Taylor Lagace works on paid media strategy for e-commerce brands, using bottom-up business forecasts to set advertising targets and evaluate acquisition performance.
Taylor Lagace is most useful for bottom-up paid media forecasts and measurement for e-commerce brands.
Best for
A DTC e-commerce brand with several products and uneven margins improving paid media forecasting and attribution.
Not for
Paid creator sponsorships for broad consumer reach and engagement.
Key takeaways
Start with founder revenue, profit, and exit goals, then test against historical business data.
Measure against Shopify sales, not attribution tools that reconstruct incomplete customer journeys.
Set product-level targets tied to contribution margin, not one blended ROAS.
Check first: Ask for the full forecasting workflow, outcome data, and references before relying on the method.
Main ideas
Build a forecast from a brand's real history, not to hit a stated goal · Make the business forecast the target the ad account is held to · Hold every product to the same margin outcome, not one blended ROAS
Audience
7584 LinkedIn followers · 4 posts published 19-27 May 2026 Posts reviewed
Taylor Lagace shares paid-media strategy for e-commerce brands, especially a bottom-up way to connect business forecasts with advertising targets.
In the LinkedIn posts reviewed, the process starts with a founder’s revenue, profit, and exit goals, then tests those goals against historical business data.
One example projects $28 million in revenue but 12.4% profit against a goal of $25 million and 20% profit, making the shortfall—not ad-platform ROAS alone—the problem to solve.
Lagace also argues for measuring results against Shopify sales and setting product-level targets tied to contribution margin.
These posts offer a useful starting point for readers interested in how paid media can connect to a company’s finances.
The distinctive thread is translating business outcomes into practical media targets.
Lagace separates the more predictable revenue from returning customers from the volatility of new-customer acquisition, then shows how a business-level target can inform channel and product targets.
One post converts a 3.0 target over 28 days to a 2.57 target over seven-day click, assuming a 16.67% delayed-attribution lift; another explains why products with different delivery costs should not be judged by one blended ROAS.
A separate post argues that attribution tools reconstruct customer journeys from incomplete data, while the agency roots reporting in Shopify sales.
Another post relays an executive’s analogy between hiring and inventory planning, extending the focus to operating models.
Readers looking for concrete explanations linking forecasts, margins, and ad-account decisions should ask for the complete forecasting workflow: the reviewed posts omit steps 5, 10, and 11 and refer readers to unavailable comments.
Taylor Lagace argues that paid media should run on a forecast built from a brand's historical numbers, not a target reverse-engineered to match a founder's wishes.
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The forecast sets the target for the ad account, and measurement starts with money that actually arrived rather than attribution dashboards.
Recurring priorities include the business economics behind the media plan, product-level margin over blended ROAS, and skepticism toward multi-touch attribution tools.
One post relays former QALO CEO KC Holiday's idea about hiring rather than advancing Lagace's own argument.
Build a forecast from a brand's real history, not to hit a stated goal
Lagace opens with business questions, not marketing ones: Is the founder building to sell in a few years or to run a long-term cash-flow business?
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What exit number would make a sale worthwhile?
What topline revenue and profitability do they want over the next 12 months?
The forecast is then built from actual historical performance across cohorts and business inputs, rather than forced to match those goals.
Its purpose is to set expectations: show where the business would land if the next 12 months looked like the last 6 to 24, identify levers to close the gap between that trajectory and the desired outcome, and clarify what the operator controls.
Inputs include aMER target, first-time and returning average order value, customer file size, recently acquired repeat rate, gross margin, cost of delivery, fixed costs, active non-recent repeat rates, and monthly churn.
The operator controls delivery cost, fixed costs, repeat rates, and churn; new customer acquisition is the lever an agency is hired to move.
In the worked roleplay, the founder wants $25M in topline revenue at 20% profit.
The model returns $28M topline at 12.4% profit.
Topline is ahead of goal, but profit falls well short, so the exercise shifts from adding revenue to fixing profit and finding the biggest lever.
Make the business forecast the target the ad account is held to
The forecast's aMER target becomes the accountability standard for categorical prospecting across Meta, Google non-brand, TikTok, and Snapchat.
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View-through attribution is excluded, and brand campaigns are held to a higher efficiency bar.
This links the profit-and-loss statement to the ad platform's daily optimization: a business-level aMER translates into a specific ROAS or CPA target inside the platform.
Because platforms report on shorter windows than the business target, Lagace accounts for delayed attribution lift between day 7 and day 28.
With a 28-day target of 3.0 and a measured lift of 16.67% after day seven, the equivalent 7-day click target is 2.57. That explains how a campaign can look below target in a short attribution window while still hitting the business goal over 28 days.
Hold every product to the same margin outcome, not one blended ROAS
Because delivery costs differ by product, Lagace argues that a single blended ROAS is not financially meaningful.
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Instead, every SKU is run to the same first-purchase contribution margin outcome.
A product with lower delivery costs can therefore run at a lower ROAS and still hit the same margin target.
This prevents a high-margin product from hiding a low-margin one and keeps the business from scaling a blended number that obscures the underlying economics.
A product with cheaper delivery can run at a lower return on ad spend and still clear the same contribution margin bar as a product that costs more to ship.
The blended aMER remains a business-level number; each SKU's target is set through its margin instead.
Attribution tools cannot see the full journey; start measurement with sales that actually happened
Lagace argues that multi-touch attribution tools are designed to lose signals.
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A buyer might see an ad on Instagram, search the brand on Google two days later, read a Reddit thread, then buy direct three days after that.
Meta claims the conversion, while tools such as Northbeam and Triple Whale reconstruct the path from partial cookie data and estimation.
Meta optimizes on signals inside its own platform and does not read an external dashboard, so Lagace argues that a dashboard's version of events cannot feed back into the algorithm spending the budget.
The alternative is to anchor reporting in Shopify sales—money that actually arrived—build the forecast from that revenue, then work backward to in-platform targets tied to business outcomes.
The post says the agency deleted its entire attribution stack rather than reconcile dashboards that contradict each other, then invites readers to book a call.
Once retention basics work, focus on acquisition efficiency and cost structure
Lagace treats new customer acquisition as the most volatile part of a forecast and returning customer revenue as the most reliable.
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They split returning customers into recently acquired and active non-recent cohorts, which behave differently but predictably.
The practical conclusion: mature brands have limited upside in retention unless the basics are broken.
If email flows, upsells, and onboarding are properly set up, the bigger opportunity lies in acquisition efficiency and cost structure.
If those basics are missing, retention work still has room.
The same forecasting walkthrough makes this distinction, then uses the forecast's aMER target to set the prospecting bar across channels rather than assuming more retention revenue will close a profit gap.
A guest's idea: for non-e-commerce businesses, people are the inventory
The post relays former QALO CEO KC Holiday's idea, not Lagace's own argument: treat hiring as inventory planning.
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Each person has designated hours, and managing their output is the same exercise as deciding how many units to stock and how many to sell.
The post quotes Holiday directly: you are managing the output of every person, each with designated hours, just as you would ask how many units you need and how many you need to sell.
Answers the question most brands start with: what goes into a paid media forecast before anyone sets a ROAS target?
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Start here for the order of operations — business goals first, model second, ad targets last.
Taylor Lagace walks through the first four steps of an 11-step forecasting process.
It starts with business questions, not marketing ones: Is the founder building to sell in a few years or to run a long-term cash-flow business?
What exit number would make a sale worthwhile?
What topline revenue and profitability do they want over the next 12 months?
The forecast is then built bottom-up from actual historical cohort performance, not fitted to those goals.
In the worked roleplay, the founder wants $25M topline at 20% profit, and the model returns $28M topline at 12.4% profit — topline ahead of plan, profit well short.
The exercise becomes finding the lever that closes the gap.
The inputs named are aMER target, first-time and returning AOV, customer file size, recently acquired repeat rate, gross margin, cost of delivery, fixed costs, active non-recent repeat rates, and monthly churn.
Levers are split between those the operator controls (delivery cost, fixed costs, repeat rates, churn) and those the agency is hired to move (new customer acquisition).
For brands planning a campaign
A brand weighing Taylor Lagace for a forecasting engagement can test the same opening questions and ask which cohort inputs would come from its own Shopify, Klaviyo, and ad platform data before accepting any target.
Shows how a financial forecast becomes something an ad account can optimize against — the bridge between a P&L and a daily media target.
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Steps 6 through 9 of the system.
New customer acquisition is called the most volatile part of a forecast; returning customer revenue is the most reliable, particularly when returning customers are split into recently acquired and active non-recent cohorts, which behave differently but predictably.
Where Klaviyo flows, upsells, and onboarding are already in place, the post argues the larger upside sits in acquisition efficiency and cost structure rather than further retention work.
The forecast's aMER target then becomes the target for categorical prospecting across Meta, Google non-brand, TikTok, and Snapchat, with view-through attribution excluded and brand campaigns held separately to a higher bar.
On delayed attribution, a 3.0 28-day target with 16.67% lift between day 7 and day 28 implies a 7-day click target of 2.57.
Targets are set per SKU against the same first-purchase contribution margin rather than one blended ROAS, so a product with lower delivery cost can run a lower ROAS and still hit the same margin.
The post again ends by pointing to the comments, so steps 5, 10, and 11 are not described in the post.
For brands planning a campaign
A brand replacing a single blended ROAS target can use this as a working template: choose the business-level aMER, convert it to a 7-day click target using measured lift, and hold each SKU to a margin rather than a blended number.
Answers the obvious objection to the forecasting posts: why not just buy a better attribution tool?
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This is the direct argument against that route.
The argument is that signal loss is baked into multi-touch attribution tools by design: a buyer might see an ad on Instagram, search the brand on Google two days later, read a Reddit thread, and buy direct three days after that.
Meta claims the conversion, while tools like Northbeam and Triple Whale reconstruct the path from partial cookie data and estimation.
Meta optimizes on signals inside its own platform, the post says, so a dashboard's version of events cannot be fed back into the algorithm that spends the budget.
Taylor Lagace says the agency deleted its entire attribution stack and now roots reporting in Shopify sales — money that actually arrived — then works backward to in-platform targets tied to P&L outcomes.
For brands planning a campaign
For a brand considering replacing Northbeam or Triple Whale, this frames the alternative as P&L-anchored reporting plus in-platform targets. Ask to see the Shopify-based reporting and target math before accepting the argument.
Answers a narrower question: how does Taylor Lagace think about team capacity?
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A short read showing the operating frame behind the media work.
A brief post quoting former QALO CEO KC Holiday, who reframes hiring as inventory planning: each person has designated hours, and managing their output is compared to deciding how many units to stock and sell. The framing is Holiday's, relayed here.
For brands planning a campaign
Limited relevance to a paid media brief. It shows the commentary extends beyond ad accounts into operating models, context rather than a reason to select them for a forecasting engagement.
Bottom-up forecasts instead of goal-matched targets2
Turning a business forecast into in-platform ad targets1
SKU-level margin targets instead of one blended ROAS1
Skepticism about multi-touch attribution tools1
Hiring and team planning as inventory management1
Bottom-up forecasts instead of goal-matched targets
Taylor Lagace builds paid media forecasts from a brand's historical performance rather than reverse-engineering a model to meet a founder's goal.
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In a two-part walkthrough of an 11-step system, the process starts with business questions (whether to sell in a few years or build long-term cash flow, exit timeline and target valuation, next-12-month revenue and profit goals), then builds the model from Shopify, Klaviyo, and ad platform inputs: aMER target, first-time and returning AOV, customer file size, recently acquired repeat rate, gross margin, cost of delivery, fixed costs, active non-recent repeat rates, and monthly churn. In the worked example, the forecast shows $28M revenue and 12.4% profit against a founder goal of $25M and 20% profit, shifting the focus from adding revenue to fixing profit.
Build the forecast bottom-up from historical cohort and business inputs. Use the gap between projection and goal to identify the relevant levers: operator-controlled factors (cost of delivery, fixed costs, returning-customer repeat rates, churn) and the acquisition lever an agency is hired to move.
For brands planning a campaign
A brand can require a forecast based on its historical data before agreeing to a target, then use the gap between projection and goal to decide whether a campaign should prioritize topline, profit, or retention.
Turning a business forecast into in-platform ad targets
The forecast's aMER target becomes the accountability standard for prospecting across Meta, Google, TikTok, and Snapchat, with no view-through attribution and brand campaigns tracked separately at higher efficiency.
Read more
A 3.0 aMER at the business level translates into a specific ROAS or CPA target in the ad platform.
A delayed attribution multiplier bridges reporting windows: with a 28-day target of 3.0 and 16.67% lift after day seven, the equivalent 7-day click target is 2.57.
This explains why a campaign can appear below target in a short window and still hit the business goal.
For brands planning a campaign
For briefs about measurement or target-setting, a brand can ask the team to show how an in-platform target maps to a business financial outcome and specify the attribution window used to measure it.
SKU-level margin targets instead of one blended ROAS
Lagace argues that each product should meet the same first-purchase contribution margin outcome, not the same ROAS, because cost of delivery varies by SKU.
Read more
A product with lower cost of delivery can run at a lower ROAS and still hit the margin target. This approach aims to prevent a high-margin product from hiding a low-margin one and to avoid scaling a blended number that does not reflect real economics.
The blended aMER target remains a business-level number; it is applied to products through margin, not copied directly onto every SKU.
For brands planning a campaign
Brands with mixed product economics can ask for SKU-level campaign targets, particularly when a single blended ROAS has hidden unprofitable spend.
Lagace argues that tools such as Northbeam and Triple Whale cannot see the full customer journey because it is not fully represented in trackable data, and that Meta's algorithm does not read an external attribution dashboard.
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They say they deleted their attribution stack and base measurement on Shopify sales, using actual banked revenue to build the forecast before working backward to in-platform targets. The piece closes with an offer to explain the system on a call.
For brands planning a campaign
A brand evaluating attribution tooling or an agency pitch can use this argument for grounding targets in order data, then test whether its existing stack changes any in-platform decision before continuing to pay for it. Treat this as a stated position, not an independently verified comparison of the tools.
A separate post relays former QALO CEO KC Holiday's reframe: for non-e-commerce businesses, people are the inventory, and managing each person's output against designated hours is the same exercise as planning how many units to stock and sell.
Read more
Applying an e-commerce inventory model to headcount and output planning.
For brands planning a campaign
This sits outside paid media and is relevant mainly when a brief targets founders or operators. It relays a single observation from another operator rather than presenting a developed method of Lagace's own.
The posts are long, numbered breakdowns of systems Lagace says clients pay thousands for, split into part 1 and part 2 and continued in the comments.
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Each ends with a direct call to action, such as booking a call or following a link in the bio. The forecast walkthrough uses a roleplay founder with deliberately aggressive goals to show how the model responds.
For brands planning a campaign
Brands can judge the teaching style directly from these posts. The public material presents a detailed top-of-funnel version of a paid engagement; ask on a call for specific client inputs and the full step sequence beyond what is published.
Taylor Lagace's posts name M&Ms, Purdy & Figg, and WildBird as clients of the paid-media forecasting system they describe.
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The same posts oppose third-party attribution platforms, naming Northbeam and Triple Whale as tools the team removed, and quote former QALO CEO KC Holiday on treating hiring as inventory planning. The service is described only as "we," with no business named; ask for the business identity and any ownership, employment, sponsorship, affiliate, or investment relationships.
M&Ms
Client
The post describes "how we build paid media forecasts for M&Ms, Purdy & Figg, and WildBird" and lays out the 11-step, bottom-up process used for those brands, including aMER targets, returning-customer cohorts, and SKU-level margin goals.
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It presents the work as current client work for a forecasting service but gives no contract term or start date.
Original disclosure
"If you've wondered how we build paid media forecasts for M&Ms, Purdy & Figg, and WildBird, here it is."
Named alongside M&Ms and WildBird as a brand for which the post's author builds paid media forecasts.
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The second part of the series uses the same unnamed roleplay brand to explain attribution windows, SKU-level targets, and cost structure as the process "we charge thousands for." This is a claim made in the post; no contract details are disclosed.
Original disclosure
"If you've wondered how we build forecasts for M&Ms, Purdy & Figg, and WildBird, here it is. The exact 11-step bottom-up system we charge thousands for."
Listed as one of three brands whose paid media forecasts the posts describe.
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The work covers historical cohort performance, customer file size, churn, gross margin, and delivery cost to produce a 12-month projection. The posts state the relationship but give no duration or indication of whether it is ongoing.
Original disclosure
"If you've wondered how we build paid media forecasts for M&Ms, Purdy & Figg, and WildBird, here it is."
Northbeam is a target of criticism, not a client or sponsor.
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The post argues that tools like Northbeam combine partial cookie data and estimates, that Meta does not read an attribution dashboard, and that the team therefore "deleted our entire attribution stack" and bases reporting on Shopify sales. The anti-attribution-software stance is central to the forecasting pitch.
The post groups Triple Whale with Northbeam, saying the attribution platform adds "another layer of estimation" and sells it as multi-touch attribution.
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It argues that this data cannot be acted on inside Meta, then offers a call to explain the alternative system. No commercial relationship with Triple Whale is stated.
QALO appears only as the former employer of KC Holiday, whose framing the post quotes: hiring as inventory planning, with each person's designated hours treated like units to sell.
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This is a quoted observation from another operator, not a stated relationship between Taylor Lagace and QALO.
Taylor Lagace's public material centres on one argument: a media plan should come from a forecast built on a brand's own sales history, not from a target set to match a founder's hopes.
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The best entry point is the two-part walkthrough of the eleven-step forecasting system, which runs from business goals and cohort-level inputs through to the target for the ad account.
Read the parts in order; the remaining steps are in the comment threads, not the posts themselves.
The shorter post on attribution tooling states the measurement position most directly and is the quickest way to test whether the argument suits you.
The post relaying KC Holiday's hiring analogy is an aside, outside paid media.
The long, numbered breakdowns mix a roleplay example with a closing invitation to book a call.
Ask for the complete method before treating the posts as a standalone playbook.
Both argue that attribution software and platform-reported numbers should not be treated as settled truth, and anchor decisions in business outcomes: Reeto M. uses blended new customer CAC as a north star, while Lagace grounds measurement in Shopify sales and the forecast built from them.
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Both name specific tools, such as Triple Whale and Northbeam, and test claims against them rather than accepting the dashboard.
Reeto M.'s posts are short experiment reports from a measurement vendor: a test of a predictive CAPI installation against an incumbent, with early results and an invitation to talk.
Readers get outcomes and product direction, not a step-by-step method they could rebuild themselves.
Lagace makes a similar case through a longer system with named inputs and arithmetic.
Both write for ecommerce brands that have hit a growth ceiling, locating the constraint inside the business rather than in the market.
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McDonnell describes a revenue level where existing systems stop working and CPA creeps up, while Lagace shows a projection that beats a founder's revenue goal but misses on profit. Both structure posts as numbered reasoning and close with a direct question to the reader.
McDonnell covers the operating model as a whole, from autonomous agents handling reorders to creative production capacity and campaign structure.
The unit of change is how the company runs, not how the ad account is targeted.
Lagace stays inside paid media economics, forecasting and measurement; their posts say little about operations outside the media plan.
Both cover ecommerce growth economics and set spending decisions from financial outcomes rather than platform metrics: Perdriau sets CAC targets from lifetime gross profit, while Lagace converts a business-level aMER into in-platform ROAS or CPA targets.
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Both treat attribution data with skepticism and teach through worked examples rather than general advice.
Perdriau focuses on retention and repeat purchase, covered through a long video masterclass and a yearly predictions scorecard; Lagace focuses on forecasting, target setting and prospecting across channels.
Perdriau teaches in conversational videos with a co-host, while Lagace uses written, numbered breakdowns.
The useful comparison is the financial framing rather than the delivery.
The posts show one measured audience figure and a clear topical signal.
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Taylor Lagace's LinkedIn account lists 7,584 followers, observed October 11, 2026.
The four complete posts cover operational paid media topics for e-commerce brands: bottom-up revenue forecasting, why they dropped third-party attribution tools, and how hiring can be planned like inventory.
That mix points to a founder, operator, and in-house growth audience rather than a broad consumer following—a reasonable fit for a DTC brand selling to people who own or run paid media budgets.
The posts do not publish demographic breakdowns, engagement data, video performance summaries, or reach per post, so the audience's geography, age, gender, seniority, employer, and occupation remain unknown.
If a campaign depends on reaching a specific role or market, ask for current LinkedIn follower breakdowns by industry, seniority, and location for a recent comparable period; any audience survey results; and recent placement results for comparable topics, depth, and formats.
The four full LinkedIn posts discuss paid media forecasting and attribution for e-commerce brands; one includes a general call to book a call.
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They do not describe sponsorship packages, creator partnerships, rates, deliverables, operational requirements, or a partnership inquiry route. Ask whether Taylor Lagace accepts sponsorships and, if so, request available formats, rates, and requirements.
Fit, provided the engagement focuses on forecasting and measurement rather than broad consumer reach.
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Brief
A DTC e-commerce brand with several products and uneven margins wants to improve paid media forecasting and attribution, and is considering replacing its current multi-touch attribution stack.
Reasons
Taylor Lagace's published work is a bottom-up paid media forecasting system for e-commerce brands, not general marketing commentary. It starts with business questions, such as whether the founder is building to sell or to run a long-term cash-flow business, then builds the model from historical cohort inputs: aMER target, first-time and returning AOV, customer file size, recently acquired repeat rate, gross margin, cost of delivery, fixed costs, active non-recent repeat rates, and monthly churn.
The material directly addresses attribution. One complete post argues that tools such as Northbeam and Triple Whale cannot see the full customer journey because it is not fully present in trackable data, and that Meta's algorithm does not read an external attribution dashboard. It says the agency deleted its attribution stack and roots reporting in Shopify sales, then works backward to in-platform targets tied to P&L outcomes.
The system connects a financial forecast to ad account decisions. The forecast's aMER target becomes the accountability target for categorical prospecting across Meta, Google non-brand, TikTok, and Snapchat, with view-through attribution excluded and brand campaigns held separately. A delayed attribution multiplier bridges reporting windows: a 3.0 28-day target with 16.67% lift after day seven implies a 7-day click target of 2.57.
It sets SKU-level contribution margin targets rather than one blended ROAS, allowing products with different delivery costs to run at different ROAS levels while hitting the same margin outcome. That helps DTC brands avoid hiding unprofitable spend behind a blended number.
The audience signal is plausible for a campaign targeting founders, operators, and in-house growth teams: the four posts are operational paid media material, and the LinkedIn account listed 7,584 followers on October 11, 2026.
Conditions
The two forecasting posts publish steps 1–4 and 6–9 of an 11-step system; steps 5, 10, and 11 continue in comment threads not included in the posts. Ask for the full sequence before treating the published system as complete.
The posts end with calls to book a call or follow a bio link. Treat the public material as a detailed top-of-funnel version of a paid engagement, not the full client delivery process.
The posts name M&Ms, Purdy & Figg, and WildBird but provide no outcome data, client references, or independent verification of the forecasting method. Ask for results or references before relying on the method.
The posts provide no audience demographics, engagement metrics, or video performance data. Ask for these before assessing audience reach or composition.
When to choose a specialist
Poor fit based on the posts.
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Brief
A consumer lifestyle or beauty brand wants a paid creator sponsorship: short-form video posts and product seeding to a broad consumer audience, measured primarily in reach and engagement.
Reasons
The four complete posts document no sponsorship or brand-partnership formats, media kit, rate card, deliverable specification, disclosed partnership example, or verified creator-partnership inquiry route. Ask whether Taylor Lagace accepts sponsorships and request the relevant materials.
The posts are long text pieces about paid media forecasting, attribution, and one hiring analogy. They target e-commerce operators and growth teams, not a broad consumer audience shopping for lifestyle or beauty products.
The only audience figure is 7,584 LinkedIn followers, on October 11, 2026. Ask for a demographic breakdown, engagement data, video performance, and placement history to assess reach and consumer audience composition.
The posts' commercial call to action is to book a call or follow a bio link, pointing toward consulting or agency services rather than sponsored content.
Before running a consumer sponsorship campaign, request short-form video performance, sponsorship history, audience geography and age, brand-safety context, and a working inquiry and contracting route.
A worked campaign example
Angle
Replace the attribution dashboard, not the media budget: a P&L-anchored forecasting sprint for a DTC brand with mixed SKU margins.
Next step
Ask for the full 11-step sequence, including missing steps 5, 10, and 11, and for anonymized client results or references on the forecasting and attribution change. Request a working session to test whether the brand's own Shopify and cohort data can produce the same input set before replacing any attribution tool.
Steps
Open with the forecasting posts' business questions rather than a ROAS target: Is the goal to sell in a few years or build long-term cash flow? What exit number would make a sale worthwhile, and what topline revenue and profitability are wanted over the next 12 months?
Pull the system's cohort and business inputs from the brand's own Shopify, Klaviyo, and ad platform data: aMER target, first-time and returning AOV, customer file size, recently acquired repeat rate, gross margin, cost of delivery, fixed costs, active non-recent repeat rates, and monthly churn.
Build the forecast bottom-up from historical performance and show the gap between projection and goal. In the published roleplay, the founder wants $25M topline at 20% profit, and the model returns $28M topline at 12.4% profit, shifting the problem from adding revenue to fixing profit.
Use that gap to choose the lever. Separate operator-controlled levers—cost of delivery, fixed costs, returning-customer repeat rates, and churn—from the acquisition lever the agency is hired to move.
Convert the business-level aMER target into in-platform prospecting targets across Meta, Google non-brand, TikTok, and Snapchat, with no view-through attribution and brand campaigns tracked separately. Apply the delayed attribution multiplier: a 3.0 28-day target becomes a 2.57 7-day click target when measured lift after day seven is 16.67%.
Set targets per SKU against the same first-purchase contribution margin, not one blended ROAS. A product with lower delivery costs can run a lower ROAS and still hit its margin target.
Replace multi-touch attribution reporting with Shopify-rooted revenue reporting, then work backward to in-platform targets. Meta's algorithm optimizes on signals inside its own platform, so a separate attribution dashboard cannot feed data back into the algorithm that spends the budget.
Sprint success measures: a documented forecast built from named inputs; a clear projection-versus-goal profit gap; a small set of levers ranked by control and impact; in-platform targets with stated attribution windows; and SKU-level margin targets a media buyer can act on.
Choose Reeto M. when the open problem is the quality of the signal reaching Meta, not the financial model behind the budget.
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The posts describe a brand where more than half of daily orders came from returning customers and where all paid spend was directed at new-customer acquisition.
Syncing customer and CRM lists hourly to Meta and excluding those audiences from prospecting did not hold down cost: platform-reported new customer CAC and blended new customer CAC kept rising.
Switching from an established conversions API provider to Angler AI's predictive CAPI came with a falling blended new customer CAC even as Meta spend rose, with marginal new customer CAC also improving.
A second post sets out how to run a two-cell A/B test of a conversions setup against an incumbent such as Elevar, Blotout, Triple Whale Sonar, Shopify, Segment, or a homegrown stack, and how Meta's incremental attribution reporting can check whether conversions credited in a seven-day click window are actually incremental.
Brands focused on repairing what Meta receives, rather than rebuilding the forecast, are closer to this comparison than to Taylor Lagace's work.
Reeto M.'s material is short and framed around a product's client-onboarding challenge, reporting early results rather than a completed method.
It does not publish the forecasting inputs, the aMER target, the attribution-window arithmetic, or the SKU-level margin targets that Taylor Lagace's posts set out.
The two posts cover conversions API quality and incrementality testing, not the bridge from a profit-and-loss statement to in-platform ad targets.
A brand needing both would be choosing between a measurement-layer fix and a forecasting rebuild, not between two versions of the same service.
Choose Charley McDonnell when the growth constraint is the operating model rather than attribution.
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One post argues that most e-commerce brands hit a wall around £80-100K per month because the systems that built that revenue — scrappy creative production, daily manual optimisation, gut-feel decisions, complicated campaign structures — cannot carry the next level.
Brands that break through, it argues, rebuild how they operate and invest in creative production capacity before they need it.
A second post describes what autonomous agents already do in operations: a bestselling product hits low inventory at 2am, an agent cross-references supplier lead times, calculates a stock-out date from sales velocity, generates a purchase order, and sends it to the supplier overnight.
Brands stalled by creative volume, manual process, or inventory decisions will get more from that diagnosis than from a forecast that assumes the media system is the problem.
Charley McDonnell writes about operations and systems, not the forecasting inputs — aMER target, cohort repeat rates, monthly churn, cost of delivery — or the conversion of a business target into a seven-day click target.
The posts also lean on a vendor's autonomous-agent capability, arguing a direction of travel more than a tested method.
This comparison is useful when diagnosis is the open question; brands needing forecasting inputs and conversion of a business target into a seven-day click target should ask for those specifically.
Choose Nathan Perdriau when the gap between a first order and a second order, rather than the forecast, is the likelier cause of poor paid media returns.
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A retention masterclass description credited to Nathan Perdriau and Angus Cowan argues that most e-commerce brands fix email and SMS while the real retention problems sit deeper, in product, customer experience, acquisition strategy, and unit economics.
It proposes measuring lifetime gross profit instead of the usual LTV figure, setting CAC targets from that number, structuring Klaviyo flows around first, second, and third orders, improving first-to-second-order conversion, segmenting customers with RFM analysis, using paid media to acquire higher-quality customers, and applying zero-party data.
A separate post scores ten 2025 e-commerce predictions and concludes that returns came from structure — better forecasting, incrementality thinking, financial alignment, systems — rather than new tactics, and that attribution-only tools struggle to drive high-leverage decisions.
If the brand suspects acquisition is bringing in customers who never come back, this comparison tests a different part of the funnel from Taylor Lagace's forecasting work.
The Nathan Perdriau material reviewed comprises a video description with timestamps and one text post.
It covers retention economics and CAC setting, but not the paid media forecasting model or the SKU-level ad targets Taylor Lagace publishes.
A brand replacing a multi-touch attribution stack would be moving sideways into retention unless retention is the underlying problem.
Ask for the calculations behind the lifetime gross profit targets and flow framework before relying on them.
This page covers four complete LinkedIn posts from Taylor Lagace: a two-part walkthrough of an 11-step bottom-up paid media forecasting system, a post arguing against multi-touch attribution tools, and a short post relaying a hiring analogy.
The posts do not carry publication dates; the account was observed October 11, 2026.
Steps 5, 10, and 11 of the forecasting system, and the comment threads the posts point to, are not available in the posts here.
Taylor Lagace's posts here include no videos, transcripts, long-form articles, audience data, or documented sponsorship formats.
Similar-creator comparisons draw on two posts each from Reeto M., Charley McDonnell, and Nathan Perdriau, published between March 2025 and July 2026.
The posts here do not establish whether earlier signature work such as books or shows exists.
