The B2B SaaS marketer's Data Audit: How to Find Your Content Moat in 6 Questions

Three of the most useful data programs in B2B content—Ahrefs' content decay research, Carta's equity benchmarks, ActiveCampaign's automation maturity model—started with a company looking at what its own product already knew and asking: could this be something our market actually needs?

This audit is a structured way to get the same information (and impact) out of the data you and your content team have access to. The audit is built around six questions, each one acting as a filter to help you refine a realistic data-driven content playbook. 

By the end, you'll know whether you're sitting on data that can be turned into industry thought leadership, what form it could take, and whether you can build an ongoing content machine around it.

The bar your data needs to clear

For your data and insights to be worth the content team spending its time on, it needs to be ‘reference-worthy’. That means “data someone outside your company would put in a slide deck and share.” It produces an insight (a benchmark, a stat, or a trend line) that an exec cites in a board meeting or a journalist quotes in an article.

At Campfire Labs, the ‘reference-worth’ bar is non-negotiable when we build data-driven content for our clients. Production quality and research sophistication matter, but they're not enough on their own. The primary question is whether the data creates something useful for a reader who has no obligation to care about your company.

The 6 questions

1. Is this data something only you can share?

The first filter: could someone replicate this with a survey tool and a few hundred respondents? Could a research firm synthesize it from public sources?

If yes, you have a starting point, but not a moat. Proprietary data lives in your product's usage logs, your platform's transaction history, and behavioral patterns inside your tool. It exists because your company exists, and not because anyone else could go looking for it.

Caveat - survey data is still better than no data! The bar to entry for great survey-driven content is still extremely high, and many companies don’t have the budget or other resources to create it. But since we’re aiming high, also spend some time thinking about what your company’s true data moat is, the data and insight that only you could share.

Ask: would this data exist if we as a company didn't exist?

2. Does it challenge a common assumption?

Benchmark data earns citations, which have value. But data that overturns what people think they know is the kind of stuff that gets shared, argued about (especially on social), and built on.

The most durable data content in any category tends to correct something that’s received wisdom, a “best practice” that isn't actually so great, or a metric people track because everyone else does. When Ahrefs published findings on content decay (showing that organic traffic to the vast majority of pages falls off regardless of quality, often within months of ranking), it traveled because it contradicted the assumption that ranking once meant holding a position for months or years.

Ask: would our findings make someone say "wait, really?" or just nod along?

3. Can it produce a benchmark?

Benchmarks are one of the most consistently engaging forms of data content in B2B SaaS because they answer a specific, recurring question: how are we performing relative to everyone else?

A CMO justifying channel investment to a CFO needs a comparison point. A content lead walking into a board review needs to know if their traffic growth is normal or lagging. Benchmarks give people a number they can actually use, which is why they get linked, cited, and revisited long after a trend piece has been forgotten.

Mixpanel’s State of Digital Analytics benchmark reports are good examples, as are their spin-off benchmark pages for target verticals. You can also go one step further and create a free tool so that folks who land on the page can benchmark their company performance against your data.

Ask: SCan your data produce that number for someone in your market?

4. Does it capture behaviour, not just opinion?

There's a credibility hierarchy in data. Behavioral data—what people actually do inside your product—is harder to obtain and more authoritative than what people report about themselves in a survey.

For example, Carta's equity benchmarks hold the authority they do because they're drawn from actual cap tables managed on the platform; not what founders reported they raised, but what the documents show. That distinction is tangible in how people cite the data, treating it as a record, not a collection of opinions.

Ask: does our data tell us what people did, or what they said?

5. Would this data change how someone does their job?

A finding can be accurate and interesting and still produce no useful action in the reader's world. Data that changes a decision someone is actively making is more valuable, in content terms.

For example, Prompt Health’s Practice Growth Index shows how healthcare practices use automation and connected ops to become more efficient in booking and serving patients. The outcomes outlined in the content are closely tied to specific actions (using real-time data in scheduling and billing tools, submitting claims within 24 hours, etc), which makes it easier for target readers to see how the data applies to them and their day to day work.

Ask: If a reader found your key finding, would it change what they do next week?

6. Can you wire tooling and workflows around it so insights surface continuously, not just when someone remembers to run the export?

A single research piece can earn press coverage and backlinks. But content programs that compound are built on a data infrastructure that generates new findings on a rolling basis, with or without someone going looking for them.

This is about implementation. Are there automated queries, scheduled data pulls, or anomaly alerts that surface shifts in your data before anyone has to notice them manually? Ahrefs publishes data studies regularly enough that it reads less like a campaign and more like a function, which suggests the infrastructure is doing some of the work of surfacing what's worth writing about. The publishing cadence is an output of the infrastructure, not a separate editorial decision.

Ask: Have we built the conditions where insights come to us, or does someone have to remember to go looking?

Three examples of B2B data content engines

Ahrefs 

The data that powers the product—link graphs, traffic estimates, content performance across billions of pages—is the same data that powers its most-cited research. Studies on content decay, link velocity, and keyword cannibalization are built on behavioral data from the product at scale. Rather than commissioning an industry survey or market research, they looked at what their infrastructure could already tell them.

Carta 

Carta built a dedicated research function, Carta Data, around cap table data nobody else holds. Because founders and operators manage equity on Carta, the platform sees actual funding rounds, compensation structures, and ownership distributions across tens of thousands of startups. That produces reports on compensation benchmarks, founder dilution trends, and pre-money valuations by stage and sector, reports that VCs, founders, and legal teams use as reference material. The content is useful because the underlying data is, functionally, irreplaceable.

ActiveCampaign 

ActiveCampaign took a different approach: user-input data. Their customer experience automation maturity model asks users to self-assess their marketing automation sophistication across several dimensions. Aggregated and segmented, that self-reported data produces benchmarks by company size, industry, and use case, showing users what the gap looks like between their current state and where high performers operate. The tool generates the data, the data produces the content, and the content brings people back to the tool.

Three different data sources, three different publishing models, but each company identified data it owned exclusively, built infrastructure around it, and created content that gave their market a reference point it couldn't find anywhere else. For a closer look at how content teams are building these kinds of programs in practice, take a look at this roundup from practitioners running benchmark report programs.

When your dataset is smaller than Carta's

It’s true that companies like Ahrefs and Carta operate marketing at a scale many B2B SaaS companies can't. A smaller dataset constrains what you can claim with statistical confidence.

But smaller and more specific data sets aren't necessarily less useful. Four hundred enterprise logistics companies using your platform represent a more immediately relevant dataset to enterprise logistics buyers than a generic survey of ten thousand "supply chain professionals." Your market is smaller, so your data is also more directly relevant to the people in it. Specificity is what makes a finding actionable for a narrow audience.

There's a version of your content strategy where one of the datasets your product already generates becomes the reference point for a question your market asks repeatedly. That doesn't happen automatically. It happens when someone looks at the data and asks the right questions about it.

Want us to run this audit on your specific data and tell you what we'd build from it?

We look at what you're sitting on, tell you whether it's publishable, and sketch out what a content program built on it could look like. The questions takesabout 3 minutes on your end.

→ Take the audit

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