Marketing Mix Modeling (MMM) is a statistical analysis technique used to estimate the impact of various marketing tactics on sales and other key performance indicators. It helps businesses allocate their marketing budget more effectively by identifying the channels and strategies that drive the most value.

While both MMM and Attribution aim to measure the effectiveness of marketing activities, they do so in different ways. MMM provides a comprehensive, cross-channel view of how various marketing activities contribute to overall sales over time, considering both online and offline channels.

In contrast, Attribution typically focuses only on digital channels, often neglecting the impact of traditional media and the interaction between different channels. Attribution also fails to account for saturation effects, where the effectiveness of additional spending diminishes. Given increasing regulations and the deprecation of third-party cookies, Attribution's functionality is becoming more limited and less reliable.

Open-source MMM refers to marketing mix modeling solutions that are developed and shared openly with the community. These solutions are typically built using open-source software, which allows users to access, modify, and enhance the underlying code to fit their specific needs.

Yes, open-source MMM can be trusted if it's supported by a strong community and regularly updated. Open-source solutions benefit from collective intelligence, transparency, and continuous improvement. At MMM Labs, we ensure that our open-source algorithms are rigorously tested and validated for accuracy and reliability.

To start an MMM analysis, you'll need sales or conversions data, marketing activities data, and non-marketing variables. Sales data can come from your POS, ERP, CRM, or web tracking tools. Marketing data should include exposure metrics and spend from all paid activities. Non-marketing variables could be promotions, discounts, macroeconomic factors, weather, and competitor activities.

No, open-source MMM packages are not all the same. Open-source provides transparency, but the models can differ significantly. Some models are Bayesian, offering a probabilistic approach that incorporates prior knowledge, while others are Frequentist, focusing on long-run frequencies of events. Additionally, some models are additive, assuming that the effect of marketing activities is summed, whereas others are multiplicative, assuming interactions between activities. Moreover, some models are built on national data, providing broad insights, while others use DMA or geo-level data for more localized insights. There are also models that consider long-term effects, capturing the prolonged impact of marketing activities, and models with nested effects, which account for hierarchical data structures. Each approach has its benefits and drawbacks. At MMM Labs, we try all of them, experiment with different formulations, and bring out the best to our users.