There is a common problem in SEO content strategy. You research keywords, you identify search volume, and you build content to rank for them. Underneath all of it sits an assumption nobody examines: that a keyword tool shows you what people actually want. Tools are useful for scale, and they show traffic trends reliably enough. But they also obscure specificity. They hide the precise questions your audience is asking, and they collapse several different intents into a single metric. By the time a strategy has been organised around search volume, the real opportunities are often the ones that were never measured at all.
Mark Williams-Cook starts somewhere else. He starts with what people are actually asking Google, rather than with a tool's prediction of it. That means the questions surfaced in People Also Ask boxes, and in the results themselves. This is messy data. It is unstructured, it needs interpreting, and it is real. People asked these questions. Google decided they were worth showing. That is signal.
Who They Are
Williams-Cook runs the agency Candour, and he created AlsoAsked.com, a tool that maps the network of related questions Google surfaces in its People Also Ask feature. The practice is built around one insight: the biggest opportunities in search are the questions people are actively asking that nobody has bothered to answer well. AlsoAsked exists to turn unstructured question data into something you can plan against.
What distinguishes the approach is methodological rigour applied to qualitative data. It is also relentlessly practical, which is the agency background showing through. He pairs the question data with whatever tooling makes it workable at scale, whether that is AlsoAsked itself, crawlers, or AI where it genuinely saves time, because the method has to run inside real client budgets. Most SEO professionals treat keyword research as a quantitative exercise. Find keywords, sort by volume and difficulty, pick the easiest to rank for. He treats it as a qualitative one instead: find the questions people are actually asking, understand the intent behind each, work out how they relate, then build content addressing whole intent clusters rather than isolated terms.
The reputation was built on finding the most profitable questions, not the most comprehensive keyword lists. Take an illustration. A question with zero recorded search volume that a hundred qualified prospects are asking is worth more than a question with ten thousand monthly searches behind which sit fifty qualified prospects. Finding those specifics is the job. He is good at it because he will spend time inside actual search result data rather than relying on tool projections.
He is also transparent about method, which I think matters more than it sounds. When he shares a finding, he explains how he got there, shows the data, and names the limits of what it supports. That is what builds the credibility. You are not being asked to take his word for anything, because you can see the work and judge it yourself.
What They Teach
The core teaching is People Also Ask mining. Google shows related questions in a box below the results. Most professionals glance at it and move on. He looks at it systematically instead: what is Google surfacing for this search, and what does that tell you about what people want to know? AlsoAsked.com scrapes the data at scale and structures it so you can work with it, which turns PAA from a curiosity into a research methodology.
The methodology works because PAA data is grounded in actual behaviour. These are not predicted questions. They are questions real people typed into Google, surfaced because they are frequently asked or algorithmically considered related. That is genuine intent, expressed in the searcher's own words. The applications are concrete. A Shopify merchant running a product category through the tool finds the specific feature questions buyers ask before purchasing. A B2B company finds the pain points its software addresses, phrased the way the buyer phrases them rather than the way the marketing team does.
Intent proximity mapping is the second pillar. Not all related questions are equally related. Some cluster tightly around a single intent variation, and others branch into distinct intent categories, and he teaches how to map the relationships. Once you see that questions A and B share related intent while question C branches into a different need, you know to structure content differently for each cluster. You also know where to expect high bounce rates. That is where users arrive expecting the answer to question A and find only the answer to question C. The mapping tells you whether to build one comprehensive piece or separate pieces for separate clusters. Some topics naturally cluster, others naturally separate, and getting that structure right improves both rankings and engagement.
Zero-volume keywords is the concept that contradicts conventional wisdom most directly. Keyword tools report zero searches for many highly specific queries, and most professionals read that as nobody looking. His argument is that zero usually means unmeasured rather than unsearched, because the tools have not caught the query yet, which happens constantly with long, conversational phrasings. Each query is individually small. Collectively these terms can represent real search interest, and some carry intensely specific intent. One person searching how to fix a squeaky office chair wheel might generate zero monthly searches. But that person is actively searching. If you hold the content that answers it, you rank, and if your business is office furniture, you have captured a qualified prospect for the cost of a paragraph.
The framework underneath all of this is that search volume is an imperfect proxy for value. High volume often means broad, low-intent searches. Low volume often means specific, high-intent ones. So the best opportunities frequently sit in the low-volume, high-intent space, where almost nobody is looking. That needs a different research method, because the tools will not show you those opportunities. You find them in actual search data, and in actual user behaviour.
Search intent shift monitoring is less discussed and just as valuable. PAA data changes over time. Questions that appear for a term this month can disappear next month while new ones emerge, and that movement means user behaviour is shifting underneath you. He teaches monitoring the shifts to spot emerging opportunities early, before they become obvious to everyone else in the market.
How It Maps to Opportunity and Authority
Williams-Cook's work is very high on Opportunity, with a significant Authority component. On the Opportunity side the connection is direct. PAA data reveals specific content gaps matching actual user needs, so if people are asking questions your competitors have not answered, you are looking at an Opportunity gap. Zero-volume keywords are untapped Opportunity by definition. Intent proximity mapping reveals the content structure that increases relevance match across related searches, which expands the set of keywords you can address at all.
The Authority component is just as important. Building comprehensive answers to the questions people are actually asking builds topical Authority. Content that answers all the related questions Google surfaces for a topic is not answering one question, it is demonstrating expertise across an intent cluster, and that produces stronger topical signals than a page answering a single question superficially.
His framework shows that Opportunity gaps are frequently Authority gaps wearing different clothes. When nobody has answered a specific question well, capturing the traffic is only half of it. You are also establishing yourself as the source for that topic. That is why the work is valuable: it identifies opportunities that build Authority at the same time, rather than trading one for the other.
The framework component is intent-driven content strategy. Instead of optimising for maximum keyword volume, you optimise for intent coverage, identifying what people want to know and building content that addresses it. That tends to produce stronger rankings, higher engagement, and better conversion. The reason is unglamorous. You are answering real questions rather than guessing at what someone might want.
The Strategy Breakdown
His methods are not interchangeable. Each one weights the two levers differently, and knowing the split helps you match the method to whatever gap your diagnostic surfaced.
PAA mining and intent proximity
Map the questions Google surfaces for a topic and how they branch, so you can see the user's likely train of thought: what they ask first, and what they ask next. Opportunity impact: very high. PAA data is direct insight into the questions users are asking right now, and the branching structure reveals the related long-tail queries sitting behind each one. A single map can hand you a cluster of content opportunities in one pass. That is keyword research run against real behaviour rather than tool estimates. Authority impact: high. Content that comprehensively answers the questions users are actively asking positions your site as the most relevant, most helpful resource on the topic. That is topical authority earned by addressing stated needs directly, and it is the engine behind his approach to content marketing.
The zero-volume keyword strategy
Deliberately target the highly specific queries tools report at zero monthly searches, on the premise that the data is incomplete rather than the demand absent. Opportunity impact: high, for niche relevance. Each query is individually tiny, but the intent is precise, the competition is close to nil, and collectively the terms add up to real search interest. The visitors they bring are often far down the funnel and ready to act, which makes this ideal for specialised B2B services and narrow product ranges. Authority impact: moderate. Answering a question that specific shows you understand the niche problem in detail. It builds focused credibility with a targeted audience rather than broad topical dominance, and that is frequently the more valuable of the two.
Monitoring search intent shift
Track how the PAA questions around your core topics change over time: what appears, what disappears, and what the movement says about the market underneath. Opportunity impact: high. When the questions shift, user needs are shifting, and adapting early means capturing new opportunities before they show up in anyone else's keyword tool. It also stops you maintaining content for demand that has quietly moved on. Authority impact: moderate. Content aligned with what people currently ask maintains credibility. Content answering last year's questions erodes trust in a way that is hard to spot from inside the organisation, because the page still looks perfectly fine.
Practical AI application
Use AI for the unglamorous work: validating existing content against PAA questions at scale, generating ideas from question data, automating tasks like redirect mapping during a site migration. Opportunity impact: moderate. AI compresses the time between having the question data and having a prioritised gap list, so you find and act on opportunities faster than a manual audit allows. Authority impact: moderate. Automated redirect mapping done properly preserves technical health through a migration, and validating coverage against question data keeps your topical signals comprehensive. He is clear-eyed about the limits. AI hallucinates, so it supports human quality control rather than replacing it. The judgement stays with you.
When to Learn From Them
Learn from Williams-Cook if your diagnostic shows an Opportunity gap. When your research says you are missing keywords competitors are capturing, PAA mining will usually reveal why, and the answer is normally that you are missing intent variations which never surface in a high-volume keyword list.
He is also the right read if you have been creating content based on what you think people want. This is a common failure, and a comfortable one. You believe there is demand for content on a topic, so you write about that topic, and the demand never gets verified against real search behaviour. His methodology forces the verification to happen first.
Then there is the case where you want high-intent, low-competition opportunities. This is the space most professionals ignore entirely. Zero-volume keywords with clear commercial intent. Highly specific questions with no good answers anywhere. Those opportunities stay invisible while you look only at high-volume keywords, and this methodology is what makes them visible.
Read him too if you believe the best content answers real questions, because the whole approach is predicated on that belief. You are not optimising for keywords, you are optimising for questions, and the distinction matters. A keyword is an aggregated metric, where a question is one person's specific intent. Content that answers questions will typically outperform content optimised for keywords alone.
Finally, this is the methodology to reach for when you are launching a new site or product on a limited budget. Rather than trying to capture every keyword, you capture the most valuable ones. It works particularly well for a startup with limited resources, because you are being surgical about where the effort goes.
Where to Start
AlsoAsked.com is the obvious starting point. Run your target keywords through it, look at what questions Google is surfacing, then spend a while just exploring rather than working toward a deliverable. Which patterns emerge, and what intent variations show up that you had not considered? That exploration alone usually surfaces content opportunities you would never have reached from a keyword list.
His video walkthroughs of the methodology are worth watching carefully. He explains his thinking, shows real examples, and works through the process of building a content strategy from PAA data. He gives the methodology away openly, and the payoff comes from understanding it well enough to apply it rigorously. The Search with Candour podcast and the Unsolicited SEO Tips series cover the same ground in ongoing form, and they are a good way to absorb how he reasons about new developments as they happen.
His conference talks go deeper into worked examples, showing zero-volume keywords that turned into meaningful traffic, and intent proximity mapping applied to a real topic. That context is what makes the methodology actionable. A method described in the abstract and a method shown working on a real site are different objects.
Then audit your own content against actual PAA data for your target keywords. For each piece, what questions is Google surfacing for the keyword you are targeting, and is the content addressing those or ignoring them? Very often you will find a page that answers the primary question and misses the related intent entirely. This audit tends to reveal quick wins, since content that already ranks can frequently rank higher once it covers the related questions, and adding a single section can move both rankings and engagement.
One last thing he emphasises. PAA data changes. A question that appears this month might disappear next month, new questions emerge, and the movement means the market is evolving underneath your content. He teaches monitoring PAA for your target keywords on a regular cycle, monthly or quarterly. When new questions emerge you have an early-mover advantage, and when questions disappear you can deprioritise the content that served them. That keeps a strategy aligned with actual user behaviour, rather than with research done months or years ago.
Part of the Expert Series. Back to the framework or the diagnostic. Part of the Marketing Universe. Explore Traffic Plus Offer : The Trust Algorithm : 4-Quadrant AI. Read the book: Marketing Curious: Working the Noise.