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 · Experimentation

Can I Stop My A/B Test Yet?

Day five, the dashboard shows 96% confidence, and someone wants to ship. Enter what you planned, where the test stands and how many times the team has looked. You get a verdict, the days remaining, and the real error rate after all that checking.

Can I stop?

No, keep running

Progress

28%

Current p-value

0.0312

28% of the planned sample. About 13 more days at current traffic. p = 0.031 looks good, but an early stop needs z ≥ 3.71; you have 2.15.

After 5 looks, a 95% test carries a real false-positive rate of about 14.2%.

Talk to us about your measurement

Worked example

A test checked 5 times at 95% confidence has a real false-positive rate near 14%, not 5%. Checked 10 times, about 19%: roughly a one-in-five chance of shipping a change that does nothing.

Why daily checking manufactures winners

A 95% threshold allows a 5% chance of a false winner when you look once, at the planned sample. Each extra look is another draw. Classic work by Armitage, McPherson and Rowe puts the real false-positive rate at about 14% after five looks and 19% after ten.

Early results swing hard because a few large orders or one promotional email can tilt a small sample. The swing that looked like a 25% lift on day four is often flat by week three.

The rules the checker applies

Planned sample reached and at least one full week run: stop and read the result once. Sample not reached: keep going, with an estimate of days left. A pre-planned early stop is allowed only if the evidence clears a much stricter O'Brien-Fleming-type boundary. And if the traffic split is off beyond chance, the result is invalid whatever it says.

Frequently asked questions

When is it safe to stop an A/B test?

When you have reached the sample size set before launch and run at least one full week, ideally two. Earlier stops are valid only under a sequential design planned in advance.

Is it ever OK to stop a test early?

Yes, for harm: broken checkout, errors, or a revenue drop on guardrail metrics justify an emergency stop. A promising lift does not.

We already peeked. Is the test ruined?

Not if you did not act on it. Keep running to the planned sample and read the result then. The damage comes from stopping when the number looks good.

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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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