Programmatic SEO is not dead. But the version that worked in 2021, thin templates, sparse datasets, variable injection, is. Here is what still works, what does not, and how to build programs that rank in 2025.
Minh PhamFounder, SEOmaticTable of Contents
TL;DR
No. But the version most people built between 2019 and 2022 is.
If your definition of programmatic SEO is: find a keyword pattern, build a template, swap city names into boilerplate copy, publish 10,000 pages, and wait for traffic, that approach is dead. It has been dead since Google's September 2023 Helpful Content Update, and the obituary has been confirmed by every core update since.
If your definition of programmatic SEO is: identify validated keyword demand, build a dataset with genuinely unique information per page, design a template that renders real content differentiation at scale, and publish in controlled batches with proper indexing infrastructure, that approach is not only alive, it is one of the most durable traffic acquisition strategies available.
The question “is programmatic seo dead” is really two separate questions: is the lazy version dead, and is the serious version dead. The answers are yes and no, in that order.
This article explains what changed, what it means for programs being built today, and how to tell whether a programmatic approach will work for your specific situation.
Programmatic SEO's credibility problem did not start with the Helpful Content Update. It started with a pattern of abuse that made entire categories of programmatic content synonymous with spam.
Between 2018 and 2022, the template-and-variable approach scaled with almost no quality floor. A publisher could build a 50,000-page location program by taking one paragraph of copy, inserting a city name variable 12 times, and publishing the result across every US city. Google's systems did not consistently filter this content, so it ranked. Traffic accumulated. The approach spread.
By 2022, the SERP for hundreds of local and comparison keyword patterns was dominated by near-identical programmatic pages with no genuine informational value. The pages technically answered the query format, “accounting services in [city]” returned pages with that phrase, without providing anything a searcher could actually use.
Google's response came in phases.
March 2024 Core Update + Spam Update: Google explicitly targeted “scaled content abuse”, defined as pages generated primarily to rank in search rather than to serve users, regardless of whether AI or human labor was used. This update deindexed large swaths of low-quality programmatic programs that had survived previous quality evaluations. The defining criterion was not AI use or template use, it was whether the content provided genuine value beyond what was available elsewhere.
What this means practically: the update created a hard line between programmatic programs built around real data differentiation and programs built around variable injection. Programs with rich datasets, city-specific statistics, real feature comparisons, genuine integration documentation, largely survived. Programs built on sparse datasets with copy that only differed by variable name largely did not.
The pattern since 2024: programmatic SEO works when the pages genuinely serve the searcher. It fails when the pages exist to capture a query pattern without providing anything meaningful in response to it. This is not a new standard, it is Google's standard made enforceable.
Google's documentation on scaled content abuse is worth reading precisely because it does not define the problem as AI-generated content or template-generated content. It defines it as content produced at scale where the primary purpose is ranking, not serving users.
The practical indicators Google has described:
None of these criteria disqualify programmatic SEO as a category. They disqualify a specific implementation: thin templates with sparse datasets producing content that is formally varied but substantively identical.
A programmatic program where each page contains:
does not meet any of these criteria. It meets them no more than a well-written editorial article does.
Rather than arguing in the abstract, here is what the data shows about programmatic programs that are performing today.
Local business directories and service-area programs built on Google Places API data, census enrichment, and real business entity information continue to index consistently and rank for local queries. The distinguishing factor is dataset depth, pages where the entity data extends beyond name and address to include ratings, review counts, business descriptions, neighborhood context, and service-specific information.
Integration hubs built by SaaS companies, one page per integration partner, are among the most consistently performing programmatic implementations. The reason is structural: integration pages are inherently differentiated because the integration documentation varies by definition. Zapier integration pages describe different workflows than Salesforce integration pages, not because of template design but because the underlying information is different.
Tool comparison programs built on accurate, regularly updated feature matrices continue to rank. G2 and Capterra data, when combined with manual verification and genuine use case analysis, produces comparison pages that serve real buyer intent. The programs that fail in this category are those using AI to generate feature comparisons from tool name inputs alone, the resulting pages contain plausible-sounding but fabricated comparisons that generate poor user signals.
Industry and role-specific use case pages, “[Product] for [Industry]”, perform consistently when the industry content reflects genuine segment-specific knowledge rather than generic product descriptions with an industry name substituted in. The differentiation test: remove the industry name and ask whether the content could describe a different industry. If yes, the content is not genuinely segment-specific.
Any program where the primary variable is a geographic location and the content differentiation is limited to inserting that location name into otherwise identical copy. Google's geographic query systems are now sophisticated enough to distinguish between pages that provide location-specific information and pages that mention a location name while providing generic content.
Using an AI model to write page content with only the primary variable as input, “write a page about accounting services in Austin” without providing any Austin-specific data, produces content that fails the scaled content abuse criteria regardless of how fluent the output is. The model cannot differentiate the page from the Denver page or the Portland page because it has no differentiating information to work from.
Publishing thousands of pages to a sitemap without internal links pointing to variation pages from indexed hub and spoke content. These programs produce “Discovered, currently not indexed” Coverage reports that persist indefinitely because Google's crawl systems have no contextual signal incentivizing priority crawl.
Building the same programmatic program across multiple domains to multiply impression volume. This is treated as a network manipulation signal and typically results in all properties being evaluated more harshly.
The more accurate framing for programmatic SEO in 2025 is not “is it dead” but “what is its ceiling.”
The ceiling question: programmatic pages, by design, provide information that is structurally similar across the variation set. Even a well-built program, rich dataset, genuine differentiation, strong internal linking, produces pages that share a template. Google's quality threshold for template-derived pages has risen and will continue to rise as AI-generated content becomes more prevalent.
This means the sustainable competitive advantage in programmatic SEO is increasingly about data quality, not template quality. Two programs targeting the same keyword pattern with the same template design will diverge in performance based on the depth and uniqueness of their respective datasets. The program with proprietary data, customer-generated review data, first-party usage statistics, unique entity attributes not available from public sources, will outperform the program built on the same public data sources every competitor also has access to.
The ceiling for programmatic programs built on public data alone is not zero, but it is lower than it was in 2020. The ceiling for programs with proprietary, regularly updated, genuinely differentiated data is higher than ever, because the quality bar has raised the floor for all competitors simultaneously, and fewer publishers are willing to invest in data quality at scale.
This is the decision most practitioners actually face when they ask whether programmatic SEO still works.
| Criterion | Programmatic SEO | Traditional Editorial SEO |
|---|---|---|
| Best for | Keyword patterns with consistent demand across 50+ variations | Individual high-value keywords requiring deep editorial treatment |
| Content unit | Dataset row → template → variation page | Brief → research → draft → edit → publish |
| Time to publish | 100 pages in hours | 1 page in days to weeks |
| Ranking timeline | 60–90 days for initial indexing and position data | 90–180 days for a new editorial article to find its ranking |
| Content differentiation | Driven by dataset quality | Driven by editorial depth and original research |
| Maintenance | Dataset updates + indexing monitoring | Content refreshes + link building |
| Fails when | Dataset is too thin to produce genuinely different pages | Topic is too competitive for domain authority to support |
| Works best alongside | Hub page + editorial spoke articles building topical authority | Programmatic variation pages capturing long-tail demand |
The answer is almost always both. Editorial content builds topical authority and ranks for high-competition informational queries. Programmatic content captures long-tail variation demand at scale. Neither approach is complete without the other.
The hub-and-spoke model exists precisely because this combination outperforms either approach in isolation. A well-written hub article about programmatic SEO builds domain authority in the topic cluster. Variation pages targeting [programmatic seo] + [industry], [programmatic seo] + [use case], and [programmatic seo] + [page type] capture the long-tail demand that no single editorial article can address.
Before building any programmatic program, run this four-question evaluation.
Check 10 representative variations in a keyword research tool. If the majority return search volume, the pattern has demand. If most return zero, the pattern does not justify a programmatic approach regardless of how strong the template is.
For every variation, ask: what information specific to this variation could I include that a searcher would actually find useful? If the answer is only the variation name itself, only the city, only the tool name, the dataset is too thin. The program will not survive quality evaluation at scale.
Search 5 representative variations. If the top results are Wikipedia, major media publications, or deep editorial guides from high-authority domains, programmatic variation pages will not rank above them. If the top results include thin directories, aggregators, or weak local pages, the pattern is winnable.
A programmatic program is not a one-time publishing effort. It requires ongoing dataset maintenance, indexing monitoring, content quality review, and batch expansion. If you do not have the tooling or team bandwidth to maintain the program after launch, the initial investment will decay faster than it compounds.
If all four questions return positive answers, the program has legitimate potential. If any return negative answers, fix the underlying problem before building, not after.
To close the loop on the “is it dead” question, here are the specific program types that are either already failing or will fail as Google's quality systems improve.
Any program targeting “service in city” patterns where the only differentiation is the city name inserted into static copy. These programs are either already deindexed from the March 2024 update or operating on borrowed time.
Comparison pages where the feature comparison content was generated by an AI model from tool names alone, not from verified feature matrices. The comparisons are plausible-sounding but factually unreliable, and user signals (high bounce rate, low dwell time) accelerate ranking loss.
Any program that takes content from another source and republishes it with surface-level variation. This was always against Google's guidelines; it is now consistently enforced.
Keyword patterns that had strong demand in 2021–2022 but have since shifted, because the underlying behavior has changed, because a dominant platform has captured the intent, or because the query pattern is being answered directly in Google's AI-generated results. Validate demand against current data, not historical volume.
The programs generating consistent, compounding traffic today share four characteristics that distinguish them from the programs that failed.
The dataset contains information that is not available from a simple public source query. It has been enriched, combined, or derived from multiple sources to produce something a competitor could not replicate by downloading the same public dataset.
The template is designed around what the dataset actually contains, not around what a generic template should look like. The dynamic sections are dynamic because the data genuinely varies. The static sections are substantial enough to carry the page even if the dynamic content were removed.
Hub page indexed. Spoke articles indexed and linking to variation pages. Related page cross-links built into the template. Sitemap submitted. Publishing in controlled batches with Coverage monitoring between each one.
Dataset refreshed when source data changes. New rows added as the keyword pattern expands. Indexing rate monitored monthly. Underperforming pages diagnosed against the dataset rather than abandoned.
That is not a dead strategy. That is one of the most scalable content acquisition systems available to a growing business.
SEOmatic is built for exactly this version of programmatic SEO, dataset-driven, template-structured, batch-published, and monitored from a single dashboard.
SEOmatic is the content infrastructure agencies and in-house SEO teams use to generate, optimize, and publish hundreds of SEO pages that rank in search and AI.
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Minh Pham
Founder, SEOmatic
Today, I used SEOmatic for the first time.
It was user-friendly and efficiently generated 75 unique web pages using keywords and pre-written excerpts.
Total time cost for research & publishing was ≈ 3h (Instead of ≈12h)
Ben Farley
SaaS Founder, Salespitch
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