Evidence-Based Content Gap Analyzer
Diagnose content coverage and prioritize meaningful gaps without duplicating existing topics.
Customise & copy
Add the context you have. The prompt updates as you work.
Include titles, URLs or identifiers, dates, formats, summaries or full text, and available performance data.
Describe segments, roles, needs, problems, objections, knowledge level, and buying context.
Include the offer, positioning, customer journey, strategic priorities, and commercially important buyer questions.
Add search data, sales questions, support tickets, customer research, comments, or audience requests. Leave blank if unavailable.
Live prompt
4 fieldsAct as a marketing content strategist. Analyze the published content library, target audience, business context, and optional evidence below to diagnose existing coverage and prioritize evidence-based content gaps. Perform only this job: do not draft content, create a full editorial calendar, or recommend generic SEO topics. Inputs: Published content library: {{published_content_library}} Target audience: {{target_audience}} Business context: {{business_context}} Optional demand or customer evidence: {{demand_evidence}} Use only the supplied information. Do not invent statistics, search volume, customer feedback, performance results, testimonials, business facts, or market conditions. Clearly label reasonable inferences. Treat a topic as a genuine gap only when the existing content does not adequately address the audience need, buyer question, search intent, or decision stage. Reject cosmetic variations, synonyms, repackaging, new formats, or minor angle changes when the underlying topic is already adequately covered. Return exactly these sections in this order: Coverage summary Give a concise overview of what the library covers, including recurring audience problems, search intents, buyer stages, formats, and commercially relevant themes. Reference specific supplied content by title, URL, identifier, or other available marker. Repeated or heavily covered themes List the themes that appear repeatedly or are strongly covered. For each, cite the supporting content and briefly explain the coverage pattern. Gap analysis Distinguish genuine gaps, weakly covered topics, and apparent gaps that overlap with existing content. For each meaningful finding, explain the unmet need, the relevant audience or buyer question, the decision stage or intent, the supporting evidence, and the overlap judgment. Prioritized content gaps Use a table with exactly these columns: Rank | Gap | Audience or buyer question | Decision stage or search intent | Evidence | Existing-content overlap check | Recommended format or angle | Priority rationale. Include only gaps supported by the supplied inputs. Rank them by audience importance, commercial relevance, evidence of demand, decision-stage value, and the extent to which existing content fails to answer the need. State when evidence is limited. Limitations and evidence notes Identify missing, ambiguous, outdated, inconsistent, or insufficient source information that affects confidence. Separate supplied evidence from inference. Be professional, clear, practical, direct, and evidence-aware. Make every major coverage finding and recommendation traceable to the supplied inputs.
Proof before you use it
A real example
Tested on gpt-5.6-luna on 2026-09-08
{
"demand_evidence": "Dealer call summaries show repeated questions about expected maintenance costs after warranty expiration, service response times in remote jobsites, operator training, and whether telematics data can support warranty claims. A recent customer survey mentions difficulty comparing quoted ownership costs across brands. There is no reliable keyword or conversion data.",
"target_audience": "Primary audience: experienced construction company owners and fleet managers overseeing roadwork and utility projects. They care about uptime, operator productivity, total cost of ownership, service availability, and fitting equipment into changing project requirements. They are skeptical of broad product claims and typically involve a finance lead and an equipment operator in the purchase decision.",
"business_context": "The company sells excavators, loaders, attachments, maintenance plans, and regional service contracts. Its strategic priority is increasing dealer-qualified sales for mid-sized contractors replacing aging fleets. The commercial team needs content that helps buyers evaluate lifecycle cost, service support, rental-versus-purchase choices, and equipment suitability before speaking with a dealer.",
"published_content_library": "Commercial construction equipment manufacturer library: 1. 'Mini Excavator Buying Guide' — URL /mini-excavator-buying-guide — Feb 2023 — buyer guide. 2. 'Excavator Maintenance Checklist' — URL /excavator-maintenance — Apr 2023 — checklist. 3. 'Compact vs Standard Excavators' — URL /compact-vs-standard — Aug 2023 — comparison article. 4. 'Jobsite Safety Basics' — URL /jobsite-safety — Jan 2024 — educational guide. 5. 'How to Finance Construction Equipment' — URL /equipment-financing — Jun 2024 — guide. 6. 'Excavator Attachments Explained' — URL /excavator-attachments — Oct 2024 — product education. Available data: the buying guide receives steady traffic; dealer referrals are the only tracked conversion source."
}A small ritual that works
How to use it
- 01Paste the published content library, including titles, identifiers, formats, summaries, dates, and available performance data.
- 02Describe the target audience, including segments, needs, problems, objections, knowledge level, and buying context.
- 03Add the business context, including the offer, positioning, customer journey, priorities, and important buyer questions.
- 04Optionally provide demand or customer evidence, then review the structured coverage analysis and prioritized gap table.
Why it works
This prompt gives the strategist a clearly bounded job: diagnose content coverage and prioritize evidence-based gaps without drafting content or producing a full editorial calendar. Its inputs cover the main evidence sources needed for that job: the published library, target audience, business context, and optional demand or customer evidence. The instructions also create useful safeguards by prohibiting invented statistics, customer feedback, market conditions, and business facts; requiring reasonable inferences to be labeled; and distinguishing genuine gaps from cosmetic variations or topics already covered. The required sections provide a practical decision structure: summarize coverage, identify repeated themes, separate genuine and weak gaps from apparent overlaps, prioritize supported opportunities in a fixed table, and document limitations. Requiring references to titles, URLs, identifiers, and other supplied markers makes major findings traceable to the source material. The prompt also adapts well across subjects, as shown by its ability to organize both cybersecurity content and construction-equipment content using the same analytical framework.
Where it fails
The prompt can present coverage-stage and adequacy judgments too confidently when the supplied library contains only titles, formats, and brief descriptions rather than full content or detailed outlines. For example, classifying content as serving particular decision stages or concluding that an article does not address a specific operational question may be reasonable, but those conclusions are inferred from sparse metadata. The prompt tells the model to label reasonable inferences, yet its required coverage summary and gap-analysis language does not provide a precise mechanism for doing so consistently. As a result, readers may mistake plausible classifications for directly supplied facts. Its prioritization can also remain qualitative when search demand, conversion data, content depth, freshness, or detailed performance information is absent.
Customisation tips
Add an explicit evidence-labeling convention, such as prefixes or tags for Supplied evidence, Inference, and Unknown, and require those labels in every coverage-stage classification, overlap judgment, and priority rationale based on incomplete metadata. Define the minimum source fields expected for each published item, including title, URL or identifier, publication date, format, summary, target intent, audience, and performance information. If stage analysis matters, add structured fields for known funnel stage and intended audience rather than asking the model to infer them from titles alone. If prioritization matters commercially, specify how to handle missing search, conversion, revenue, freshness, and content-depth data. You can also add a rule that the model must say when a conclusion cannot be established from the supplied descriptions and should recommend source review instead of treating the topic as a confirmed gap.
Watch-outs
- Brief titles and summaries may not establish the full scope of an article, so overlap judgments should be treated as qualified unless detailed content is supplied.
- Terms such as early-stage, mid-stage, late-stage, implementation, and vendor evaluation can be inferred from topic and context; they should not be presented as directly supplied facts unless the inputs state them.
- A topic can look absent because the source description is incomplete. The prompt should preserve the distinction between not supplied, not covered, and not adequately covered.
- Qualitative phrases such as repeated questions, strong interest, or high commercial relevance do not quantify demand, frequency, revenue impact, or conversion impact.
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