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Nerve Centre KPIs · Audit Profile · Sentiment Settings Big Cartel is a hosted commerce platform for artists, makers, musicians, and small indie brands - deliberately narrow (small catalogues, low/free fees, fast time-to-live). Merchants run lean with no analyst and no BI seat, so the audit focuses on the executive pulse the built-in reports miss: OAuth token health, catalogue + image/description completeness, sold-out / out-of-stock hygiene against the plan product cap, refund-rate spikes (the product-trust signal), order/fulfilment SLA, and cross-channel comparisons against Ad / Email / Website-performance siblings to surface ad-spend-on-OOS, email-attribution share, and slow-PDP cart loss.

What this audit checks

Authentication & access

  • OAuth2 bearer token present and attaches as ‘Bearer
  • account_id resolves - /v1/accounts/ returns 200 with the store profile
  • Token scope includes read access to products / orders / customers (no 403 on resource reads)
  • Validate probe /v1/accounts/ is the cheapest auth check (single account object)
  • Plan tier readable - plan_name + product_limit present for plan-cap signals

Catalogue & image completeness

  • Products with zero images (image_count = 0) - convert poorly, free lift to fix
  • Products with empty description - SEO + conversion drag, AI-fill candidate
  • Sold-out products still listed on the storefront (status = sold-out) - quiet revenue leaks
  • Active product count vs plan product_limit > 90% - upgrade-or-delist pressure
  • Draft / hidden products lingering >30d (abandoned listings)

Inventory hygiene

  • Tracked products at zero stock (total_stock <= 0) on active listings (OOS but visible)
  • Low-stock tracked products below reorder threshold across the catalogue
  • Recent bestseller now sold-out (top-velocity SKU transitioned to status=sold-out)
  • Stale inventory - active product updated_at > 90d (likely dead listing)

Refunds & customer trust

  • Refund rate > 5% on 30D vsP (product-quality / expectation mismatch)
  • Rolling 24h refund rate > 2x 30D baseline (anomaly spike)
  • Cancellation rate > 3% (stock or expectation problem)
  • Concentrated repeat-refunder - single customer with multiple refunds (fraud / dissatisfaction signal)

Order & fulfilment SLA

  • Pending orders aged > 24h (payment-gateway hiccup or manual-review drag)
  • Paid-but-unshipped orders aged > 48h (broken shipping promise)
  • Avg time-to-ship > 72h on 30D vsP (slow ops for a hand-packed maker)
  • Order volume drop > 20% vsP without a known cause (silent acquisition fall)

Cross-channel: leak vs Ads / Email / Website-performance (the killer area)

  • Active ad spend on sold-out / OOS Big Cartel products - daily spend on products that can’t convert
  • Email-attributed revenue share < 15% (email under-utilised) OR drop > 20% vsP when an email tool is connected
  • High-value customers (top-spend P90) unengaged on email > 90d - win-back opportunity
  • Top-velocity products with LCP > 4s on a connected website-performance source (slow PDP → cart loss)

Severity thresholds

SignalWarnCritical
products_no_image_count110
products_no_description_count110
sold_out_listed_count15
plan_product_usage_pct90100
oos_tracked_count110
low_stock_count110
inventory_staleness_days6090
refund_rate_pct35
refund_spike_multiplier1.52
cancellation_rate_pct23
pending_orders_aged_24h315
unshipped_paid_aged_48h520
avg_time_to_ship_hours4872
order_volume_drop_pct1520
shipping_pct_of_revenue1215
ads_on_oos_daily_spend125
email_revenue_share_pct1510
high_value_unengaged_count35
slow_pdp_top_product_count13

Data sources

  • GET https://api.bigcartel.com/v1/accounts/{account_id} - Auth probe + plan tier + product_limit + currency (cheapest validate)
  • GET https://api.bigcartel.com/v1/accounts/{account_id}/products - Catalogue completeness + image/description coverage + sold-out + stock hygiene
  • GET https://api.bigcartel.com/v1/accounts/{account_id}/orders - Revenue + refund-rate spike + fulfilment SLA + country mix
  • GET https://api.bigcartel.com/v1/accounts/{account_id}/customers - Customer count + repeat rate + top-spender concentration