Business Questions - G33-Moviles-2026-1/Wiki GitHub Wiki

Type 1 — App telemetry

Which feature/flow has the highest crash rate (crashes per 1,000 sessions)?

Rationale/decision: prioritize hotfixes where the app fails most and improves perceived stability.
On which screens/actions does response time exceed the target threshold during peak hours?

Rationale/decision: optimize “few taps / fast discovery” when users are walking and in a hurry.

For a user near a building (or with approximate location), what are the top N “Available now” rooms ordered by estimated walking time, including the room status last-updated timestamp?

Rationale/decision: reduces wandering time and increases trust with timestamps.
For a user, which available rooms match a specific gap based on capacity/equipment?

Rationale/decision: turns the users needs into actionable recommendations (“My gaps and intentions” → “Recommended Rooms”).
For each room shown as available, what is its reliability level (e.g., Scheduled Free, User-validated, Conflicting reports) and when was it last validated?

Rationale/decision: delivers direct value by making uncertainty visible and increasing user confidence (instead of “accuracy” only for analytics).
For a specific room, which verifiable utilities does it have: equipment (projector/computer), power outlets/chargers availability, space type, Big tables?

Rationale/decision: supports fast decisions for studying/resting/group work without “insider knowledge”.

Which filters (building, floor, capacity, equipment) are applied most often, and in what sequence (e.g., building → capacity → equipment)?

Rationale/decision: simplify UI (defaults, quick filters) and reduce taps when users are in a rush.
What characteristics do the most favorited rooms share (building/location, capacity, equipment, proximity to frequently visited class buildings, real availability history)?

Rationale/decision: improve recommendations (stronger priors) and suggest “similar rooms” that actually help users.
What is the most common way users upload/import their schedule (Google/Apple Calendar sync, ICS/PDF file, manual), and which method has the highest drop-off by step?

Rationale/decision: invest in the winning method and remove friction in the losing one (permissions, formats, UX).
What percentage of users rely on Recommended Rooms (location + gaps) vs manual search, and which path reaches “room selected” faster (time to success)?

Rationale/decision: validate whether the recommendation engine reduces time/stress or needs recalibration.

Where are the demand hotspots (searches/views/intent-to-reserve) by building + time window, and how do they compare to official occupancy?

Rationale/decision: identify overcrowding, rebalance resources, and inform space policies.
Which are the most used tags (chilling, studing, teaching) in bookings (by building + time window)?

Rationale/decision: identify the common ways in which the rooms are being used.

Where in the app do students spend the most time browsing?

Rationale/decision: identify most used screens on the app so they can be used to put adds and the dev team can shift the GUI to benefit that functionality or others.
How does the use of personalized room recommendations influence navigation efficiency and repeat usage under different contextual conditions (peak hours vs. off-peak)?

Rationale/decision: evaluate whether personalized recommendations improve efficiency and repeat usage to decide if the team should prioritize, adjust, or reduce investment in the recommendation engine based on contextual performance.