From numbers on a screen to decisions you can act on
The process is practical and grounded in your existing data. No new tools required to get started.
Start with what you already have
Most independent publishers already have access to more data than they realize. Google Analytics, Google Search Console, WordPress or Ghost stats, Substack analytics if you cross-publish. The platform works with these existing sources. There's no proprietary tracking to install and no requirement to switch platforms.
The first step is understanding which data sources you have access to and what each one measures. They don't all measure the same things, and they sometimes give conflicting numbers for good reasons. That context matters before you can make sense of any individual metric.
The platform covers how to access and navigate each major data source, what its limitations are, and which questions it can and cannot answer. Not every question you have as an editor has a data answer. Knowing that upfront saves a lot of confusion.
Reading format performance
Different content formats generate different patterns in your analytics. A long-form guide that ranks well in search might show high traffic but low average session duration. A personal essay might have the opposite pattern. Neither is necessarily performing badly. They're just performing differently for different reasons.
The platform walks through how to read time-on-page, scroll depth (where available), return visit rates, and traffic source breakdowns in relation to content format. The goal is to understand what each format is actually doing for your publication, not to optimize for a single metric.
This section also covers how to think about format experimentation. If you've been writing primarily in one format and want to know whether a different approach might work for your audience, there are ways to test that and read the results without drawing conclusions from too little data.
Mapping your topic landscape
Topic analysis is where analytics most directly informs editorial decisions. Which subject areas are drawing consistent traffic? Where does your audience come back for more? Which topics are you covering primarily because you find them interesting, and where does that overlap with what your readers are actually looking for?
The platform explains how to group and tag your content by topic so you can compare performance across categories, not just individual posts. A single post performing well doesn't tell you much. A whole topic area performing consistently is a different kind of signal.
It also addresses how to think about topic areas where you're competing with much larger publications. Sometimes the data will show you that a topic you write about is getting search traffic, but almost none of it is coming to you. Understanding why that happens, and whether it's worth trying to address, is part of reading your data honestly.
Making editorial decisions with data
The point of all this isn't to turn writers into analysts. It's to give independent publishers enough data literacy to make editorial decisions with more confidence and clarity.
Doubling Down
When a topic, format, or approach is showing consistent signals of resonance with your audience, the platform helps you read those signals clearly and think through what doubling down actually looks like in editorial terms.
Reconsidering Approach
When the data suggests something isn't working, the platform helps you think through whether that's a topic issue, a format issue, a distribution issue, or simply a mismatch between what you're writing and what your current audience is looking for.
Updating Existing Content
Sometimes the editorial decision isn't what to write next but what to update. Identifying older content with declining traffic, or content that could perform better with revisions, is a practical use of analytics that many independent publishers underutilize.