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.

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Causal Attribution

Build a model that tells you exactly what your ads are worth

Causal models that link ad spend directly to revenue and profit — not correlation, not last-click. Built for e-commerce, DTC, and retail teams who need to justify every dollar.

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Investment:$5,000 – $15,000

3–4 weeks

Build timeline

Causal

Inference methodology

Monthly

Model update protocol

Deliverables

What you get

Custom causal ROI model connecting ad spend to revenue and margin

Channel-by-channel revenue attribution with confidence intervals

Scenario planning model (increase/decrease spend by X%)

Executive summary dashboard with key ROI metrics

Monthly model recalibration protocol

Interpretation guide for your internal team

Investment

$5,000 – $15,000

Fixed project scope

Timeline

3–4 weeks

Book a Consultation

Process

How it works

Data Integration

We pull spend data from every active channel and connect it to revenue outcomes at the right granularity — daily, weekly, or campaign level depending on your data maturity.

Causal Model Build

We construct a causal model — not a correlation — using methods like Bayesian structural time-series or difference-in-differences depending on your data.

Calibration & Validation

We back-test the model against periods where we know the outcome, then stress-test the assumptions before presenting results.

Handover & Training

We deliver the model, dashboard, and a training session so your team can run scenarios and update inputs as conditions change.

Ideal Client

This service is for you if…

E-commerce brands spending $50K+ per month across channels who need to justify budget

DTC companies preparing for board presentations or investor reviews

Retail businesses wanting to compare digital vs in-store ad effectiveness

CMOs who've been burned by last-click attribution and want something rigorous

What clients see

2.3×

Average ROAS improvement

Clients who reallocate budget based on the model typically see material ROAS improvement within 90 days.

35%

CAC reduction potential

Identifying which channels are truly incremental allows brands to stop funding non-incremental spend.

Common questions

How is this different from what Meta or Google tells me?

Platform-reported ROAS is based on their own attribution models, which naturally credit their platforms generously. Our models are platform-agnostic and use your own outcome data.

What sample size do I need?

Typically 6+ months of daily spend and revenue data across the channels you want to model. Less is possible with certain methodologies.

Do I need a data engineer for this?

No. We handle data extraction and cleaning. If you can give us export access to your ad accounts and revenue data, we can do the rest.

Other services

Marketing Analytics Audit

A comprehensive review of your data stack, KPIs, and attribution gaps. We surface exactly where your measurement is broken and what it's costing you.

$3,000 – $8,000

Conversion Rate Optimization

Structured testing frameworks that eliminate guesswork. We design experiments with proper statistical power, monitor them correctly, and deliver conclusions you can act on with confidence.

$4,000 – $12,000

Multi-Touch Attribution

A custom multi-touch attribution model that accurately maps how each channel contributes to conversions — across the full customer journey, not just the last click.

$6,000 – $20,000

Currently accepting new clients

Ready to prove your marketing ROI?

Book a free 30-minute consultation. No commitment, just 30 minutes of clarity on what's actually driving your results.

Book Free Consultation

No commitment. Just 30 minutes of clarity.