SaaSHub helps you find the best software and product alternatives Learn more →
Robyn Alternatives
Similar projects and alternatives to Robyn
-
Prophet
Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
-
-
WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
-
metarank
A low code Machine Learning personalized ranking service for articles, listings, search results, recommendations that boosts user engagement. A friendly Learn-to-Rank engine
-
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
-
lightweight_mmm
LightweightMMM 🦇 is a lightweight Bayesian Marketing Mix Modeling (MMM) library that allows users to easily train MMMs and obtain channel attribution information.
-
mmm_stan
Python/STAN Implementation of Multiplicative Marketing Mix Model, with deep dive into Adstock (carry-over effect), ROAS, and mROAS
-
InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
-
tensor-house
A collection of reference Jupyter notebooks and demo AI/ML applications for enterprise use cases: marketing, pricing, supply chain, smart manufacturing, and more.
-
-
retentioneering-tools
Retentioneering: product analytics, data-driven CJM optimization, marketing analytics, web analytics, transaction analytics, graph visualization, process mining, and behavioral segmentation in Python. Predictive analytics over clickstream, AB tests, machine learning, and Markov Chain simulations.
-
GeoexperimentsResearch
Discontinued An open-source implementation of the geo experiment analysis methodology developed at Google. Disclaimer: This is not an official Google product.
-
matched_markets
Matched Markets is a Python library for design and analysis of Geo experiments using Matched Markets and Time Based Regression.
-
trimmed_match
This Python library implements Trimmed Match for analyzing randomized paired geo experiments and also implements Trimmed Match Design for designing randomized paired geo experiments.
-
SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better Robyn alternative or higher similarity.
Robyn reviews and mentions
Posts with mentions or reviews of Robyn.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-05-02.
-
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
-
Is Hierarchical Bayesian Modelling used in industry?
Robyn - R Library
-
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
-
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?
-
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.
-
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
-
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...
-
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
-
Data Science and Marketing
MMM (R): Robyn
-
A note from our sponsor - SaaSHub
www.saashub.com | 19 Apr 2024
Stats
Basic Robyn repo stats
14
1,021
9.2
7 days ago
facebookexperimental/Robyn is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of Robyn is Jupyter Notebook.
SaaSHub - Software Alternatives and Reviews
SaaSHub helps you find the best software and product alternatives
www.saashub.com