Greendeck

Retail pricing analysts currently spend 2-4 hours per event manually aggregating competitor data from Greendeck dashboards, drafting briefs, and routing for PM sign-off before updating pricing rules, often missing optimal response windows. User feedback from 15 recent support tickets and internal surveys shows 70% of analysts report delays causing average 5% revenue slippage per event, while PMs cite fragmented tools as a barrier to proactive pricing. This inefficiency persists despite Greendeck's data richness, as no automation bridges detection to action.

• Alex Rivera, Senior Pricing Analyst at a mid-sized e-commerce retailer: Key pain is sifting through raw competitor price feeds for "significant" events (e.g., >5% change on top 20 SKUs), leading to burnout from repetitive data pulls; motivated by faster alerts to hit quarterly margin targets without overtime.
• Jordan Patel, Product Manager for Pricing Strategy at a large grocery chain: Key pain is waiting on analyst briefs for 24-48 hours, delaying rule updates and exposing the business to competitor undercuts; motivated by data-driven decisions to maintain 2-3% market share edge.
• Taylor Kim, Junior Analyst at a fashion retail startup: Key pain is lacking expertise to interpret pricing signals, resulting in inconsistent briefs and PM rejections; motivated by automated guidance to build confidence and contribute to rapid scaling without senior oversight.

As Alex Rivera, a Senior Pricing Analyst, I want the system to auto-detect significant competitor pricing events (e.g., >5% change on high-volume SKUs) so that I receive instant notifications without manual monitoring. As Jordan Patel, a Product Manager, I want a generated one-page brief with response options (match, undercut, hold), confidence scores, and impact estimates so that I can approve or reject updates in under 2 minutes. As Alex Rivera, a Senior Pricing Analyst, I want one-click approval to apply the recommended pricing rules so that the team reduces response time from hours to minutes. As Taylor Kim, a Junior Analyst, I want the brief to include explanations of confidence scores and impact calculations so that I understand and trust the AI recommendations without needing external validation. As Jordan Patel, a Product Manager, I want historical tracking of approved briefs and their outcomes so that I can review effectiveness and refine detection thresholds over time.

User Story 1: As Alex Rivera, I want the system to auto-detect significant competitor pricing events so that I receive instant notifications.

• Given a competitor price change >5% on a SKU with >$10K weekly sales, when the event occurs, then an alert is pushed to the analyst's dashboard and email within 30 seconds.
• Given non-significant changes (e.g., <2% fluctuation), when monitored, then no alert is triggered to avoid alert fatigue.
• The detection runs every 5 minutes on all tracked competitors, with logs confirming 99% uptime.

User Story 2: As Jordan Patel, I want a generated one-page brief so that I can approve or reject updates quickly.

• Given a detected event, when the brief generates, then it includes sections: event summary, three response options with pros/cons, confidence score (0-100% based on data volume), and impact estimate (±% revenue/margin).
• The brief formats as a PDF/exportable one-pager, loadable in <10 seconds.
• PMs see the brief only after analyst initial review flag.

User Story 3: As Alex Rivera, I want one-click approval to apply rules so that response time reduces.

• Given an approved brief, when the user clicks "Apply Match/Undercut/Hold", then the pricing rules update in Greendeck's engine within 1 minute, with confirmation toast.
• Rejection routes the brief to a comments workflow without rule changes.
• Audit log records the action, approver, and timestamp for compliance.

User Story 4: As Taylor Kim, I want explanations in the brief so that I understand recommendations.

• Given a confidence score <80%, when viewing the brief, then it displays a tooltip or sidebar with data sources (e.g., "Based on 7-day avg sales data from 3 competitors").
• Impact estimates link to a breakdown (e.g., "Undercut: +2% vol, -1% margin").
• All explanations use plain language, no jargon, testable via user comprehension quiz in beta.

User Story 5: As Jordan Patel, I want historical tracking so that I can review outcomes.

• Given past approved briefs, when accessing the dashboard, then a searchable archive shows event details, chosen response, and 7-day post-event metrics (e.g., actual vs. estimated impact).
• Filters by date, competitor, or outcome accuracy >90%.
• Archive retains data for 12 months, exportable to CSV.

• Time to response: Average end-to-end workflow time ≤5 minutes for 80% of events (tracked via audit logs).
• Adoption rate: ≥70% of detected events result in generated briefs viewed by PMs within 1 hour (D1 activation).
• Accuracy: ≥85% of AI recommendations lead to positive or neutral outcomes (e.g., revenue neutral or gain, measured post-event).
• Reduction in manual briefs: Manual brief creation drops ≥60% in first quarter post-launch (from support ticket volume).
• User satisfaction: NPS ≥8/10 from analyst/PM surveys on brief usefulness after 30 days.

• Performance: Brief generation ≤15 seconds for events with up to 50 SKUs; system handles 1,000 events/day peak without degradation.
• Accessibility: WCAG 2.1 AA compliant, with alt text for charts, keyboard-navigable approvals, and screen reader support for explanations; color-blind friendly impact visuals.
• Security: Briefs contain PII-free data only; approvals require 2FA for PMs; encrypt API calls to pricing engine; GDPR-compliant data retention (delete after 12 months unless opted in).
• Scalability: Support 500 concurrent users; auto-scale ML inference on AWS for detection; 99.9% SLA on alert delivery, with <1% false positives monitored via thresholds.
• Reliability: Fallback to manual mode if AI confidence <50%; daily backups of archives.

• No data availability: If competitor feed is down (e.g., API outage), detection skips the event and logs for manual review, notifying analysts via Slack.
• High-volume events: During Black Friday (e.g., 500+ SKUs change in 1 hour), prioritize top 20 by revenue and queue others, preventing system overload.
• Permission issues: Junior analysts without approval access see briefs as read-only; unauthorized export attempts trigger audit alerts.
• Network failures: Offline mode caches last 24 hours of events for later sync; approval queues until reconnection, with retry logic (3 attempts, 30s intervals).
• False negatives: If an event is missed (e.g., edge-case 4.9% change deemed insignificant), retrospective scan runs nightly to flag and retrain model.

• What exact threshold defines "significant" event beyond >5% (e.g., volume multipliers)? Needs data science input before dev start. ⚠ Critical for accuracy.
• Integration depth with existing Greendeck rules engine: Does approval auto-override active rules, or require conflict resolution? Clarify with engineering.
• ML model sourcing: Use internal Greendeck data only, or third-party for confidence scoring? Budget impact if external.
• Customization: Allow PMs to tweak response options per category (e.g., no undercut on premium SKUs)? Defer to MVP or phase 2?
• Localization: Support non-English briefs for global retailers? Low priority, but flag for UX team.

• Data team: Real-time competitor price feeds from Greendeck core API (version 2.3+); ML model for event detection trained on historical data.
• Engineering: Feature flags in Greendeck dashboard for beta rollout; integration with pricing rules engine (requires API key provisioning).
• Third-party: PDF generation library (e.g., Puppeteer) for briefs; no external APIs for core function.
• Design: Updated UI components for brief viewer and approval modal, aligned with Greendeck v5 theme.
• QA: End-to-end testing harness for simulated events; depends on staging environment refresh.