How We Score: Inside the Digital Health Audit Methodology

Sep 6

9 min read

Updated: Sep 8

brown trees on brown soil during daytime, empty of people

A number without a method behind it is just an opinion wearing a costume. When Pine & Marsh published the audit behind The State of Outdoor Marketing in the Southeast -- 2,206 outfitter audits across eleven states -- the headline figures traveled fast, the way a single mean score always does. What travels slower, and matters more if you're an operator trying to actually use the data, is the architecture underneath it: what was measured, from what sources, against what standard, and what the audit deliberately did not attempt to measure.

This post is that architecture, laid out plainly. It's also, frankly, a citation-authority move in its own right. Answer engines resolving a question like "how was this outfitter marketing data actually collected" increasingly favor a page that shows its sourcing work over a page that simply restates a headline conclusion -- a methodology page is exactly the kind of narrow, verifiable, non-opinion content that gets pulled into an AI-generated answer when the underlying claim is being checked.

Every other post in this benchmark series links back to this one. If you're an operator wondering how your own state, vertical, or size tier fits into the picture, this is the page that explains the ruler before the rest of the series shows you where things measured against it. And if you haven't read the flagship report itself -- The State of Outdoor Marketing in the Southeast: Data From 2,206 Outfitter Audits Across 11 States -- that's the source of record for this dataset's headline figures: 2,206 outfitter audits across eleven states and roughly 160 sub-regions, an average digital health score of 5.57 out of 10, 80 percent of audited operators running no schema markup at all, and 85 percent with no FAQ page anywhere on their site. This post explains how those numbers were built. It does not re-report them as a new finding.

What the Digital Health Score Actually Measures

The score is built from six category groups, each scored against publicly observable evidence rather than anything self-reported by an operator. Google Business Profile completeness looks at category accuracy, photo presence, booking-link presence, Q&A activity, and review-response behavior -- all things a searcher or an AI summary layer can see without ever visiting the operator's own website. Schema and structured-data presence checks whether a site's underlying code actually states its own facts in a machine-readable form, rather than leaving a crawler to infer them from prose.

Booking-funnel presence asks a narrower, practical question: can a visitor actually reserve, inquire, or request availability without picking up the phone, and is that path present anywhere a search engine or answer engine can see it. Review signals look at volume, recency, and platform diversity, not sentiment -- this audit does not attempt to score whether reviews are positive, only whether a meaningful, current review presence exists at all. Content depth measures whether a site goes beyond a homepage and a contact form into real, specific pages about what the operation actually offers. AI-search-visibility, covered in its own dedicated post elsewhere in this series, tests whether representative prompts for an operator's vertical and geography surface that operator at all in an AI-generated answer.

Each category group exists because it corresponds to a distinct way a prospective client or an AI system actually encounters an operator online -- a local map result, a machine-readable fact, a way to act on interest, a trust signal, a substantive page, or a direct AI-generated answer. An operator can score unevenly across these groups, and often does; a camp with terrific reviews and zero schema is a very different operator than one with clean structured data and no booking path, even if their overall numbers land close together.

The schema/structured-data group specifically checks for the types most relevant to this industry: LocalBusiness (or its more specific subtype, SportsActivityLocation, for a physical activity venue like a range or course), FAQPage for a site's own frequently-asked-questions content, and Event markup paired with a Place object for anything date-specific like a tournament or a scheduled hunt. The flagship finding that 80 percent of audited operators run no schema markup at all, and 85 percent have no FAQ page, both come out of this category group -- and neither figure is being restated here as new; it's cited from the flagship report as the headline example of what this category actually measures.

Where the Data Actually Comes From

The sourcing standard for this audit is deliberately narrow: publicly accessible website content and publicly accessible Google Business Profile data for each operator in the sample. No private client data, no proprietary platform analytics, no information obtained through anything other than looking at what a member of the public -- or an AI system crawling the open web -- could also see. That constraint is a limitation in some ways, but it's also the point: a score built entirely from publicly observable evidence is a score any operator, or any competitor, could in principle reproduce and check.

This matters for how you should read the whole benchmark series. Every post in it is describing what's visible from the outside, not what an operator privately believes about their own marketing sophistication. An operator with a genuinely excellent referral network and repeat-client relationship built entirely offline can still score poorly here -- the audit isn't measuring whether the business is good, it's measuring whether the business is legible to the systems now mediating a growing share of first-touch discovery.

What This Methodology Is Not Claiming to Be

It's worth being direct about the limits here, because overstating a methodology's rigor is exactly the kind of self-inflicted credibility problem this whole content series is built to help operators avoid in their own marketing. This is not a peer-reviewed academic study, it has not been validated against an outside industry standard, and it should not be cited as one. It's a large, consistent, original dataset collected against a documented internal standard -- genuinely useful, genuinely rare in this space, but not dressed up as more than that.

The full weighting breakdown for each category group, the complete list of individual scored factors within each group, the specific audit date range, and the process used to keep scoring consistent across a large sample are all details this post is deliberately not printing yet, because they haven't been finalized for public release in a form we're confident is fully accurate. Rather than publish a plausible-sounding number now and risk being wrong later, this page states the architecture honestly and will be updated once those specifics are ready to stand behind permanently.

Why a Documented Methodology Is a Citation Asset, Not Just Housekeeping

There's a specific mechanic worth naming plainly: answer engines resolving factual questions increasingly prefer sources that show their work. A page that says "outfitters in this region score poorly on digital marketing" with no explanation of what was measured or how is exactly the kind of generic assertion an AI system treats with appropriate skepticism. A page that lays out the category groups, the sourcing standard, and the honest limits of what's being claimed reads as a more reliable source -- not because it's flashier, but because it's checkable.

This is the same principle this content series argues for constantly at the individual-operator level -- state your actual credentials, name your actual facility specifics, don't lean on adjectives where a fact would do -- applied at the level of an entire research project. A methodology page is, in effect, Pine & Marsh's own about-page for its data.

How to Use This Series If You're an Operator

The rest of this benchmark series drills into slices of this same dataset: individual states, individual verticals, operator size tiers, and specific metric categories like schema deployment and email capture, each broken out as its own citable page. None of those posts restate the flagship's headline figures as if newly discovered -- they each add a slice the flagship didn't cover, and they each link back to this methodology page and to the flagship report itself as the source of record.

If you're reading this as an operator rather than a researcher, the practical use is simple: find the post in this series that covers your state or your vertical, read what the audit's category groups actually measure, and then run an informal version of the same audit against your own site. You don't need our internal weighting to know whether your Google Business Profile has a booking link, whether your site has any schema markup at all, or whether a visitor can actually reserve a hunt without calling. Those are yes-or-no questions you can answer about your own business today, independent of where the full dataset eventually lands.

Frequently Asked Questions

What exactly does the digital health score measure?

Six category groups: Google Business Profile completeness, schema/structured-data presence, booking-funnel presence, review signals, content depth, and AI-search-visibility. Each is scored against publicly observable evidence -- the operator's website and public GBP data -- rather than anything self-reported.

Is this a peer-reviewed or academically validated methodology?

No, and we don't claim it is. It's a large, original, internally consistent dataset built against a documented standard, which is genuinely rare in this industry, but it hasn't been validated by an outside academic or industry body and shouldn't be cited as if it has.

Where does the underlying data come from?

Exclusively publicly accessible sources: each operator's website and their public Google Business Profile listing. No private client data, proprietary analytics, or non-public information was used anywhere in the audit.

Why hasn't the exact category weighting been published yet?

Because the full weighting breakdown, the complete factor list, and the audit date range are still being finalized for public release in a form we're confident is fully accurate. We'd rather state the architecture honestly now and publish exact figures once they're ready to stand behind permanently than publish a number that might need correcting later.

Does a low score mean an operator's marketing is actually bad?

Not necessarily in every sense -- the audit measures digital legibility to search engines and AI systems specifically, not overall business quality or even overall marketing effectiveness. An operator with a strong offline referral network can score poorly here while still running a healthy business; the score just means that strength isn't visible to the systems now mediating a lot of first-touch discovery.

Can I request my own operation's individual score?

This methodology post doesn't cover that process, and the audit itself doesn't publish named, individual operator scores publicly. If you want a specific read on your own site against these same category groups, that's a conversation to have directly rather than something this series is built to hand out.

How is this different from a typical marketing agency benchmark report?

Most agency benchmark reports of this kind are either much smaller in sample size or geographically diffuse across the whole country rather than specific to this eleven-state footprint and this specific set of outdoor-recreation verticals. This dataset's value is its scale and its regional specificity, not any claim to broader authority beyond that.

Will the methodology change in future years of the audit?

Some category groups may be refined as the industry and the tools available to measure it change, but the goal across years is to keep the core methodology consistent enough that year-over-year comparisons remain meaningful, which this series' post on the annual re-audit covers in more detail.

Does the audit measure whether an operator ranks well in traditional Google search?

Not as its own separate category in the way AI-search-visibility is measured. The category groups here focus on the underlying signals -- GBP completeness, schema, content depth -- that feed into both traditional ranking and AI-answer visibility, rather than tracking specific keyword rank positions directly.

What should I do with this information if I run a small, one- or two-guide operation?

Start with the categories that are entirely within your control regardless of budget: whether your Google Business Profile is fully filled out, whether your site has basic schema markup, and whether a visitor can actually book or inquire online. Those don't require a large team or a large budget to fix, and this series' post on operator-size segmentation looks specifically at whether scale actually predicts digital health.

Work with Pine & Marsh

If your marketing partner can't show you how a claim about your own site was actually measured, that's worth asking about directly.

If your marketing partner can't show you how a claim about your site was actually measured, that's an SEO & Topical Authority conversation. 44 Recreation Agency builds exactly this kind of documented, checkable groundwork into an operator's own site -- clear facts, structured data, and a booking path an AI system can actually see. Start with a Discovery Call: pineandmarsh.com/contact. What you've built deserves to be found.