--- title: Editorial Analytics & Content Gap Analysis for Publishers description: "Content gap analysis from live reader behavior: what your audience searches for and never finds, archive pieces with live demand, and what to stop writing." url: https://www.artificialpoets.com/use-cases/editorial-intelligence/ site: Artificial Poets type: page date: 2026-08-09T19:46:31+00:00 modified: 2026-08-25T19:31:48+00:00 --- # See What Readers Want Before You Commission It "What should we publish next — and what does our audience want that we've never given them?" [See it on my numbers](/request-a-demo/) Horse & Rider · Ride TV · Equine Network · Equus Magazine · The Score · Equine Network Lockup ## Commissioning without the signal The problem - **A third of output is unread** — The Guardian cut weekly output by a third after its own analytics showed nobody was reading a large share of it. - **The archive is a dark library** — Thousands of pieces that would still be read sit unsurfaced, because nothing connects what a reader wants now to what you already wrote. - **Demand is visible, if you look** — Readers tell you what is missing every day, in the searches that return nothing and the topics they reach for and cannot find. ## How you see demand before you commission - **Gaps, surfaced** Every query that returned nothing is a topic your readers wanted. The engine collects them and shows you the gaps. - **Archive, back in play** Pieces that would still be read are served when a reader reaches for them. Your back catalogue earns again. - **What to write next** Demand signals from search, reach and completion, in one view. Commission on what readers already asked for. ## Readers tell you what to publish next. Most newsrooms cannot hear it. What you see Every day, readers search your site for things you have not written and leave when they do not find them. They reach for a topic from an article and find nothing connecting to it. Pieces in your archive that would still be read sit where no one can surface them. The Artificial Poets Platform collects all of it. Searches that returned nothing become a list of topics with proven demand. Reach patterns show which subjects readers move toward and which they abandon. The archive stops being a dark library, because a piece from three years ago is served whenever a reader today is reaching for it. The output is a view of demand your editors can commission against, built from what your readers already did rather than what a survey says they might. **Introducing** ## Artificial Poets Platform One engine behind every solution on this site. It learns your archive and your readers, then acts inside your CMS, your templates and your ad stack. - It learns your archive: **Every story you have published, current again** — The engine understands each piece by what it is about, not when it ran or where it was filed. A feature from 2019 competes for the next slot on merit with one from this morning. - It reads the visit: **What a reader wants, without asking** — Interest builds from what someone actually does in the session. No login, no third-party cookies, nothing leaving your domain. Useful on the second pageview, not the tenth visit. - It chooses: **The right next read, not the popular one** — Someone comparing products and someone following a running story want different things. A most-read list gives both the same five links and serves neither. - It serves: **There before the reader leaves** — The feed, the recommendations, the search answer and the signup ask all run on the same engine, in your templates and your ad stack. Any slot that arrives with them is yours to sell. - It proves: **A lift you can defend, or we say so** — Every deployment runs beside titles that did not get it, plus a serving pause. That is how a result becomes a number you can take to a board instead of a vendor claim. ## FAQ ### We already pay for Chartbeat, Parse.ly and GA4 — what does this add? Those tools report the performance of what you published. Content gap analysis needs the half your analytics cannot see: demand with no page to land on — searched-not-found queries, intents the matcher can't fill, archive pieces readers would take but never encounter. And it isn't another dashboard to stop opening by week three: the output is a commissioning-cycle brief, not a screen. ### Where does the gap signal actually come from? From your own readers, not from keyword tooling. Demand comes from on-site search that returned nothing useful and from what readers reach for article to article; coverage comes from your vectorized library. If the signal were external search volume, this would be SEO tooling with a new name, and it would point you at fights rather than gaps. ### What does it give me on a Tuesday morning? A gap map you can take into a commissioning meeting, not a dashboard to check. Two of its four zones cost nothing new to act on: archive gold is inventory you already paid for, and overserved is work you can stop doing. It is designed to reduce workload before it adds any. ### Will metrics start dictating what the newsroom writes? No. It is a map, not a mandate — commissioning stays with editors. For a smaller team the largest line item is usually what to stop: the Overserved quadrant is effort already being spent on demand that isn't there. Fewer, better commissions is a workload decision editors make, not one a model makes for them. ### We publish eight stories a week. Is there enough signal at our size? Honestly, it depends on your traffic, and it is worth testing rather than assuming. Smaller titles get sparser demand signals, and the zones that stay reliable longest are the ones drawn from your own library rather than from reader volume. The baseline month will show you which side of that line you are on. ### Is this trained on other publishers' audiences? No. The interest graph is built from your library and your readers' behavior on your properties — first-party data, anonymous profiles, no PII, not pooled across customers. A niche audience's demand map is precisely what a generic model cannot have. ### How do we introduce this without a fight in the newsroom? By being clear about what it does: it reports what readers looked for and did not find, and it never writes, commissions or publishes anything. Nothing is automated into the news agenda. Editors keep the same pin, exclude and constrain controls that govern everything else the engine touches. ## Related - [Search That Understands the Question](https://www.artificialpoets.com/use-cases/intent-search/) — "Readers search my site and leave empty-handed — can search understand what they're actually asking?" - [How Equine Network Grew Multi-Page Sessions 46%](https://www.artificialpoets.com/customers/equine-network-rollout/) — Equine Network enabled the Artificial Poets Platform on two titles and left the rest unchanged. Over nine months the enabled titles grew multi-page sessions 46%; across the comparison titles the same measure fell 16%. - [You Can’t Get the Traffic Back. You Can Get the Session Back.](https://www.artificialpoets.com/resources/traffic-back/) — The economics of session depth, and the causal evidence behind it: +53% pageviews per user, 95% CI [+48%, +58%], with the modelling assumptions written down. ## Unlock new revenue with our AI Artificial Poets Platform can help you with [Book a demo](/request-a-demo/) ## Questions this page answers ### What is content gap analysis for publishers? It is reading demand against coverage: what readers search for and do not find, what they reach for from every article, and which archive pieces would still be read if anything surfaced them. Those signals are logged as a by-product of serving and read back as a gap map. ### How does the gap map work? Demand comes from on-site search and the interest graph; coverage comes from the vectorized library. Each topic cluster lands in a zone. Two of the four cost nothing new to act on: archive gold is inventory you already paid for, and overserved is work you can stop doing. ### How much does editorial intelligence cost? There is no public price list; pricing follows network size and formats. The intelligence is the exhaust of a serving engine rather than a separate analytics product, so it arrives with the deployment instead of adding another subscription and another dashboard to check. ### Who is it for? Editors and commissioning leads deciding what to publish next with a smaller team than they had. It is built to reduce workload before it adds any: the first two zones it surfaces are things you can stop doing and things you already own. ### Does this mean an algorithm decides our coverage? No. It reports what readers looked for and did not find; what to do about that stays an editorial decision. Nothing is auto-published, nothing is auto-commissioned, and the same pin, exclude and constrain controls that govern serving apply to everything the engine touches. ### How do I get started? The signals accumulate from the moment indexing starts, so the four-week measurement-only baseline is already producing the first gap map before anything is served. Authorization, CMS access and infrastructure access are the requirements, and integration is carried out by our team.