The DCG Framework in Action: A Worked Example

This is a worked example, not a case study. There is no real clinic behind it, no real numbers, and no client whose results we're describing. What follows is a made-up dermatology clinic used to show how Angryturtle's Diagnosis, Cost, Growth (DCG) framework actually runs step by step — what a diagnosis surfaces, what gets fixed first, and what a practitioner should watch for afterward. If you want the mechanics of the framework without a fabricated before/after, this is that page.

DCG is genuinely how we structure a GBP engagement, whether it's self-serve inside Angryturtle or run by our managed team. Diagnosis finds the actual constraint, not the symptom the owner assumed. Cost work fixes the constraint. Growth is what compounds once the foundation is no longer broken. The example below walks through all three using a fictional clinic, because the sequence is the useful part, not any specific percentage.

Table of contents

• The starting point
• What the diagnosis actually looks at
• The root cause, not the symptom
• Cost: fixing the constraint first
• Growth: what compounds once the foundation is fixed
• What to track instead of a percentage
• The lesson
• FAQ

The starting point

Picture a dermatology clinic in South Delhi. Two doctors, a decent walk-in reputation, nothing wrong with the medicine. The owner's theory of the problem is simple: "we need more Google reviews — our competitors have hundreds and we're still under a hundred."

That's a reasonable guess. It's also, in this scenario, wrong — or at least not the constraint that matters most. Figuring out why is what Diagnosis is for.

What the diagnosis actually looks at

Angryturtle's Rank OS scores a profile across five weighted dimensions — relevance, review health, freshness, entity authority, and AIO readiness (see the Rank OS glossary entry for the full weighting). In this worked example, the score comes back low, and the dimension breakdown is what tells the real story:

• Relevance is weak because the GBP categories selected don't match what patients actually search for. The clinic is listed under a generic category, not the specific one for its specialty.
• Review health is weak, but not because the reviews are bad — there just aren't many, and new ones are trickling in slower than nearby competitors'.
• Entity authority is weak. NAP consistency is broken across directories: an outdated phone number in one place, the wrong business name format in another, a listing that was never claimed at all.
• Freshness is weak. Photos are years old, the services list is thin, nothing has been posted in months.
• AIO readiness is weak too — when you ask an AI assistant a question a prospective patient might ask, this clinic doesn't show up as a citable answer.

None of that is a percentage. It's a map of where the profile is actually broken, ranked by how much each dimension is dragging the score down.

The root cause, not the symptom

Here's the part that matters. The reviews aren't the constraint. The category is.

If a profile is filed under the wrong primary category, it becomes invisible for the exact searches that would send it the highest-value traffic — "dermatologist near me," not "doctor near me." Every review that clinic has ever collected was generated on a profile that Google wasn't showing for those searches in the first place. Fixing review volume without fixing the category is like turning up the volume on a radio that isn't tuned to a station.

That reframe is the entire value of Diagnosis. The owner asked for more reviews. What the profile actually needed was a category fix, then everything else in the right order behind it.

Cost: fixing the constraint first

Cost is the phase where the constraint actually gets fixed, using direct GBP editing that pushes changes live to Google rather than sitting in a dashboard waiting for someone to act on it manually.

The category gets corrected to the specific specialty, with relevant secondary categories added underneath it. The services section gets rebuilt with real, specific entries instead of generic placeholders — using the language patients actually type into search, not clinical shorthand. NAP records across directories get corrected so the phone number, address, and business name format match everywhere, closing the entity-authority gap. Missing attributes get filled in. Photos get replaced. A review-request flow gets switched on so new reviews start arriving on a schedule instead of sporadically.

None of this is glamorous. It's mostly closing gaps that had been sitting open for months, sometimes years. The category correction is the one piece of this that changes what the profile is even eligible to show up for — everything else improves how well it performs once it's showing up.

Growth: what compounds once the foundation is fixed

Growth is the phase where effort starts compounding instead of evaporating. Direction requests are usually one of the first metrics to move once relevance and freshness improve, because they track people who've already decided to visit and just need the profile to be findable. Calls tend to follow once the services section actually matches what people are searching for, because the profile is now answering the question instead of quietly ignoring it.

Review velocity should also start climbing here, not because of any gating tactic — Angryturtle doesn't do that, and neither should anyone else — but because a request flow sent at the right moment after a good visit converts far more reliably than hoping people remember to leave one unprompted.

Share of local voice is the metric that tells you whether all of this is actually landing in the results that matter, measured across a geo-grid rather than a single search from a single point. A profile that was invisible for its category can only start earning share of voice once the category itself is fixed — which is exactly why Diagnosis had to come before Growth, not the other way round.

What to track instead of a percentage

A specific uplift number for this clinic would be fiction, so here's what a real practitioner tracks instead, and why each one matters:

Rank OS score, dimension by dimension. Not the single number alone — which dimension moved, and whether the move matches what was actually fixed. If entity authority moves but relevance doesn't, the NAP cleanup worked but the category or services section still needs attention.

Geo-grid position for the corrected category, using rank tracking across a service area rather than a single search result. A category fix that isn't showing up across the grid within a reasonable window is worth investigating before assuming it worked.

Direction requests and calls in GBP Insights, read as a trend over weeks rather than a single data point, since both figures are naturally noisy month to month.

Review velocity and review health, tracked through review sentiment and response rate, not just star average — a clinic can have a strong rating and a still-weak review-health score if volume and response rate are both thin.

AIO readiness, checked through Ask Maps question coverage — whether the profile shows up as a citable answer when someone asks an AI assistant the kind of question a prospective patient would actually ask.

Angryturtle's Insights & Performance reporting is built to surface exactly these trend lines rather than a single vanity percentage, which is a large part of why we don't publish invented uplift figures on pages like this one — the tool itself is designed to show a client their own real numbers, not a stand-in for them.

The lesson

The owner's instinct wasn't crazy. More reviews genuinely help. But more reviews on a profile that's invisible for its highest-value searches don't fix the actual problem, and no amount of review-generation effort compounds until the category is right.

That's the order DCG insists on: find the real constraint, fix it before anything else, then let Growth work do its job on a foundation that isn't broken underneath it. Whether that's run through Angryturtle's self-serve tools or by our managed local SEO team, the sequence doesn't change — only who's doing the clicking does.

See how DCG applies to your business →

FAQ

Is this a real client result? No. This is a worked, illustrative example built to show how the DCG framework runs in practice. There is no real clinic, no real client, and no real figures behind it.

Why not just show a real case study instead? Because a fabricated number is worse than no number, and we'd rather teach the framework honestly than dress up an invented result as evidence. If we publish a real client outcome in the future, it will be labeled as one, with a name or a clearly disclosed anonymization method, not presented as this page is.

What's the single most common misdiagnosis in a GBP audit? In our experience, it's assuming low review count is the root cause when the real constraint is a wrong or missing GBP category, which limits which searches the profile is even eligible to appear for.

Does Rank OS work the same way for every industry? The five dimensions and their weights stay consistent, but what counts as "relevant" within the Relevance dimension is industry-aware — a dermatology clinic and a real estate brokerage are judged against different category and service norms.

Can Angryturtle actually push these fixes live, or does it just flag them? Both. The platform's direct editor writes changes straight to Google rather than only listing recommendations, and the same fixes can be handled by our managed team if you'd rather not do the clicking yourself.