Comments (5)
Thanks for the comments. Indeed the explanations are generally vague ... and I believe is not only because privacy but also because the approach might depend on the nature of the lift test.
That being said, if we are able to go provide a simple (does not need to be fancy and if it follows an heuristic is great!) but effective framework for MMM calibration, this could be a key factor against competitors 😉.
from pymc-marketing.
Yes, you can add the lift test measurements as observations. The way in which you go about doing this depends on what the lift test actually measures and how the experiment is performed. For the HelloFresh project, we added them as imperfect observations of the incremental CAC of media channels.
from pymc-marketing.
I'm curious @juanitorduz, could you share some links on the prior information stuff?
from pymc-marketing.
Hey! I have not gone into depth regarding this subject but is my immediate task.
-
At the moment I am using costs as priors as done in Google's LightWeight MMM, see https://github.com/google/lightweight_mmm/blob/main/lightweight_mmm/models.py#L350
-
Where I have read lift test being included in priors in Uber's work: Bayesian Time Varying Coefficient Model with Applications to Marketing Mix Modeling, see Section 4.1.2 4.1.2 Experimentation Calibration (It was a while since I read this)
from pymc-marketing.
The Google code seems to set a prior on the scale of the coefficient, but it doesn’t constrain its mean, using something like a Gamma or LogNormal, and the Orbit paper doesn’t explain what they really did. They just say that they “ingest some observations as priors”. They don’t explain how they do that.
The approach that we had followed was quite crude: we assumed that the lift test was the observation of a random variable. The mean of the distribution was computed from the estimate of the target thing that was being measured during the period of the lift test. So it was nothing very fancy, and it could be improved by incorporating how the lift test was performed. I’m not sure how many details I can share about this though, so I’m being vague on purpose.
from pymc-marketing.
Related Issues (20)
- Plot specific channels in contribution_curves
- Negative values not allowed for MMM channel variables
- Improve posterior predictive output of ParetoNBD
- Release 0.3.1 HOT 1
- Ruff linter and formater HOT 1
- marginal return HOT 1
- Error in MMM Quickstart, mmm for DelayedSaturatedMMM() specified but model.fit used HOT 3
- CLV Quickstart example fails HOT 6
- Posterior predictive referenced incorrectly in plot_channel_parameter HOT 3
- plot_components_contributions fails with Matplotlib ParseException HOT 3
- closer access to Michaelis-Menten curve fit HOT 1
- Implementing a wider selection of variable transformations HOT 1
- Illustrate use of `thin_fit_result` in notebook HOT 1
- sample_posterior_predictive doesn't scale X data HOT 3
- Forward pass on a given spend HOT 2
- ValueError on fit if target part of DataFrame
- Wrong type hint in `_create_likelihood_distribution` HOT 1
- idata is removed if fit is called HOT 4
- Error when yearly seasonality is enabled
- install pymc in the recommended manner for remote workflows
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from pymc-marketing.