Predicting Video Revenue Through Marketing Mix Modeling and Bayesian Forecasting

Lack of a Predictive Framework

There was no operationalized process to convert marketing spend plans across channels into revenue projections. Media planning decisions were made without a data-driven foundation, making it difficult to justify budget allocations or predict outcomes with confidence.

Manual and Unscalable Processes

Existing processes were largely manual and not designed to handle multiple upcoming releases simultaneously. As the release pipeline grew, the absence of a structured, repeatable MMM framework became a critical bottleneck for the planning team.

No Sensitivity Analysis Capability

The client had no mechanism to simulate how changes in spend across individual channels — such as TV, digital, or print — would impact total revenue. Without dynamic sensitivity analysis, it was impossible to model different budget scenarios or optimize spend allocation before committing to a plan.

Fragmented Historical Data

Marketing spend and revenue data across past titles were not consolidated or structured for modeling. This made it difficult to identify which channels had historically delivered the greatest revenue impact and to benchmark new releases against comparable titles.

01.

Data Collection, Validation, and Similarity Analysis

Marketing spend data, spend scenarios, and historical revenue data were gathered and validated for completeness and accuracy. Comparable past video titles were identified and benchmarked based on genre, marketing strategy, and release scale. These benchmarks were used to inform model calibration and improve the relevance of revenue forecasts for new titles.

02.

Marketing Mix Modeling Using the Robyn Framework

Marketing mix models were built using the Robyn framework, ensuring a minimum R² of 60% to guarantee acceptable model accuracy. The MMM framework quantified the incremental revenue contribution of each marketing channel — including TV, digital, print, and outdoor — enabling the client to identify which channels were driving the most impact and where spend was being over or under-allocated.

03.

Bayesian Revenue Forecasting Using PyMC

Bayesian revenue forecasting models were trained using PyMC, targeting a Mean Absolute Percentage Error (MAPE) of less than 25%. This approach provided probabilistic revenue projections for upcoming titles, accounting for uncertainty and enabling more robust planning across different release scenarios.

04.

Scenario Sensitivity Analysis

Multiple marketing spend scenarios were simulated and analyzed to measure the revenue impact of reallocating budgets across channels. A weekly spend calendar was built based on finalized scenarios, giving the media planning team a clear, actionable distribution plan for each upcoming release.

05.

Dynamic Tableau Dashboard

A dynamic Tableau dashboard was developed and deployed on Tableau Online, integrating outputs from both the Robyn and PyMC modeling frameworks. The dashboard allowed the team to interactively modify marketing spend allocations across channels and visualize updated revenue projections in real time — transforming a previously manual process into a self-serve, data-driven planning tool.

Data-Driven Revenue Forecasting

For the first time, the client had a statistically validated framework to translate marketing spend plans into revenue projections, replacing guesswork with model-backed predictions for each upcoming title.

Optimized Marketing Spend Allocation

The MMM framework identified the incremental revenue contribution of each channel, enabling the team to reallocate budgets toward high-impact channels and reduce spend on activities with diminishing returns.

Dynamic Scenario Planning

The sensitivity analysis capability allowed the media planning team to simulate multiple spend scenarios and compare projected revenue outcomes before finalizing any campaign, significantly improving the quality of planning decisions.

Scalable, Repeatable Process

The framework was designed to be reused across future titles with minimal rework, giving the client a scalable solution to manage a growing release pipeline without increasing manual effort.

Real-Time Insights via Tableau

The interactive Tableau dashboard gave business users direct access to model outputs, enabling self-serve scenario planning and real-time revenue projections without requiring technical intervention for every new simulation.