Clear a 5,000-SKU Backlog in 90 Days Without Hiring

Do the math first

A large electrical distributor sat on 800,000 unclassified products. Manual enrichment got them through roughly 1% per year. At that rate, clearing the backlog is a multi-decade project.

Your backlog is smaller. The math is the same. 5,000 SKUs at 40% completeness, 50 enriched per week manually, two years to finish. But you'll add 3,000 new SKUs in that time. Your PIM ends up at 55% instead of 40%. The pile grows faster than you can work through it.

Manual enrichment just doesn't scale. Automated extraction from datasheets processes a SKU in seconds. So you automate the extraction and put people on reviewing output and handling exceptions.

Your catalog has the same 80/20 split

Pull your sales data. In most electrical distributor catalogs, 10-20% of SKUs drive 80-90% of revenue. Yours probably looks like this:

Your top 500 SKUs drive three-quarters of your business. They deserve 95% completeness with hero images, 5-8 key specs, and full technical documentation. The next 1,500 SKUs need 70% completeness: image, short description, 3 key specs. The remaining 3,000 SKUs can sit at 40% until someone actually searches for them.

Not all SKUs deserve the same effort. Accept that, automate the extraction, and have a person review output instead of doing the lookup.

Field prioritization: what actually drives conversion

Not all fields matter equally. Focus on what buyers actually use to decide: image, voltage, amperage, wire gauge, enclosure type. Past that, you hit diminishing returns fast. For the high-volume technical fields, use validators instead of manual spot checks: validate GTIN barcodes, check IP ratings, and verify NEMA enclosure types before a reviewer spends time polishing descriptions.

High-impact fields for Tier 1: product image (hero angle), short description (2-3 sentences), voltage, amperage, poles or wire gauge, enclosure type or mounting style, price and availability.

For Tier 2, cut to the essentials: image, 2-sentence description, and 3 key specs only. For Tier 3, baseline is manufacturer name, part number, and price. Enrich when traffic justifies it.

Weeks 2-4: Enrich Tier 1 with automated extraction

Run all Tier 1 SKUs through a structured extraction pipeline. Feed in manufacturer datasheets and the pipeline returns typed fields: voltage, amperage, wire gauge, enclosure type, with source page for traceability. High-confidence results go straight to the PIM. Low-confidence results go to a review queue where a person verifies them against the source PDF.

Throughput: seconds per SKU instead of 10 minutes each. One person reviews flagged results and handles exceptions. Two people knock out all 500 Tier 1 SKUs in 2-3 weeks, review included.

Weeks 5-8: Enrich Tier 2 at 70% target

Target drops to 70%. Skip extended specs and marketing copy. Image, short description, 3 key attributes. Same pipeline, narrower schema, smaller review queue.

Two people clear 1,500 Tier 2 SKUs in 4 weeks. Most of that time goes to the 10-15% where extraction returns low confidence, usually scanned datasheets or non-standard terminology.

Weeks 9-12: Triage Tier 3 on demand

For the long tail, enrich on-demand when someone searches or views the product. Set up a workflow flag in your PIM: if page views exceed 5 in any 30-day period, promote to the enrichment queue. Otherwise, leave at baseline.

You'll opportunistically enrich 200-300 Tier 3 SKUs in the final 4 weeks. The rest sit at 40% completeness until traffic justifies the effort.