CommerceIQ

PMs and analysts at brands like Procter & Gamble or Unilever face a fragmented workflow: they pull past campaign data from spreadsheets, cross-reference retailer guidelines via PDFs, and align with internal goals in emails, often taking 4-8 hours to draft a spec. This results in inconsistent specs, engineering delays of 2-3 days for configuration, and errors like overlooked ROAS targets. User feedback from 15 beta interviews shows 80% cite the "blank page syndrome" as a blocker, with one PM stating, "I waste a full afternoon just structuring the brief before we even start testing."

• Sarah Thompson, Senior PM at a CPG Brand: Manages Amazon promotions for household goods; her key pain is reconciling historical ROAS data with Walmart's bid caps, leading to over-budget campaigns; motivated to launch faster to hit quarterly targets without burning out on admin work.
• Raj Patel, Retail Media Analyst at a Beauty Brand: Analyzes Walmart data and supports spec creation; pain point is inconsistent formatting that confuses engineering, causing 20% rework; driven by reducing errors to focus on optimization insights.
• Emily Chen, Campaign Manager at an Electronics Brand: Oversees seasonal Amazon pushes; struggles with integrating brand goals like sustainability messaging into specs; motivated to eliminate manual translation to scale campaigns across multiple retailers.

As a Senior PM like Sarah, I want to answer guided questions on campaign parameters so that the system generates a complete spec without me starting from scratch.
As a Retail Media Analyst like Raj, I want the generated spec to include recommended automation rules based on past data so that engineering can configure faster with fewer revisions.
As a Campaign Manager like Emily, I want suggested A/B test variants in the spec so that I can validate ideas like bid adjustments before go-live.
As a Senior PM like Sarah, I want an auto-generated go-live checklist in the spec so that I catch retailer-specific compliance issues early.
As a Retail Media Analyst like Raj, I want to export the spec as a shareable PDF so that I can distribute it instantly to engineering and stakeholders.

User Story 1 (Guided Questions to Spec Generation):

• Given a user selects "New Promotion Spec" and answers all 5 questions (product category, target ROAS, retailer, budget range, seasonal moment), when they click "Generate," then a structured spec PDF is displayed within 30 seconds containing sections for objectives, rules, tests, and checklist.
• Given incomplete answers (e.g., missing ROAS), when they click "Generate," then the system flags required fields with error messages and prevents generation until resolved.
• The spec must incorporate user inputs exactly, e.g., ROAS target of 4.5x appears verbatim in the objectives section.

User Story 2 (Recommended Automation Rules):

• Given historical campaign data exists for the category/retailer, when the spec generates, then it includes 3-5 rules like "Pause bids below 3x ROAS" pulled from top-performing past campaigns.
• If no historical data, the system defaults to retailer-standard rules (e.g., Amazon's 10% budget pacing) and notes "Limited data; review manually."
• Rules must be editable inline before export, with changes tracked in a revision log.

User Story 3 (Suggested A/B Test Variants):

• Given inputs like budget range $10k-$20k, when generating for Amazon, then the spec lists 2-3 variants such as "Variant A: 20% bid uplift on high-ROAS SKUs vs. Baseline."
• Variants must tie to goals, e.g., seasonal Black Friday input triggers traffic-focused tests.
• Each variant includes expected impact estimates based on aggregated past data (e.g., +15% conversion).

User Story 4 (Go-Live Checklist):

• The generated spec always includes a 10-item checklist tailored to retailer, e.g., "Verify Walmart vendor compliance" for Walmart campaigns.
• Given a generated spec, when items are marked complete, then progress bar updates and locks on export if <80% done.
• Checklist must export with the spec, preserving mark states in PDF.

User Story 5 (PDF Export):

• Given a generated spec, when clicking "Export," then a downloadable PDF is created with branded CommerceIQ header, user inputs, and all sections.
• The PDF must be under 2MB and include hyperlinks to retailer docs where applicable.
• Export logs the action with timestamp and user ID for audit trails.

• Time to spec generation: Average ≤ 5 minutes per user session (measured via UI timestamps), target 75% reduction from current 4-hour manual baseline.
• Adoption rate: ≥ 60% of active PM/analyst users generate at least one spec in the first month post-launch.
• Engineering handoff time: ≤ 1 day from spec delivery to config complete (tracked via ticket metadata), down from 2-3 days.
• Spec revision rate: ≤ 15% require engineering feedback loops (via post-generation surveys).
• User satisfaction: Net Promoter Score ≥ 8/10 from in-app feedback on spec usefulness.

• Performance: Spec generation must complete in ≤ 30 seconds for 95% of requests; page load times ≤ 2 seconds. Use caching for historical data queries.
• Accessibility: WCAG 2.1 AA compliant, including screen reader support for question forms and ARIA labels on generated tables; color contrast ≥ 4.5:1.
• Security: All inputs encrypted at rest; role-based access ensures only PM/analyst roles can generate specs; audit logs for all generations/export with PII redaction. No storage of sensitive budget data beyond session.
• Scalability: Handle 1,000 concurrent generations during peak seasons without >5% error rate; auto-scale backend via AWS Lambda.
• SLAs: 99.9% uptime for the generator module; support response ≤ 4 hours for generation failures.

• No historical data: System generates a basic spec with placeholders and prompts user to upload data manually; fails gracefully without crashing.
• Invalid inputs: ROAS target <1x or budget >$1M triggers validation warnings; Walmart selection without category defaults to error page if unsupported.
• Network failure during generation: Queue inputs and retry on reconnect, notifying user via toast; store draft for 24 hours.
• Permission issues: Non-authorized users (e.g., interns) see locked UI; on role change mid-session, save and redirect to login.
• High-volume seasonal spike: If query volume >500/min, throttle to 100/min with queue status; previous failure showed 20% drop-off without this, so monitor via Datadog.

• How do we source and weight historical data for rule recommendations? Need ML team's input on data freshness (last 12 months?). ⚠ Critical for accuracy before dev start.
• Should the generator support custom questions beyond the 5 core ones, like brand-specific KPIs? Defer to v2 or include as extensible fields?
• Integration depth with existing CommerceIQ dashboards: Pull live ROAS trends or just static uploads? Requires UX review.
• Multi-retailer specs: Allow Amazon+Walmart combo or force single selection? Test with 5 users for feasibility.
• Localization: English-only initially, or add Spanish for LATAM brands? Low urgency, post-launch.

• Data Team: Access to sanitized historical campaign database (past ROAS, rules) via Snowflake API; must be live before alpha testing.
• Engineering: Feature flag in CommerceIQ backend (e.g., "ai-spec-gen-v1") for staged rollout; depends on existing user auth service.
• ML Team: Pre-trained model for rule/A/B suggestions, integrated via internal API; timeline aligns with Q3 sprint.
• Third-Party: Amazon Selling Partner API for real-time constraints (e.g., bid caps); Walmart Developer Portal for compliance data pulls.
• Design: Figma mocks for question flow and spec template finalized; no blockers, but sync weekly.