Mass Analytics

Mass Analytics, founded in 2013 by Dr. Ramla Jarrar and Dr. Firas Jabloun, is a specialized technology company headquartered in London, United Kingdom. The firm operates in the marketing analytics sector, providing software and consultancy aimed at democratizing marketing effectiveness measurement for businesses of all sizes. The founders' backgrounds are deeply rooted in data science and analytics; Dr. Ramla Jarrar holds a Ph.D. in Data Science and has over two decades of experience in marketing analytics, while Dr. Firas Jabloun holds a Ph.D. in electrical engineering and has extensive experience as a quantitative analyst and in software development. Their combined expertise underpins the company's mission to make complex marketing data analysis accessible.

The core of Mass Analytics' business revolves around Marketing Mix Modeling (MMM), a statistical analysis technique used to quantify the impact of various marketing activities on sales or other key performance indicators. This allows clients to measure the return on investment (ROI) for each marketing channel and optimize their budgets accordingly. The company serves a diverse client base, including major brands like TESCO and MONOPRIX, as well as media agencies and companies that wish to bring their analytics capabilities in-house. Revenue is generated through a hybrid model that includes licensing its software suite and providing consultancy, training, and managed services.

The company's flagship product is a proprietary software platform that includes several tools. "MassTer" is the end-to-end MMM software that automates and accelerates the modeling process. It is complemented by "MassFeeds" for data preparation and "Insight" for budget optimization and simulation. This suite enables users to load and visualize data, build econometric models, and use those models to forecast and simulate the outcomes of different marketing plans. By using historical data, the platform identifies the key drivers of sales and helps businesses make data-driven decisions to re-allocate budgets from lower-performing channels to those with higher ROI. The approach combines machine learning with statistical regression to provide a dynamic and comprehensive view of marketing performance.

Keywords: Marketing Mix Modeling, marketing effectiveness, MROI, marketing analytics, budget optimization, predictive analytics, sales forecasting, marketing ROI, data-driven decision making, econometric modeling, MassTer software, media effectiveness, marketing measurement, consultancy, data science, statistical analysis, return on investment, channel optimization, marketing strategy, advertising measurement