Content Scoring: Stop Publishing on Instinct

Content scoring is the process of evaluating individual pieces of content against defined criteria, such as business relevance, audience fit, search demand, and conversion potential, to prioritise what gets created, promoted, or cut. Done properly, it replaces editorial instinct with a repeatable framework that connects content decisions to commercial outcomes.

Most content teams do not have one. They operate on a mix of gut feel, whoever shouted loudest in the last meeting, and a vague sense that more content equals more results. It rarely does.

Why Most Content Decisions Are Made Badly

Early in my career, I ran a brainstorm for a Guinness campaign. The agency founder handed me the whiteboard pen and walked out to a client meeting. I was not prepared for it, and the room knew it. What I learned that day was not about Guinness. It was about the difference between having an opinion and having a framework. Opinions fill whiteboards. Frameworks make decisions.

Content planning in most organisations works the same way. Someone has an idea. Someone else agrees. A brief gets written. The piece gets made. Nobody asks whether it was the right piece to make, whether it serves a real audience need, or whether the business will be any different for having published it.

The result is a content library full of articles that rank for nothing, convert no one, and exist primarily because a quarterly plan needed filling. I have seen this pattern in agencies, in-house teams, and at brands spending significant sums on content production with no mechanism for deciding what actually matters.

Content scoring is the corrective. It is not complicated, but it does require discipline, and it requires you to be honest about what your content is actually for.

What Is Content Scoring and What Should It Measure?

A content score is a composite number assigned to a piece of content, or a content idea, based on how well it performs or is likely to perform against a set of weighted criteria. The criteria vary by organisation, but the most commercially useful ones tend to cluster around four dimensions: strategic fit, audience relevance, search and discovery potential, and conversion contribution.

Strategic fit asks whether the content supports a current business priority. A piece about enterprise procurement workflows might score high for a B2B software company targeting CFOs, and near zero for a consumer brand. The question is not whether the content is good in the abstract. It is whether it is good for this business, right now.

Audience relevance scores how closely the content matches the needs, language, and intent of a specific segment. This is where a lot of content fails. It is written for a generalised reader who does not exist, rather than for a specific person at a specific stage of a decision. If you have done proper audience research, this criterion is straightforward to apply. If you have not, your scores will be guesswork.

Search and discovery potential covers organic search volume, keyword competition, and the likelihood of the content being found without paid promotion. Tools like SEMrush’s market penetration analysis can help contextualise where content fits within a broader acquisition strategy, but the score should reflect realistic traffic potential, not aspirational rankings.

Conversion contribution is the hardest to score in advance, but it is the most important. Content that sits at the top of the funnel and drives no downstream behaviour is not necessarily worthless, but it needs to earn its place in the plan. Attribution is imperfect, and I have spent years watching teams over-credit last-click and under-credit the content that actually shaped the decision. But some honest approximation of conversion role, whether that is direct, assisted, or brand-building, should be part of every content score.

How to Build a Content Scoring Model

Building a scoring model is less technical than it sounds. The challenge is not the maths. It is the discipline of agreeing on what matters before you start scoring.

Start by listing the criteria that genuinely reflect your content goals. If your primary objective is lead generation, conversion contribution should carry more weight than brand reach. If you are in a category where awareness drives long-term purchase, the weighting shifts. The model should reflect your actual strategy, not a generic template borrowed from a blog post.

Assign a weight to each criterion. The total should add up to 100. A typical distribution for a B2B content programme might look like this: strategic fit at 25 points, audience relevance at 25 points, search potential at 20 points, conversion contribution at 20 points, and production feasibility at 10 points. The last one matters more than teams admit. A piece that scores brilliantly on every other dimension but requires six weeks and a film crew is not a priority if you are working to a monthly publishing cadence.

Score each criterion on a scale of one to five, multiply by the weight, and sum the results. A piece scoring 4 out of 5 on each criterion in the above model would achieve 80 out of 100. Anything below 50 should be questioned seriously before it enters production. Anything below 35 should probably not be made at all.

The number is not the point. The conversation the number forces is the point. When a content idea scores 38, someone has to explain why it should still go ahead. That explanation is either a good one, in which case the model needs recalibrating, or it is not, in which case you have just saved a week of production time.

If you are thinking about how content scoring fits into a broader go-to-market approach, the Go-To-Market and Growth Strategy hub covers the strategic frameworks that sit around decisions like this, including how content planning connects to audience development, channel strategy, and commercial targets.

Scoring Existing Content: The Audit Use Case

Content scoring is not only a planning tool. It is one of the most effective ways to audit an existing library and decide what to update, consolidate, or retire.

When I was growing an agency from around 20 people to over 100, one of the consistent problems we inherited from clients was content debt. Brands that had been publishing for five or more years often had hundreds of pieces that were never audited, never updated, and actively diluting the authority of the pages that mattered. The solution was not to produce more. It was to score what existed and make hard decisions about what deserved resources.

Applying a scoring model to an existing library surfaces three categories of content. First, high scorers that are underperforming technically, perhaps because of weak internal linking, outdated information, or poor on-page optimisation. These are worth investing in. Second, mid-range pieces that could be consolidated with similar content to create a single stronger asset. Third, low scorers with no realistic path to relevance. These should be removed or redirected.

The instinct is always to keep everything. Publishing feels like progress, and deleting feels like waste. But a 300-page content library where 60 percent of pages receive no organic traffic is not an asset. It is noise. Scoring gives you the evidence to make the cuts that gut feel never quite justifies.

Behavioural analytics tools can support this process significantly. Understanding how users actually move through content, where they drop off, and which pieces generate meaningful engagement versus passive scrolling adds a layer of real-world validation to your scores. Hotjar’s user behaviour data and similar tools give you the qualitative signal that search data alone cannot provide.

The Funnel Stage Problem Most Scoring Models Miss

I spent a long time earlier in my career over-indexing on lower-funnel performance. It looked clean on dashboards. Clicks, conversions, cost per acquisition. The numbers were trackable and the narrative was simple. What I eventually understood was that a significant portion of what performance channels were being credited for was going to happen anyway. The person was already going to buy. We were just the last touchpoint they passed through.

Content scoring models often make the same mistake. They weight conversion contribution so heavily that top-of-funnel content, the kind that introduces new audiences to a brand and shapes how they think about a category, consistently scores low and gets deprioritised. Over time, the content programme narrows to a cluster of high-intent, low-volume pieces that serve existing demand rather than creating it.

This is the clothes shop problem. Someone who tries something on is far more likely to buy than someone who never enters the store. Top-of-funnel content is the equivalent of the window display that gets people through the door. If your scoring model cannot account for that role, it will systematically undervalue the content that drives long-term growth.

The fix is to score funnel stage explicitly. Assign a separate dimension to where the content sits in the customer experience, and make sure the model rewards a balanced mix rather than defaulting to conversion proximity. A content programme that only scores well on bottom-funnel pieces is a programme that is harvesting existing demand, not building future pipeline. Vidyard’s analysis of why go-to-market feels harder touches on exactly this tension between short-term capture and long-term audience development.

Integrating Content Scoring Into the Planning Cycle

A scoring model that lives in a spreadsheet and gets consulted once a quarter is not a planning tool. It is a compliance exercise. For scoring to change how content decisions get made, it needs to be embedded into the regular planning rhythm.

The most practical approach is to score content ideas at the point of briefing, before any production resource is committed. This means the scoring criteria need to be accessible to everyone who generates content ideas, not just the strategist or the SEO lead. If the person writing the brief cannot apply the model, the model is too complicated.

Monthly or quarterly reviews should include a scoring pass on the content pipeline, not just the publishing calendar. The question is not only what is scheduled to go out, but whether the mix of scores across the pipeline reflects the strategic priorities for that period. If every piece in the next six weeks is a mid-funnel product explainer, that is a signal the model is surfacing, not a coincidence.

Annual content audits should apply the scoring model to the full library, with particular attention to pieces that were high scorers at briefing but underperformed in practice. The gap between predicted and actual performance is where the model improves. If a piece scored 78 at briefing and generated no meaningful traffic or engagement, the criteria or the weighting needs revisiting. The model should get sharper over time, not stay static.

Agile planning principles are worth considering here. Forrester’s work on agile scaling highlights the importance of iterative review cycles, and the same logic applies to content. A scoring model that never gets updated based on performance data is a model that stops reflecting reality.

Common Mistakes in Content Scoring

The first and most common mistake is scoring for the wrong outcomes. Teams that measure success primarily through pageviews will build scoring models that optimise for traffic, not business impact. The model reflects the metrics you reward, so if your reporting culture celebrates impressions over pipeline contribution, your scores will chase impressions.

The second mistake is using scoring to justify decisions already made. I have seen this in agency settings more times than I care to count. A senior stakeholder has a content idea they are committed to. The scoring model gets applied, and somehow the idea scores well enough to proceed. If the model is being gamed, it is not a model. It is a rubber stamp with extra steps. The criteria need to be agreed in advance, applied consistently, and treated as binding, not advisory.

The third mistake is treating all content types the same. A long-form thought leadership piece and a product comparison page serve different purposes, sit at different funnel stages, and should be scored against different benchmarks. A single universal scoring model applied across all content types will produce scores that are technically consistent but strategically meaningless.

The fourth mistake is over-engineering the model. I have seen scoring frameworks with 14 criteria, decimal weightings, and sub-scores for sub-criteria. Nobody uses them after the first month. A model with five clear criteria, honest weightings, and a five-point scale will be used consistently. A model that requires a PhD to apply will not.

Growth strategy frameworks, including how to structure content planning within a broader commercial model, are covered in more depth across the Go-To-Market and Growth Strategy hub. If content scoring is part of a wider effort to tighten your planning process, the surrounding articles are worth reading alongside this one.

What Good Content Scoring Actually Looks Like in Practice

I judged the Effie Awards for a period, which means I spent a significant amount of time reading case studies from brands that had genuinely connected content and creative investment to business outcomes. The ones that stood out were not the ones with the biggest budgets or the most sophisticated attribution models. They were the ones that had been ruthlessly clear about what they were trying to achieve and had made deliberate choices about what not to do.

Content scoring, at its best, is that same discipline applied to the planning process. It is not about finding a formula that tells you what to write. It is about building a shared language for prioritisation that keeps the team aligned on what the content programme is actually for.

In practice, that means a brief that includes a score alongside the rationale. It means a pipeline review where low-scoring pieces get challenged before they consume production time. It means an audit process that treats the content library as a portfolio to be actively managed, not an archive to be left alone.

The teams that do this well tend to publish less and perform better. Not because volume is inherently bad, but because the discipline of scoring forces them to think harder about each piece before it gets made. That thinking is what most content programmes are missing.

Growth hacking frameworks sometimes position content as a volume game, and there is a version of that argument that holds for early-stage audience building. But as Crazy Egg’s growth hacking overview notes, sustainable growth comes from compounding gains, not from flooding channels with undifferentiated output. Scoring is how you build the compound.

About the Author

Keith Lacy is a marketing strategist and former agency CEO with 20+ years of experience across agency leadership, performance marketing, and commercial strategy. He writes The Marketing Juice to cut through the noise and share what works.

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