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  1. Research Research
  2. A Machine-Learning Approach to Small-Hotel Revenue-Management A Machine-Learning Approach to Small-Hotel Revenue-Management

A Machine-Learning Approach to Small-Hotel Revenue-Management

RoomPriceGenie makes revenue management possible for smaller hotels, unlocking great value potential for them. But lack of internal data makes this difficult. The project intends to make robust pricing simple and ubiquitous.

Brief information

School:

Computer Science and Information Technology

Status:

Ongoing

Period:

30.08.2021 - 30.08.2023

Overview

RoomPriceGenie is an SaaS company unique in providing dynamic pricing for smaller hotels. It
is a difficult problem – which is one reason that RoomPriceGenie is the only provider targeting
this market.
The number of data points provided by smaller hotels is much fewer than for larger hotels,
and the mindset of small-hotel managers tends to be less quantitative than that of the
revenue-managers in large hotels. The solution RoomPriceGenie provides, therefore, needs to
be a) simple to use, b) intuitive, c) accurate and d) cheap to provide. These constraints can
make the problem very challenging in certain cases. RoomPriceGenie would like to use this
Innosuisse project to make pricing solutions that fulfil these constraints for all hotels,
including the more difficult ones.
The project will use a different source of data to the existing pricing model – namely the
reservation data – that previously would have been considered too sparse to use as small
hotels have comparatively little data. However, by combining various data sources in a robust
and complete algorithmic framework, RoomPriceGenie will have a complete and easily
scalable solution that will enable the business to grow at a much faster rate.
In addition to instant-set-up, high quality pricing, this project will also provide forecasts and
diagnostic indicators that will make the user experience much more of a joy, further increase
revenue and also increase trust in the system. This would greatly improve both free-trial to
subscription conversion rates, and customer retention rates.

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Facts

Type of project

Forschung

Internal organisations involved
  • Computer Science and Information Technology
Funding
  • Innosuisse - HSLU als Hauptforschungspartnerin (Main Research Partner)
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Links

  • RoomPriceGenie

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Persons involved: internal

Project manager
  • Sita Mazumder
Project Co-Head
  • Daniel Pfäffli
Member of project team
  • Florian Bär
  • Fabian Gröger
  • Reza Kakooee
  • Daniel Pfäffli
  • Marc Pouly
  • Pascal Wullschleger

Brief information

School:

Computer Science and Information Technology

Status:

Ongoing

Period:

08/30/2021 - 08/30/2023

Project Head

Prof. Dr. Sita Mazumder

Co-Head Algorithmic Business Research Lab

+41 41 757 68 81

Show email

Project Co-Head

Daniel Pfäffli

Highly Specialised Senior Research Associate

+41 41 757 68 28

Show email

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