MMM Model

8 articles

MMM - Guidelines for Feature Selection

Rockerbox's Marketing Mix Modeling (MMM) measures the impact of your marketing on your target KPI—typically revenue or conversions (signups, subscriptions, and so on). We break down your marketing in…

Updated by Eddie Chou

MMM Feature Metrics - Spend, Impressions, or Clicks

Customers often ask whether we model media against spend, impressions, or clicks. At Rockerbox, we generally use daily spend when available. Here are the reasons we find spend to be a better metric:…

Updated by Eddie Chou

MMM - Including Non-Spend Features

This guide outlines how Rockerbox can incorporate additional non-spend features into Marketing Mix Modeling (MMM) and the specific metrics needed for inclusion. For details on spend-related features,…

Updated by Eddie Chou

Model Fit for MMM

This documentation is designed to help you understand the expectations and estimate KPIs regarding the fit of your MMM model. Understanding Model Fit. 1. Purposeful Simplicity. Our MMM models are int…

Updated by Emily Shreero

Bayesian Methodology

Bayesian MMM at Rockerbox. What is Bayesian MMM? Bayesian MMM applies Bayesian statistical methods to marketing mix modeling. Unlike traditional statistics that view probability as frequency, Bayesia…

Updated by Ron Jacobson

Setting Priors

Overview. Rockerbox uses a Bayesian methodology in our marketing mix models. An important step in building models is setting priors. Setting priors in Bayesian models allows for the incorporation of…

Updated by Eddie Chou

MMM - Features Included in Modeling

Rockerbox Marketing Mix Modeling (MMM) helps brands explore the relationship between marketing investments and other factors and their impact on revenue based on historical data. These factors includ…

Updated by Eddie Chou

MMM - External Factors

Rockerbox includes external factors, variables that influence business performance but are outside the direct control of the advertiser, in our modeling. We source a standard set of these factors, in…

Updated by Eddie Chou