The traditional gap between collecting survey data and acting on it is an analyst. Someone has to export the responses, clean them, run the numbers, read the open-ended comments, and assemble a document leadership will actually read. At most venues, that someone does not exist, which is why survey data so often accumulates without ever becoming a decision.
Infotap closes that gap with an AI analysis engine that takes a venue from raw responses to a finished executive report on demand. This post walks through what actually happens between a guest tapping submit and a report landing in an inbox.
How responses arrive
Every completed survey, whether it came through the Wi-Fi gateway, a QR code, or an emailed link, lands in the same response store in real time, tagged with its source, location, and timestamp. At a deployment like CVG International Airport, that means roughly 22,000 responses a month flowing in continuously rather than arriving as a quarterly batch. The analysis engine works against this live store, so a report run on Tuesday afternoon reflects Tuesday morning's guests.
Statistical analysis
The first stage of the engine is quantitative. Responses are validated and cleaned, incomplete submissions are handled consistently, and the engine computes the distributions, means, and segment comparisons an analyst would build by hand. Crucially, it does the cross-tabulation work that usually gets skipped: satisfaction by location, by day part, by guest segment, with sample sizes attached so a reader knows which differences are meaningful and which are noise.
This is the stage where scale pays off. A venue collecting a few dozen responses a month can only report an average. A venue collecting thousands can say precisely where and when an average moved.
Sentiment and themes
Open-ended responses are where the richest feedback lives and where manual analysis usually gives up, because reading eight hundred free-text comments is nobody's afternoon. The engine reads all of them. Each comment is scored for sentiment and grouped into themes, so "the line at the coffee place near gate B12 was twenty minutes" and "waited forever for coffee in Concourse B" resolve into the same operational finding rather than two isolated anecdotes.
The output is a ranked list of what guests are actually talking about, weighted by frequency and sentiment, with representative comments attached. Leadership reads three themes instead of eight hundred comments and loses nothing that matters.
Persona synthesis
Beyond individual questions and comments, the engine synthesizes response patterns into guest personas: recognizable profiles of who is moving through the venue and how their experiences differ. The business traveler racing to a gate and the family with a two-hour layover report different frustrations and value different amenities. Seeing satisfaction through those profiles turns a single blended score into a set of distinct experiences a venue can design for deliberately.
The finished report
The final output is a publication-ready executive document: headline findings up front, each finding connected to a specific recommendation, supporting data included without drowning the reader in it. Every report is stored as a time-stamped snapshot in the report library, so this month's report can be compared against last month's to show whether the changes made in between actually moved the numbers.
No export, no spreadsheet, no analyst on staff. The distance between a guest's 28-second survey and a leadership decision collapses to however long it takes to open the report. That is the point of the entire pipeline: not to produce data, but to remove every step between data and action.
