] | [Company] | [Contact] | [SKU x qty] | Delivery [date] | Custom [yes/no] | Score [X] | MOQ [ok/exception] | Key verbatim: "..." | Suggested next action: send quote within 24 hours.
Customer promise
"[First name] (sales rep) will contact you within [SLA] with a detailed quote. Request reference: RFQ-[id]." Never promise an exact price in chat unless it is from the public rate card. See bot handoff (#12).
How to configure the bot on Shopify?
Setup Shopify quote chatbot in four technical layers.
1. Entry Points
Configurable PDP "Request a Quote" widget, /pages/quote page, cart CTA if qty > threshold, proactive intent for high-value cart abandonment.
2. Catalog Data
Sync SKU, variants, MOQ metafield, lead_time_stock, lead_time_prod, public price (not negotiated quote price). Virtina: the bot maps natural language to catalog attributes (Virtina, NLP catalog).
3. Business Rules
MOQ filter, minimum lead time calculation, qualification score, auto-incremented RFQ codes.
4. Integrations
Webhook → CRM, customer confirmation email, Slack #rfq-new, Shopify draft order option if fixed price list. See train Shopify bot, Qstomy integration.
Which KPIs should be measured on the quote bot pipeline?
The quote chatbot KPIs prove commercial ROI, not just support deflection.
Leading KPIs
Flow completion rate: target > 65%
Fields completed / RFQ: target ≥ 6/8
Time to CRM: < 60 seconds post-submission
% filtered by MOQ: volume disqualified before sales
Lagging KPIs
Time to first human contact (target < 4 h for high scores). Time for quote sent vs promised. Conversion rate RFQ → order (Heeya: +20-35% qualified pipeline vs manual). Rate of incomplete RFQs avoided (Virtina benchmark: 2-3× faster turnaround).
Monthly review
Top 3 flow drop-offs (which step?), top missing specs, question adjustment. See chatbot KPIs.
What mistakes sabotage a quote chatbot?
Five quote chatbot anti-patterns to avoid at launch.
Common errors
Disguised form: 15 fields at once, not a conversation
Invented price: LLM quotes an amount not approved by finance
No MOQ: sales receives requests for 5 units
Empty CRM: email only, zero context on volume/lead time
Vague SLA promise: "we'll get back to you" with no timeframe
Guards
Exact prices and deadlines = human or rules engine only. Gold set test with 30 real RFQs before production. Legal review of quotes T&Cs (valid for 30 days, subject to stock, etc.).
How does Qstomy handle e-commerce quote requests?
Qstomy deploys a conversational quote flow connected to the Shopify catalog and CRM.
Quote features
rfq_start intent: guided 8-field flow
Live MCQ check: Shopify metafields sync
Lead time calculation: stock vs production
RFQ Score: automatic AE routing
CRM Webhook: structured brief + transcript
Handoff: pre-filled section 7 brief
Quantified DTC Scenario
Light B2B packaging brand, 45 RFQs/month via email (60% incomplete). Qstomy flow deployment: 71% completion, average fields 7.2/8, time to CRM 42 sec, first contact time 3.5 h vs 28 h before, RFQ → order conversion +24%, sales discovery time -85%.
Explore AI support, AI sales agent, Shopify, request a demo.
Which operational playbooks should be used to launch the quote bot?
Playbook 1: fields mapping (2 h)
List 8 section 3 fields, MOQ per top 20 SKU, stock/lead times. Notion document "RFQ Policy".
Playbook 2: MVP flow (1 day)
6 steps: product, qty, lead time, contact, recap, confirmation. Test 10 internal scenarios.
Playbook 3: CRM + Slack (3 h)
Webhook, custom properties, #rfq-new notification, SLA by score.
Playbook 4: website entry points (2 h)
Configurable PDP widget, /quote page, large cart CTA. Proactive message qty > threshold.
Playbook 5: sales training (1 h)
Read RFQ brief, SLA by score, never re-ask for fields already captured.
Playbook 6: W+4 review (45 min)
Section 9 KPIs, drop-off rate per step, 2 questions to add or remove.
Useful links
A quote starts with the right questions. The chatbot asks them one by one, 24/7, so your team starts Monday with complete files, not empty emails.
Enzo
June 28, 2026
