Prophet
Robyn
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Prophet | Robyn | |
---|---|---|
221 | 14 | |
17,743 | 1,026 | |
1.2% | 3.1% | |
6.2 | 9.2 | |
22 days ago | 7 days ago | |
Python | Jupyter Notebook | |
MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Prophet
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Moirai: A Time Series Foundation Model for Universal Forecasting
https://facebook.github.io/prophet/
"Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well."
- prophet: NEW Data - star count:17116.0
- prophet: NEW Data - star count:17082.0
- Facebook Prophet: library for generating forecasts from any time series data
- prophet: NEW Data - star count:16196.0
- prophet: NEW Data - star count:15889.0
Robyn
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Mixed Marketing Modeling Approach for attribution?
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
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Is Hierarchical Bayesian Modelling used in industry?
Robyn - R Library
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Can Ads Be GDPR Compliant?
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
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Thoughts on Exposure Metrics Based Marketing Mix Modeling (MMM)?
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?
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My Favorite Off-the-Shelf Data Science Repos, What Are Yours?
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.
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Marketing Spend Optimization
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
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data engineering should not be an issue for Data Scientists
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
- Reviewing Marketing Mix Model - concerned about a few issues...
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Media Mix Modeling. What variables should I use? What would be a good R^2?
Take a look at Robyn by Facebook Meta, if you haven't
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Data Science and Marketing
MMM (R): Robyn
What are some alternatives?
tensorflow - An Open Source Machine Learning Framework for Everyone
MMM-BurnIn
darts - A python library for user-friendly forecasting and anomaly detection on time series.
EconML - ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
scikit-learn - scikit-learn: machine learning in Python
causalml - Uplift modeling and causal inference with machine learning algorithms
xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
lightweight_mmm - LightweightMMM 🦇 is a lightweight Bayesian Marketing Mix Modeling (MMM) library that allows users to easily train MMMs and obtain channel attribution information.
greykite - A flexible, intuitive and fast forecasting library
mmm_stan - Python/STAN Implementation of Multiplicative Marketing Mix Model, with deep dive into Adstock (carry-over effect), ROAS, and mROAS
MLflow - Open source platform for the machine learning lifecycle
mta - Multi-Touch Attribution