Basic fields are fundamental to content analysis. For instance, URL and Title are virtually always in an analysis, and for traditional inventories and audits form the backbone for every row in the spreadsheet (each row has a unique URL).
Fields: Date Last Updated, File Format, File Group, Title, URL, Unique Content IDYour brand captures what is unique about your organization and how it projects that uniqueness (or your organization may have multiple brands). Fields to capture brand can be fairly mechanical (such as what page template is used, which may project the brand in some blunt ways such as your logo) or very much not mechanical (like voice and tone).
Fields: Tone, VoiceCategorization is essential for content analysis. Some of the uses include:
Most content analysis is toward the goal of making some sort of content transformation. In particular, there are fields you may add that are for that decision you make about what to do with the content.
Fields: Bucket, Disposition, Effort, Resourcing, [Target] FieldAlthough many websites are far too organizationally focused (rather than focused on the site visitor), sometimes fields to capture organizational dimensions are helpful in a content analysis, partly because this may capture who needs to act on changes. Revenue-based fields can also very concretely help prioritize efforts.
Fields: Author, [Category] Revenue, DivisionQuality indicators can either be the entire point of an analysis (when testing a hypothesis or understanding the pervasiveness of a problem) or they can be used to inform action (for instance, if a page has a lot of different quality issues then perhaps it should just be rewritten).
Fields: Audit Comments, Date Published, Has [Problem], Near Text Duplicate, [Problem] Count, [Problem] Example, RedundantTechnical fields can be essential to an analysis, and they have the advantage of usually being relatively easy to gather. That said, we shouldn't use that as an excuse to impress each other with huge inventories with lots of technical fields that don't provide much value to our actual goals. Some technical fields can prove very useful (like the count of pages in each PDF).
Fields: Crawl Depth, H1 Count, Images Without Alt (count), Landmark Count, MIME Type, Meta Description, Meta Keywords, PDF Page CountUltimately we want to make it easy for users to complete certain tasks. Sometimes we can directly capture this type of metric, but usually we need to confine ourselves to metrics that are more removed from the actual user experience.
Fields: Audience, Nielsen H1 Visibility, Nielsen H1 Visibility Rationale, Nielsen H1 Visibility Score (LLM), Nielsen H10 Help & Docs, Nielsen H10 Help & Docs Rationale, Nielsen H10 Help & Docs Score (LLM), Nielsen H2 Real-World Match, Nielsen H2 Real-World Match Rationale, Nielsen H2 Real-World Match Score (LLM), Nielsen H3 User Control, Nielsen H3 User Control Rationale, Nielsen H3 User Control Score (LLM), Nielsen H4 Consistency, Nielsen H4 Consistency Rationale, Nielsen H4 Consistency Score (LLM), Nielsen H4 Structural Sub, Nielsen H5 Error Prevention, Nielsen H5 Error Prevention Rationale, Nielsen H5 Error Prevention Score (LLM), Nielsen H6 Recognition, Nielsen H6 Recognition Rationale, Nielsen H6 Recognition Score (LLM), Nielsen H7 Flexibility, Nielsen H7 Flexibility Rationale, Nielsen H7 Flexibility Score (LLM), Nielsen H8 Aesthetic, Nielsen H8 Aesthetic Rationale, Nielsen H8 Aesthetic Score (LLM), Nielsen H9 Error Recovery, Nielsen H9 Error Recovery Rationale, Nielsen H9 Error Recovery Score (LLM), Nielsen Overall Score, Page Views, Reading Level, [Success Event] Count