Questions we hear often

A content scoring system assigns each piece of published content a composite performance number based on a set of weighted metrics. Instead of looking at a dozen different analytics columns and trying to mentally synthesize them, you see a single score that reflects how well each piece is doing against the goals that matter for your business.

The score is designed to be actionable. A low score on a piece that took significant effort to produce is a signal to either refresh it or stop investing in that topic area. A consistently high-scoring topic cluster is a signal to publish more there.

It depends on your business and what you're trying to achieve with content. Typical inputs include organic traffic, time on page, scroll depth, form completion rate, and downstream CRM events like demo requests or contact form submissions.

We don't use a fixed formula. The weights are calibrated to your specific pipeline — a company where content primarily drives inbound demo requests will weight conversion events more heavily than one where content is primarily supporting an outbound sales motion.

Standard analytics tools show you metrics per piece — traffic, bounce rate, session duration. Those numbers are useful but they don't tell you how to prioritize. A scoring system synthesizes multiple metrics into a single signal that's directly tied to your publishing decisions.

The other difference is interpretive clarity. Analytics dashboards answer "what happened?" A scoring system answers "so what do we do about it?"

The engagement is designed to be low-burden on your team. For the initial audit and scoring build, you'll need someone available for a kick-off call, a mid-point review call, and the final enablement sessions. That's typically four to six hours of team time over eight weeks, not counting the enablement sessions themselves.

We do the heavy lifting on data analysis and model building. We'll need access to your analytics and CRM, but we don't require someone to manage us day-to-day.

No. The scoring system is specifically designed for content and marketing generalists. If you have an analytics specialist, great — they'll appreciate the model's structure. But the system is built to be readable and actionable by a content strategist or marketing manager without a data background.

At minimum: read access to your Google Analytics 4 property, read access to your CRM (HubSpot, Salesforce, or similar), and a content inventory — even a rough spreadsheet list of published URLs works as a starting point.

If you have Search Console data, heatmap data, or email performance data, those can add useful signal to the model. But they're not required to get started.

You own everything. The scoring model, the documentation, the editorial plan — all of it lives in your tools and is yours to use and modify. We include 30 days of email support after the handoff for questions that come up as your team starts using the system independently.

If you want ongoing support after that, our Ongoing Advisory option covers quarterly recalibration and strategy sessions.

Probably not yet. The scoring system needs a meaningful amount of historical data to generate reliable signals — generally six months of analytics data and at least 50 published pieces. Before that threshold, the patterns aren't stable enough to build a useful model on.

If you're in early publishing mode, the most useful thing right now is probably to make sure your analytics tracking is set up correctly so you'll have good data to work with when you are ready.

It matters in that it affects what signals are available to include in the scoring model. A basic GA4 setup without conversion events configured will produce a less nuanced score than one with full funnel tracking. But basic setups can still produce useful scoring models — we work with what you have and are transparent about the limitations.

In some cases, we'll suggest a few specific tracking additions before we start building. Nothing complex — usually just making sure key conversion events are properly tagged.

Yes, with some nuance. The scoring model can be adapted to handle different content formats, with format-appropriate metrics for each. A video piece won't be scored on the same metrics as a written article — the model accounts for that.

Multi-format content libraries do add complexity to the build, which affects scope and timeline. We'd factor that in during the initial scoping conversation.