Anish S. writes about B2B marketing and growth, focusing on how businesses diagnose what is holding results back.
In six LinkedIn posts, the recurring thread is to look beyond the requested tactic: check what happens after a lead clicks, whether existing content is being used, how customers are treated before a sale, and whether an AI project changes the way work is done or only speeds it up.
The posts offer a practical, diagnostic perspective for readers interested in marketing strategy, operations, and measurement.
Anish S. connects marketing decisions to the processes around them.
A post about a prospect burned by three agencies makes the point through questions about sales cycles, stalled deals, and what counts as a good lead—not a pitch deck.
Another argues that a content audit can reveal whether the problem is generating demand or making better use of existing pages.
The AI example extends the same reasoning: reconsider a five-round approval process instead of merely producing copy faster.
The writing moves from a familiar complaint to a different diagnostic question, giving readers ways to examine their own business.
The posts offer explanations and anecdotes rather than documented case studies.
The search-statistic post, for example, gives a specific figure without its source or measurement method; ask for both before relying on it.
Across these LinkedIn posts, Anish keeps returning to a practical question: where is the real constraint in a business?
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The answer may be a weak sales handoff, forgotten website content, an outdated approval process, or search reporting that counts clicks but misses visibility. The posts favor diagnosing the whole system before adding another channel or producing more output.
Find the business problem before choosing a marketing channel
Anish argues that a client’s first request—such as SEO or Google Ads—may not identify what is holding growth back.
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Start by understanding the sales cycle, lead quality, and where deals stall; the right response could be paid media, a website fix, or a change to the sales process. In another post, they call this “curiosity before capability”: ask what went wrong before pitching a solution.
A prospect burned by three agencies described campaigns that reported impressions but never examined the CRM or asked whether the leads could close. Anish says the discovery call instead focused on the sales cycle, stalled deals, and what counted as a good lead.
For small businesses, Anish sees demand leaking in everyday interactions before a sale, not just in ad targeting.
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A business may not be able to outspend a larger competitor, but it can improve how it handles calls, messages, reviews, and complaints. Treating these moments as separate from marketing leaves their effect on trust and conversion unmanaged.
Anish describes an untracked phone call, a reply to a direct message, or a front-desk response to an annoyed prospect as moments that can shape a decision. A thoughtful review response can also help people discover the business.
Anish argues that some companies need a clear view of what they already have more than they need new content.
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Without an inventory, teams may keep publishing while old pages are outdated, underused, or broken. An audit helps distinguish a demand-generation problem from a failure to use existing material; each calls for different fixes and budgets.
The post points to years of blog posts, landing pages left over from ended campaigns, product pages tied to past launches, and resource pages linking to deleted PDFs.
Redesign the process instead of putting AI on top of it
Anish distinguishes changing a process from automating its existing steps.
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The key question is whether the process would be built the same way from scratch today. They also note that redesign can challenge established roles and approval layers, not just save time.
A five-round creative approval chain made more sense when producing one variant took a week. If AI can produce dozens quickly, Anish argues, the approval process—not the writing—may be the real bottleneck.
Anish argues that click-based reporting can miss brand visibility when search results answer questions without sending people to a website.
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They propose tracking whether a brand appears in AI-generated answers, featured snippets, and knowledge panels alongside conventional traffic measures. The post gives a specific no-click-search figure but does not cite its underlying data; verify the number before reuse.
Anish says a brand may appear on page one or in an AI Overview while analytics show no visits from that search. The post highlights the gap between visibility and measurable traffic.
Answers the basic question, “what does this person actually do?”
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This is the clearest short statement in Anish S.’s posts of their positioning: diagnosing which lever matters, not selling a channel.
The post contrasts two opening calls.
Five years ago, a client arrived saying “we need SEO” or “we need Google Ads”; now, they arrive saying they are spending money and do not know what is working.
The argument: first understand the business well enough to know which lever matters now.
The answer might be paid media, a website fix, or “your sales process is where the leads are dying.”
Anish S. argues that clients who ask for clarity are the ones who scale because they are not committed to a channel in advance.
This is a positioning piece, not a how-to: it describes each lever in one line and names no client, metric, or campaign.
For brands planning a campaign
Use this to frame a discovery conversation around diagnosis rather than execution. If a brief arrives with a channel already specified, this writer’s stated approach is to question whether that channel is the real constraint before committing budget.
Answers “how does this person run a client relationship?”
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It is the most concrete account of their discovery process and the failure pattern they say they are addressing.
Anish S. recounts a discovery call where the prospect opened with “we've been burned by three agencies, why would you be any different?”
Instead of pitching, they asked what went wrong each time.
The same pattern emerged all three times: the agency promised results, ran campaigns for 90 days, delivered a report full of impressions, and never asked what happened after the click.
No one spoke to the sales team, looked at the CRM, or checked whether the leads could close.
The call continued with questions about the sales cycle, where deals stall, and what a good lead looks like.
The prospect said the difference was feeling that Anish S. was curious about their business rather than their budget.
The post sums this up as “curiosity before capability.”
It is an anecdote without named clients or numbers, so treat it as a stated approach, not documented performance.
For brands planning a campaign
Doubles as a buyer’s checklist for selecting a fractional strategist or agency: ask whether the first call probes the sales process and CRM, and whether reporting connects to closed deals. It also sets the expectation that a first meeting will focus on diagnostic questions, not a capabilities presentation.
Answers “what does this person think is actually broken in small-business marketing?”
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It gives the clearest single argument about where demand leaks, in plain operational language.
The post argues that small-business marketing starts with interactions nobody measures: the untracked phone call, the reply to a DM, the way a front desk handles someone already annoyed.
A small business cannot out-spend larger competitors on media, the post says, but can out-behave them in the moments that shape a decision, most of which happen before the sale.
Big companies script these interactions for consistency, which the post frames as forgettable sameness; small businesses have no script or process, so quality depends on who answers the phone that day.
The mistake, according to the post, is owners separating “marketing” from “customer service.”
A response to a Google review is distribution; a thoughtful DM reply is a conversion asset.
The post closes by offering a framework to readers who comment, making it a lead-capture post rather than a case study.
For brands planning a campaign
For local or small-business clients, this shifts the question from how much to spend on ads to how the business answers the phone and replies to messages. The post’s failure mode: funding more media without fixing the handoff between interest and purchase.
Answers “what does this person do first on a content engagement?”
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It clearly explains why they audit before producing and what readers can check on their own sites.
The post lists a familiar inventory: three years of blog posts, landing pages untouched since a 2023 campaign ended, product pages built for a launch that already happened, and resource pages linking to PDFs someone deleted.
The claim: this is not a strategy gap but a visibility gap.
You cannot prioritize what you cannot see, and most people running sites know little beyond the homepage and whatever is ranking that quarter.
The post distinguishes between a demand-generation problem and a failure to use what the business has already built; these require different fixes and budgets.
It also argues that producing new content feels like progress while existing pages with real traffic and intent sit outdated or work against conversion.
The post shows no audit process, tool, or example spreadsheet.
For brands planning a campaign
Sequence an audit before new production. If a client’s issue is unused or decaying pages, a plan focused only on new content spends budget twice: once on the new asset and again on traffic the old asset already had.
Answers “what does this person say about AI and marketing operations?”
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It is the sharpest post here on why faster execution is not the same as a changed process.
The post argues that most AI adoption takes an existing process, drops a chatbot on top, and measures time saved on a task nobody questioned in the first place: “automation with better PR.”
Companies that get value instead ask whether they would build the process this way if it did not already exist.
The marketing-operations example is specific: a client wants AI to write and approve creative and report faster, but the real unlock is recognizing that a five-round approval chain made sense when producing one variant took a week and makes far less sense when fifty variants can be produced before lunch.
The bottleneck was the process, not the writing.
Anish S. also states the uncomfortable consequence: rethinking a process usually means eliminating a role, meeting, or approval layer that used to justify someone’s job.
Speeding up the old process protects the org chart; redesigning it does not.
The post names no client or tool and reports no measured result.
For brands planning a campaign
When scoping AI or operations work, sell process redesign rather than speed.
Expect internal resistance: the current bottleneck may serve an internal purpose.
A useful opening question for a client: would they build the process this way from zero?
Answers “how does this person think about search and measurement now?”
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It explains in one short read the shift underlying their arguments about content and visibility.
The post states that 64.82% of Google searches in 2026 produce no outbound click, up from 50% in 2019, because answers arrive through AI Overviews, featured snippets, and knowledge panels.
The conclusion: visibility and traffic have decoupled.
A brand can rank on page one or be cited in an AI answer and still record no sessions, so click-based measurement frameworks understate what is happening.
The advice is to shift clients from “traffic we can measure” to “presence we can verify,” tracking whether a brand appears in AI-generated answers, snippets, and panels rather than only its ranking positions.
The figure is presented without a linked source, and the post shows no tracking method or example report.
For brands planning a campaign
Build citation and presence tracking into the measurement plan alongside sessions.
Warn clients in advance that click-only reporting will look like failure during a period when this writer says search behaviour has changed.
Treat the 64.82% figure as Anish S.’s claim, not an independently checked statistic.
Anish describes a shift in what clients ask for: instead of requesting SEO or Google Ads, they arrive saying they are spending money but do not know what is working.
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The response starts with business questions — what the sales cycle looks like, where deals stall, what makes a good lead for the sales team — and only then recommends a channel, which might be paid media, a website fix, or the sales process itself.
Another post describes a discovery call with a prospect who had been burned by three agencies.
Anish did not pitch; they asked what had gone wrong each time, summing up the standard as "curiosity before capability."
Question-led discovery: ask about the sales cycle, where deals stall, and what makes a good lead before recommending tactics.
For brands planning a campaign
Relevant for an agency or client seeking a strategist who starts with a diagnosis, not a channel brief.
Expect early conversations to focus on the sales process and lead quality rather than deliverables.
A sample content plan should show how the recommended work follows from that diagnosis.
Anish argues that small businesses lose more demand in everyday interactions than in ad targeting: an untracked phone call, a reply to a direct message, or the way a front desk handles someone who is already annoyed.
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Small businesses cannot outspend bigger competitors on media, the post says, but can outperform them in these moments.
A response to a Google review, an answer to a DM, or an in-person complaint resolution can function as a distribution and conversion asset.
The common mistake, Anish writes, is separating marketing from customer service.
The post invites readers to comment to receive a framework but does not say what it includes. Ask to see the framework before using it in a campaign.
For brands planning a campaign
Fits briefs on conversion, retention, and service businesses where interest converts in conversation rather than on a page. A campaign could pair paid media with response-time standards and scripts for front-line channels, rather than treating them as a separate department.
The recurring claim is that most companies do not have a content problem but a "we forgot what we have" problem: years of blog posts, landing pages from ended campaigns, and resource pages linking to deleted files.
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Anish calls this a visibility gap rather than a strategy gap: you cannot prioritize what you cannot see. A full site audit, they argue, distinguishes a demand-generation problem from a "not using what we already built" problem, since each requires a different budget and fix.
For brands planning a campaign
A brand considering Anish for content work should expect an inventory and performance audit before new production, and should be prepared for a recommendation to publish less rather than more.
AI as workflow redesign, not a faster legacy process
Anish argues that most AI efforts amount to automation with better public relations: the same workflow with a chatbot on top.
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The question that separates useful work, the post says, is whether the workflow would be built this way if it did not exist yet.
The concrete example is marketing approval chains designed for a time when producing a variant took a week.
When fifty variants can be produced before lunch, five rounds of sign-off become the bottleneck, not the writing.
The post states plainly that redesigning a workflow usually means removing a role, a meeting, or an approval layer that used to justify someone's position.
The post names organizational resistance but offers no change-management method beyond a reframing question. Ask what support they provide for managing the change.
For brands planning a campaign
Relevant for marketing-operations or AI-adoption campaigns where the client wants process change rather than faster output. The work implies internal change management and stakeholder sensitivity, so a campaign should name who owns the workflow and who will be affected by redesigning it.
Anish states that nearly two thirds of Google searches now end without an outbound click, up from about half in 2019, and that visibility and traffic have therefore decoupled: a brand can rank on page one and be cited in an AI Overview while analytics show no sessions for the week.
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The recommendation is to move clients from "traffic we can measure" to "presence we can verify," tracking appearances in AI-generated answers, featured snippets, and panels rather than ranking positions alone, and rebuilding reporting around that.
The 64.82% figure is given without a cited source, so a brief should treat it as the creator's stated number and verify the underlying data before quoting it.
For brands planning a campaign
Suits briefs about search strategy, content investment, and reporting. A campaign plan could include a presence-tracking method and revised client reporting that captures AI citations and panel appearances, not just clicks.
The posts name no client, employer, sponsor, partner, or portfolio business.
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Several use first-person plural — “we see this constantly in marketing operations,” “I'll send you our framework,” and a discovery-call story in which the writer asks about a prospect's sales team — suggesting Anish S. works inside a team or agency that sells marketing services.
They never name that business, so no brand relationship can be recorded.
The only company named in the posts is Google, which appears as the search platform whose AI answers are changing client visibility, not as a client, sponsor, or partner.
Anish S. writes short LinkedIn posts, usually opening with a claim that the obvious answer is wrong, then offering a reframe.
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Start with the client thread: posts about discovery calls that begin with the sales cycle, where deals stall and what makes a good lead for the sales team before any channel is recommended; and a post arguing that phone calls, direct-message replies and review responses are marketing, not a separate customer-service function.
Site and operations posts extend the same habit: auditing pages a company already owns instead of publishing more, and asking whether an approval chain built for slow production should survive when AI can produce dozens of variants.
A newer line of argument covers search, shifting from counting clicks to tracking whether a brand appears in AI answers, snippets and panels.
Read three or four in sequence to see the pattern: familiar problem, reframe, concrete example, then a question or an offer of a framework in exchange for a comment.
Both write about search visibility as something earned through substance rather than volume.
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David Arato argues in a post on information gain that a practice-area page repeating what 400 other law firm sites say gets ignored.
Firms stand out by writing about specific case types, real outcomes and a point of view nobody else in their market has.
That instinct echoes Anish S.'s point that clients now buy clarity about what works rather than another channel.
Arato's examples stay within law firm marketing—practice-area pages, blog posts, FAQs, press releases and mill-produced content—and recommend writing from case experience. Anish S. ranges wider, covering sales processes, front-desk responses, AI approval chains and search measurement, without legal-specific detail.
Both treat AI-era search as a problem of being understood and measured, not just ranked.
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Azhan Shafique describes a surgical instrument manufacturer that had certificates, memberships and backlinks but was inaccurately described by ChatGPT and Google AI. That overlaps with Anish S.'s claim that a brand can be cited in an AI answer while the analytics dashboard shows nothing.
Shafique writes execution reports based on named client situations—reindexing pages, correcting entity information, and checking keyword difficulty and search intent one by one—and closes by naming Agenex Digital. Anish S. questions the process around the work rather than describing platform tactics, and rarely names clients.
Both write about integrating AI into marketing work.
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Mitesh Patel's post on Claude Skills describes writing a creative process once so briefs, hooks and copy don't need to be re-explained in every conversation.
That echoes Anish S.'s argument that the approval chain, not the writing, becomes the bottleneck once dozens of variants can be produced.
Both also frame search as splitting into SEO, answer-engine and AI-recommendation layers.
Patel's material offers ecommerce and agency creative teams a hands-on playbook, with skill templates and a step-by-step setup.
The SEO/AEO/GEO post ends with a three-part action framework.
Anish S. writes more as an agency principal describing how discovery calls run, with fewer step lists and tool-level details.
The posts do not say who follows Anish S. or whether the audience matches a particular B2B buyer group.
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The posts cover agency-client relationships, small-business marketing, content audits, search visibility, and AI workflows; that shows the subjects of the content, not the professional makeup of its audience. Before proposing a placement, ask for recent audience survey results or another documented source that shows the roles, industries, and seniority of the people Anish reaches.
Anish S.'s posts do not set out a partnership product.
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They include no sponsorship menu, media kit, rate card, disclosure term, editorial-approval rule, or dedicated partnership inquiry route.
Every post is written for a marketing, agency, or small-business owner audience rather than for brands shopping for placement.
The closest item to an offer is a comment-to-message prompt in one post that promises to send a customer-experience framework—a lead-capture step for their own consulting work, not a bookable paid placement.
For a buyer, any collaboration would likely be negotiated case by case as a consulting, fractional-strategist, or client relationship rather than through a defined sponsored-content slot.
Ask them directly to confirm scope, deliverables, pricing, and disclosure.
A strong fit is a fractional-strategy retainer for a client already spending money but unable to say what is working, where the engagement reaches beyond a single channel and into the sales process.
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Brief A: a B2B professional-services firm with 40 to 80 staff and several locations.
It spends roughly 15,000 dollars a month across paid search and a blog that has not been audited in two years.
The sales team says leads arrive but do not close.
The marketing manager has a dashboard full of sessions and impressions, with no way to connect either to a signed contract.
The proposed engagement is a three-month fractional retainer with access to the CRM and two people on the sales team, plus a sample content plan due in the first three weeks.
Why this fits Anish S.'s writing.
The client's opening question echoes the shift in buyer behaviour Anish describes: not “we need SEO” but “we are spending money and we do not know what is working.”
The post sets out a diagnostic sequence that could point to paid media, the website or the sales process itself.
A post about burned prospects shows how a first engagement should start: with questions about the sales cycle, where deals stall and what the sales team considers a good lead, before any capabilities pitch.
The audit post argues for an inventory and performance review as the first deliverable, rather than new production.
It also distinguishes a demand-generation problem from a failure to use what has already been built, since each calls for a different budget.
For a services firm that converts interest in conversation, the untracked phone call, unanswered direct message and front-desk response are framed as distribution and conversion assets—directly relevant when leads exist but die at handoff.
The commercial shape also matches what Anish's posts describe.
The nearest thing to an offer is a comment-to-message prompt promising a customer-experience framework.
The practical route for a brand or agency is a consulting or fractional relationship negotiated case by case, as Brief A proposes.
A 30-minute discovery call is a reasonable ask: the posts describe discovery as question-led, not presentation-led.
One point to verify before committing: the audit post does not show the process, tools or sample output.
Ask to see those, along with the customer-experience framework referenced in the touchpoint post.
Use the first sample plan to assess the method, not as sales material.
When to choose a specialist
A poor fit is a placement brief with a fixed commercial format and channel mandate, on a tight deadline.
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Brief B: a software brand wants three sponsored LinkedIn posts across the next quarter, priced against a guaranteed reach figure, with a media kit, an agreed disclosure line and creative delivered within ten working days of signing.
The channel is already decided; the sales team and CRM are out of scope, and the brand explicitly does not want strategy work.
A related mismatch is a pure paid-media scale-up: double spend across two channels in sixty days, with reporting on impressions, clicks and cost per lead.
Why this does not fit.
Anish S.'s posts do not specify sponsorship formats, pricing, disclosure terms or an inquiry route, so they offer no basis for quoting or guaranteeing reach.
The only items resembling an offer are comment-to-receive prompts, which capture leads for Anish's consulting work rather than promote a bookable paid slot.
The posts also provide no information about readers' roles, industries or seniority.
A reach guarantee would be unsupported.
And the stated approach runs against Brief B's premise.
The AI post says that making an existing workflow faster preserves the org chart and the old ceiling; the clarity post says clients who arrive committed to a channel are the ones who do not scale.
A brief that removes diagnosis from scope and rules out process questions is close to the opposite of the approach described.
If a brand still wants to test a placement, ask directly for audience composition, a rate card and disclosure terms.
Impressions alone would need a second measurement layer to show outcomes.
A worked campaign example
A worked angle for Brief A, framed for the 30-minute call rather than beforehand.
Position the work as “fix the room before buying more traffic.”
In the first five minutes, make the case that the firm is probably not short of interest; it is losing prospects between interest and purchase.
The first month should reveal what is already happening, not buy more media.
Use the middle fifteen minutes for questions, not a pitch.
Ask: What does your sales cycle look like from end to end?
Where do deals stall?
What does the sales team consider a good lead, in its own words?
Who owns the site beyond the homepage?
What did you spend last quarter, and what can you attribute to it?
Who answers the phone and messages on a Tuesday afternoon?
Then state the hypothesis plainly: the likely constraint is the handoff, not the ad account, and the first three weeks will test that.
Sample content plan, 90 days, built for this campaign:
Weeks 1 to 3, inventory and measurement baseline.
List every live page, its traffic and its stated intent.
Flag pages built for finished campaigns, resource pages linking to files that no longer exist, and pages that rank but convert nothing.
Separate the firm's demand-generation problem from its unused-asset problem; each calls for a different budget.
Deliverable: a short diagnostic memo with a prioritised fix list, not a spreadsheet for its own sake.
Weeks 3 to 8, pre-sale touchpoint standards.
Set a response-time target for inbound calls and direct messages.
Write one response pattern for negative and positive reviews.
Define exactly where marketing hands a lead to sales and what information goes with it.
Deliverable: a one-page standard and a named owner for each step.
Weeks 6 to 12, presence tracking.
Add a presence section to the existing report, covering whether the firm appears in AI answers, featured snippets and panels, alongside sessions and rankings.
Warn the client that a click-only dashboard will look flat during this window.
Anish cites 64.82 percent of 2026 Google searches ending without an outbound click; the post does not link a source for that number.
Use it internally as a stated claim, and verify the underlying data before including it in any client-facing document.
How to read the 90 days.
Track cost per qualified opportunity and stage-to-stage conversion alongside the presence count.
Expect the first month to produce findings before leads.
This angle depends on the client opening the CRM and bringing the sales team into the conversation; without that access, the diagnostic becomes another impressions report.
Consider David Arato when the client is a law firm or another practice-area professional-services firm whose pages read like every competitor's.
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The two posts make the information-gain argument directly: content that adds something new is treated as a resource, while content that repeats what already exists gets ignored.
Law-firm content is generic by design because content mills and AI both produce the average of what already exists.
If the diagnosis is "our practice-area pages are interchangeable and we need substance drawn from real experience," this is a closer match than a generalist strategist.
The second post extends the same argument to AI search, pitching content work at answers and citations, not only Google rankings.
This is a poor fit for a campaign aimed at improving pre-sale handoffs.
Anish S.'s posts do not explain how to improve response times, front-desk behaviour or leads dying between interest and purchase, and they do not address sales-process questions or CRM access.
A firm whose leads arrive but do not close is being offered a content-substance fix, which may target the wrong layer of the problem.
Consider Azhan Shafique when a site already has authority — certifications, regulatory references, industry memberships, a long history and a decent backlink profile — but search engines and AI assistants describe the business inaccurately or not at all.
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One post describes exactly that case at a surgical instrument manufacturer, where the fix was the information layer: who the company is, what it manufactures, its locations, product categories, expertise and history, plus re-indexing key pages.
The other covers the decision that comes before it, arguing that AI-generated keyword lists provide volume without difficulty, intent or a view of who currently ranks, and that low-competition terms with individually checked intent come first.
For a client whose funnel works and whose discoverability does not, that ordering is more useful than a broad strategy retainer.
The work described is SEO and generative-engine optimisation on a manufacturer's site and a new site, so it does not touch the sales-side handoff covered in Anish S.'s posts or say whether the work can run alongside a services sales team. The keyword post reads as a positioning statement for one agency; it shows how they argue about keyword selection, not a campaign result.
Consider Mitesh Patel when the client is an ecommerce or DTC brand, or an agency producing creative for those brands, and the constraint is creative operations alongside search presence.
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The Claude Skills post names a concrete operational problem — every chat starts from zero, brand context is re-pasted, and the brief format is re-explained — and a specific fix: write the creative process once as a reusable file so research, briefs, hooks and copy all draw on the same instructions and can be shared across a team. The second post sets out the SEO, AEO and GEO split for product brands, including where AI product recommendations come from, offering a usable framing for a brand trying to work out why it ranks but is not recommended.
This is a poor fit for a campaign aimed at B2B professional-services retainers.
The examples are ecommerce product discovery and DTC creative production, not B2B professional-services retainers, and the posts do not deal with sales cycles, CRM access or leads stalling at handoff.
Both posts end with a gated next step — comment to receive a playbook — while Anish S.'s posts invite a conversation, so a buyer wanting an unprompted sample of the method has less to judge.
This profile draws on six LinkedIn posts written by Anish S., covering client discovery and agency selection, small-business pre-sale touchpoints, website and content audits, AI workflow design, and search measurement.
The posts are presented in full rather than as headlines or snippets.
The posts carry no publication dates, so the profile describes recurring arguments and habits of thought rather than a timeline of how Anish S.'s positions changed.
The posts mention no books, shows, courses or other long-form work.
Several posts use first-person plural language about clients, which suggests the writing comes from inside a team selling marketing services, but no employer, client, sponsor or partner is named anywhere in the posts, so no brand relationship is described.
Audience composition and any commercial or sponsorship terms are also absent, so nothing here should be read as a claim about who reads the posts or what a placement would cost.
The comparison section draws on published LinkedIn posts from three other creators, dated March to September 2026, covering law firm content, technical AI-search work, and AI creative workflows for ecommerce brands.
Those posts are available as published writing; nothing in them shows that a compared creator is available for hire.
Where a figure appears in the posts without a cited source, such as the stated share of Google searches ending without a click, it is presented as the creator's claim rather than as verified data.
