# Taylor Lagace

Taylor Lagace works on paid media strategy for e-commerce brands, using bottom-up business forecasts to set advertising targets and evaluate acquisition performance.

- Profile: https://b2binfluencer.marketing/creators/taylor-lagace/
- LinkedIn: https://www.linkedin.com/in/taylor-lagace-5080a9b2
- LinkedIn followers: 7,584
- Topics: ecommerce, paid-ads
- Based on 13 dated posts, latest 2026-07-14

## Summary

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.
- Check first: Ask for the full forecasting workflow, outcome data, and references before relying on the method.

### 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.

## Main ideas

- **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? 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.
- **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. 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.
- **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. 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.
- **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. 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.
- **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. 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.
- **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. 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.

## Work to read

- [How we build paid media forecasts for M&Ms, Purdy & Figg, and WildBird (part 1)](https://www.linkedin.com/posts/taylor-lagace-5080a9b2_if-youve-wondered-how-we-build-paid-media-activity-7465058703805898752-4HjJ)
- [The same forecasting system, part 2: from model to ad account targets](https://www.linkedin.com/posts/taylor-lagace-5080a9b2_if-youve-wondered-how-we-build-forecasts-activity-7465417588211339264-J7L9)
- [Why this agency deleted its attribution stack](https://www.linkedin.com/posts/taylor-lagace-5080a9b2_if-youre-paying-for-northbeam-or-triple-activity-7463245984517562369-vXWq)
- [Hiring as inventory planning, borrowed from a former QALO CEO](https://www.linkedin.com/posts/taylor-lagace-5080a9b2_for-non-e-commerce-businesses-humans-are-activity-7462521206794276864-jY8x)

## Brand relationships

- **M&Ms** (client)
- **Purdy & Figg** (client)
- **WildBird** (client)
- **Northbeam** (mention)
- **Triple Whale** (mention)
- **QALO** (mention)

## Partnerships

The four full LinkedIn posts discuss paid media forecasting and attribution for e-commerce brands; one includes a general call to book a call. 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.

## Alternatives

- Reeto M.: Choose Reeto M. when the open problem is the quality of the signal reaching Meta, not the financial model behind the budget. 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.
- Charley McDonnell: Choose Charley McDonnell when the growth constraint is the operating model rather than attribution. 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.
- Nathan Perdriau: 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. 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.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/