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.
from pymc-marketing.
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)
- Improve custom MMM class user experience
- Pull out `_create_likelihood_distribution` method as function HOT 2
- Run formatter on the notebooks
- CLV models don't have working repr without model fit HOT 1
- The model_config parameter of the Pareto/NBD is throwing an error when used HOT 4
- Clarify / checks for prior configurations
- compute_sigmoid_second_derivative is wrong HOT 4
- mmm.plot_posterior_predictive() does not work HOT 4
- Rename sections header on website
- Add a job that tests all notebooks upon every release
- Add a new "General/Misc" sub-section to Examples for cross-model-class features
- Run example code in doc strings in CI
- Inquiry Regarding Definition of Recency in pymc-marketing Package HOT 2
- Returning parameters for saturation & carryover effect HOT 1
- Add Codespaces config file? HOT 6
- Return Xarrays in `CLV` RFM Utility Functions?
- Raise Exception when `GammaGammaModel` does not converge properly HOT 1
- Add heterogeneous `discount_rate` broadcasting to `customer_lifetime_value` function?
- Remove `future_t=0` predictions from `expected_customer_lifetime_value` function
- Issues with pymc_marketing.plot_frequency_recency_matrix() HOT 5
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