mta VS Robyn

Python library of a bunch of attribution models

Some other marketing topics to be aware of: forecasting - Prophet is an interesting library for this, you'll definitely need some domain knowledge to fit the forecast, it really shouldn't be used to just fit and go otherwise you'll probably end up with some bad results, Media Mix Modeling - FB-Robyn is a library with quite a bit of potential, Multi-Touch Attribution - MTA is a decent python library for this, but you'll have pretty significant data requirements to actually have accurate results, these approaches tend to be pretty susceptible to survivorship/selection bias, survival analysis - Lifelines is a pretty good python package for this, this sort of analysis is useful for determining churn likelihood or time until next purchase.

This is a nice library that implements several multi-touch attribution models beyond the simpler heuristic based ones. One word of caution about these sort of attribution models is the attribution always adds up to 100%. Attribution models typically don't take exogenous factors into account - things that potentially influence whether the customer would have purchased anyway regardless of marketing touchpoints. They also tend to be quite sensitive to selection bias. If you have a touchpoint that requires a customer perform some behavior that can be related to a base level of interest, the model will overweight the attribution of that touchpoint - think things like an abandoned cart remarketing journey. The customer has already shown an inherent interest in the product by placing the product in the cart.

With all this talk about Google and other platforms deprecating 3P tracking in favor of more aggregate "tracking", my team is considering a marketing mix modeling tool. One that comes to mind is this tool - Robyn

Robyn - R Library

Furthermore, both Google and Meta have quietly conceded that a lot of the digital attribution data they generate is pretty bunk. It’s why Meta developed Robyn, which uses MMM techniques that have long been used to measure effectiveness of traditional channels: https://github.com/facebookexperimental/Robyn

Here's my first question - are there other approaches that model media spending and media exposure metrics separately? I have noticed that Robyn has included both exposure metrics and media spending and got them transformed via a non-linear model called the Michaelis-Menten function to establish the spend-exposure relationship, as you can find it here. What if we want, or is it possible to keep only media exposure metrics instead of media spending? If yes, what would be the alternative approaches apart from Logarithmic Regression, Ridge Regression, Bayesian approach, etc?

Here are my top off-the-shelf data science models for Marketing. Would be interested which other marketing data science tools you use?

Product Recommendation on Your Website with Metarank (https://github.com/metarank/metarank)

Metarank is a tool that helps you easily build an advanced recommendation engine for your products or content on your website. To get started you only need historical performance data of your products (e.g. number of clicks) and additional metadata like product rating, genre, ingredients or price. In a YAML file, you define the features and the model parameters (e.g. number of iterations, modeling technique). The API service integrates with Apache Flink and can be easily integrated into Kubernetes clusters.

User Journey Analysis on your Website with Retentioneering (https://github.com/retentioneering/retentioneering-tools)

Retentioneering helps you to understand the user journey on your website. Retentioneering is a Python library that allows you to easily connect your Google Analytics data (in Bigquery). You define user-id, event-type and time stamp. From this data input a comprehensive graph network is created with gains and losses as you know it from a customer journey. In addition, customer segments are created that have a similar customer journey. This reduces the complexity of a purely descriptive view of the data.

Marketing Mix Modeling with Robyn (https://github.com/facebookexperimental/Robyn)

Less third-party cookie means less attribution models. The answer to this is Marketing Mix Modeling. Marketing mix models are regression models that use statistical probability to calculate the effect size of marketing channels and other independent variables. The advantage is that business context can be modeled much more realistically. For example, Google Searches for the own brand can be integrated to determine the share of the own brand strength in the revenue. Likewise, offline advertising measures can be modeled with other metrics in this context (e.g. offline advertising with GRPs). Robyn takes into account adstock effects, ROAS calculation and multicollinarity in the marketing channels. In addition, with simple functionality, budgets can be optimized using the predictions and results from marketing tests can be integrated into the model for calibration.

Try this https://facebookexperimental.github.io/Robyn/ And for more advanced project you can try this https://siyasgupte.medium.com/causal-impact-understand-the-inner-workings-to-optimize-your-results-506ed442619

For Data Scientists super interesting but still in WiP: We are implementing a advanced Marketing Mix Model soon. It combines bascially Prophet + Ridge Regression + Nevergrad (it is already a R library from Meta: https://github.com/facebookexperimental/Robyn

Take a look at Robyn by Facebook Meta, if you haven't

MMM (R): Robyn