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Conversational shopping lifts AOV
A beauty retailer embedded a conversational shopping assistant across PDP/PLP, increasing average order value and guiding bundles.
Objectives & KPIs
- Guide shoppers conversationally to bundles and add‑ons.
- Improve re‑ranking and recommendations impact.
- Measure uplift and attribution.
+12%
Average order value
+9%
Upsell rate
↑ CTR
On recommended items
< 2 weeks
Pilot live
Solution architecture
Assistant uses recs + intent signals to suggest bundles, adjusts ranking, and feeds analytics back.
Signals → Intent Recs API → Items Chat → Bundle suggestions Analytics → AOV & CTR
We applied prompt guardrails, PII filtering, and rate limits. Clear escalation and analytics were embedded from day one.
Key integrations
- Recommender API
- Shopify
- GA4
- Messaging (optional)
Channels
- On‑site assistant
- PLP overlays
- Email follow‑ups
Implementation
1) Discover
Goals, KPIs, data sources; success criteria and analytics events.
4) Launch
Pilot, QA, calibration; gradual rollout with dashboards.
2) Design
Flows, prompts & guardrails, UX, privacy filters, integration plan.
5) Handover
Docs, runbooks, enablement, and iteration roadmap.
3) Build
Connect data, implement logic, tag analytics, harden security.
Results & evidence
- AOV: +12% vs baseline
- Upsell: +9% rate
- Higher CTR on recs:
- Better discovery experience:
Significance verified via standard tests; GA4/BI dashboards tracked lift and seasonality.
Testimonial
“Bundles felt natural—customers discovered more without pressure.”
— Client stakeholder
Governance & security
- Least‑privilege access and environment isolation.
- PII filters; no training on raw logs.
- Transparent citations or decision trails.
- Audit trail for changes and rollbacks.
Runbooks
Operational SOPs for updates, incident response, and periodic reviews.
Lessons learned
- Keep suggestions minimal
- Explain value briefly
- Track per‑segment lift
Next steps
- Personalize by cohort
- Add post‑purchase cross‑sell
- Test seasonal bundles
