MMM for Ecommerce

Grow revenue by spending where growth actually comes from

Ecommerce growth rarely comes from one channel working in isolation - paid search catches demand that social awareness created, email retains customers that ads acquired, and organic captures the overflow. Marketing Mix Modelling measures how these channels genuinely interact, so budget decisions reflect the whole revenue engine, not one dashboard's version of it.

See your revenue drivers

12-Month Revenue Decomposition Model

Unified incremental revenue breakdown across all trading streams

Baseline/Organic 41% Paid Media 33% Owned Channels 17% Promotions 9%

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"Most ecommerce brands can name their paid media ROAS. Few can say with confidence how much of their revenue paid media actually created."

The problem

Growth is being reported everywhere and explained nowhere

Ecommerce brands are drowning in revenue data and starved of revenue understanding. MMM resolves this by modelling the business as one system, not a set of disconnected channel reports.

01 Fragmented data Shopify, ad platforms, email tools and marketplaces each hold a slice of the revenue story, with no shared view of the customer.

02 Misleading attribution Platform-reported ROAS takes credit for revenue that promotions, seasonality or brand demand were already driving.

03 Poor investment decisions Budget follows whichever channel's dashboard looks best this week, rather than the channel actually growing the business.

04 Inefficient marketing spend Blended CAC rises even as individual platform ROAS looks healthy, because the interactions between channels are invisible.

Platform Limits

A ROAS number can't tell you what to do next

Traditional metrics What they miss Better business measurement

Blended or platform ROAS✗ Doesn't separate promotional and seasonal lift from marketing impact✓ Contribution modelling that isolates each driver of revenue

Last-click attribution✗ Ignores the channels that built demand before the final click✓ Channel interaction effects across the full funnel

Weekly platform dashboards✗ Compare this week to last week, not what would have happened without spend✓ Baseline versus incremental decomposition

Discount-driven revenue spikes✗ Get read as 'marketing performance'✓ Promotional lift measured and separated from channel contribution

Revenue going up is not the same as marketing working. MMM is how you tell the difference.

The Method

From scattered revenue data to a growth plan

1Data IntegrationConnect dataShopify orders, ad platform spend, email/SMS performance and promotional calendars are unified into one dataset.

2History AnalysisUnderstand historyThe model learns how each channel, plus pricing and seasonality, has actually behaved across your trading history.

3Bayesian EngineModel contributionBaseline demand and promotional lift are separated from genuine incremental revenue by channel.

4Marginal ReturnsIdentify saturationEach channel's response curve shows where extra spend keeps growing revenue and where it plateaus.

5Active ExecutionOptimise budgetA concrete reallocation plan translates the model into next quarter's budget, with expected revenue impact.

Attribution vs Incrementality

Channels don't work alone, so stop measuring them that way

Paid Social Exposure➔Paid Search Volume +18% branded search volume lift

Paid social exposure increases branded search volume by an estimated 18%.

TV / Video Exposure➔Direct / Organic Traffic Sustained baseline lift for 2-3 weeks

Upper-funnel video exposure lifts direct-to-site traffic in the following 2-3 weeks.

Retention (Email & SMS)➔Extended Customer LTV Ad spend amplification multiplier

Retention channels amplify the revenue return from acquisition spend by extending customer lifetime value.

💡Insight: Cutting paid social to protect ROAS often shows up two weeks later as a drop in branded search and email list growth - a cost that platform dashboards never attribute back to the channel that caused it.

What we do

From channel data to a revenue growth plan, automatically

UnderstandWhat is happening?Pangolin models how every channel, promotion and seasonal pattern contributes to revenue, continuously, as new data arrives.

ExplainWhy is it happening?Every contribution figure comes with the reasoning behind it, including cross-channel interaction effects.

PredictWhat's likely to happen next?Response curves forecast how revenue will respond to different spend levels ahead of your next planning cycle.

OptimiseWhat should we do?AI-generated budget recommendations turn the model into a specific reallocation plan, approved by a human before it goes live.

Platform Interface

Revenue contribution, modelled and ready to action

LIVE DECOMPOSITION REPORT Pangolin Continuous-MMM Sync Active

Modelled Allocations & Opportunity Cost

Paid socialContributing 22% of revenue

Paid social: +22% recommendedHighly incremental (low saturation reached)

Promotional discountingContributing 9% of revenue

Promo discounting: 9% of revenue at margin costDiminishing baseline returns

Projected Growth Plan

Expected quarterly lift+£240,000quarterly revenue at current total budget

Projected impact+£240,000 quarterly revenue

Deploy Reallocation

Built for DTC Teams

A unified view for every stakeholder

CMONeeds a growth story built on what actually drove revenue

• A unified view of every revenue driver including promotions and seasonality.

• A model-backed narrative for the board.

Head of GrowthNeeds to know which channel to fund next quarter

• Channel interaction effects that explain knock-on impact.

• A ranked view of where the next pound of spend performs best.

Performance MarketerNeeds to defend budget decisions beyond platform ROAS

• Contribution figures independent of any single platform's reporting.

• Clear evidence when platform numbers and business results disagree.

Finance TeamNeeds revenue growth explained in terms the P&L recognises

• Contribution and margin impact by channel, not just top-line revenue.

• A defensible basis for marketing budget decisions.

Clear parameters

Frequently Asked Questions

What does Marketing Mix Modelling add for an ecommerce brand specifically?

Traditional attribution models claim credit for self-reported value on a single platform basis. Pangolin's MMM approach reconciles platform metrics against actual Shopify/GA4 order history, capturing seasonality, promotional curves, baseline organic demand, and multi-channel overlaps simultaneously.

Can MMM account for promotions and sales events?

Yes. Our platform isolates promotional discounting and seasonal events (such as Black Friday or Cyber Monday) from normal advertising spend. This ensures discount spikes are quantified separately and aren't incorrectly attributed to ad platform performance.

Does this replace my Shopify or GA4 analytics?

No. GA4 and Shopify analytics remain key to daily store management, custom funnel tracking, and inventory flows. Pangolin works alongside them, ingesting daily transaction reports to perform continuous, aggregate incrementality calculations.

How does MMM handle channel interactions, like social driving search?

Pangolin isolates time-lagged cross-correlation coefficients. For example, our algorithms calculate the specific coefficient of branded keyword search volume lift that occurs in the days following a Facebook or TikTok ad spend push.

Is this only useful for large ecommerce brands?

While historically limited to large enterprise brands, Pangolin's automated pipeline makes MMM accessible to mid-market and rapidly scaling DTC operations. Typically, we recommend having at least 12 months of solid historical transaction data to begin training the models.

How quickly can I see results?

Initial models and budget allocations are computed within days after connecting your store, marketplaces, and advertising channels. The model's baseline accuracy and confidence intervals stabilize over consecutive weekly synchronization runs.

Your revenue has a story. Most dashboards can't tell it.

See how each channel is actually contributing to your growth.

Book a demo