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

A/B Test Sample Size Calculator

Most failed tests were lost before launch: too little traffic for the lift anyone could realistically expect. Enter your conversion rate, the smallest lift that would change a decision, and your daily sessions to see what the test really requires.

Sessions per variant

66,946

Total sessions

133,892

Duration

45 days

Detects 2.4% → 2.64%. Plan for 7 full weeks.

Talk to us about your measurement

Worked example

At a 2.4% conversion rate, detecting a 10% relative lift (2.4% to 2.64%) at 95% significance and 80% power takes about 67,000 sessions per variant. At 3,000 sessions a day across both versions, that is roughly 45 days.

Choosing the lift to detect

Pick the smallest lift that would change what you do, not the lift you hope for. Halving it roughly quadruples the sample. If the answer comes back at four months, test a bolder change, move the test to a higher-traffic page such as the product page rather than a single collection, or test on add-to-cart as a validated leading metric.

Run in whole weeks

E-commerce traffic converts differently on weekdays, weekends and paydays. Even if you reach the sample in nine days, run to fourteen. Avoid launching tests across major promotions such as Black Friday, where shopper intent shifts and results rarely transfer to normal weeks.

Frequently asked questions

How long should an A/B test run on a Shopify store?

Long enough to reach the calculated sample, rounded up to whole weeks, with a minimum of two weeks for most stores. Low-traffic stores often find the honest answer is that small tweaks cannot be tested; bolder changes can.

Does testing more variants need more traffic?

Yes. Each extra variant adds an arm and also tightens the significance level each comparison must meet, so the sample per arm rises too.

Should I use relative or absolute lift?

Relative lift (+10% of the baseline) compares easily across pages. Absolute lift (+0.24 points) is clearer when you are estimating revenue. The calculator supports both.

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