For years, hotels have relied on Google Analytics as their primary reference to analyse user behaviour on their websites.
It is a solid, widely adopted and often indispensable tool. However, its main limitation is structural: Google Analytics was built as a general-purpose platform, designed for any type of website — not for a complex, seasonal and highly anticipatory industry like hospitality.
In tourism, and especially in hospitality, knowing website traffic alone is not enough.
What really matters is understanding real demand: when people are searching, for which dates, how far in advance, how many nights they plan to stay, and what happens at the most critical stage of the funnel — the booking engine.
These are the insights that drive pricing, revenue and sales strategies. And this is precisely where Google Analytics shows its limits.
Traffic vs demand: the first major misconception
Google Analytics is very effective at answering quantitative questions: how many visits a website receives, where users come from, which pages they view and how long they stay online.
All useful metrics — but ones that tell only part of the story.
Hotels need very different answers. They must know which dates users are searching for, how many nights they are planning, which room types they are evaluating and how many searches result in no availability.
Most importantly, they need to understand how demand is evolving compared to the same period last year.
Google Analytics captures none of this. The risk is real: a hotel may see growing traffic without knowing that demand is concentrated on low-season dates, that guests are shortening their stays, or that a significant share of demand is being lost due to lack of availability.
Without real demand data, pricing, marketing campaigns and revenue strategies are optimised blindly.
Lead time: a critical metric GA cannot see
In hospitality, lead time is one of the most valuable variables: it shows how far in advance potential guests start searching for a room.
Knowing whether users search 90 days in advance during high season, only a few days ahead in low season, or last minute on weekends fundamentally changes how hotels plan prices, offers and campaigns.
Google Analytics, however, cannot read lead time.
It does not interpret searched check-in dates, recognise stay intent or connect searches to subsequent behaviour.
It is like doing revenue management without a calendar: technically possible, but strategically ineffective.
The biggest blind spot: the booking engine
The booking engine is the core of the hotel funnel.
This is where sales happen — and where most drop-offs occur.
Yet Google Analytics, not even GA4, can truly describe what happens inside this critical step.
It does not show how many searches are performed, which room types attract the most interest, how many searches end with no availability, or how conversion changes day by day for specific dates. Without this information, hotels cannot know whether low conversion is driven by price, availability or friction in the booking engine.
The result is simple and dangerous: you don’t know why you are selling — or not selling.
Without benchmarking, numbers lose meaning
In tourism, no metric has absolute value.
Everything must be read in relation to the market. Hotels do not operate in isolation: they compete within a destination, a compset and a constantly changing environment.
Google Analytics is not designed for hotel benchmarking.
It does not allow hotels to compare their demand with the destination average, analyse searches year over year or understand how market ADR is evolving.
Without this context, even apparently positive metrics can be misleading.
The most important missing metric: how demand changes day by day
One of the most critical needs for hotels is understanding how demand evolves over time — not monthly, but day by day.
Knowing whether searches for a specific date are accelerating, slowing down or shifting earlier allows hotels to adjust pricing and campaigns before the market moves.
Google Analytics cannot answer questions such as:
How many searches for August 15 did we have today compared to yesterday?
How is interest for New Year’s Eve changing versus last year?
Is conversion improving or worsening on key dates?
Without these answers, forecasting and revenue management remain assumptions, not strategies.
How Optimand overcomes the limits of Google Analytics
Optimand was created to solve these gaps. It is a Real Demand Analytics platform built exclusively for hospitality, combining demand analysis, booking engine analytics, benchmarking and pricing data into a single, coherent ecosystem.
Optimand enables hotels to accurately and reliably track:
- all booking engine searches,
- searched and booked lead time,
- demand by date, market and period,
- day-by-day changes in demand and conversion,
- PMS integration to connect demand, sold rooms and revenue,
- destination benchmarks based on aggregated and anonymised data.
As a result, web analytics, user behaviour, market data and sales performance stop living in separate silos and become part of a unified view.
Conclusion: GA is necessary, but not sufficient
Google Analytics remains a useful and often essential tool. But on its own, it cannot effectively support pricing, distribution and revenue decisions in hospitality.
Hotels need a vertical platform, built specifically for the industry, capable of understanding not just who visits the website, but who is really searching, what they are searching for and when. Optimand fills exactly this gap.
The outcome is tangible: more direct bookings, reduced OTA dependency, more accurate pricing and strategies driven by real data — not interpretations.
