There is no longer a need to place emphasis on obtaining specific key words when conducting "keyword research." By 2026, though you may still identify key words using tools such as Ahrefs or SEMrush, you will not find value in doing so because search engines now utilize algorithms to identify and rank content based on the relationships between topics. Instead of optimizing for one search term on a page about B2B SaaS Marketing Strategy, we can build an entire cluster of pages around the topic of B2B SaaS marketing in order to establish a foundation for the creation of additional content around common themes (such as positioning, CAC, onboarding, retention, attribution). This allows us to create a structured set of content that links and supports one another, thereby creating stronger connections among the pages. Through this restructuring of content into a cluster format using pillar pages (which serve to define primary entities) and additional pages (that deepen sub-topics), we can also create related documents (such as definitions, related concepts, FAQs, and internal links that reinforce authority) that improve our chances of ranking for multiple relevant keywords, rather than just one. The emphasis has changed from ranking for the phrase(s) to owning the subject.
Keyword research is still useful but the way we approach and use it may change. It’s no longer about individual terms and search volume. It’s about understanding topics, entities and intent across the entire discovery landscape. Traditional keyword research can still be a good starting point for topic clusters but it's not the whole strategy. Search and discovery now operates across SEO, PPC, social media, content marketing and AI visibility. Users don’t just type short, high-volume queries into Google anymore. They ask natural language questions in AI tools, search inside social platforms and discover brands through different methods. Instead of targeting individual keywords and chasing down rankings and traffic, we focus on building authority around entities such as brands, products, services and problems and mapping content strategically so that we can serve the right audience at the right time in their search journey. How we conduct research is also likely to shift away from third-party tools that report on rankings and search volumes. Google Search Console, for example, can often be more valuable at showing us where a site already appears but isn’t ranking in top positions. These kind of queries help us spot content gaps and striking distance opportunities. From there, we can use the data to expand into supporting cluster content across blog, landing pages, video, paid search and social, strengthening topical authority everywhere the audience searches. We’re also seeing a shift toward analysing conversational, natural language queries like questions people ask AI systems and use in social search vs shorter phrases they might search on Google. In 2026, brands that think in topics and the broader search landscape rather than just rankings will be the ones who get ahead.
Keyword research is not dead in 2026, but keyword research based on standalone search terms will be. In investment casting, machining, search intent and context are more important than single terms. Today, engineers ask complex questions about tolerances, alloys, lead times and secondary machining. Search engines assess level of authority on content elements such as material, process, certification and application. Our approach has evolved from a running behind volume to mapping of topics and their connections. We still assess keywords as signals but not as targets: we want topical authority in manufacturing capabilities, not rankings for keywords. This approach adjusts the structure of content and internal alignment. Rather than fragmenting with single-phrase pages, we cluster around core entities, but with long-tail subpages. Sales discussions lead content planning; FAQs are cluster content. Internal links flesh out materials to processes and finishings, helping search engines understand relationships. That pulls in engineers later in the buying process because the content speaks to real-world technical considerations. This will help you see topic clusters for the codependent reflection that they are, not merely as SEO strategy.
Question One: Keyword planning will always have relevance; however, it is no longer the leading strategy but rather a tactical assistance. In 2026 chasing after individual search terms, is comparable to building a house by only concerned with the nails. You will have the proper hardware, but be missing the blueprint to adequately build your final product. We are migrating away from matching string, but instead starting understanding "things", where the importance of understanding an entity's context when being evaluated is stronger than the weight of counting up a phrase. The proof we continually find is that when 1 page is optimized for only 1 search term, it will lose to a page that optimizes for the entire related entity. Question Two: The new strategy we are working toward uses "Entity Mapping". Instead of using a list of 50 keywords related to your industry, you have established the main areas of subject matter, as well as the relationship of all areas in that technical domain. The use of topic cluster is to demonstrate to the algorithm that you own the entire neighborhood of that subject - and not just one house. For example, if we were promoting "Staff Augmentation", we would establish a graph with other topics, and establish delivery governance, vetting rubrics, and engineer velocity to create true topical authority. This establishes to the search engines that you are an authority and not just answering their question. For most teams, a transition to an entity-focused based model is a cultural change, as they are used to running SEO campaigns based around spreadsheets. Teams must shift from measuring the "safer" high-volume metrics, to the more "complicated" topical depth. This is harder to execute, but provides a larger moat to protect against AI-driven search algorithm changes over time.
Hi, I'm Christian from Hometime, an Airbnb management company in Australia. I help drive organic traffic to our website through search engines + AI searches and I can help answer some of your questions with regard to SEO. No, keyword research isn't dead in 2026. New keywords pop up daily, and established keywords can still be tapped into with aggressive link-building + cornering with low-competition variants. If you can spot an emerging, low-competition keyword early, you can rank high for that keyword for an extended period of time without making many changes. However, you need to have a finer understanding of how the landscape has changed: which keywords will trigger AI overviews, which keywords are likely to get clicks, and which can help you expand your topical coverage for AI searches? If your KPIs include conversion, then you also need to take that into account. A shift to topic clusters and entity SEO is thus essential in the modern SEO landscape, as opposed to just ranking for individual keywords. Ask yourself these questions: 1. What other keywords are associated with your main keyword? 2. Should you discuss these all on the same page, or is it better to create various pages then tightly interlink them? 3. When customers land on a page, what other related topics are they most likely to click on to take them closer to the purchase stage? Shaping the customer's journey now goes hand-in-hand with improving ranking. Please let me know if you have any questions, happy to answer them :) For attribution, you can use: Christian Suzon Hometime | Airbnb management (https://www.hometime.io/airbnb-property-management)
No. But here's what IS actually dead: - Building pages for individual keywords with no entity context - Using search volume as an excuse for content value - Treating a keyword list as a content strategy (I'm still seeing this!) - Ignoring semantic relationships between topics in favor of exact-match targeting Adobe bought Semrush for around 1.9 billion dollars a few weeks ago. Officially or unofficially, keyword research is not dead. It is still one of the base metrics that you rely on. The only thing that has changed is that you have to be way smarter while planning keywords. We need to consider the exact entity relationships, we need to consider searcher's intent, we need to consider how connected it is with the knowledge graph, cluster depth score, and like 15 more criteria. The whole process got larger, the tasklist got bigger, and it's more competitive than ever. However, I think the fundamentals are very basic. Google's Knowledge Graph has over some hundred billion facts. When you are writing a content, Google will look for: "Is this page a trusted node in the entity graph for this topic?". Google won't ask "Does this page contain the keyword 17 times?" You need to focus on entity salience scoring, where every page is designed to strengthen a site's topical authority signal for a specific entity cluster, not rank for a keyword. Use Google's Natural Language API on your existing blog. It will show you which entities Google extracts from your page and their salience scores. If your page is about kitchen plumbing but the API returns "home improvement" as the dominant entity, you have a salience problem, not a keyword problem. Also, Look for cluster depth score. How many unique sub-questions within a topic does your content actually resolve? Use People Also Ask scraping tools (like AlsoAsked) and map every branch. If your cluster answers 12 of 80 sub-questions, you're a thin node, and Google treats you like one. Another thing we do is pull Google Search Console data and look at query sequences. what did users search before and after landing on the page? Tools like Semrush's Keyword Gap combined with GA4 path exploration give a rough version of this.
Keyword research isn't dead in 2026--it's the foundation that informs entity-based SEO and topic clusters at Cleartail Marketing, where we've driven 278% revenue growth for a B2B client in 12 months and 14,000% traffic spikes for others among our 90+ clients. We start with tools like Ahrefs and SEMrush to uncover core entities tied to client services, like "Google AdWords campaigns" or "LinkedIn outreach," then cluster related topics around them--buyer intent terms feed pillar pages on revenue growth, with spokes on tools, steps, and results. For one client, this shifted us from isolated terms to a "LinkedIn lead gen" entity cluster, generating 400 emails monthly and 40+ qualified calls, proving keywords validate clusters that build authority. Ongoing monitoring via Google Analytics ensures clusters evolve, turning research into sustained rankings without ditching volume or competition data.
Keyword research isn't dead in 2026, but "one page = one keyword" is. I run marketing across FLATS(r) properties (Chicago, San Diego, Minneapolis, Vancouver) and I've seen the biggest lifts come when Google can clearly understand *entities* (property, neighborhood, amenities) and when users get the next best answer without bouncing. At The Miller (Vancouver Waterfront), I shifted from chasing terms to building an entity-first content set around what we actually are: "Vancouver Waterfront apartments," "coworking lounge," "wellness/day spa," "fitness," and "pet-friendly living," then connected those with tight internal links and consistent on-page language. When we paired that with richer media (illustrated floorplans, 3D tours, video tours), we saw a 7% increase in tour-to-lease conversions and a 4% lift in organic search traffic over six months--because the content answered intent and the site made it easy to commit. My topic clusters are built around resident/prospect journeys, not keyword variations: "Work-from-home living" (coworking, WiFi, meeting space, quiet hours), "Move-in basics" (how things work, what to do first), and "Neighborhood life" (walkability, waterfront, commute). The same way Livly feedback showed a repeat "oven start" confusion and we fixed it with FAQ videos (30% less move-in dissatisfaction), SEO works better when clusters are built from real questions people keep asking. I still do keyword research, but only as QA: I use it to confirm I'm covering the language people use and to spot gaps, then I measure performance with UTMs so I can tie cluster pages to tours/leads (UTM tracking drove a 25% lead-gen improvement for us). If a topic cluster can't be measured down to a tour action or lead source, I treat it as branding--not SEO.
Keyword research isn't dead--it's just no longer a spreadsheet exercise where you "pick a term and write a page." I've spent 13+ years building lead-gen systems for service businesses and we've tracked $140M+ in revenue, and the only SEO that reliably turns into calls is the kind that maps to real jobs, real locations, and real conversion paths. My shift is: build the entity map first (services, sub-services, service areas, brands, credentials, problems, pricing signals), then use keywords as validation--not the strategy. For a local home services client, we restructured the site into service entities ("roof repair," "storm damage," "metal roofing") with supporting proof nodes (warranty, financing, before/after, reviews, FAQ) and tied each to the right GBP categories + on-page schema; rankings were nice, but the win was cleaner attribution and higher close-rate leads because the content matched the actual estimate conversation. Topic clusters still matter, but I build them around "decision moments," not "topics." Example: instead of a generic cluster on "roofing," we built a hub for "Is my roof repairable or do I need replacement?" with spokes like "insurance claim checklist," "how long repairs take," and "what fails inspections," and we paired it with Local Service Ads + conversion-focused landing pages so we captured demand *today* while the organic authority compounded. If you want a practical 2026 filter: if a page can't be tied to (1) a service entity, (2) a location entity, and (3) a measurable action (call/form/booking) in analytics, it's not SEO--it's content therapy.
Keyword research isn't dead in 2026; it's just been demoted from "strategy" to "input." I manage marketing for FLATS(r) across Chicago/San Diego/Minneapolis/Vancouver, and the wins come from mapping real-world entities (building, neighborhood, amenities, transit, policies) and using keywords mainly to validate how people phrase those relationships. At The Lawrence House, we shifted from chasing phrases like "Uptown luxury apartments" to building an entity graph around the property + Uptown: rooftop lounge, 6,000-sq-ft fitness center/pool, pet policy, Red Line access, and on-site retail like Heritage Outpost and Larry's. Then we organized content as clusters that answer the next question a renter has (FAQ hub, neighborhood "coffee/gyms" spokes, and floorplan pages), and tightened internal linking via Engrain sitemaps + unit-level video tour pages. That mix helped us lift organic search traffic 4% in six months and improved tour-to-lease conversion by 7% once rich media (illustrated floorplans/3D/video) was baked into those clusters. Entity-based SEO got way easier once I treated resident feedback as "query demand." We saw repeat Livly tickets about "how do I start the oven?" right after move-in, so we produced maintenance FAQ videos and distributed them through onsite teams; move-in dissatisfaction dropped 30% and reviews improved--then we repurposed the same answers into structured FAQ content that naturally targets long-tail without obsessing over a single term. My current rule: build one canonical "truth" page per entity (property, amenity, policy, neighborhood spot), and only do keyword research to catch missing modifiers (price/AMI, pet limits, parking, commute). If you can't draw the cluster on a whiteboard and explain how a prospect moves through it, you're not doing topic strategy--you're just collecting keywords.
Keyword research isn't dead--it's just not the starting point anymore. I've been building websites for 20+ years, and the biggest shift I've seen with our contractor clients is that Google now rewards businesses that answer the *full question*, not just match a phrase. When an HVAC company ranks for "furnace repair near me," they also need content about "when to replace vs. repair" and "how long does a furnace last"--because Google groups those as one entity: furnace problems. For our local service clients, I build topic clusters around the actual customer journey. An electrician doesn't just need "emergency electrician Lancaster OH"--they need a cluster: "power outage troubleshooting," "circuit breaker replacement cost," and "when to call an electrician vs DIY." We link those tightly, use consistent terminology (the entity), and suddenly they're ranking for 12 related searches instead of one. One pest control client saw their call volume jump 40% in 90 days because we stopped chasing keywords and started owning the topic of "seasonal pest prevention." I still use keyword tools, but only to validate I'm speaking the language people actually use and to find gaps. If someone's searching "why does my outlet spark," that's a signal to build content around electrical safety--not just stuff keywords. The proprietary lead gen system we built works *because* we match how people think, not just what they type.
No, keyword research isn't dead in 2026--it's the starting point for our Semrush-certified strategies at BullsEye, where we've driven calls for Coral Springs clients since 2006. We shifted to entity-based SEO by prioritizing Google Business Profile entities over isolated terms, auditing and optimizing one client's profile in Boynton Beach to boost local rankings and conversions tracked in real-time reports. Topic clusters now organize our content around geo-specific entities like Broward County services, with hubs on GBP optimization linking to spokes on listings and analytics--delivering 300% more leads for SEM clients in the first month without long-term contracts.
Having run JPG Designs for over 15 years, I've seen that keyword research isn't dead, but it has transitioned into intent-based mapping for voice search and mobile-first indexing. Users no longer just type "HVAC RI"; they ask conversational questions like "who is the best HVAC company in Rhode Island," requiring us to target the semantic intent behind the query. We now build "Service Entities" for clients like Advanced HVAC by using structured data and schema markup to define the business as a local authority. This strategy shifts the focus from isolated search terms to creating an ecosystem where technical expertise, local references, and trust signals are inextricably linked for the search engine. Our topic clusters are organized by service profitability, grouping technical FAQ sections with mobile-optimized call-to-actions to capture highly motivated leads. By prioritizing accessibility and 2-second load speeds, we ensure these clusters rank for the "near me" searches that now drive the vast majority of local business discovery.
Keyword research isn't dead--it's just evolved into a discovery tool rather than the strategy itself. I still use tools like Google Keyword Planner and WordStream every week for clients in mortgage and real estate, but now I'm mining those keywords to understand *entities* and user intent, not to assign one term per page. Here's what actually works: I identify a core entity (like "FHA loans" for a mortgage client), then map supporting keywords into content clusters around related concepts--down payment requirements, credit score needs, property eligibility, first-time buyer programs. That one client saw their organic mortgage application leads jump 40% in four months because we stopped fighting for "FHA loan rates" and started owning the entire FHA ecosystem. The shift happened when I noticed our top-performing blog posts weren't ranking for their target keyword--they were ranking for 15-20 related long-tail variations we never optimized for. Google was already connecting the dots between entities; we just formalized it. Now when I audit a page in Yoast, I'm checking if it supports the entity hub, not if it hits 2% keyword density. One tactical move: I take our clients' most-asked questions (from sales calls, customer service tickets, email replies) and turn those into topic clusters because that's literally the language Google's entity models are trained on. A Portland mortgage broker client had 50+ emails asking about Oregon's first-time buyer programs--we built a cluster around that entity and it now pulls traffic from 80+ keyword variations we never specifically targeted.
Keyword research isn't dead in 2026, but "keyword-first SEO" is. I run three agencies (Get Found Fast, Roofing Contractor Marketing, OBL Marketing) and after 15+ years of SEO + paid + web, the pages that win are built around what Google can *verify* about the business and what users can *complete* fast. My shift is entity-first architecture: I define the entity set (company, services, service areas, people, offers, credentials, FAQs) and then I use keywords only to name/label those entities and catch language variations. Practically, that means tighter internal linking by entity type (service - related service - proof/support content), plus schema that matches the entity graph (Organization/LocalBusiness + Service + FAQPage + Review where appropriate), so Google and users don't have to "guess" what a page is. Example: for a Denver-area service business we rebuilt the site so every core service had a single canonical URL, then supported it with 6-10 "topic cluster" pages that answered one question each (cost ranges, timelines, common failure points, comparisons). In Search Console we saw impressions spread across hundreds of long-tail queries without chasing them individually, and the bigger win was conversion rate--after conversion-focused redesign, form submissions per 100 visits improved by ~28% because pages were clearer and faster on mobile. My 2026 filter is: if I can't connect a piece of content to a specific entity + a specific on-site action + a measurable funnel stage in analytics, I don't publish it. Topic clusters still matter, but they're built to deepen entity understanding and reduce friction, not to "rank for a term."
Keyword research isn't dead in 2026--it's the entry point for entity SEO, helping us map Detroit-specific entities like "furnished lofts near Little Caesars Arena" from my short-term rental listings. We're shifting by prioritizing entities over single terms: instead of chasing "Detroit rentals," we target "Detroit bachelor party venues" tied to nightlife spots like TV Lounge, building topical authority via my site's area guides. Topic clusters form the core--hub page on "Detroit Area Guide" links to clusters on sports arenas, biking paths, and hospitals, linking back to pet-friendly loft properties. This drove a 25% booking increase last year as Google recognized us as the go-to for traveling nurses and event-goers. Results? Organic traffic to properties like Detroit Riverwalk Loft jumped 35%, filling off-peak weekends without paid ads.
Not dead--"keyword research" just stopped being a spreadsheet game and became language + intent intelligence. I've been building search-optimized brands at Brand911 since 2016 (and before that I did fraud/risk + private investigation), so I treat queries like evidence: what people actually ask, how they ask it, and what Google needs to understand about the *thing* behind the words. What I do now is map entities first (person/company, services, locations, credentials, competitors, and the problems we solve), then use keyword research to validate the phrasing. Example: for a local service business, instead of chasing "best [service] + city" variants, I build one authoritative service page that clearly defines the entity + attributes (who/what/where), add an FAQ section built from real "near me" and voice-style questions, and support it with 4-6 cluster articles that answer adjacent intent without keyword stuffing (something I've warned against for years). Entity-based SEO also forces consistency across the web, not just your site. For local brands, I'll align the exact NAP, categories, and service descriptions across core directories and the Google Business Profile, because Google's local ranking factors (relevance/distance/prominence) get reinforced by consistent entity signals--not by repeating the same exact keyword 50 times. My 2026 "topic cluster" shift is basically: one pillar per core service/entity, clusters per sub-problem, and internal links that reflect real decision paths (cost, timeline, comparisons, "what to do if..."). Keyword tools still matter, but I use them like a polygraph--confirm the language, spot gaps, and make sure we're matching how people speak *before they ask it* (especially for voice search).
Keyword research isn't dead in 2026--it's evolved into entity discovery for smarter clusters. As FLATS(r) Marketing Manager across Chicago, San Diego, Minneapolis, and Vancouver, I've optimized SEO for multifamily sites like The Heron, turning innovative features into ranking powerhouses. We're shifting by prioritizing entities like "ORI Cloud Bed Sofa" and "Pocket Office," creating pillar pages with supporting clusters on floorplan specifics (e.g., A6 Studio at 539 sq. ft., C14 at 581 sq. ft.) and reconfiguration benefits, all internally linked for topical authority. At The Heron, this cluster around expandable ORI apartments--tying Cloud Bed's disappearing Queen bed to Pocket Closet's walk-in storage--accelerated lease-ups 25% faster via embedded unit tours, slashing unit exposure 50% without extra costs. For townhomes, we clustered "Chicago townhome rentals" entities: custom oak staircases, vaulted ceilings, in-unit laundry, linked across Edgewater neighborhoods like Andersonville--driving qualified traffic that sustained occupancy amid market shifts.
Keyword research isn't dead in 2026--it's evolved into a starting point for entity signals in Google's E-E-A-T framework, especially for competitive home services like HVAC and plumbing where CPCs exceed $50. With 18 years ranking contractors via Foxxr's data-driven SEO, we map keywords to entities like "emergency plumber" or "roof leak repair," then cluster content around service intent, citations, and voice-optimized long-tails--58% of voice searches target local businesses per BrightLocal. For a St. Petersburg roofer, we built a cluster linking service pages, GBP audits, and original research on storm damage trends; organic leads jumped 40% in six months by dominating entity prominence over isolated terms. This shifts focus to GEO for AI overviews, ensuring contractors appear in ChatGPT answers with structured data and authoritative clusters, not just SERP scraps.
Keyword research isn't dead in 2026--it's evolved into the foundation for entity-based SEO at FLATS(r), where I manage marketing for properties like The Draper in Uptown Chicago. As Marketing Manager overseeing a $2.9M budget and revamping SEO that grew organic traffic 4% in six months, I've seen it pivot from isolated terms to entity mapping. We're shifting by identifying Uptown entities like "rooftop pool amenities near Argyle Red Line" over generic "Chicago studios," tying them to rich media like 3D tours and video libraries linked via Engrain sitemaps. This cut unit exposure by 50% during lease-ups without extra costs. Topic clusters anchor on hubs like our "Benefits of Living in Uptown Chicago" blog, linking to spokes on dining at Spacca Napoli, recreation at Montrose Beach, and AMI floorplans--boosting tour-to-lease conversions 7%. Monthly UTM analysis optimized this, lifting engagement 10% across properties.