Ranking for your own name when someone else has it

A real case in entity architecture. Two people share the name Doug Lord. One is a 45-year coastal engineer with decades of government citations. The other builds digital authority systems. This is what happened when I fixed my structured data — including the regression three days later, and what that means for how long this actually takes.

The baseline — day one morning

In May 2026, I searched my own name cold — incognito, no personalisation, no login. The results were instructive.

Positions 1 and 2 belonged to a coastal engineer. Also named Doug Lord. Based in Newcastle. Forty-five years of experience in coast and estuary management. Indexed across government PDFs, Engineers Australia publications, LinkedIn, and his own domain. A well-established entity with deep citation signals across authoritative sources.

My domain — douglord.com — did not appear at all. Not on page one. Not anywhere visible.

When I searched logged in, the picture was different: my sites surfaced, my LinkedIn headline was visible, my ventures appeared. Personalisation was doing the disambiguation work that my structured data wasn't doing.

Root cause

The coastal engineer has stronger entity signals — not because he's more important in any absolute sense, but because his digital footprint is more structured and consistent. Government documents, academic citations, a long-standing domain, stable LinkedIn with specific location. All machine-readable trust signals pointing at the same entity.

My domain had no Person schema. No machine-readable assertion of who I am, what I do, or how my properties relate to each other. Four domains — douglord.com, digitaldominator.com.au, authority44.ai, og01.ai — that Google had no instruction to treat as a connected graph.

During the audit, I also found a character-level typo: one domain was referenced as ogo1.ai instead of og01.ai. Google tries to resolve sameAs URLs. A non-existent domain breaks the graph silently — no error, no warning, just a dead link in the entity chain. This kind of gap doesn't appear in any standard SEO audit.

The fix

Three things, deployed in a single session:

1. Person schema — added to douglord.com as a JSON-LD block with entity anchor, geographic signal (Northern Rivers, NSW), sameAs graph connecting all four domains, founder relationships, occupations, and family entity adjacency to documented Australian artists Michael Johnson, Franklin Johnson (Yellow House, Sydney 1969–72), Matthew Johnson (Olsen Gallery), and Anna Johnson (Vogue, Vanity Fair, Artist Profile).

2. llms.txt — rewritten with full identity context, explicit disambiguation against the coastal engineer by name and location, citation guidance for AI systems, and the complete entity picture across digital, artistic, and musical practices.

3. This page — a case study hosted on douglord.com itself. Indexed content on the domain. More pages means more surface area for Google to confirm the entity. The Article schema on this page links back to the Person entity, strengthening the graph further.

// Entity anchor
"@type": "Person",
"@id": "https://douglord.com/#person",

// Geographic disambiguation (away from Newcastle)
"address": { "addressLocality": "Northern Rivers", "addressRegion": "NSW" },

// Connected graph — four domains, one entity
"sameAs": [
  "https://digitaldominator.com.au",
  "https://authority44.ai",
  "https://og01.ai",  // was ogo1.ai — broken sameAs, now fixed
  "https://ogcollection.gallery/artists/douglas-lord"
],

// Family entity adjacency — known graph connections
"relatedTo": [
  { "name": "Michael Johnson", "description": "Australian abstract painter, New York. Uncle." },
  { "name": "Franklin Johnson", "description": "Yellow House Sydney 1969–72. Uncle." },
  { "name": "Matthew Johnson", "description": "Painter, Olsen Gallery. Cousin." },
  { "name": "Anna Johnson", "description": "Writer, Vogue, Vanity Fair. Cousin." }
]

The initial result — same evening

Within hours of deploying, cold search had shifted meaningfully.

The coastal engineer dropped from positions 1–2 to 8–9 in cold search. douglord.com appeared at position 3 from being completely absent. The initial movement was real and fast — entity signals can shift SERP positions within hours of a deploy when the content is clear enough.

The regression — three days later

Three days after deploying, "doug lord" in Chrome (logged in, personalised) showed the coastal engineer back at position 1. douglord.com had dropped to position 5. The initial movement had partially reversed.

This is expected, not a failure. Here is why it happens:

Google's index has multiple layers. The initial movement came from the freshness layer — Google's fast-refresh crawl picked up the new schema quickly and the entity signal shifted. But the deeper ranking signals — inbound links, citation volume, domain authority, SE data sources — hadn't changed yet. After a few days, the deeper signals reasserted themselves and partially pulled the ranking back toward its previous state.

"Doug lord" and "Douglas lord" are two separate battles. The coastal engineer's citations almost universally use the short form "Doug Lord" — government PDFs, Engineers Australia documents, Salients Consulting. His entity owns the short form in the knowledge graph. "Douglas lord" (the full name) is a different query where the entity graph is less established — that's where the initial gains were stickiest.

What the regression tells you about timeline

Entity-based changes follow a predictable propagation curve:

The fix is a foundation, not a finish line. The schema tells Google who the entity is. The subsequent work — Wikidata entry, Wikipedia article, backlinks from controlled properties, FAQ citations in AI systems — builds the authority that makes the ranking permanent.

The OG Collection signal

Position 1 in both searches — logged in and cold — is held by ogcollection.gallery/artists/douglas-lord. This is a plain HTML page on a Cloudflare Pages static site. No Search Console. No backlinks. No schema markup. No sitemap submission.

It ranks because the content entity signal is clean and specific: "Douglas Lord works at the boundary of sculpture, installation, and found material. Drawing from the Northern Rivers landscape..." That's a precise, unambiguous description of a distinct person in a distinct location doing a distinct thing.

It's also the same Douglas Lord. The sculptor, the digital strategist, and the musician are the same person — and once the schema and llms.txt asserted all three identities together with the family entity connections, Google had enough signal to understand the full picture.

The detail worth sitting with: this page has never been submitted to Google Search Console or Bing Webmaster Tools. No sitemap was ever filed for it. It was found, crawled, ranked, and cited entirely through normal discovery — because submission is not what drives ranking. Search consoles are diagnostic dashboards; they report on indexing, they do not cause it. What made the page rank was that it was reachable by crawlers and unambiguous about who it described. Two things. Nothing else.

The crawler-access test

The OG Collection result raised a question worth testing directly. If a bare HTML page with no schema could rank and get cited, while douglord.com — with far richer structured data — lagged behind on AI citation, something other than content quality was at play. So I ran a controlled comparison across the property network: same entity, same author, different infrastructure settings.

The variable turned out to be at the edge, not in the markup. One platform-level setting — a managed robots.txt layer — was injecting crawler directives above each site's own rules. Because the first matching user-agent block wins, those injected directives were the ones that counted. The AI discovery crawlers — the exact agents that read a page before an AI system can cite it — were being turned away at the door.

The crawl logs made the effect unambiguous:

That is the whole story in two rows. The page that was reachable got crawled and cited. The page that was blocked — despite a complete Person schema, a connected sameAs graph, and a full llms.txt — was invisible to the same crawler. The richest entity markup in the world does nothing if the crawler that reads it is refused at the edge.

Once identified, the fix was a single toggle per property — disabling the managed layer so each site's own crawler-permissive rules served as written. The test confirmed the hypothesis: the entity architecture was never the constraint. Crawler access was. It is a clean demonstration of why authority has to be measured at every layer — content, structure, and exposure — not just the one that shows up in a standard SEO audit.

AUTHORITY44 metrics implicated

douglord.com scored 31/100 on AUTHORITY44 at baseline — rated Weak. The gaps mapped directly to three of the six canonical scoring dimensions:

The fixes were structural, not cosmetic. No new backlinks acquired. No paid promotion. No content marketing campaign. Just entity architecture — telling machines clearly and correctly who the entity is, what it does, and how its properties connect.

What comes next

Results will be updated here as they come in.