Who does L&Y Decision work with?

L&Y Decision works with e-commerce brands, DTC companies, and retail businesses, typically with revenues between $500K and $50M, who are serious about making evidence-based marketing decisions. They specialise in small and medium businesses ready to graduate from gut feeling.

What data access does L&Y Decision need to get started?

Read-only access to ad platforms (Meta Ads, Google Ads), analytics (GA4 or equivalent), and commerce data (Shopify, WooCommerce, etc.). L&Y Decision never touches campaign settings, budgets, or creative.

How quickly will e-commerce brands see results from decision science?

Most engagements deliver initial insights and recommendations within 2–4 weeks. Implementation of those recommendations typically produces measurable impact within 60–90 days. Causal inference and attribution model projects take 4–8 weeks by design.

Do you need an in-house data team for marketing attribution modeling?

No. L&Y Decision handles data extraction, transformation, and modeling entirely. If you can grant read access to your platforms, they do the rest. Many clients have no dedicated data function at all.

What is causal inference in marketing?

Causal inference in marketing is a statistical method that isolates the true incremental impact of advertising: measuring what would have happened without the ads (the counterfactual). Unlike correlation-based attribution, causal methods such as geo-holdout experiments, difference-in-differences, and Bayesian structural time-series prove that ads caused revenue, not merely correlated with it.

What is marketing mix modeling (MMM)?

Marketing mix modeling (MMM) is a statistical technique that quantifies how each marketing channel, paid search, social, email, TV, out-of-home, contributes to sales. Unlike last-click attribution, MMM uses aggregate time-series data to measure true channel contribution, accounting for external factors like seasonality and price changes.

How do CMOs and founders justify marketing analytics investment to their CFO or board?

L&Y Decision builds board-ready marketing ROI models that CFOs and investors can audit. Platform-reported attribution overclaims by 30–200% due to cross-channel double-counting. Causal models, using geo-holdout experiments and difference-in-differences analysis, isolate true incremental revenue. Clients typically discover they can reallocate 20–35% of ad spend to higher-performing channels, reducing customer acquisition cost by 25–35% on average.

Is L&Y Decision suitable for small and medium-sized businesses or small-cap public companies?

Yes. L&Y Decision was built specifically for brands that want enterprise-grade measurement without an in-house data science team. Typical clients are e-commerce, DTC, or retail brands with revenues between $500K and $50M, spending $10K–$100K per month on paid advertising. L&Y Decision handles all data extraction, modeling, and interpretation. No data engineering resources required.

Free tool · Marketing mix

Marketing Budget Reallocation Simulator

Average ROAS tells you which channel worked. Marginal ROAS tells you where the next dollar should go, and the two often disagree. Enter what each channel spends and returns today and see how a marketing mix model would move the budget.

Incremental revenue, reallocated

$257,257

Today

$255,500

Change

+$1,757

Meta: $40,000 → $48,000 · marginal ROAS 1.43x → 1.32x

Google Search: $30,000 → $31,600 · marginal ROAS 1.36x → 1.32x

TikTok: $15,000 → $12,900 · marginal ROAS 1.26x → 1.32x

YouTube: $15,000 → $7,500 · marginal ROAS 1.05x → 1.29x

Budget moves toward channels whose next dollar earns most, until those returns even out. Each channel stays between 50% and 200% of today's spend, where its curve can be trusted.

Talk to us about your measurement

Worked example

A $100,000 monthly budget split across Meta ($40,000 at 2.6x), Google Search ($30,000 at 3.4x), TikTok ($15,000 at 1.8x) and YouTube ($15,000 at 1.5x) earns about $255,500 in incremental revenue. Moving money toward Meta and Search until each channel's next dollar returns about the same, near 1.32x, raises that to about $257,300 for the same spend, and shows that YouTube's next dollar returns well under its 1.5x average.

Average versus marginal ROAS

Every channel saturates. The first dollars reach the most likely buyers; later dollars reach people who were less likely to buy or would have bought anyway. A channel can show a strong average ROAS while its next dollar returns far less.

The simulator models each channel as revenue = a × spend^b, calibrated so the curve passes through your current spend and incremental revenue. The exponent b is the saturation setting: lower values flatten faster. Marginal ROAS is b × average ROAS at the current spend.

Where the inputs should come from

Use incremental ROAS, not platform-reported ROAS. Good sources are a recent marketing mix model, a geo holdout or a conversion-lift study. Platform numbers usually overstate return, and they overstate it unevenly between channels, which pushes budget the wrong way.

If you have no measured saturation, start with the defaults: search and retargeting saturate fastest, prospecting video and new channels slowest. Then test the biggest recommended move before rolling it out.

Why the moves are capped

Response curves are only reliable near the spend levels they were estimated from. The simulator keeps every channel between half and double its current spend. Larger moves need a new test, not a bigger spreadsheet.

Frequently asked questions

What is marginal ROAS?

The revenue earned by the next dollar of spend, as opposed to the average across all dollars spent. Budget is best allocated when marginal ROAS is equal across channels.

Is this a marketing mix model?

No. It uses the same diminishing-returns logic an MMM produces, applied to numbers you provide. A real MMM estimates those curves from your own spend and sales history, including seasonality, pricing and carryover.

What saturation should I use for each channel?

Measured curves are best. As a starting point, branded search and retargeting saturate fastest (around 0.4), broad prospecting and video slowest (around 0.7 to 0.8).

Should I cut a channel whose marginal ROAS is below 1?

Not automatically. Below 1 the next dollar loses money on revenue alone, but channels can feed others or build demand that shows up later. Treat it as a strong signal to test a lower spend.

More free tools

Geo Holdout PlannerHow long a geo holdout must run, the smallest lift it can detect, and what the test costs.

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Book a free 30-minute consultation. Bring your ad account numbers; leave knowing which channels earn their budget.

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