Product Data Enrichment for Jan-San Distributors
A facility manager shopping for a disinfectant is not searching by your six-digit item code. They are searching for "EPA disinfectant kills C. diff in 4 minutes" or "Green Seal neutral floor cleaner concentrate." A building service contractor pricing a bid wants the cost-in-use per ready-to-use gallon, not the price of the concentrate jug. If your catalog answers with a SKU and a one-line manufacturer blurb, the order goes to the distributor whose page actually answers the question.
Jan-San is a spec-and-compliance category wearing a commodity costume. Two can liners that look identical on a price file behave nothing alike once you compare resin, gauge, and seal. Two disinfectants at the same price point carry completely different kill lists and dwell times. The data that decides the sale — EPA registration numbers, contact times, dilution yields, dispenser compatibility, certifications — is exactly the data that gets stripped down to a name and a pack size when it lands in your system.
Anglera sits alongside your PIM and does the work your PIM only stores. It gathers, cleans, and enriches every SKU against real buyer signals — how the EVS director, the BSC, the school custodial supervisor, and the foodservice operator actually search, compare, and decide — then writes structured, complete data back to your source of truth.
Attributes thin jan-san (janitorial & sanitation) distributors catalogs miss
The Jan-San categories where thin data quietly loses the order
Your catalog spans wildly different spec profiles, and each one has its own way of failing on bad data:
- Disinfectants & sanitizers — won by EPA registration number, kill claims (norovirus, C. diff spores, TB, MRSA, SARS-CoV-2), contact/dwell time, and EPA List N/List K membership. Missing the dwell time loses the healthcare and K-12 buyer outright.
- Floor care chemicals — strippers, finishes, sealers, and neutral cleaners decided on dilution ratio, solids content, pH, and certification. Buyers compare cost-in-use, not jug price.
- Can liners — gauge (mil/micron), resin (LLDPE vs HDPE), gallon capacity, dimensions, and seal type (star, gusset, flat). A liner with no resin or seal data can't be cross-referenced or substituted.
- Paper & dispensers — ply, sheet count, roll length, core size, recycled content, and the dispenser system it actually fits.
- Floor pads, mops & equipment — pad color code, diameter, RPM rating; autoscrubber path width, run time, and voltage.
A price file gives you a name and a pack. Buyers need the other 15 fields, and they need them filterable.
The attributes Jan-San buyers actually filter and decide on
Generic enrichment fills in "color" and "brand." It misses the fields that move a Jan-San buyer from browsing to buying.
A disinfectant page that converts shows the EPA reg number, the kill list with each organism's contact time (10-second sanitize vs 4-minute sporicidal), whether it's on EPA List N, the dilution ratio, and the dispenser/closed-loop system it works with. A floor finish page shows percent solids, coats recommended, and coverage per gallon so a BSC can model labor and cost.
The single most important number in this vertical is rarely on the product itself: cost-in-use — the diluted cost per ready-to-use gallon. A $40 concentrate that yields 64 RTU gallons beats a $12 ready-to-use jug, but only if your data does the math the buyer is doing in their head. Enrich for that and you stop competing on sticker price.
Compliance and spec data you can't fake — and can't ship without
Jan-San carries regulatory weight that most catalogs handle badly. Buyers in healthcare, education, and foodservice are bound by purchasing policies that require specific documentation, and a missing field is a hard disqualification, not a soft one.
That means every relevant SKU needs the SDS (Safety Data Sheet) linked, GHS pictograms and HMIS/NFPA hazard ratings surfaced, EPA registration verified for any product making a public-health claim, and NSF food-contact categorization (A1, D2, etc.) for warewashing and foodservice chemicals. Green-cleaning mandates make Green Seal (GS-37, GS-1), EPA Safer Choice, and UL ECOLOGO certifications a filter, not a footnote. And because aerosols, sanitizers, and some chemicals ship as DOT hazmat with a UN number and freight class, that data drives both compliant shipping and accurate quoting.
This is structured extraction work — pulled from spec sheets, SDS PDFs, and EPA databases, then mapped to your schema. It's exactly the work that gets skipped when a human is reformatting line items by hand.
Cross-reference and substitution: the Jan-San buyer's real shortlist
Jan-San runs on equivalency. A BSC standardizing a new building, or a distributor defending an account against a competitive quote, needs to know which of your SKUs replaces the incumbent product. "This liner equals brand X's 1.5-mil 56-gallon star-seal," or "this neutral cleaner matches the discontinued item the customer used to buy."
Those cross-reference and fitment relationships almost never come on the supplier feed. Building them requires comparing resin, gauge, dimensions, dilution, and certification across products — then expressing the match in the buyer's language. When that data exists, you win substitution and conversion business instead of losing it to whoever's quote is in front of the buyer.
Thin catalogs can't do this because the comparison fields aren't there to compare. Complete, normalized attributes make cross-reference, dispenser-compatibility groupings, and "good/better/best" merchandising possible.
How buyer-signal enrichment changes the outcome
Anglera processes whole Jan-San categories in parallel — not one SKU at a time. It reads spec sheets, SDS documents, EPA listings, and certification databases; extracts the kill claims, dwell times, dilution yields, resin and gauge specs, and certs; normalizes them to your taxonomy; and writes complete, search-ready records back to the PIM and commerce platform you already run.
The difference shows up where it counts: SKUs that surface for "60-second sanitizer" or "FSC roll towel for a 12-inch dispenser," facets that actually let a buyer narrow by kill claim or certification, and cost-in-use data that reframes a price comparison in your favor.
Your PIM stores the data. Anglera does the work of filling it — typically in about 30 days, on your real catalog. The fastest way to see it is to let us run a category of your own SKUs and compare the before and after.
Frequently asked questions
How is this different from the manufacturer content my suppliers already send?
Supplier feeds and syndicated content give every distributor carrying that line the same blurb and a handful of fields — so your page looks identical to your competitors' and is missing the spec data buyers filter on. Anglera extracts the structured attributes (kill claims, dwell times, dilution yields, resin and gauge specs, certifications) from spec sheets, SDS documents, and EPA listings, maps them to your taxonomy, and writes unique, buyer-specific content back. Many distributors run both: syndicated data as a baseline, Anglera for the gap-filling and differentiation that actually ranks and converts.
Do I have to replace my PIM?
No. Anglera is not a PIM and not a CRM — it sits alongside whatever you run (Akeneo, Salsify, inriver, SAP, NetSuite, and the major commerce platforms). Enriched, normalized SKUs flow back into your system of record. Your PIM stores the data; Anglera does the work of filling it.
Can it handle the compliance data — EPA, SDS, NSF, certifications?
Yes, that's a core part of the work. Anglera pulls EPA registration numbers and kill claims, links SDS documents, surfaces GHS/HMIS hazard data, captures NSF food-contact categories, and verifies Green Seal, Safer Choice, and ECOLOGO certifications — then structures them as filterable attributes mapped to your schema. These are the fields that disqualify a SKU in healthcare, education, and foodservice purchasing when they're missing.
Will it build cross-reference and substitution data for things like can liners?
Yes. By normalizing the comparable fields — resin, gauge, dimensions, dilution, certification — across products, Anglera can build equivalency and cross-reference relationships ("this liner replaces brand X's 1.5-mil 56-gallon star-seal") and dispenser-compatibility groupings. That's what powers substitution selling and competitive defense, and it rarely arrives on a supplier feed.
How fast does this run on a real catalog?
Anglera processes whole categories and supplier catalogs in parallel, not one SKU at a time. A typical implementation runs about 30 days, and we'll enrich a real category of your own SKUs during the demo so you can compare the before and after on your data, not a sample.
What does 'buyer-signal enrichment' actually mean for Jan-San?
It means enriching every SKU against how your buyers search and decide — the EVS director who needs a sporicidal with a documented dwell time, the BSC who buys on cost-in-use, the school bound by a green-cleaning policy, the foodservice operator who needs NSF. Anglera scores and fills each product for those signals rather than just reformatting the supplier's copy, so the right SKU shows up, filters correctly, and answers the question that closes the sale.