Should we stop chasing the algorithm and focus solely on the user?

What does Google really mean by this statement?

Google has been repeating this mantra for years: users first, algorithm second. The idea seems simple. Instead of dissecting every update and chasing ranking signals, we should analyze what our audience truly seeks and respond to it.

The search engine asserts that its job is precisely to align its results with the real expectations of users. If your content better meets a search intention than a competitor's, you should naturally rank better. In theory, this eliminates the race for technical signals and refocuses the profession on the value provided.

However, this idealized view obscures a fact: Google translates user expectations into hundreds of algorithmic criteria that no one fully understands. Between pure intention and final ranking, there is a black box with its own rules, biases, and approximations.

How does Google measure these expectations?

Google relies on behavioral signals: click-through rates, time spent on page, bounce rates, post-click actions. It cross-references this data with human evaluations via its Quality Raters and massive A/B testing on SERPs. The goal is to detect when a result disappoints or satisfies.

But these signals remain imperfect proxies. A user might spend 5 minutes on a page because it's poorly structured and hard to read, not because it's excellent. Google is improving its interpretations, but measuring satisfaction remains approximate and subject to side effects.

Why is this statement coming out now?

Google promotes this narrative to counter the obsession of SEOs with manipulation techniques. For years, the industry has sought shortcuts: keyword stuffing, link networks, mass-generated content. Each update aimed to correct these misuses.

With the rise of AI-generated content, the risk of result pollution skyrockets. Google wants to remind us that real quality prevails, that the algorithm evolves to detect it, and that short-term tactics always end up failing. It's also a way to absolve itself: if your site is not performing, it's because you are not meeting user expectations, not because the algorithm is faulty.

• Google claims to align its results with real user expectations, not with arbitrary technical criteria
• The engine measures satisfaction through behavioral signals and human evaluations, but these proxies remain imperfect
• This communication aims to dissuade manipulation tactics and hold content creators accountable
• Between user intent and final ranking, there is always a complex and opaque algorithmic layer
• Understanding expectations is not enough: you also need to master how Google interprets them technically

Does this statement truly reflect ground reality?

Partially. It is true that sites that precisely answer a search intent tend to perform better. When you decode what a user really wants behind a query and provide exactly that, you increase your chances of ranking.

But to claim that chasing the algorithm is pointless? That's oversimplistic and misleading. Two pieces of content can equally address an intent. The one that wins is often the one that also masters technical criteria: domain authority, HTML structure, speed, internal linking, freshness, semantic density. Ignoring these signals under the pretext of focusing on the user is shooting yourself in the foot.

Where does this "user-first" approach show its limits?

In competitive niches, everyone produces quality content. When ten sites perfectly meet the same intent, what differentiates them? Backlinks, domain age, Core Web Vitals, the level of technical detail. Google does not openly admit it, but these algorithmic criteria weigh heavily.

Another problem: Google interprets expectations through its own biases. It favors certain formats (lists, tables, videos) even if they are not always the best way to answer a question. It prioritizes freshness on evergreen topics where old content remains perfectly valid. It overvalues known brands, even when a specialized site provides more value.

[To be verified]: Google claims that its algorithm automatically detects quality, but the criteria remain vague. The Quality Raters Guidelines provide clues, but nothing guarantees that the algorithm applies them faithfully. We regularly observe SERPs where mediocre content from well-known actors surpasses expert resources from less authoritative sites.

When does this rule clearly not apply?

For high commercial intent queries, the perfect answer for the user is not necessarily the one that ranks. Google favors sites with massive backlinks, brand mentions, and advertising presence. A comprehensive buying guide on a small site may lose to an average page from a large media outlet, even if the former better addresses the intent.

Another case: YMYL topics (Your Money Your Life). Here, Google places a disproportionately high value on authority and E-E-A-T signals to the point where content alone is never enough. A perfect medical article written by an anonymous person will never outshine average content authored by a recognized physician on an established medical domain.

What concrete steps should you take to align content with user expectations?

Start with a thorough search intent analysis. For each target keyword, decipher what the user is really seeking: a definition, a comparison, a tutorial, a list of solutions? Analyze the current SERPs to understand which format Google deems relevant.

Next, create content that addresses this intent better than the already ranked results. More depth, more clarity, concrete examples, numerical data, a more readable structure. If everyone is producing lists of 10 points, offer a guide organized by use cases. If competitors stay surface-level, dig deeper.

But don't stop there. Ensure that this content is also technically optimized: consistent Hn tags, relevant internal linking, fast loading times, appropriate schema markup. User experience also depends on technical performance, which Google measures through specific signals.

What mistakes should be avoided in this user-centric approach?

A classic mistake: confusing what you think is useful with what the user is actually looking for. You may find it fascinating to explain the history of a technology, but if the intent behind the query is purely practical, you miss the mark. Test your hypotheses with real data: Search Console, semantic research tools, user feedback.

Another trap: neglecting post-click satisfaction signals. Excellent content that is poorly structured will lead to a high bounce rate. A slow page will frustrate even interested users. The intent is well addressed, but the experience disappoints, and Google captures this through its behavioral metrics.

How can you verify that your approach is working?

Monitor real engagement metrics in Google Analytics and Search Console: average time on page, bounce rate, pages per session, conversions. If your content truly meets expectations, these indicators should be above the average for your site.

Compare your average position and CTR for each page. An abnormally low CTR for a good position indicates a problem with the title/meta description. A high bounce rate despite a good CTR signals that the promise is not fulfilled. Cross-reference this data to identify where the gap between intent and response widens.

• Analyze the real intent behind each target query before creating content
• Decode current SERPs to understand which format Google favors
• Create more comprehensive, better-structured, and more actionable content than existing results
• Simultaneously optimize technical signals: speed, HTML structure, internal linking, schema markup
• Test your hypotheses with real data: Search Console, user feedback, engagement metrics
• Monitor post-click satisfaction signals to detect discrepancies between promise and reality