Comments (6)
Hey Mario!
Your article looks amazing 🎉! Thanks for sharing it with us.
Let us know if something seem unintuitive or had trouble finding documentation/example about something specific (we are working on adding more across the board but in case there was any thing specific).
Cheers!
from lightweight_mmm.
Thanks, Pablo!
I hope this encourages more people to try the tool.
The only thing that was not very clear was what to do with the organic data.
Is my thought correct that we don't need to add it as a media channel?
My models were slightly more accurate out-of-sample when I added it with cost=1 for everything, but it was not significant enough for me to trust it.
from lightweight_mmm.
Hello Mario!
Apologies for the delay in the response.
Good question! Could you give us a few more pointers on the nature of this organic data? There might be nuances depending on how it is generated or where it comes from.
That said, let me give a couple pointers on how that can be added (or not), here are some options:
- Do not use it. If it is not very relevant or traffic is very minor.
- Use it as a paid media channel (similar to what you did in your attempt to add it). You can introduce a fake cost for it (eg. average of costs of other channels, or average cost of a channel that is similar).
- Use it as a non media variable. In LMMM you can add
extra_features
for the model to incorporate, this could be promotions that you run, competitors data, inflation data, ... but one could use organic data in certain scenarios.
I might be able to correct the given answer (or add on it) based on your response.
Let me know if something is unclear.
from lightweight_mmm.
I have also added a Community Spotlight section in the README and linked your article.
from lightweight_mmm.
Thanks for the highlight!
My organic traffic data (impressions) comes from Youtube and Google Search Console. Youtube is the largest by far.
I will try the extra_features
approach (I completely forgot about it).
Everything is clear for now.
I am currently testing a few changes to my campaigns based on running the optimizer over the model with Audiences, Devices, and Demographics as channels.
The results make intuitive sense, so let's wait for the data :)
from lightweight_mmm.
Sounds great Mario! I would love to hear about the results so please feel free to reach out to me directly if you want.
Let us know if you run into any other problems with the library.
Closing this one but feel free to re-open.
from lightweight_mmm.
Related Issues (20)
- What is the best way to contribute to this library?
- INVALID_ARGUMENT: Python buffer protocol is only defined for CPU buffers. HOT 1
- In trying to plot the the response curves I get an error
- NumbaWarning: Compilation is falling back to object mode WITH looplifting enabled because Function "histogram" failed type inference due to: non-precise type pyobject During: typing of argument at /usr/local/lib/python3.10/dist-packages/arviz/stats/density_utils.py (979)
- Potential Improvments on Price and Extrafeature
- ValueError: Einstein sum subscript 'c' does not contain the correct number of indices for operand 1. HOT 1
- Priors of tested media channels HOT 3
- All predictions start with an offset HOT 1
- Kpi is smaller than spend on response curve
- GPU Performance Issues HOT 1
- Adstock Normalization
- What is budget in media optimization
- previous_extra_features data in predict function
- Not able to install the packages
- What are media_mix_model.trace["media_transformed"], media_mix_model.trace["coef_media"] ? Media Contribution Calculation Understanding HOT 6
- Model prediction decomposition HOT 2
- JAX GPU with Apple M2
- Response Curve : Adstock and Carryover models HOT 14
- Feature Names HOT 2
- RuntimeError (This version of jaxlib was built using AVX instructions) on importing `optimize_media` from `lightweight_mmm` in a conda environment on M1 mac.
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 lightweight_mmm.