Marketing Science

Advanced analytics for marketing optimization and measurement

Marketing Science combines statistical modeling, econometrics, and machine learning to measure marketing effectiveness and optimize budget allocation. We help organizations move from intuition-based to data-driven marketing decisions.

Core Capabilities

Marketing Mix Modeling (MMM)

Decompose sales drivers, measure ROI, and optimize budget allocation across channels

Response Curves & Saturation

Model diminishing returns, identify optimal spending levels, and understand saturation points

Customer Lifetime Value (CLV)

Predict customer value, optimize acquisition costs, and improve retention strategies

Attribution Modeling

Multi-touch attribution, incrementality testing, and cross-channel measurement

Media Optimization

Real-time budget reallocation, campaign performance tracking, and tactical execution

Business Impact

15-30% improvement in marketing efficiency
Clear ROI measurement for every marketing dollar
Data-driven budget allocation recommendations
Reduced waste from oversaturated channels
Improved forecasting accuracy for marketing outcomes

Learn More: Articles & Insights

View all articles

Deep dives into methodologies, case studies, and implementation guides

5 min read

Mixed Integer Programming for MMM Budget Optimization

Learn how Mixed Integer Programming (MIP) with discretized response curves solves marketing budget allocation problems using branch-and-bound methods.

Sep 9, 2025

1 min read

Part 3 | Hands-On Implementation: Building a Causal MMM from Scratch

A practical tutorial implementing causal marketing mix modeling with DoWhy, including code examples and a complete toy dataset

Aug 28, 2025

16 min read

Featured

Practical optimizer scenarios in MMM: A step-by-step guide

Walk through real-world MMM optimization scenarios using Mixed Integer Programming. See how to discretize response curves, formulate constraints, and solve practical reallocation problems with actual numbers.

Aug 21, 2025

8 min read

Part 2 | Beyond Attribution: How Causal Inference complements Modern MMM

Exploring structural causal models using DAGs, counterfactual reasoning, and frameworks like DoWhy and PyMC-Marketing

Aug 20, 2025

8 min read

Part 1 | Inside Google Meridian: Causal MMM at scale

How Google's Meridian brings causal inference to marketing mix modeling at global scale

Aug 15, 2025

See Our Work

Real-world implementations and case studies demonstrating impact

Marketing Science

CLTV-driven customer acquisition strategy

Customer lifetime value modeling combined with lookalike audience optimization for targeted acquisition, improving customer quality and reducing acquisition costs.

View case study →

Ready to Transform Your Marketing Science?

Let's discuss how we can help you implement data-driven solutions that deliver measurable business impact.

Start a Conversation Explore More Articles