# Description
Changes:
* On Created and Check Out Bookings daily models, the determination of New Dash bookings now uses `int_core__booking_summary`. This does not change the output, but will be more consistent for other KPIs.
* Adds the categorisation of `dash_source` in all daily metric models that depend on Verification Requests (Guest Journeys). This affects GJ Created, GJ Started, GJ Completed, GJ w. Payment and Guest Payments. Note that the next aggregations (monthly, mtd, agg_monthly, agg_mtd) are not created yet. This will be done in a separated PR.
# 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
Main changes:
* Remove Cancelled Bookings previous logic.
* Speeds up Main KPIs by adding a couple of indexes in `mtd_aggregated_metrics`. Tested in prod, to me it looks like it's quite fast now.
# 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: #24637
# Description
First commit - should be an exact replica of the previous PR on Check-Out:
Adds Cancelled and Not Cancelled Created Bookings as absolute figures. Adds Cancellation Rate only for aggregated models.
I also added the removal of duplicate bookings. Otherwise the rates might be affected, and this should be cleaner anyway.
Second commit:
Small changes on the unique key description of the model for check-out, doesn't impact the model output.
# 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: #24637
# Description
Adds Cancelled and Not Cancelled Check-Out Bookings as absolute figures. Adds Cancellation Rate only for aggregated models.
Edit: I also added the removal of duplicate bookings. Otherwise the rates might be affected, and this should be cleaner anyway. Will do the same for Created Bookings.
This won't make these new metrics appear in KPIs.
# 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: #24637
# Description
Adds new category Dash Source for Check-Out bookings KPIs
# 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
Changes:
* Major changes on int_kpis__metric_daily_invoiced_revenue. Now data is retrieved from the account codes, following the previous analysis. This actually reads from a work done on Accounting Codes, with the default aggregations set as a seed. I've also included here new revenue streams for new dash, and the split of guesty fees between resolutions and real API fees. However, New Dash services are still not visible as final metrics. I'll do this separately.
* Propagation of changes in intermediate models. This will affect currently displayed metrics, but not show any new metric.
Since this is quite dense, let's discuss.
# 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: #26531
# Description
Context: I'm intending to work on Account Managers reporting, that mostly will include reporting at Deal level the Resolutions Payouts as well as the new Retained metrics. While checking the great increase on Resolutions Payouts for October 2024:
I decided to take a quick look into the main players... and surprise surprise we have Guesty:
So Guesty represents 37k over the 73K of October. 50%. Not bad.
The main issue is that we've been aware for months now (since Churn efforts, mostly) that we're not reporting in KPIs those deals that are NOT created in Core. Most notably, API deals which includes... well, Guesty. So creating this kind of in-depth Account Managers improvement without reporting Guesty I think it would be very misleading. Note that the overall figures (Global dimension) are still accurate, though.
What's new:
* A new model named `int_kpis__dimension_deals` that basically retrieves Deals from both Core (as before) and Hubspot. It combines information from both and mostly assumes Hubspot as a better source of information than Core - although we do not have the Main Billing Country there afaik.
* Propagates changes, mostly in the monthly by deal view of Main KPIs. Here I confirm that now Guesty appears, and it only has metrics that come from Xero (APIs Revenue, Total Revenue, Resolutions, etc)
# 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: #25829
# Description
Adds `is_end_of_month_or_yesterday` for Main KPIs.
Apparently, the fact that we do not show the ongoing value of the month on some tabs of Main KPIs is the main blocker for Ben C to consistently use Main KPIs, which he actually retrieves from the SH legacy reporting. Since I'm sceptical about the data shown there, I want to remove this blocker.
It will require a small PR on PBI as well.
# 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.
- [NA] 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: #25342
# Description
Aggregated model for guest kpis metrics.
This model aggregates the values for all basic metrics for guest kpis, it has a granularity per metrics of timeframe (YTD, MTD, WTD) and all other dimensions needed (has_payment, has_id_check & main_billing_country_iso_3_per_deal).
I decided not to include the calculated metrics because it adds much more complexity to the model specially when you start considering all possible combinations between dimensions and how this would affect the calculations, in this case conversion_rate, revenue_rate and average_csat_score. I think this could all be managed much simpler inside PBI and since the granularity of the model is not as deep as the daily model, this should help with the loading time for the report.
# 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.
- [ ] I have checked for DRY opportunities with other models and docs.
- [ ] 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: #24604
# Description
Creates the models for KPIs for New Dash - Chargeable metrics. In essence, computes 4 metrics:
- Chargeable Services
- Chargeable Amount (in GBP)
- Chargeable Bookings (unique over a time period and dimension, not additive)
- Chargeable Listings (unique over a time period and dimension, not additive)
This is done by creating:
- A Weekly Metric and Monthly Metric model. Here we keep the granularity of id_booking / id_accommodation to be able to compute the uniqueness.
- A Daily, Weekly and Monthly Aggregated models. Same as usual.
- Integrates everything to the existing model for Product New Dash Agg Metrics
- Exposes everything into reporting
NB: I removed on "by_host" in Created Services - I forgot to clear it out.
# 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: #20809
# Description
This PR aims to do 2 things:
1. Create the first metric daily model for New Dash - Chargeable Services
* PK at Booking, Date and Service. I added a few more dimensions such as Accommodation and Business Type. The idea is that while Daily Unique Bookings/Accommodations Charged will be close (if not the same) as Charged Services, by having the ID we can compute Monthly/Weekly Unique Bookings/Accommodations Charged in a proper manner. Besides this, we would still compute additive metrics in the future such as the sum of Charged Services and the sum of Service Total Price in GBP.
* `IMPORTANT`: as discussed in the daily, I changed New Pricing models containing "Charged" columns to "Chargeable". This affects the new model for New Dash KPIs, but also `int_core__booking_summary` and `int_core__booking_service_detail`.
2. Small fixes on New Dash - Created Services.
* Mainly, there were some inconsistencies with what was actually written (and really applied) in the schema entry as for what was the PK of the model vs. what was stated in the model. I just re-ordered the columns and re-specified correctly the PK.
# 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: #20809
# Description
This PR has 2 commits:
- The first one handles the removal from the computation any user that has not an id deal properly set. I just created a boolean field in int_core__user_host that identifies if the host has no id_deal. Then apply the new condition in the 2 main usages of New Dash info.
- The second one cleans New Dash KPIs. Since we do not have anymore users without deals, it means that the identification of the host/account is going to be exactly the same if done by id_user_host or id_deal. I hated having id_user_host in KPIs so I've removed it :)
Lastly, this should speed up massively the execution. Not because there's improvements on the code but rather the reduction of data.
# 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: #20809
# Description
Changes:
* Fixes weekly dates for KPIs. Before, joins were not working (but nothing was really used).
* Computes weekly new dash created services and exposes it to reporting
# 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.
- [ ] 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: #20809
# Description
This PR handles the computation of KPIs for New Dash, focusing on Created Services.
New dimensions configured in `business_kpis_configuration` and applied in this new models for `NEW_DASH_CREATED_SERVICES`:
* `dim_host`,
* `dim_has_upgraded_service`,
* `dim_new_dash_version`,
* `dim_pricing_service`
New daily metric model `int_kpis__metric_daily_new_dash_created_services`
* Follows a similar pattern as for the rest of daily metric models. The only difference is that is aggregated to `id_booking` to ensure we can handle count distinct of bookings per different time granularities.
* Reads from the new pricing tables `int_core__booking_summary` and `int_core__booking_service_detail`. The main filters applied are selecting only new dash users and only services created after the user move timestamp to new dash.
An additional metric model at monthly level is created `int_kpis__metric_monthly_new_dash_created_services`
These finally go to a dimension aggregated model (`dimension`, `dimension_value`), respectively:
* Daily: `int_kpis__agg_daily_new_dash_created_services`
* Monthly: `int_kpis__agg_monthly_new_dash_created_services`
A final model aims to aggregate the different dimension aggregated metrics for New Dash: `int_kpis__product_new_dash_agg_metrics`
* It computes a `time_granularity` aggregation
* Here I will add additional metrics (such as revenue) once we have them.
A final model reading from the previous is exposed to reporting: `kpis__product_new_dash_agg_metrics`
# 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: #20809
# Description
Replacement PR from the abandondes one for daily metrics for guest KPI model
# 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.
- [ ] I've picked the right materialization for the affected models.
# Other
- [ ] Check if a full-refresh is required after this PR is merged.
daily model for guest products
Related work items: #23371
# Description
New model:
* int_kpis__agg_dates_main_kpis - Serves as the skeleton of dates and dimensions for Main KPIs. It's aggregated since it follows a similar aggregation strategy. It's a single model to feed both Main KPIs visualisations. Note boolean fields are real booleans (true/false) while before these were integers (1/0). This also affects downstream models.
Main KPIs flow adaptations to new skeleton model:
* int_monthly_aggregated_metrics_history_by_deal
* int_monthly_churn_metrics - additionally, calls usual KPIs macro instead of old one
* int_mtd_vs_previous_year_metrics
Reporting changes to ensure report is not down:
* mtd_aggregated_metrics - adaptations on booleans (true-1, false-0)
Cleaning:
* get_kpi_dimensions macro is no longer used
* int_dates_by_deal model and schema entry
* int_dates_mtd_by_dimension model and schema entry
* int_dates_mtd model and schema entry
# 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: #23763
# Description
Adds listing metrics model. It's a bit similar as the Deal metrics, but here we can aggregate by deal :)
I'm aware that int_kpis__metric_daily_listings takes quite a bit (3 min). To be optimised later on.
# 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.
- [ ] I have checked for DRY opportunities with other models and docs.
- [ ] 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: #23567
# Description
Changes:
* Creates lifecycle_daily_deal, metric_daily_deals and agg_daily_deals. These follow a different strategy due to the nature of the metrics
* Modifies the dimension macro to ensure deal dimension is included in all models except these ones
* Fixes production issue on currently deployed deal lifecycle and metrics
# 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: #23566
# Description
Here I created a weekly aggregated model for guest journey metrics.
I changed the daily_dimension week start and end to iso so it matches the week date obtained by Postgres for this aggregation
# 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: #23373"
# Description
Here I created a weekly aggregated model for guest journey metrics.
I changed the daily_dimension week start and end to iso so it matches the week date obtained by Postgres for this aggregation
# 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: #23373
