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Mmm_stan Alternatives
Similar projects and alternatives to mmm_stan based on common topics and language
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Robyn
Robyn is an experimental, AI/ML-powered and open sourced Marketing Mix Modeling (MMM) package from Meta Marketing Science. Our mission is to democratise modeling knowledge, inspire the industry through innovation, reduce human bias in the modeling process & build a strong open source marketing science community. (by facebookexperimental)
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orbit
A Python package for Bayesian forecasting with object-oriented design and probabilistic models under the hood. (by uber)
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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.
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Bayesian-Marketing-Mix-Modeling
The project analyses the impact of different marketing tactics on the sales of items. The problem is a multivariate-modeling problem as there are 3 different tactics of marketing. Since, the impact of marketing medium cannot be negative we will be using Bayesian model for regression.
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pymc-marketing
Bayesian marketing toolbox in PyMC. Media Mix (MMM), customer lifetime value (CLV), buy-till-you-die (BTYD) models and more.
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stan
Stan development repository. The master branch contains the current release. The develop branch contains the latest stable development. See the Developer Process Wiki for details.
NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better mmm_stan alternative or higher similarity.
mmm_stan reviews and mentions
Posts with mentions or reviews of mmm_stan.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-05-16.
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Looking for advice on developing a MMM: Should I use Robyn or PyMC?
Question: Which Rabbit Hole to fall into? So far, it seems the best solution is either Robyn (most visualizations, lots of support / courses, can be automated on Google Cloud Platform, but uses R and apparently can't forecast), or PyMC (which uses Markov Chain Monte Carlo (MCMC). Additionally, there are some good github projects (MMM_STAN) that use Pystan (that use a domain specific C++ syntax to specify the model and data). Or, should I just use PyMC (in my native python) and then adapt to Robyn once I understand the basics? Does anyone have any thoughts or opinions?
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Marketing background + data science
Here's an example of using marketing mix modeling to determine ROI for things like sales and marketing budgets. This kind of work would be more valuable in established companies and departments that have the data infrastructure to support it. I'd argue that there's a ton of work involve to get to that point in most companies, though.
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Media Mix Modeling. What variables should I use? What would be a good R^2?
This is a thorough example worked out using a Bayesian google model https://github.com/sibylhe/mmm_stan
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A note from our sponsor - InfluxDB
www.influxdata.com | 2 May 2024
Stats
Basic mmm_stan repo stats
3
315
0.0
almost 2 years ago
sibylhe/mmm_stan is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of mmm_stan is Jupyter Notebook.
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