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# Description This PR creates a new model that depends on int_monthly_aggregated_metrics_history_by_deal. The idea is that this is used for Churn computation (Booking Churn, Revenue Churn, Listing Churn) later on. The idea is relatively simple. Measure how much a Deal has been contributing to a Global amount (sum of metric for all deals) over the preceding period of 12 months. You will notice that there's 2 computations, the "additive" and the "average" one. This is because we still need to align with Matt/Suzannah on which approach makes more sense, but we need data for it. I'm not sure the namings are good though so happy to see your suggestions. You will also notice that there's no filter by deal_lifecycle_state = '06-Churning'. This will be done in a separated model, whenever we attribute this model to the mtd computation. The reason is simple - this model stays at deal level, thus meaning we can do the dimension aggregation and even a lifecycle aggregation if needed, depending on the needs. Be aware that this effectively means that MTD KPIs models will depend on the "monthly by deal" models. This has some cons in terms of dependency management but cannot be overcome since we the metric total revenue depends on many subsets. In essence, I don't see another way of doing it unless doing a massive KPIs refactor. I prefer to wait until the Product KPIs discussions are finished and then we see how we approach it. # Checklist - [X] The edited models and dependants run properly with production data. - [X] The edited models are sufficiently documented. - [X] The edited models contain PK tests, and I've ran and passed them. - [X] I have checked for DRY opportunities with other models and docs. - [X] I've picked the right materialization for the affected models. # Other - [ ] Check if a full-refresh is required after this PR is merged. Related work items: #22691 |
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