Frederick Lansky is a fractional CMO who focuses on ecommerce growth, conversion improvements, and measuring marketing by its effect on sales. Lansky's posts discuss practical fixes such as checkout speed, email flows, and tracking whether advertising leads to transactions.
Frederick Lansky is a fractional CMO focused on ecommerce growth.
Their posts make a practical case for fixing conversion problems and judging marketing by whether it produces sales—not just clicks or polished dashboards.
They’re useful for founders and marketers weighing where to focus next, from checkout speed and email flows to channel measurement and strategic decisions.
Lansky’s recurring idea is to measure the business result that matters, then address the constraint the evidence reveals.
In one example, they say an eight-figure Bitcoin ATM company tied its marketing channels to ATM transactions, paused paid search when clicks brought no extra transactions, and tested local Google Maps listings against a control group.
They report a 15% rise in Maps click-through and seven figures in added revenue.
The same focus shapes their ecommerce advice: before increasing ad spend, check for unnecessary checkout steps, slow mobile load times, and welcome, abandoned-cart, and post-purchase emails that may need attention.
(Lansky’s checkout and email checklist) They also draw a boundary between AI-assisted execution and strategy, arguing that choosing the wrong option for a particular business takes judgment beyond asking a tool to agree with a plan.
(Lansky’s view on AI and strategy) Together, these posts offer readers a grounded way to question activity that looks successful but may not be moving sales.
Frederick Lansky writes about ecommerce growth as a measurement and fundamentals problem, not an advertising problem.
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Across the posts, their recurring argument is that marketing spend should be judged by whether it produces sales—ATM transactions in one account, Shopify orders in another—and that the fastest gains often come from unglamorous fixes: checkout steps, mobile load speed, the email list and neglected lifecycle flows. They also argue that local listings are a real channel for businesses with physical locations, and that AI can’t supply strategic judgment because a model will agree with whatever plan it’s given.
Judge marketing by transactions, not dashboards
Lansky’s core position: a channel earns its budget only when it shows up where the business actually gets paid.
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Impressions, clicks and agency dashboards matter less than the revenue event.
Their method is to define that event first, instrument it, then hold every channel to it.
For a Shopify store, they reduce the test to a question a founder can ask an agency: which clicks turned into orders you wouldn’t have gotten anyway?
At an 8-figure Bitcoin ATM company with roughly 550 locations, paid search delivered 12.1M impressions and 24,597 clicks without producing any additional ATM transactions, so Lansky paused it.
Before changing campaigns, they made ATM-level transactions the source of truth and rebuilt reporting in Looker Studio so each channel could be traced to real revenue.
From then on, every channel had to show up in transactions to keep its budget.
Lansky argues that fixing checkout usually makes more money than the next campaign, and that most brands above $3M skip it because it’s boring.
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When growth slows, the reflex is to increase the ad budget or hire a new agency; the cheapest money is usually in funnel friction and stale automation deferred quarter after quarter. They offer a short, checkable list instead of general growth advice.
Lansky’s checks include how many clicks it takes to get from a product page to a completed order, how fast the site loads on a phone over a normal connection, and when the welcome, abandoned-cart and post-purchase emails were last edited.
They tie this approach to growing iChess.net to seven figures with no outside money through email, funnels and sales to an existing list.
The business was sold and is now part of Chess.com.
Lansky treats list building and lifecycle email as revenue channels a brand controls, not add-ons to a paid media plan.
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They return to the same failure pattern: money goes to automation and design while assets that compound—the list, the flows and the site itself—go unbuilt or unmaintained.
Lansky opens the newsletter with two examples: a brand paying an agency $30,000 for an automated funnel while nobody was building its email list, and another about to spend $20,000 on a redesign while its cart took 30 seconds to load.
For multi-location businesses, treat each location as its own listing
For businesses with physical locations, Lansky says the key question is where customers actually find them—which may not be the channel the marketing plan assumes.
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Their method is to run local listings as a tested program with a control group, measuring location revenue rather than listing metrics alone.
In the Bitcoin ATM account, Google Maps turned out to be where people actually find an ATM.
Each of the roughly 550 locations was treated as its own local listing and tested against a control group.
Maps click-through rose 15% in one quarter, which Lansky attributes to seven figures of extra revenue at the ATMs.
Lansky argues that a model that agrees with whatever it’s given can’t help with the strategic challenge of choosing which of ten reasonable options is wrong for a particular business.
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They don’t dismiss the tools—they use them for first drafts and busywork—but distinguish execution from judgment. For strategic advice, they say, qualification comes from having owned a P&L and lived with the decision.
Lansky points out that asking AI whether a strategy is good gets agreement—and pitching the exact opposite plan gets agreement again. Over the past year, they say, they’ve watched founders hand major decisions to AI, sometimes with bad results that an operator would have spotted in five minutes.
Publishes a monthly newsletter for ecommerce founders and operators
Several posts direct readers to Common Sense Conversion, which Lansky describes as a monthly write-up on the unglamorous work that makes ecommerce brands money and the hard truths about ecommerce growth.
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It’s the stated home for the positions above, including the checklist they’d apply before a brand spends agency-sized budgets.
The first issue is described as containing two opening examples—the $30,000 agency funnel with no list building, and the $20,000 redesign alongside a 30-second cart load—plus what Lansky would check before spending that kind of money.
The newsletter framing is consistent across the posts; nothing in the posts here shows the position changing over time.
Shows how to tell whether paid marketing is producing money rather than clicks.
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It is the clearest example of a method applied end to end: pick the real revenue event, instrument it, test against a control, and cut what does not appear.
An 8-figure Bitcoin ATM company with about 550 locations had flat revenue for over a year because marketing happened online while transactions happened at the machines.
Frederick Lansky describes making ATM-level transactions the source of truth and building Looker Studio reporting that tied every channel back to real revenue.
Paid search had produced 12.1M impressions and 24,597 clicks but no additional transactions, so it was paused.
Google Maps turned out to be where people actually find an ATM; each location was treated as its own local listing and tested against a control group.
Maps click-through rose 15% in one quarter, worth seven figures in extra revenue.
After that, every channel had to show up in transactions to keep its budget.
For brands planning a campaign
A brand hiring for conversion and accountability should expect budgets defended with transaction data and control-group tests, not dashboard metrics. Before a campaign starts, ask which business event counts as revenue and how it will be instrumented.
Shows how this person decides where to spend effort first and what an ecommerce audit looks like in practice.
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A newcomer gets three checkable items rather than general growth advice.
The argument is that fixing checkout usually makes more money than the next campaign, and that most $3M+ brands skip it because it is boring.
The piece lists what to check first: how many clicks it takes to get from a product page to a finished order, how fast the site loads on a phone on a normal connection, and when the welcome, abandoned-cart and post-purchase emails were last edited.
Frederick Lansky says iChess.net grew to seven figures with no outside money through email, funnels and selling to an existing list, then was sold and is now part of Chess.com.
For brands planning a campaign
If a brand is weighing a larger ad budget against a conversion project, this sets out the stated priority order. It also suggests the first work product from an engagement would be a short list of concrete store and email checks.
Shows what this person counts as misplaced spending, in two sentences.
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Useful for a newcomer who wants the short version before reading longer examples.
Two short failure patterns are named: a brand paying an agency $30,000 for an automated funnel while nobody was building its email list, and another about to spend $20,000 on a redesign while its cart took 30 seconds to load. Both are opening examples in the first issue of a newsletter about the boring parts of ecommerce that make money.
For brands planning a campaign
Use this as an intake checklist: if funnel automation or a redesign is on the table, confirm that list building and cart speed are not the actual constraints.
Shows what this person thinks cannot be delegated to tools: judgment formed by owning a profit-and-loss result.
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Frederick Lansky points out that asking an AI whether a strategy is good gets agreement, and pitching the opposite plan gets agreement again.
AI is useful for execution, first drafts and busywork, they say.
Strategy means knowing which of ten reasonable options is wrong for a particular business.
The post says founders have handed large decisions to AI over the past year, with calls that an operator would have spotted as bad in five minutes, and recommends talking to someone who has owned a P&L and lived with the decision.
For brands planning a campaign
Test this person's strategy judgment on a real trade-off rather than tooling. Ask for an example of an option they recommended rejecting and why.
Shows how Frederick Lansky describes the engagement and who it is aimed at, in their own words.
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It also outlines the qualification step a brand would go through.
This post opens by pointing to a recent client review, then describes the service: fractional CMO work specializing in 8-figure DTC ecommerce on Shopify and WooCommerce, with SaaS also possible.
It offers a free 45-minute growth consult to businesses above roughly $500,000 a year in revenue that feel they have hit a ceiling.
The post says the call would otherwise cost $250 on Upwork, would be treated like a paid consult, and is intended to help build the portfolio with two more clients.
For brands planning a campaign
The stated target is 8-figure DTC ecommerce, the entry offer is a free consult, and the stated reason for it is portfolio building. Ask to see the client review referenced at the start of the post.
Publishes a newsletter aimed at ecommerce founders and operators4
Tie marketing spend to transactions, not dashboards2
Fix the checkout before funding the next campaign2
Own the email list and the flows around it2
Treat local listings as a channel for multi-location businesses1
Use AI for execution, not for strategy1
Fractional CMO engagements with a revenue floor and platform scope1
Tie marketing spend to transactions, not dashboards
Lansky describes an 8-figure Bitcoin ATM company with about 550 locations where paid search generated 12.1M impressions and 24,597 clicks without producing extra ATM transactions, so they paused it.
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Before changing campaigns, they made ATM-level transactions the source of truth and rebuilt reporting in Looker Studio to trace every channel back to real revenue.
After that, each channel had to show up in transactions to keep its budget.
They apply the same test to a Shopify store: if the agency dashboard looks great but the bank balance has not moved in a year, ask which clicks produced orders the brand would not have gotten anyway.
Made actual transactions the source of truth and rebuilt channel reporting before changing any campaign.
For brands planning a campaign
A brand working with Lansky should expect revenue-linked measurement as the first deliverable and be prepared to pause channels that cannot show up in sales, rather than just platform metrics.
Lansky argues that repairing a checkout usually makes more money than the next campaign, and that most $3M+ brands skip it because it is boring.
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Their checks include the number of clicks from a product page to a completed order, site load speed on a phone over a normal connection, and when the welcome, abandoned cart and post-purchase emails were last edited.
They point to their own history growing iChess.net to seven figures with no outside money through email, funnels and selling to an existing list.
The business was sold and is now part of Chess.com.
For brands planning a campaign
For a DTC brand whose growth has slowed, expect an audit of funnel steps, mobile speed and lifecycle email before any recommendation to increase the media budget.
The examples Lansky uses to open their newsletter: a brand paying an agency $30k for an automated funnel while nobody was building its email list, and another about to spend $20k on a redesign while its cart took 30 seconds to load.
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They argue that the fastest money is usually in the unglamorous parts of the business—a recurring subject of the newsletter they write.
For brands planning a campaign
A brand should expect retention assets, especially list growth and lifecycle flows, to be treated as revenue channels rather than add-ons to a paid media plan.
Treat local listings as a channel for multi-location businesses
In the ATM account, Lansky found that people used Google Maps to locate an ATM, so they treated every location as its own local listing and ran tests against a control group.
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Maps click-through rose 15% in one quarter, which they attribute to seven figures of additional revenue at the ATMs.
For brands planning a campaign
For a brand with physical locations, expect a per-location listing program and controlled tests rather than one national campaign, with location-level revenue as the measure of success.
Lansky writes that AI will agree if you ask whether your strategy is good, then agree again if you pitch the exact opposite plan.
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They use it for first drafts and busywork but argue that strategy is a different job: knowing which of ten reasonable options is wrong for a particular business. They say founders who handed AI their big calls made decisions an operator would have caught in five minutes, and frame owning a P&L and living with the decision as the relevant qualification for giving that advice.
Positions hands-on P&L ownership, not tooling, as the basis for strategy recommendations.
For brands planning a campaign
A brand evaluating Lansky should expect to be challenged on its plan, not simply validated, and should not expect strategic calls to be delegated to an AI tool.
Fractional CMO engagements with a revenue floor and platform scope
Lansky introduces themself as a fractional CMO specializing in 8-figure DTC ecommerce brands on Shopify and WooCommerce, with SaaS also in scope.
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They offer a free 45-minute growth consult, which they compare to a $250 paid consult on Upwork, to businesses doing over $500k a year that feel they have hit a ceiling. They say they are looking for two more clients to add to their portfolio.
Lansky names SaaS as an additional fit, so the scope is not ecommerce only.
For brands planning a campaign
The stated revenue floor and platform focus mean a brand below roughly $500k a year, or outside Shopify, WooCommerce and SaaS, is likely outside Lansky's scope.
Publishes a newsletter aimed at ecommerce founders and operators
Several posts direct readers to Common Sense Conversion, which Lansky describes as a monthly write-up on the unglamorous work that makes ecommerce brands money and hard truths about ecommerce growth, including the checklist they would apply before a brand spends agency-sized budgets.
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For brands planning a campaign
The recurring audience is ecommerce founders and operators making budget and funnel decisions—the audience a brand or sponsor would reach through this work.
Posts mention one owned business, the Common Sense Conversion newsletter, and iChess.net, which Lansky says they grew and which was sold and is now part of Chess.com.
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They do not mention sponsor or affiliate relationships; ask for disclosure. Unnamed client and agency work appears in the posts but cannot be attributed to a named brand; ask for attributable examples.
Common Sense Conversion
Owned business
Lansky started Common Sense Conversion, a newsletter about ecommerce conversion and the routine fixes that make brands money.
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Posts invite readers to subscribe and mention the first issue.
Start with the short first-person posts, where the working method is easiest to see.
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Most follow the same shape: a specific number or failure from a client account, the check they would run instead, and a pointer to their newsletter.
The Bitcoin ATM account is a good example: paid search ran 12.1M impressions and 24,597 clicks without moving ATM transactions, so Lansky rebuilt reporting to require every channel to prove itself in revenue.
Their checkout and lifecycle-email posts show how they approach a brand that already has traffic and is about to spend more on ads; the AI-and-strategy post shows where they draw the line between tools and judgment.
Common Sense Conversion, their monthly newsletter, is where they say the same material appears.
Follow the comparison accounts below for the narrower craft of email deliverability, Klaviyo builds, and store platform choices, rather than the measurement-and-accountability argument that runs through their writing.
Raymond writes from first-hand client work about the lifecycle email a brand already owns.
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One post covers images disappearing from every live flow after a brand switched vendors; another walks through reactivating a dormant Klaviyo list with a sampled send and deliverability monitoring. Both writers treat the automations running quietly in the background as places where revenue is won or lost.
Raymond focuses on email and flow hygiene: whether a flow is broken and whether a list can be safely reactivated. Lansky works one level up, tying every channel to a revenue event and asking whether a budget should exist at all.
Shahria works on the same Klaviyo and ecommerce stack, describing flows, welcome and abandoned-cart automations, segmentation, sign-up forms, and account optimization as one system that should keep working without the owner.
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Lansky makes the same case: the list and flows are revenue assets, not add-ons.
Shahria writes as an implementer who builds the setup; the post about a responsive WooCommerce build documents a delivery, not an argument about where money should go. Lansky's material is advisory and measurement-first, aimed at deciding budgets and calling out spending that does not produce sales.
MAHI advises small ecommerce businesses and builds their stores.
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Both writers treat platform choice as a fit question rather than a default.
MAHI compares Shopify and WooCommerce by launch speed, staff usability, app cost, and flexibility, and runs one store on each.
Lansky makes a similar practical comparison when discussing whether to fix a checkout before funding a campaign.
MAHI's material is aimed at helping a small business get found and sell online, with checkout options and a portfolio of live stores as proof. Lansky writes for brands already spending at scale, where the questions are attribution, channel budgets, and accountability for results.
LinkedIn is the only channel with published audience information in the posts here.
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Frederick Lansky's posts show 1,209 followers on 2026-10-09.
That is a reach measure, not a measure of audience composition: Frederick Lansky's posts do not say anything about audience demographics or survey data on geography, age, gender, employer, role, or seniority.
Frederick Lansky's posts show content aimed at ecommerce operators and founders—conversion fixes, checkout friction, email flows, paid-search accountability, and fractional CMO work.
For a DTC brand assessing Frederick Lansky for fractional CMO work, the useful question is whether the LinkedIn audience contains the buyers and operators who would act on that content, not whether the posts are about ecommerce.
Request recent LinkedIn audience demographics (follower and post-viewer breakdowns) or an audience survey covering company type, revenue band, role, and geography.
Also request recent results from comparable conversion or retention engagements so audience reach can be matched to the needs of a DTC brand assessing Frederick Lansky for fractional CMO work.
Frederick Lansky's posts describe service-based commercial relationships, not sponsorships, affiliate arrangements, sponsored posts, or a media kit.
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They work as a fractional CMO for large ecommerce brands, take on a small number of clients at a time, and use a free 45-minute growth consult as the entry step.
Another recurring channel is a monthly newsletter, Common Sense Conversion, where readers are asked to subscribe; no advertising or sponsorship terms are published for it.
For buyers, the practical route is a consulting engagement rather than a paid placement.
Ask about pricing, availability, and scope, which the posts do not specify.
Fractional CMO engagement
Buyer requirement
Aimed at 8-figure DTC ecommerce brands on Shopify or WooCommerce, with SaaS businesses also named as a fit.
Expect a senior marketing lead working inside an existing team, not a fixed package of deliverables.
Measurement centers on business results: in one client account, marketing spend and channels were tied to ATM-level transactions, budgets were cut where channels produced no extra transactions, and tests ran against a control group.
Buyers should be ready to share transaction- or order-level data, not just platform dashboards.
Free 45-minute growth consult
Buyer requirement
Open to businesses doing over $500k a year that have hit a ceiling.
They present the call as the kind of work normally charged at $250, treat it as a paid consult, and aim to add two more clients.
Come with a specific growth problem and the numbers behind it.
The post gives no calendar link or booking process, so the practical route is a direct message or comment.
A DTC ecommerce brand doing roughly $3M–$10M a year on Shopify, with growth flat for a year or more, wants a fractional marketing lead judged on orders and revenue, not dashboard metrics.
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Lansky states the service directly: fractional CMO work specializing in 8-figure DTC ecommerce brands on Shopify and WooCommerce, with SaaS also in scope.
The offer includes a free 45-minute growth consult for businesses doing over $500k a year that feel they have hit a ceiling.
They also state a goal of adding two more clients.
The brand's target size, platform and entry step match the offer.
The method makes the fit specific.
In one client account described in the posts, an 8-figure Bitcoin ATM company with about 550 locations had flat revenue for over a year because marketing ran online while transactions happened at the machines.
The first move, they say, was to make ATM-level transactions the source of truth and rebuild reporting so every channel traced back to real revenue before touching any campaign.
Paid search was then paused after 12.1M impressions and 24,597 clicks produced no extra transactions.
Local listings became the working channel, and changes were tested against a control group.
Maps click-through rose 15% in a quarter, which they attribute to seven figures in extra revenue at the machines.
The same test applies to stores: if the agency dashboard looks good but the bank balance has not moved in a year, ask which clicks produced orders the brand would not have gotten anyway.
Their conversion priorities are concrete and checkable: how many clicks it takes to get from a product page to a finished order, how fast the site loads on a phone on a normal connection, and when the welcome, abandoned-cart and post-purchase emails were last edited.
They argue that fixing checkout usually makes more money than the next campaign, and that most $3M+ brands skip it because it is boring.
They cite two failure patterns: a brand paying an agency $30,000 for an automated funnel while nobody was building its email list, and another about to spend $20,000 on a redesign while the cart took 30 seconds to load.
Two points merit a discussion before hiring: pricing, availability and scope, which the posts do not specify; and the client review referenced in the service post, which is not included.
Ask for those terms and more outcome detail from past engagements; the ATM case is the only one described.
The posts also portray a lead who will test a plan rather than simply confirm it, which suits a brand that wants its assumptions challenged, not one looking for a contractor to execute a settled plan.
When to choose a specialist
A brand wants paid distribution for a launch—sponsored posts or a placement package across this person's channels, with a rate card, audience demographics such as geography, role, buyer type or company size, and reach or CPM-style guarantees to justify the spend.
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The posts describe service-based commercial relationships: fractional CMO work and a free growth consult as the intake step, plus a monthly newsletter, Common Sense Conversion, whose call to action is to subscribe.
They do not publish sponsorship or advertising terms for the newsletter, a partnership inquiry route or pricing.
Ask about availability, rates and audience data before considering a paid placement.
The only audience figure given is 1,209 LinkedIn followers; the posts provide no demographics or survey data on geography, age, gender, employer, role or seniority.
A reach-based campaign would therefore buy an unquantified audience.
The posts focus instead on conversion, attribution, checkout friction, email flows and judging marketing spend.
A brand that wants strategy endorsed rather than examined is also a poor fit.
The posts say that asking an AI whether a strategy is good gets agreement, and pitching the opposite plan gets agreement again.
They say founders have handed large calls to AI over the past year, including decisions an operator would have caught in five minutes.
The recommendation is to talk to someone who has owned a profit-and-loss result and lived with the decision.
A brand that wants its existing plan validated, or big calls handled by tooling, should expect documented disagreement instead of a deliverable.
A worked campaign example
A worked angle for the good-fit brief: a $4M Shopify brand, revenue flat for four quarters, whose team plans to raise paid social spend by 40% and wants a fractional CMO to run conversion and accountability.
Angle: defend the next dollar of media spend with order-level proof, and fix checkout before increasing the budget.
Days 1–2 — define the revenue event.
Choose the Shopify order that counts, such as paid orders net of returns and cancellations, and report it beside channel spend so paid social, paid search, email and organic each carry a number in orders rather than sessions.
The ATM case offers the template: nothing moved until transactions became the source of truth and reporting was rebuilt before a campaign changed.
Days 3–10 — run the three checks named in the posts.
Count the clicks from a product page to a completed order and remove any unnecessary step.
Test load time on a phone on a normal connection.
Find the last-edit date on the welcome, abandoned-cart and post-purchase emails.
Put the results in a one-page list: what is likely losing money, what the fix costs, and what it is worth if it works.
Days 10–30 — test one channel against a control before scaling it.
The ATM account ran control groups at the location level.
For a single online store, the equivalent is to hold one channel or one geographic split at flat spend and compare orders with a matched group that received the extra budget.
The rule: no channel keeps its budget unless it shows up in orders.
Campaign line: "Your next 40% in ad spend is a bet — price it in orders first."
Supporting evidence comes from two newsletter failure patterns: $30,000 for funnel automation with no list building, and $20,000 for a redesign with a 30-second cart.
The first-month deliverable is a spend map showing which channels are defended by the orders they caused and which go on the cut list, plus a ranked fix list for checkout and lifecycle email.
Bring to the consult: trailing twelve months of revenue and channel spend, order-level data from the store, the current click path to checkout, mobile load times, and the last-edit dates for the three email flows.
Expect the call to challenge rather than confirm the plan: the posts say AI will agree with any strategy and that a useful review comes from someone who has carried a P&L.
Ask about pricing, availability and scope, which are not published.
A brand with physical locations should also ask about the local-listing test: location-level listings with control-group testing were the channel that worked in the ATM network.
Choose Raymond C. when flat revenue stems from the email and SMS programme, not the whole funnel.
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Both posts focus on these failures.
In one, a brand switched email vendors and every image in every live flow — welcome, abandoned cart, post-purchase — went blank.
The images stayed that way for a month, until a customer wrote in about an empty receipt.
The old vendor had hosted them on its own server and took them down when the brand left.
In the other, a brand returned to Klaviyo after nearly a year of sending straight out of Shopify, with no recent engagement to build on.
Raymond C. pulled the Shopify open data, split the list between people who had opened at least twice in the last three months and everyone else, then ran a 25% random sample and tracked the groups separately: repeat openers landed around 50%, everyone else around 10%, and the blended rate near 40%.
The decision not to raise the sample to 50% or 75% to reactivate faster was deliberate: more low-engagement addresses in the send drag down the average.
The post links a blend sliding into the low 30s to slipping Google Postmaster reputation on the domain, explicitly as a read rather than a rule.
The plan is a 6-to-10-campaign sampling window, after which responders are pulled back and the rest suppressed.
This is the right specialist for a broken or dormant lifecycle programme, a vendor migration, or sending health that is capping everything else.
The posts cover email and SMS, not channel-level revenue attribution, paid-media testing against a control, checkout or site-speed work, or budget decisions.
A brand that has not repaired its flows or measured its channels would still need the other half of the job, and the order of fixes depends on what is costing money.
There are two posts, with no client outcome figures beyond the sample result.
Choose Shahria Naz when the tools are not built properly or the store needs to be built or rebuilt.
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The posts describe end-to-end Klaviyo implementation: flows, welcome and abandoned-cart automation, segmentation and targeting, campaign setup, sign-up forms, email design and copy, and account optimization.
The argument is that a good campaign brings results, but a well-implemented account does more: flows, segmentation, forms, campaigns, and journeys pay off when they work together, reaching the right customer at the right time rather than simply making the email look good.
The second post covers a WordPress and WooCommerce e-commerce build, with a custom Elementor layout, mobile-first design, responsiveness, and SEO.
A brand whose Klaviyo account was set up once and never restructured, or whose WooCommerce store is on a theme nobody can maintain, gets a specific builder rather than an abstract review.
The work described is implementation and build, not commercial ownership.
Neither post shows attribution between channels, a budget decision, media testing against a control, or accountability to a profit-and-loss result.
The posts do not establish whether this person would advise a brand to pause a channel when transactions do not move.
The store work is WordPress and WooCommerce; the good-fit brief in the fit section is a Shopify brand, so the platforms only partly overlap on the build side.
The two posts are from July 2025 and September 2026.
Choose MAHI UDDIN AHMED when the problem is getting a store built and found, or deciding which platform to use.
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One post puts two versions of the same shop side by side — same products, same owner, one getting three orders a day and one taking orders while the owner sleeps.
It traces the difference to visibility on Google versus being limited to a Facebook page, customers leaving a chat before a price comes back, and orders that cannot scale out of a conversation.
The post lists what a proper site changes: it can be found on Google around the clock; prices, photos, and reviews are visible without waiting; and bKash, Nagad, card, and cash-on-delivery checkout are handled automatically.
The other post answers the Shopify-versus-WooCommerce question based on running one store on each platform, with named live work: MYNAH MART on Shopify and Royal User UK on WooCommerce, both taking cash-on-delivery orders daily.
The conclusion is that the platform matters less than two things: whether it loads fast on a phone and whether the owner can manage it without calling a developer.
This is the right supplier when a build, migration, or platform decision is the blocker.
The scope is store build and platform choice, largely for small businesses the post identifies as being in Bangladesh, the UK, and the US.
The posts do not cover marketing budget allocation, channel attribution, lifecycle email programmes, or accountability for revenue against spend.
For a brand at the scale and flat-growth profile in the fit section, this is a build supplier, not the fractional lead who decides where the next dollar of media goes; hiring on this evidence would leave the measurement problem untouched.
Frederick Lansky's five full LinkedIn posts include a paid-search and attribution account at a Bitcoin ATM network, a checkout and lifecycle email checklist, agency spending without list building, a post on AI and strategy, and a description of a fractional CMO practice.
No publication dates are given for the posts; profile information was current as of 9 October 2026.
No newsletter issues, videos, transcripts, or independent coverage are available.
A client review referenced in one post does not appear here, so the posts do not verify past engagement outcomes beyond the ATM example.
Frederick Lansky's posts here do not document a sponsorship or paid-placement offer; that absence applies only to the posts here.
