The magic word
Those technologies are fantastic but at early stage, not always appropriate or adapted.
After years of researches, using the latest,
we encountered too much limitations and decided to create an accesible dedicated NLP.
For matching to work, you need to gather as much as possible the "global picture", more is better.
Without sufficient data and found level of interactions, results is inconsistent. We overcame those challenges.
Selecting the optimal aggregate is key to matching.
Working with a limited mapped criteria number, is more about mapping than matching.
With few criteria, matching rates & room codes is limited for a simple reason: All subsets, criteria, are too deeply intrinsically imbricated.
This domino effect is prevalent in room type description as it mixes lingo, abreviations and other B2B terms, with more customer oriented descriptions.
Mastering this domino effect is a challenge.
A common example
Sometimes, you will have "single use" in the description, and sometimes you will have "1ad+0" or "1Pax".
If you are unable to map paxes, you cant do the policy match. this is one example among many others.
Going duck hunting with a sniper rifle is not ideal.
From mapping to matching
Covering essentials criteria, specifications, policies, paxes, limitations and bonuses, we use
Fuzzy logics and NLP to datacross all aspects of a room.
For a qualified matching you need to find out how mixed subsets criteria interact and impact themselves.
28 mapped subsets to play with. matching is customized, targeted, filtered.
From static to dynamic
For room codes, a static matching is prefered.
Our software can "fill the holes" too.
For rates and lead-ins, a dynamic matching is a must.
Our software has an hybrid bridge to retreive suppliers API policies.
You get a sorted by rate list of room type groups.
Different market strategy, different needs.
From availability to markup
You now have an exhaustive updated inventory per hotel.
By comparing the supply chain rate all the way through,
you extract the lead-in rate and optimal markup for your business model.
Be competitive and more independent.
From lingo to readable
One of our core tech component is called the "normalizer" and is part of the NLP.
Our software is able to entirely rewrite (normalize) the room description based on the 28 mapped subsets.
Errors, lingo and B2B abbreviations are converted into proper terms, in english.
Everything is clear and readable, unified & fluid.
How it works
Profiling your target
Matching is about finding the optimal aggregate across room types.
The profiler interface is here to define your objectives and target what you are after.
Access our power level user interface and experiment aggregate customization, ongoing developments.
Just click the presets or play with the options, understand better about mapping and matching.
You move the cursor over how deep the matching to go. You are in command.
Set it broad & use your own algorythms afterward, or deep in the differentiation.
Save your profile for online and API usage.
Automated presets & deep customization
Balcony / patio
Dynamic rates matching
1 - Per hotel
You match rooms at the availability "recheck" level, after cache.
You instantly compare a suppliers large number at the hotel id avail. query method.
You do not need much suppliers to immediatly see the benefit of finding the supplier lead-in rate.
Gross profit increases by 25% to 30%.
2 - Per destination
You compare destination options and hotels and right spot where are the best deals around.
For B2B, call centers, DMC, travel agencies, this is a formidable way of increasing your margins.
You can now sort an entire destination by profit.
You are able to send your clients to better and cheaper places while skyrocket you profit and competitiveness.
Gross profit increases over 300%.
automated mapping & matching
elimitates 95% of hard work
automatic elimination of errors
quick mapping results
reduce mapping costs
higher converstion rates
lower your purchase costs
no bookings errors
use the API or the UI
upload your inventories
drag and drop online tools
live matching activity dashboard
dedicated manipulation interface
For new profitable models
The below graph represents the averaged GP margin per room extracted from availability queries across multiple destinations,
performed by a DMC, over a period of 18 months.
The bedbanks leading rate was systematicaly matched against a public price (BAR) whenever it was available.
7 bedbanks + 1 affiliate APIs
120 to 300 hotels per search, 4 & 5 stars only, all rooms
10 to 30.000 per search. Hundreds searches performed.
Pushing the limits
how to read the graph
Despite doing availability search at various period before check-in, our technology right spots 5% of rooms with a X3 markup coef or more. bedbank price $100, public price $300.
Searching over about 100 hotels, you always end up with 30 to 50 top deals across the destination where you are at least with a 100% to 200% markup.
Getting closer to the check-in, it is lowering but still a lot at 50% and even more.
Get the destination availabitity top deals
with a GP margins of over 300%
all day long !
We also have an algorithm able to spot, across bedbanks, the highest potential margin deals for any destination,
without having a matched BAR or OTA API !
Disintermediation & uberization
Mapping room types brings benefits across the hospitality chain, but also exposes rates.
When connected to multiple API, you are able to find the optimal market GP margin and markup per room, datacrossing all rates.
This is a fantastic business intelligence tool to keep your prices in line accross your business model.
But on the other hand, rates are compared and visible across the chain, leveling the price competition playing field.
Mapping and matching technology disturb business models, allows more OTA to be competitive,
diminish affiliate programs dominance, brings to travel agencies, DMC recovered margins.