Comments (3)
The Google Cloud Docs contain a guide on how to do this with a DL model:
https://cloud.google.com/ai-platform/docs/clv-prediction-with-offline-training-train
This paper proposes Gaussian Processes for CLV modeling, which should be within the capabilities of pymc
:
If there is interest in adding a spend model with covariates, the GP model is worth exploring further.
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GP model sounds interesting, we could perhaps start with a developer notebook just to see it works well before implementing a packaged model?
from pymc-marketing.
That seems like the sensible approach.
Can someone more familiar with GP modeling in pymc
assign themselves to this? Right now my focus is on replicating all functionality of the lifetimes
library.
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Related Issues (20)
- Negative delta x causes sampling error
- Speed up sphinx builds
- MMM Example Notebook returns KeyError: 'dist' HOT 2
- Example NB plots are compressed HOT 7
- Negative custom priors (again!) HOT 2
- Handle recent pymc deprecation warnings HOT 1
- Memory concerns with large `l_max` values HOT 9
- Add License to headers
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- Pull out fourier transformation from MMM method HOT 3
- BorkenPipeError on ps.ParallelSampler() HOT 5
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- Can each channel contributions on any input data be extracted? HOT 1
- bug in clv.utils.rfm_train_test_split() HOT 7
- Missing module / class docstrings HOT 1
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- Add `py.typed` file HOT 1
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