Session Continuity Review
A focused check of login, cart, and content resume states when a user leaves one device and returns on another.
From THB 28,000 per review
Bangkok · App behavior analysis
We trace how people actually move between phone, tablet, and desktop inside your app—then hand your team a ranked list of handoffs that break mid-task.
Cross-device user behavior analysis is our daily craft: reading event sequences, resume states, and identity links so a booking started on a commute phone can finish on a laptop without starting over.
The Cross-Device Journey Audit samples real paths across at least two device classes, tags the breaks, and returns a remediation backlog your engineers can estimate.
Typical span: 3–4 weeks · Fixed project fee from THB 85,000
Open the audit detailsEach engagement is human-led from our Lat Krabang base—document review, workshops, and written findings—not a hosted analytics product.
A focused check of login, cart, and content resume states when a user leaves one device and returns on another.
From THB 28,000 per review
Clarify how first-touch, last-touch, and cross-device paths should be credited so marketing and product reports stop contradicting each other.
THB 18,500 for the pair of sessions
A half-day working session that turns raw cross-device observations into a brief your design and engineering teams can act on.
From THB 42,000 per workshop
“The audit showed our desktop resume page never received the booking draft ID from mobile. We had argued about conversion quality for months; the sample paths made the missing field obvious.” — Nalinee S., product lead
Short write-ups on handoffs, identity stitching, and funnel exports that keep device context intact.
A practical look at the moments users leave a phone session and expect the same place on a laptop—and why event logs often miss the handoff.
How to talk about linked devices in stakeholder meetings when deterministic IDs are partial and probabilistic matches are imperfect.
Household tablets blur individual paths. Here is how to interpret shared-device noise without discarding useful behavior signals.