Question 1

Who does L&Y Decision work with?

Accepted Answer

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.

Question 2

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

Accepted Answer

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.

Question 3

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

Accepted Answer

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.

Question 4

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

Accepted Answer

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.

Question 5

What is causal inference in marketing?

Accepted Answer

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.

Question 6

What is marketing mix modeling (MMM)?

Accepted Answer

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.

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