Hotellab
Providing a booking platform for hotels.
| Date | Investors | Amount | Round |
|---|---|---|---|
| - | investor | €0.0 | round |
| investor investor | €0.0 | round | |
* | N/A | N/A | Secondary |
| Total Funding | 000k | ||
| USD | 2021 | 2022 | 2023 |
|---|---|---|---|
| Revenues | 0000 | 0000 | 0000 |
| % growth | - | 33 % | 144 % |
| EBITDA | 0000 | 0000 | 0000 |
| Profit | 0000 | 0000 | 0000 |
| EV | 0000 | 0000 | 0000 |
| EV / revenue | 00.0x | 00.0x | 00.0x |
| EV / EBITDA | 00.0x | 00.0x | 00.0x |
| R&D budget | 0000 | 0000 | 0000 |
Source: Dealroom estimates
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Hotellab.io, established in 2020 by Anatoly Burdakov, is a company operating from Tallinn, Estonia, providing a revenue management system for the hospitality industry. The firm secured $200,000 in a Seed funding round on June 9, 2021, with Iskra Ventures and Startup Lab as investors.
The company's core offering is a software platform that assists hotels in optimizing pricing and enhancing profitability through data-driven decisions. It serves a range of clients, from independent hotels and serviced apartments to hotel chains and resort properties. The business model is based on providing its software as a service, which integrates with existing Property Management Systems (PMS) and Channel Managers to automate pricing and inventory decisions.
Hotellab's platform utilizes a machine learning algorithm that reports a 96% accuracy in demand prediction by analyzing data such as historical bookings, competitor pricing, and flight search data. Key features include dynamic pricing, demand forecasting, competitor rate tracking, and advanced analytics. The system can be personalized for each hotel by considering over 100 demand parameters, allowing for a tailored algorithm. It offers a hybrid autopilot mode, giving users the choice between manual, semi-automated, or fully automated pricing updates. Furthermore, the platform provides end-to-end analytics, connecting PMS data with Google Analytics to track the entire booking journey and evaluate the performance of advertising campaigns.
The platform is designed to cater to hotels with 15–150 rooms as well as hotel clusters with over 300 rooms. It aims to increase Net RevPAR (Net Revenue Per Available Room) by combining revenue management with digital marketing analytics. This allows hoteliers to make informed decisions regarding website promotion and optimize their distribution channel strategies based on real-time demand. Hotellab also offers a pricing simulator for educational purposes, developed in partnership with academic institutions.
Keywords: revenue management system, hotel pricing optimization, dynamic pricing, hospitality technology, SaaS, demand forecasting, competitor analysis, NetRevPAR, property management system integration, hotel analytics, booking pace analysis, yield management, marketing analytics, automated pricing, rate shopper, hotel profitability, business intelligence, accommodation booking, travel technology, hotel data analysis