It is the only tool to thoroughly analyze the "reservation behavior" of guests and make unit price setting using artificial intelligence / machine learning technology from real time big dataPatent applied

Using services

Individual plan

Research plan

We will provide big data gathered for calculating accommodation rates for the market research tool.
Various data of accommodation facilities throughout Japan are available including ADR transition by hotel category, when · where · how many hotels are planned to be constructed.

An example of investigable items

  • Average room price(ADR)
  • Review Info
  • Event data
  • Regional data
  • Facility information to be constructed
  • Daily and monthly occupancy rate survey(Daily release schedule)

Macro survey at municipal and prefectural level is also possible from hotel individual data.
All of these data can be downloaded indefinitely with a CSV file in the research data plan, so manual work time is reduced and overwhelming operational efficiency is expected.

Basic plan

I would like to collect reviews of company's own information and compare reviews with competitors.
We released so many voices that we wanted to know only event information to grasp the demand in the neighborhood.

An example of available items

  • Company facility information
  • Company's review information
  • event information
  • Competitive comparison
  • Regional data

Review information and event information of conflicts that we have collected by hand until now are available instantly.
Compared with competition Because it is instantly visualized with 1 click, it is easy to use tool for anyone.
Those who are taking time to in-house analysis, competition comparison, please examine it.

Utilization of real artificial intelligence

IBM Watson's machine learning AI, depth learning, natural language analysis, image recognition AI utilization

At IBM WATSON SUMMIT 2017 on April 27th and 28th, Representative Tanaka will talk about IBM's Chief Developer on artificial intelligence technology.

Four features

  • Thorough analysis of guest's "booking behavior"

    "Room price per competitor's accommodation", "number of stocks", "number of reviews", "photos of rooms", "property information of houses" All these are the standards for guests to select accommodation facilities . Metro engine collects big data related to these "booking behaviors" every day, calculates room price by using artificial intelligence and machine learning processing. Revenue management that relied on historical data analysis had limitations, as the Chinese New Year, the sudden change of event schedule, the fluctuation of cherry blossoming conditions, etc., fluctuate daily from year to year.

  • No need to read past stay data

    In past revolution management it was essential to read past data. I believe that it is not possible to verify whether the past room price was appropriate in the first place, and that the unit price setting based on historical data such as the rise of accommodation in recent years will lead to machine loss. The metro engine will calculate future prices by utilizing "real time" data. For this reason, we can instantaneously grasp trends by city, town, village, prefecture, from the competitive room price strategy.

  • Cumbersome information input unnecessary

    In order to use the metro engine, complicated input work such as the address and the number of rooms is unnecessary. It is automatically entered from more than 35,000 facilities in Japan.

  • Connected with site controller · PMS

    By linking with site controllers and PMS, it will be possible not only to make any analysis related to revenue management, but also to be able to use metro engine tools while using existing systems. (coming soon)

Artificial intelligence changes bidding price

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