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Pillar content that out-writes the manufacturers.Informational sessions, doubled.

Editorial cut of the Northern Hydraulics engagement (2022–2025). Over six months, senior subject-matter pillar-and-cluster content on the core fitting categories roughly doubled organic sessions from informational and pillar pages, and pushed Northern's pillars to the top of AI-Overview answers for six category terms the manufacturers used to own. This was one discipline inside the full engagement; the qualified-lead growth is reported on the anchor study.

Client
Northern Hydraulics
Scale
~8,500 SKUs
Engagement
2023 – 2024 · 6 months
  • ×2

    Organic sessions from informational and pillar pages

  • 6

    Category terms where the pillars now top the AI-Overview answer

  • 0

    Pages LLM-ghostwritten; senior subject-matter authored

  • 6 mo

    Pillar-and-cluster build on the core fitting categories

The situation

Where they started.

Northern Hydraulics sells JIC and NPT fittings, adapters, and assemblies to engineers and MRO buyers. They had ~8,500 SKUs across 150+ categories and almost no editorial layer. A buyer searching how to size a JIC swivel or why an NPT seal weeps landed on a manufacturer’s page. Northern wasn’t in that conversation. The distributor was invisible at the question stage.

When we picked up content, the catalog rebuild was already in flight and the SKU pages were getting structured. What was missing was the layer above the SKUs: the reference content engineers read before they buy. We set a baseline. Informational and pillar pages drove a small, flat slice of organic sessions, and on six core category terms the top AI-Overview answers cited the manufacturers rather than the distributor selling the part.

The constraint

What made it hard.

Hydraulic fitting content is unforgiving. Get a thread callout or a pressure rating wrong and an engineer leaves and doesn’t come back. That ruled out LLM ghostwriting, which produces fluent copy that is confidently wrong on dash sizes and seal materials. Every pillar had to be written or closely directed by someone who knew the parts, then checked against spec. Slower to produce, and the only way the pages would hold up to the audience reading them.

What we ran

The work, phase by phase. What shipped, and when.

  1. Weeks 1–4

    Map the category questions

    Pulled the real questions engineers and MRO buyers ask across the core fitting categories, then mapped each to a pillar page with its supporting cluster. Chose six category terms where manufacturers held the AI-Overview answer and Northern could outwrite them.

  2. Weeks 3–16

    Write the pillars, senior-led

    Subject-matter writers produced the pillar pages on JIC and NPT selection, sizing, and failure modes. No LLM ghostwriting. Each page checked against spec tables before publish.

  3. Weeks 8–22

    Build the clusters

    Wrote the supporting cluster articles under each pillar and internally linked them into the matching catalog categories, so a question page led to the parts that answer it.

  4. Weeks 16–26

    Measure and re-cut

    Tracked sessions per pillar and AI-Overview answer placement on the six target terms, then revised the pages that under-indexed and expanded the clusters that pulled.

This is what a Editorial Authority engagement at this scale looks like in practice.

Why it worked

The mechanism. What actually moved the number.

Manufacturers write spec sheets. They don’t answer the buyer’s actual question, which is usually a sizing, compatibility, or failure question with a part at the end of it. A distributor that answers the question well, and links straight to the part, is a better result for that query than a PDF datasheet. That is why the pillars could displace manufacturer pages in AI-Overview answers on category terms. The page was simply more useful to the person asking.

Senior-led writing is what made it hold. Engineers can tell within a paragraph whether the author has handled the part. Pages that read as correct earned the dwell time and the links, and AI-Overview systems pulled from the pages that read as authoritative. The catalog rebuild gave these pages clean SKUs to point at, so the editorial layer compounded with the build instead of sitting beside it.

What happened

The results. As measured, dips included.

Over six months, organic sessions from informational and pillar pages roughly doubled (×2). The curve was not linear. The first pillars sat for several weeks before they indexed and began to pull, which is normal for new reference content, and the cluster pages lagged the pillars they hung under. The second half of the window is where most of the gain landed.

On the six category terms we targeted, Northern’s pillar pages now top the AI-Overview answers, displacing the manufacturer pages that used to own them. This study claims the content output only. The overall qualified-lead growth and the AI-search citation count across the top commercial queries are reported on the anchor and AI-search cuts of the same engagement, so no number is counted twice.

The number that mattered

×2

Organic sessions from informational & pillar pages

6-month pillar-and-cluster build

These read like our senior people wrote them, because effectively they did. We stopped sending buyers to the manufacturers to get their questions answered.
Head of E-commerce, Northern HydraulicsIndustrial hydraulics distributor · ~8,500 SKUs

Measurement notes

How these numbers were measured.

A metric without a source is an assertion. Every number on this page is listed below with how it was counted.

Organic sessions from informational/pillar pages roughly doubled (×2)
Google Analytics + Search Console, organic-only, filtered to the pillar and cluster URL set. Baseline is the 30-day average before the first pillar published; comparison is the same page set at the 6-month mark.
6 category terms where pillar pages now top the AI-Overview answer
Bi-weekly AI-Overview answer checks on the six target category terms. Baseline at kickoff recorded the manufacturer page as the cited source; re-checked at month 6 with Northern’s pillar as the top-cited answer.

Attribution. Published with the client’s sign-off. Metrics are as measured in the sources listed above.

Part of a bigger engagement

The rest of the Northern Hydraulics engagement.

  • Full engagement

    Industrial hydraulics distributor~8,500 SKUs

    Named · with consent

    +43.5%

    Qualified leads a month, across the full engagement

    2022 – 2025 · client CRM

    1,840 to 2,640 qualified leads a month. The full-stack rebuild behind it.

    Northern Hydraulics is an industrial hydraulics distributor — ~8,500 SKUs of JIC/NPT fittings, adapters, and assemblies sold to engineers and MRO buyers. Over a three-year relationship (2022–2025) we ran the whole growth function: brand and design, a Magento 1 to headless replatform, AI-search and technical SEO, an AI-assisted catalog rewrite, pillar content, paid ads, and cold outbound. Qualified leads went from 1,840 to 2,640 a month, up 43.5%.

    Full Growth OwnershipWebsite DevelopmentAI Search & GEOEditorial AuthorityOutbound Email2022 – 2025
  • Industrial hydraulics distributor~8,500 SKUs

    Named · with consent

    8,500

    SKUs replatformed off Magento 1

    6 months · schema-complete from day one

    8,500 SKUs off Magento 1, onto a storefront AI can read.

    Northern Hydraulics ran an 8,500-SKU catalog on Magento 1, which hit end-of-life with no security patches and product markup AI search engines couldn't parse. Over 6 months we replatformed the whole catalog to a headless Next.js storefront on Shopify Hydrogen, schema-complete from day one, and carried the JIC/NPT spec quote flow across intact. Mobile category-browse INP dropped from 600ms+ to under 200ms. This was the web-dev cut of the full engagement; the qualified-lead growth is reported on the anchor study.

    Website DevelopmentAI Search & GEO2022
  • Industrial hydraulics distributor~8,500 SKUs

    Named · with consent

    ×8.5

    AI-Overview citations, top 50 commercial queries

    4 → 34 · 6-month engagement

    4 to 34 AI-Overview citations on the top 50 commercial queries.

    Northern Hydraulics is an industrial hydraulics distributor with ~8,500 SKUs. On the top 50 commercial queries, AI Overviews were citing its manufacturers, not its own pages, so it sat in AI answers exactly 4 times. Six months of product-schema and answer-shaped page work took that to 34 AIO citations (×8.5), making the distributor the citable source instead of its suppliers.

    AI Search & GEOCatalog AI2023
  • Industrial hydraulics distributor~8,500 SKUs

    Named · with consent

    12%

    Reply rate, cold outbound to a built distributor list

    8-week run · engineered sender reputation

    A 12% reply rate to a cold distributor list. Deliverability, engineered first.

    Cold outbound email to a built list of hydraulics distributors and OEM buyers, run over 8 weeks as one part of the full Northern Hydraulics engagement. Deliverability came first: SPF, DKIM, and DMARC set up, sending domains warmed, then a tight 5-touch sequence to a researched list. The list returned a 12% reply rate.

    Outbound Email2024

More case studies

Other distributors. Same standard of proof.

  • Industrial hydraulics distributor~8,500 SKUs

    Anonymized · NDA

    +43.5%

    Qualified leads per month, on the August baseline

    Aug 2024 – Jan 2025 · client CRM

    1,840 to 2,640 qualified leads a month. No new ad spend.

    An ~8,500-SKU hydraulics distributor was stuck at 1,840 qualified inbounds a month. AI Overviews were citing its manufacturers, not its own category pages. Six months of catalog and AI-search work later, January closed at 2,640. That’s +43.5% over the August baseline, and we didn’t add a dollar of ad spend.

    Catalog AIAI Search & GEOAug 2024 – Jan 2025
  • Industrial automation distributor~12K SKUs

    Anonymized · NDA

    ×8.5

    AI Overview citation count, top 50 commercial queries

    4 → 34 citations · 6-month engagement

    4 to 34 AI Overview citations in six months. One Standard retainer.

    A mid-market automation distributor with ~12K SKUs was ranking well and still bleeding organic share, because AI Overviews were eating the click-through on informational queries. Six months on the Standard editorial retainer took its AI Overview citations on the top 50 commercial queries from 4 to 34. Organic leads from informational pages doubled.

    Editorial Authority2025
  • Industrial hydraulics distributor~8,500 SKUs

    Anonymized · NDA

    8,500

    SKUs replatformed in six months

    Magento 1 → Next.js + Shopify Hydrogen

    8,500 SKUs off Magento 1, onto a storefront AI engines can read.

    The same hydraulics distributor’s commerce stack was at end-of-life. Magento 1, schema AI Overviews couldn’t read, and 600ms-plus INP that was killing add-to-cart on mobile category browsing. A six-month replatform moved all 8,500 SKUs onto a headless Next.js + Shopify Hydrogen storefront, with a complete schema graph live on the first deploy.

    Website DevelopmentAI Search & GEO2024

Your catalog, same standard

Want a number like these for your catalog?

Held to the same standard as the studies above: we measure where you stand today — organic and AI-search visibility across your top categories — and show you the baseline before we propose a thing.