parse-sprinklr

To normalize Sprinklr Excel exports, you parse the platform sheets to resolve monthly schema drift and pillar mappings, then insert the structured results as posts, campaign_period_summaries, and platform_benchmarks into Supabase.

Sprinklr data imports fail because exports have inconsistent schemas across months. You can resolve this schema drift and handle missing columns gracefully by enforcing idempotent upserts during the data normalization process.

Yes, you can group Instagram Stories slides into a story-level metric set. The normalization process supports grouping multiple slides into unified metrics across Instagram, Facebook, and YouTube Shorts formats.

The best way to load Sprinklr campaign summaries into Supabase is to parse the Excel export, normalize platform sheets and pillar mappings, and perform idempotent upserts to ensure proper campaign and period associations without duplicating records.

Yes, Supabase supports idempotent upserts for ingesting social media platform benchmarks. This ensures that re-running the data import for Facebook, Instagram, and YouTube Shorts does not create duplicate posts or campaign summaries.