One shared base to launch product across markets
Two maisons inside a global luxury goods group run go-to-market delivery through a single Airtable system: a large product catalog with localization tracking, seasonal wear-test quality assurance captured on mobile, and automated competitor scraping that feeds pricing and marketing decisions. Marketing and Product Marketing teams share one source instead of stitching together spreadsheets, emails, and static decks.
The problem
Launching a product across markets meant tracking catalog attributes, quality testing, competitor pricing, and localized content in separate places. Storage limits blocked wear-test photos, and manual email reporting slowed approvals and data quality checks.
What they built
A shared Airtable base now runs a 148-field product catalog with API-driven updates and localization tracking, fed by daily scraping of competitor specs and pricing that triggers automated alerts. Wear-test photos and pass and fail outcomes are captured on mobile and shared through interfaces.
The outcome
Marketing and Product Marketing teams get timely alerts on competitor moves, and one set of master data drives the website and marketing materials. Wear-test evidence lives in structured records, so audit traceability improved and season-over-season material trends are visible for the first time.
Inside the solution
Product Launch and GTM Delivery
Content Production & Localization
- Product catalog management with API updates and localization tracking
- Daily web scraping of competitor specs, pricing, and variants
- Mobile capture of wear-test results shared via Interfaces
- Automated notifications for new competitor releases
- Seasonal material trend analysis and failure tracking
- Data quality QA via grouping and color coding
- Manual competitor monitoring and pricing checks
- Storage limits blocking before and after photos
- Fragmented product data and onboarding materials
- Slow updates to website content and migration QA
- Lack of season-over-season material trend visibility
- Manual data entry from emails and manual data quality checks
- Timely alerts on competitor moves and pricing
- Improved compliance and traceability for quality audits
- Centralized master data for web and marketing
- Season-over-season insight on material failures
- Reduced manual email reporting and faster approvals
- Faster detection of data quality issues
- Marketing
- Product Marketing
- Website or CMS
- External scraping scripts
- PIM systems
- Analytics dashboards
Product launch and GTM delivery
Run product launches across markets from one base: a linked catalog with wear-test QA evidence, competitor price and release signals, localization status per market, and launch milestones, all visible in a published dashboard, kanban pipeline, and localization tracker. Automations alert your team the moment a competitor moves, flag products when a wear test fails, and kick off localization for every new product, while AI fields draft launch-readiness summaries, QA notes, and competitor alerts from the data in each record.
From fragmented launch prep to one connected GTM base
Copy the templates
Connect the feeds
Capture QA on mobile
FAQ
Frequently asked questions
The catalog and competitor data live in linked tables within the same base, so a product record and the competitive signals around it stay connected without duplicating data entry.
Mobile capture feeds photos and pass and fail results directly into linked records, and Interfaces share the results with quality and after-sales teams, removing the storage limits and attachments that used to block visibility.
Daily scraping scripts and APIs pull competitor specs, pricing, and variant data straight into the base, and automated notifications alert teams the moment a new release or price change appears.
Localization status is tied to the specific product record it affects, so translated content and its source stay in sync as items move toward launch in each market.
Not for the workflows built so far. The structured catalog, QA, and competitor data here would be a strong foundation for future AI-assisted content or trend analysis work.