> ## Documentation Index > Fetch the complete documentation index at: https://docs.vortexiq.ai/llms.txt > Use this file to discover all available pages before exploring further. # Missing Descriptions, BigCommerce > Missing Descriptions for BigCommerce stores. Tracked live in Vortex IQ Nerve Centre. **Metrics type:** [Key Metrics](/nerve-centre/overview#metrics-types-explained) • **Category:** [Ecommerce Platform](/nerve-centre/connectors#connectors-by-type) ## At a glance > **Percentage of products without a description.** **Products without descriptions have 30-50% lower conversion rates** than products with full descriptions. Each missing description is a directly attributable revenue leak: the shopper can't see what they're buying, can't form intent, and bounces. The card surfaces the pool size so merchants can prioritise content remediation against the catalogue's revenue contribution. | | | | ----------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **What it counts** | The percentage of active products in the BigCommerce catalogue with `description = NULL`, empty string, or whitespace-only content. Counts active SKUs only (excludes draft, archived, or hidden products). | | **Sample type** | Backend API data from BigCommerce catalogue, refreshed on the standard data refresh. | | **Why missing descriptions matter** | (1) **Conversion**: products without descriptions convert 30-50% below catalogue average. (2) **SEO**: empty product pages have no copy for search engines to index, so they don't rank for product queries. (3) **AI-source citation**: ChatGPT, Perplexity, Claude need product copy to recommend the product in conversational shopping. (4) **Merchandising**: thin product pages erode trust and brand premium. **Each missing description is a known revenue leak with a one-time fix that pays back permanently.** | | **Reading the value** | (1) Below 5%: healthy; remaining gaps are likely temp/test SKUs. (2) 5-10%: attention zone; address in normal cadence. (3) 10-20%: investigation zone; prioritise high-revenue product gaps. (4) Above 20%: alert state; structural catalogue health issue, often from automated import without copywriting. (5) Cross-reference `bc_revenue_by_brand` to identify whether high-revenue products are in the gap. | | **Currency** | percent + absolute count. | | **Time window** | snapshot (current state). | | **Alert trigger** | `missing_desc > 5` (BAD threshold at 20%). | | **Sentiment key** | `missing_desc` (LOWER\_IS\_BETTER in `SentimentClassifier`; GOOD ≤ 5%, BAD ≥ 20%). | | **Roles** | owner, marketing, operations | ## Calculation ``` missing_desc (%) = COUNT(products WHERE (description IS NULL OR LENGTH(TRIM(description)) = 0) AND status = 'active') ÷ COUNT(products WHERE status = 'active') × 100 ``` ## Worked example A UK-based BigCommerce general-merchandise store, missing descriptions reading on Wednesday 15 May 26. | Metric | Value | Status | | ------------------------------------------ | -----------------------------------------: | --------- | | Total active products | 552,448 | - | | Products missing description | 552,448 | - | | **Missing description %** | **100.0%** | **Alert** | | Top-100 revenue products with descriptions | 0 | - | | Estimated revenue impact | 30-50% conversion drag on entire catalogue | - | **Missing descriptions: 100.0%** (catastrophic; the entire active catalogue lacks descriptions). The card flags as Action Needed in red. What the catalogue health reading is telling us: 1. **The store has 552,448 active products and zero have descriptions.** This is a structural data import issue, the merchant likely migrated from another platform without bringing product copy across, or imported via a feed that doesn't include description fields. 2. **Why 100% missing is unusually severe even for marketplace-style stores.** Even drop-shipped catalogues typically have 50-80% description coverage from supplier feeds. 100% missing suggests the description field was never mapped during import, or was imported into the wrong field (e.g., into `meta_description` instead of `description`). 3. **The revenue impact at this scale.** * **Conversion drag**: 30-50% below catalogue average means roughly half the potential revenue from the catalogue is foregone every day. * **SEO**: zero ranking for long-tail product queries; every product page is a missed organic entry point. * **AI-source citation**: zero discoverability via ChatGPT, Perplexity, Claude product recommendations. * **Merchandising**: every PDP looks empty and untrusted, eroding the entire site's brand impression. 4. **Triage priority, fix the top 1,000 first, not all 552K equally.** * Pull the top 1,000 products by 90D revenue. * Generate descriptions for those (AI-assisted is acceptable; human review on top 100). * Expected revenue lift on the top 1,000: 15-25% within 60 days (compounded across conversion + SEO + AI sources). * Then expand to top 10,000, then top 100,000. 5. **Practical fix paths:** * **Re-import from supplier feed** if descriptions exist in the source: re-map the description field and re-run the catalogue import. Fastest fix if the data exists. * **AI-assisted generation**: Vortex IQ's content actions can generate descriptions from product title + attributes + image, bulk-applied with merchant review on the top 100. Practical for catalogues above 1,000 products. * **Manual authoring**: only feasible for catalogues below 1,000 products or for the top revenue-contributing tier. * **Hybrid**: AI-generate for the long tail; human-author for the top 1,000 by revenue. 6. **Recommended ship sequence:** * **Day 1-2**: Identify the top 1,000 products by revenue contribution; export to CSV. * **Day 3-7**: AI-generate first-draft descriptions; human review the top 100. * **Day 8-14**: Re-upload to BC; verify descriptions render on PDPs. * **Day 14-21**: Re-audit conversion rate on the top 100 vs the rest of the catalogue. * **Result**: revenue lift on the top 1,000 typically 15-25%; missing\_desc card moves from 100% to 99.8% (still red because the long tail dominates the percentage). * **Subsequent quarters**: scale AI-generation to top 10,000 and beyond. 7. **Why the percentage may stay red even after meaningful work.** The metric is unweighted (treats all active products equally). Fixing the top 1,000 of 552,448 moves the metric by 0.18 percentage points, not visible. **Revenue uplift is the proxy metric** to track instead during the early-fix phase. After 50K+ products are fixed, the percentage will start to move materially. The diagnostic flow: 1. **Read the percentage.** Above 20% triggers alert. 2. **Stratify by revenue contribution** (top 1K, top 10K, rest). 3. **Apply tiered remediation** (human-authored top 1K, AI-generated long tail). 4. **Track revenue uplift on the fixed cohort** as the leading indicator. 5. **Re-measure missing\_desc** after each batch. Rapid-response playbook: | Time horizon | Action | | ------------ | ----------------------------------------------- | | First 1 hour | Read missing %, identify scale of catalogue. | | First day | Stratify top 1,000 by revenue. | | First week | AI-generate first drafts; human review top 100. | | Day 14 | Upload + re-audit conversion on fixed cohort. | | Quarter 2-3 | Scale to top 10,000+. | ## Sibling cards merchants should reference together | Card | Why merchants reach for it | | ----------------------------------------------------------------------------------- | ------------------------------------------------------------ | | [`missing_seo`](/nerve-centre/kpi-cards/bigcommerce/missing-seo-title-count) | SEO metadata gaps; descriptions feed SEO too. | | [`sku_coverage`](/nerve-centre/kpi-cards/bigcommerce/sku-coverage) | SKU completeness; descriptions are part of catalogue health. | | [`bc_revenue_by_category`](/nerve-centre/kpi-cards/bigcommerce/revenue-by-category) | Category-level revenue exposure of missing-desc gaps. | | [`bc_product_margin`](/nerve-centre/kpi-cards/bigcommerce/gross-margin) | Margin tier; prioritise high-margin missing descriptions. | | [`gsc_clicks_trend`](/nerve-centre/google_search_console/gsc_clicks_trend) | Organic clicks; thin descriptions block search ranking. | ## Reconciling against the vendor's own dashboard **Where to look in BC:** Products → Product list with the description column visible; Products → Bulk Edit to filter for empty descriptions. **Why our number may differ:** | Reason | Direction | What to do | | --------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------- | --------------------------------------------------------------------------------- | | **Whitespace handling.** BC may treat " " (whitespace) as a description; Vortex IQ trims and treats as empty. | Vortex IQ higher | Bulk-update whitespace descriptions to truly empty if visible difference matters. | | **Visibility filter.** BC may include hidden products by default; Vortex IQ excludes them. | Vortex IQ may be lower | Match BC's filter setting. | | **HTML entities.** A description of `

` is technically not empty but visibly empty. Vortex IQ may treat HTML-empty as content; BC sees it as content too. | Match | Check via DOM. | **Cross-connector:** complement with `gsc_indexable_pages` and search ranking cards. **Quick rule:** when disputes arise, export the BC product list to CSV with the description column and count empty rows directly. ## Known limitations / merchant FAQs **Q: Our missing description rate is 30%. We can't author 30% of our catalogue. What do we do?** Tier the work. Author for the top 1,000-2,000 products by revenue (which usually drive 80% of revenue) using a mix of AI-assisted and human-authored copy. The remaining long tail can be AI-generated with lighter review. **You don't need to fix every product equally; you need to fix the products that drive revenue.** **Q: We use AI to auto-generate descriptions. Is that bad for SEO?** Done well, no. Google's spam policies target low-quality, mass-produced content; well-prompted AI generation that includes specific product attributes, materials, sizing, and use cases passes Google's quality bar. **Always have human review for the top 100-500 by revenue** to ensure the AI-generated copy reflects the brand voice and accuracy expectations. **Q: Our descriptions are short (one sentence). Does that count as missing?** No, Vortex IQ counts only truly empty descriptions. A short description is content; it just may convert poorly. For copy-quality assessment (length, structure, persuasive elements), see the merchandising audit cards in Vortex Mind. **Q: We use specifications and feature lists instead of paragraph descriptions. Are those counted?** If they're stored in the `description` field, yes. If they're stored in a custom field or attribute, no. Vortex IQ checks the canonical description field per BC's API. **Q: Hidden products are excluded, but draft products we're working on count?** Drafts are also excluded (status filter). The card focuses on the live shopper-facing catalogue. Products in any non-active state don't affect the metric. **Q: We just imported 100,000 products and the metric jumped to 80% missing. Is that real or a glitch?** Real. Bulk imports that don't map the description field create exactly this pattern. **Re-import with the description field mapped** is usually the fastest fix; if the source data lacks descriptions, switch to AI-assisted generation. **Q: How does missing description affect AI-source traffic specifically?** Generative search engines (ChatGPT browsing, Perplexity, Claude) rely on product copy to answer "what should I buy" queries. **Pages without descriptions are typically excluded from AI-source candidate sets** because the model has no content to summarise. As AI search traffic grows (currently 1-3% of ecommerce sessions but growing 10x year-over-year), missing descriptions become an increasingly meaningful traffic gap. **Q: Is there a difference between description and meta description for this card?** Yes. Meta description is for SEO snippet (handled by `missing_seo`); description is the on-page product copy shown to shoppers (handled by this card). Both matter, separately, for different reasons. *** ### Tracked live in Vortex IQ Nerve Centre *Missing Descriptions* is one of hundreds of KPI pulses Vortex IQ tracks across BigCommerce and 70+ other ecommerce connectors. Nerve Centre runs the detection layer; Vortex Mind investigates the cause when something moves; Ask Viq lets you interrogate any number in plain English. [Start for free](https://app.vortexiq.ai/login) or [book a demo](https://www.vortexiq.ai/contact-us) to see this metric running on your own data.