Cassandra utilizes machine learning-based Marketing Mix Modeling to analyze both online and offline media performance, enabling brands to measure the true impact of their advertising investments without relying on user-level data. By providing a personalized media allocation plan, Cassandra helps businesses optimize their advertising budgets to maximize ROI and improve long-term sales outcomes.
Funding
Funding not disclosed
Founders
Product
Problem
Many brands struggle to accurately measure the return on investment (ROI) of their advertising spend across both online and offline channels due to limitations in traditional attribution models and increasing privacy regulations. This makes it difficult to optimize media allocation and maximize the impact of marketing investments on long-term sales.
Solution
Cassandra is a machine learning-based Marketing Mix Modeling (MMM) platform that analyzes online and offline media performance to provide brands with a comprehensive understanding of their advertising ROI. The platform leverages advanced algorithms to measure the long-term effects of advertising campaigns, predict future performance, and generate personalized media allocation plans. By understanding the causal relationship between marketing investments and sales outcomes, businesses can optimize their advertising budgets and improve overall marketing effectiveness.
Target Audience
Cassandra is designed for marketing teams, marketing analysts, and data analysts at technology, retail, travel, education, and eCommerce companies, as well as marketing agencies. Ideal clients spend over $30k on ads each month and have at least two years of historical data.
Features
- Measures online and offline media ROI without relying on user-level data
- Analyzes the long-term impact of advertising campaigns to determine true media investment effectiveness
- Predicts future performance and provides AI-powered budget allocation recommendations
- Validates causal relationships between investments and sales through incrementality testing frameworks
- Refreshes MMM models monthly to improve accuracy with new data
- Connects to various data sources via connectors or CSV uploads
- Offers a demo dataset for testing the platform