# Description
Changes:
* Creation of a deal-based model that contains when a "deal has appeared in new dash". This is tricky because a Deal can still have multiple users, thus it needs to be attributed to a date. I've chosen the first user appearance for the rest of the metrics.
* Adaptation of dimension deals in KPIs to include a client type, that indicates if the deal is from APIs or not (Platform, i.e., Dashboard).
* Xero metrics by Business Scope. This is the previous "dash source" that I need to change in the previously worked models. I decided to include APIs in the segmentation since in most cases we distinguish old dash from new dash by just "anything that is not in new dash". This is very wrong for invoicing metrics, in which we have APIs. So this actually properly computes a client segmentation by scope.
Note that I'll need to handle the monthly/mtd metrics/agg for these 2 metric models (Resolutions + Invoiced revenue) separately.
# 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: #27356
# Description
This is a extremely simple but rather important PR.
It just sets the cutoff for KPIs reporting to April 2022. This affects 1) Main KPIs, 2) Guest KPIs and 3) Account Managers report
Motivation behind this is to have accurate data. Early 2022 might still be shitty, but at least we have a source of truth to compare against (on revenue side, finance P&L)
I set a dedicated variable because currency rates is reading from the same start date, and I intend only to modify KPIs cutoff.
# Checklist
- [X] The edited models and dependants run properly with production data.
- [NA] The edited models are sufficiently documented.
- [NA] The edited models contain PK tests, and I've ran and passed them.
- [NA] I have checked for DRY opportunities with other models and docs.
- [NA] I've picked the right materialization for the affected models.
# Other
- [ ] Check if a full-refresh is required after this PR is merged. **I need to manually run a full-refresh on daily listing segmentation that is incremental**
Adds dedicated start date for KPIs
Related work items: #26712
Adds the following metrics:
- Guest Journey with Payment
- Guest Journey Payment Rate
by both visions (global and by deal id)
**Important**: it does not expose these metrics to the dashboard, this will be done after we have feedback from Ben R. on the paid GJ without GJ completeness. Missing steps to make them appear is to adapt `int_core__mtd_aggregated_metrics` and `int_core__monthly_aggregated_metrics_history_by_deal` and the respective reporting counterparts.
It adapts:
- `int_core__mtd_guest_journey_metrics`
- `int_core__monthly_guest_journey_history_by_deal`
the approaches are similar in the sense that we join with `int_core__verification_payments` and filter by a PAID status, that has been defined in the `dbt_project.yml` in a similar manner as we did with cancelled bookings. It can happen that the same verification request has multiple payments (see screenshot), which in this case we keep the first date in which the paid payment happens. The volume is quite low anyway.
code for the screenshot:
```
with pre as (
select
id_verification_request,
count(distinct icvp.id_payment) as total_paid_payments
from intermediate.int_core__verification_payments icvp
where icvp.payment_status = 'Paid'
group by 1
)
select
case when total_paid_payments > 2 then 'more than 2'
when total_paid_payments = 2 then '2'
when total_paid_payments = 1 then '1'
end as payment_volume_category,
count(1) as vr_volume
from pre
group by 1
order by 2 desc
```
I also added a missing reference in `schema.yaml` int about `int_core__mtd_guest_journey_metrics`
Related work items: #18105
This is a first approach to compute some easy metrics for the "deal" based business kpis. At this stage, it contains the information of bookings (created, checkout, cancelled) per deal and month, including both historic months as well as the current one. This do not contain MTD computation because it's overkill to do a MTD at deal level (+ we have 1k deals, so scalability can become a problem in the future)
Models:
- **int_dates_by_deal**: simple model that reads from **int_dates** and just joins it with **unified_users** to retrieve the deals. It will be used as the 'source of truth' for which deals should be considered in a given month, basically, since the first host associated to a deal is created (not necessarily booked)
- **int_core__monthly_booking_history_by_deal**: it contains the history of bookings per deal id in a monthly basis. It should be easy enough to integrate here, in the future and if needed, B2B macro segmentation.
In terms of performance, comparing the model **int_core__monthly_booking_history_by_deal** and **int_core__mtd_booking_metrics** you'll see that I removed the joined with the **int_dates_xxx** in the CTEs. This is because I want to avoid a double join of date & deal that I tried and I stopped after 5 min running. Since this computation is in a monthly basis - no MTD - it's easy enough to just apply the **int_dates_by_deal** on the last part of the query. With this approach, it runs in 7 seconds.
Related work items: #17689
This PR aims to provide a first draft for business KPIs, at this stage **quite minimal**.
It mostly contains a MTD and Monthly display approach. No dimensions are created.
The models created are:
- **int_dates_mtd**: mainly it reads from int_dates to retrieve those days needed for a MTD/monthly display
- **int_core__mtd_booking_metrics**: it contains the booking metrics, ending in a format of date|value|value last year|increment
--> here we could add, in the future, additional metrics from other sources (ex: guest journey, etc)
- **int_core__mtd_aggregated_metrics**: it transforms the data coming from the different mtd intermediate sources to be easily displayed in the dashboard. This is a good candidate for macros :)
- **core__mtd_aggregated_metrics**: copy-paste of the its int_core version, including a bit of model documentation
The dashboard is functional but in another PR. I can show you how it looks like at the moment you are going to check the PR
Related work items: #17214
