Project Name
Built a Marketing Mix Model That Proved Which Channels Were Actually Driving ROI for a D2C Brand
The client is a North American consumer goods and D2C brand managing a sizeable marketing budget across multiple channels without a rigorous attribution model capable of accounting for the full range of marketing and external factors driving sales performance.
Budget allocation decisions relied on platform-reported metrics and marketing intuition, neither of which could separate the contribution of a paid social campaign from the effect of a seasonal sales peak, a pricing change, or a competitor promotion running in the same window.
Without a unified, statistically rigorous attribution framework, every budget reallocation decision lacked the evidence base to defend it internally or optimise it systematically across channels.
Millions allocated across channels with no statistical proof of which ones were actually driving sales, and platform metrics actively making the problem worse.
- No reliable attribution model: Budget decisions were based on platform metrics and intuition, without a statistical way to separate true channel impact from baseline and overlap effects.
- Inflated platform reporting: Each platform over-attributed conversions, leading to double counting and distorted comparisons across channels.
- External noise not isolated: Seasonality, competition, and pricing shifts skewed performance signals, often misattributing outcomes to marketing activity.
- No budget optimization framework: Spend reallocation decisions lacked a unified, testable model, making them largely subjective and unvalidated.
- No visibility into saturation: The business couldn’t identify diminishing returns, leading to inefficient overspending in already saturated channels.
Ksolves, an AI-first company, built a Marketing Mix Model using econometric regression to break down sales performance across marketing channels, pricing, seasonality, competition, and macroeconomic factors. The core principle was independence from platform attribution, each channel’s impact is derived from actual sales variation in historical data, ensuring a single unified, evidence-based view for all budget decisions.
- Econometric Marketing Mix Model: Built a multi-variable regression model using historical sales, channel spend, pricing, and external factors to generate statistically robust contribution estimates independent of platform attribution logic.
- Channel Contribution Decomposition: Separates channel-driven revenue from baseline sales and external effects, revealing true incremental impact after adjusting for seasonality, pricing, and competition.
- Diminishing Return Response Curves: Models spend-to-return relationships per channel to identify saturation points and optimize marginal ROI across investments.
- Budget Optimization Scenario Planning: Simulates multiple allocation scenarios using response curves, enabling data-backed budget decisions before actual spend commitments.
- Seasonal & External Factor Controls: Incorporates seasonality, competitor activity, macroeconomic trends, and pricing shifts to isolate pure marketing-driven performance signals.
Technology Stack
| Category | Technology |
|---|---|
| Analytics | Marketing Mix Modelling (Econometrics) |
| AI/ML | Diminishing Return Response Curves |
| Analytics | Budget Optimisation Engine |
| Platform | Marketing Analytics Dashboard |
From platform metrics and marketing intuition to a statistically rigorous attribution model that tells the brand exactly what each channel is contributing, and what reallocation is worth.
- Statistically validated attribution model: Channel contributions are now derived from historical sales data, replacing conflicting platform claims with a single defensible framework.
- 3 over-invested channels identified: Diminishing returns analysis highlighted three channels with excess spend and declining marginal efficiency.
- 15% media ROI improvement projected: Scenario modelling indicates potential ROI gains through strategic budget reallocation across higher-performing channels.
- External noise fully isolated: Seasonality, competition, pricing, and macro factors are controlled, ensuring attribution reflects true marketing impact.
Relying on platform metrics is not true attribution, but it is fragmented storytelling where each channel overstates its value. For this D2C brand, it created conflicting narratives, contested budget decisions, and no clear view of what truly drove sales versus external factors like seasonality, pricing, or competition. Ksolves replaced this uncertainty with a unified econometric model that reveals real channel impact. It identified three over-invested channels, projected a 15% media ROI uplift through reallocation, and isolated external noise from marketing performance.
Are You Still Allocating Marketing Budgets Based on Platform Metrics and Intuition?
