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

Sample Ratio Mismatch (SRM) Checker

Q: How much imbalance is normal?

It depends on volume. 5,050 against 4,950 is ordinary noise; 505,000 against 495,000 is a serious mismatch. Percentages alone mislead, which is why the test matters.

Q: Can I fix a test with SRM by reweighting?

No. You do not know which shoppers went missing, so no adjustment removes the bias. Find the cause, fix it and rerun.

Q: Does my testing tool check SRM automatically?

Some enterprise platforms do. Many lightweight tools and Shopify apps do not, or bury the warning. Check it yourself on every test.

If you set a 50/50 split and one side ends up with noticeably more sessions, the test may be measuring which shoppers got counted rather than which experience works better. Enter the counts and planned split to find out in seconds.

Verdict

SRM detected

p-value

< 0.000001

Chi-square (df 1)

24.50

Control: 40,700 vs 40,000 expected (50.88% vs 50%)

Variant B: 39,300 vs 40,000 expected (49.13% vs 50%)

p < 0.0005: assignment or tracking is broken. Do not read this test's conversion results; find the cause and rerun.

Talk to us about your measurement

Worked example

A planned 50/50 test with 40,700 control and 39,300 variant sessions gives chi-square ≈ 24.5 and p < 0.0001: a clear mismatch. That result cannot be trusted, however good the conversion numbers look.

Where mismatches come from in e-commerce

Common culprits: a variant that loads slower and loses impatient mobile shoppers before the analytics tag fires; redirect tests that break on some browsers or ad-click URLs; bot filtering applied after assignment; and theme or app conflicts that stop the testing script running on one version.

Each one removes a non-random slice of shoppers from one arm. The remaining groups are no longer comparable, so the conversion difference is contaminated in a direction you cannot know.

How the check works

The checker compares observed sessions with the counts your planned split implies, using a chi-square goodness-of-fit test. It flags a mismatch at p < 0.0005, the strict level large experimentation platforms use, and warns between 0.0005 and 0.01. Run it before reading any conversion metric.

Frequently asked questions

How much imbalance is normal?

It depends on volume. 5,050 against 4,950 is ordinary noise; 505,000 against 495,000 is a serious mismatch. Percentages alone mislead, which is why the test matters.

Can I fix a test with SRM by reweighting?

No. You do not know which shoppers went missing, so no adjustment removes the bias. Find the cause, fix it and rerun.

Does my testing tool check SRM automatically?

Some enterprise platforms do. Many lightweight tools and Shopify apps do not, or bury the warning. Check it yourself on every test.

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