Most hotels see the bookings.
Not the searches.
By the time a reservation lands, the decision was made days or weeks ago. The competitive moment — when the guest was still picking — has already passed.
Google Analytics knows someone searched. It doesn't know which nights, how many guests, or whether they were the same person who came back three times before booking elsewhere.
A spike of searches for August arrival in February is a pricing decision waiting to happen — if you can see it. Most hotels don't.
One POST per search. Per-night breakdown.
A small snippet on your booking-engine page. Every search — date pick, guest count, room type — sends one lightweight event to Peaqplus.
A search for "May 10–14, 1 guest" becomes 5 rows: arrival night, three stay nights, departure night. Every date can be analyzed as arrival demand, stay demand, or departure demand independently.
We capture date + property + session ID. No email, no IP, no name, no PII. GDPR-clean by design — legitimate-interest analytics, not personalized tracking.
Enabled by a single setting once your booking engine is wired up. Vendor-agnostic — any booking-engine provider can send the same event in the same format.
Four reports. One signal.
Lead-time analytics, daily summary, search-to-booking conversion, and a forward-looking calendar. Each answers a different question; together they're the demand picture that doesn't wait for reservations.
The 12 × 12 lead-time matrix.
Twelve rows (search month) by twelve columns (target month). Read across a row: where are people who searched in February looking to stay? Read down a column: who searched, and when, for an August arrival? The diagonal is "same-month searches"; the off-diagonals are the lead-time picture you couldn't see before.
- Switch between arrival, stay-night, and departure views — the per-night breakdown means all three are first-class
- Year-over-year overlay — see how this season's lead-time shape compares to last
- Pickup overlay — overlay actual bookings so the unconverted-intent gaps surface visually
Daily volume, with the top three most-searched dates.
Day-by-day, how many searches arrived and what they were searching for: the Top 1, Top 2, Top 3 most-popular target dates plus a long-tail "other" share. A morning rhythm: open this report, see what guests asked for yesterday before you start your other work.
- Trend chart at the top — daily volume over a chosen range (default: current month)
- 30-day lookback table — date, total searches, the three most-searched targets
- A long-tail peak — say, an unexpected long weekend — shows here before bookings land
Search → booking conversion, daily.
For every day in your range: how many searches came in, how many individual bookings, how many group bookings, total pickup, and the conversion percentage. Sort by volume, by pickup, or by conversion. The "many searches, few bookings" rows are the price-or-availability questions you didn't know to ask.
- Three-axis mixed chart — search-count line, pickup bars (individual + group, stacked), conversion-percent line
- Discussion threads anchor here — every row can carry a team conversation tied to the data
- "High searches, low conversion" → pricing or availability question. Reverse: low search, high conversion → underpriced segment
60 days of demand, before bookings land.
A calendar view: every cell coloured by search intensity, classified against the previous 30 days' top-five most-searched days. Red is a demand peak. Orange is elevated. Green is a normal day. The classification refreshes daily as patterns shift.
- 60+ days forward — so demand surfaces before the booking peak
- Hover any cell to see the search count, lead-time, and target-date breakdown
- Feed for the forecast module — "warm" dates with low OTB are exactly the dates that move pricing decisions
Intent data, woven into the loop.
The search signal isn't a separate report you remember to open. It flows into the screens where you already make decisions.
Demand signal becomes pricing input — the calendar surfaces warm dates with low OTB right where you make rate decisions.
Pulse Chat reads the search data natively. "How is next month's demand looking?" gets a real answer, not a generic one.
Top Searches rows are discussable. Tag a teammate on a high-search-low-conversion row; the thread becomes a decision with an owner.
The 30–60 day lead-time signal feeds forecast accuracy — demand visible before reservations land sharpens the projection.
Intent only. No personal data.
The captured record is the search itself — date range, property identifier, an opaque session UUID, and an optional flag for source context. No email. No IP address. No name. No personalized behavioural profile. This is legitimate-interest analytics under GDPR, not consent-required tracking — confirm with your DPO if you have one, but you typically won't need a new cookie-banner entry for this.
Not currently integrated? It's a single POST.
The integration is source-agnostic: any booking-engine vendor (or your website developer) can fire the tracking event in the same format. We share the spec, sometimes the booking-engine team integrates it directly, sometimes your web developer drops it into the widget. Either way, it's typically a day of work and you're streaming intent data.
Tell us during the demo what you're using. We'll share the integration spec and connect with your booking-engine vendor or developer.
Common questions.
See the intent signal on a property like yours.
In our 45–60 minute walkthrough, we run Peaqplus on our live demo environment — a simulated property with search data that moves day to day — and walk through the 12 × 12 lead-time matrix, the daily summary, the search-to-conversion chart, and the calendar, all live, all populated. Bring a question about your own demand patterns; we'll show you what the data would look like.
No setup fee. A standard one-event integration — typically a day's setup work for your booking-engine vendor.