Comments (7)
I should be able to write something about the continuous contractual setting! I'm feeling inspired 😅
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I'll work on adding a notebook for the sBG model for discrete/contractual settings.
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I'm making progress on ^... but I'm knocking my head on a sampling error. Hopefully, I create a PR soon!
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Is the goal to add more than one model to each quadrant? The CNC quadrant now has @larryshamalama 's BG/NBD distribution (#16), but there are several other models available in that quadrant. I wasn't sure if the idea was to get one per quadrant max or if the goal was broader than that. Also, the distribution added in #16 is named ContNonContract
, which seems to imply that there aren't going to be other continuous non-contractual distributions.
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Is the goal to add more than one model to each quadrant? The CNC quadrant now has @larryshamalama 's BG/NBD distribution (#16), but there are several other models available in that quadrant. I wasn't sure if the idea was to get one per quadrant max or if the goal was broader than that. Also, the distribution added in #16 is named
ContNonContract
, which seems to imply that there aren't going to be other continuous non-contractual distributions.
Sorry for not replying to your question initially. Effectively, there are other continuous non-contractual distributions, as I realized later on (e.g. ParetoNBD). We briefly touched upon this point in #128
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Discrete contractual is covered by #133. Looks like a PR to address #176 would cover the final quadrant.
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We have a distribution block for Continuous Contractual, but no modeling options per #279. I've gotten questions on LinkedIn about modeling purchases made through active memberships and/or phone app sessions, so there's demand for it.
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Related Issues (20)
- Stale `mmm_tvp_example` notebook HOT 16
- Run slow notebooks at in frequent schedule
- mlflow module not showing up on the documentation page HOT 5
- Migrate to StrEnum
- Document Pandas to Xarray to custom models
- Run tests with dependency changes
- Collect feedback on MLflow logging HOT 2
- Hard vs (or and) Soft parameterizations for MMM HOT 2
- Is there a way to do nested models using pymc_marketing HOT 7
- Location for experimental functionality HOT 1
- Add "stale documentation" banner HOT 3
- Question on lift test calibration HOT 3
- Arbitrary `Prior` class transformations
- Assume default priors for `adstock_from_dict` and `saturation_from_dict` functions
- Add time-slice cross validation notebook.
- Reproducibility Issues in mmm.fit() HOT 11
- Remove deprecations before 0.9.0 release
- Ensure same results with `ModelBuilder.fit` random_seed
- Fix UML failures (with forks?)
- MMM class cannot be used with sklearn pipeline HOT 4
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