LodgIQ’s demand indicators require the use of hotel data but we never sell a hotel’s data.
LodgIQ is built on a modular framework that can scale to any size property.
To fully benefit from the insights of vacation rental data, hotels would need to be located in or near active vacation rental markets. Location is not a factor in any other aspect of the technology.
For LodgIQ One, you can sign-up here and get started today using our self-service module. For our full-featured LodgIQ RM product, click here to get the process started. Your dedicated account representative will reach out to begin the implementation process. A PMS, CRS, or Channel Manager will be required for rate push options.
You can view a list of all our integrations.
Yes, all LodgIQ products allow the user to toggle between multiple properties via a simple dropdown menu within the application.
Our technology is currently only available in English but other languages are scheduled for future development.
Big data comes from traditional sources like call centers, point-of-sale and financial transactions as well as digital sources like social channels and web applications.
Small data is data in a volume and format that makes it accessible, informative and actionable. Small data typically provides information that answers a specific question or addresses a specific problem.
We start at the market level and then come down to neighborhood level and then to a specific property level. We combine features from history and recent trend data to forecast guest arrivals at market segment level. We then overlay extraneous data like events, flights, weather and news to compute the impact. Based on these forecasts we conduct a convex optimization with constraints to generate pricing.
Data analytics is the process of identifying patterns and extracting insights from data through data analysis, predictive analysis, statistical analysis, and data mining. Predictive analysis is particularly useful for hospitality. Outcomes can be predicted based on historical data. Naturally, hotel data has a lot of variables, each carrying a different level of influence in determining outcomes.
Data science makes insights actionable by employing a variety of techniques drawn from mathematics, statistics, pattern recognition, probability models, machine learning, etc. Data scientists are responsible for cleansing, structuring and exploring data sets through the use of models and algorithms to drive better business decisions.
In its simplest form, machine learning gives computers the ability to learn from data sets without being programmed to do so. Data scientists build algorithms which are applied to sample data sets to train the computer on what to look for within live data sets. These algorithms learn from historical relationships and trends within the data to produce reliable, predictive analytics.
Accuracy Goals for Hotel Forecaster
DBAs 0-3: 3%
DBAs 7-30: 5%
DBAs 60-90: 10%
Accuracy Goals for Market Forecaster
DBAs 0-7: 2%
DBAs 8-30: 3%
DBAs 30-60: 5%
DBAs 60-90: 8%
User experience is a principle of design that is solely-focused on how a user interacts with a platform. With a thorough understanding of user behavior, technology can be designed to simplify the paths users take to achieve their desired goals. A streamlined user experience reduces the number of clicks the user takes and ensures the technology is laid out in a logical, intuitive manner.
Absolutely not. Our goal is to empower our users to make strategic, well-informed business decisions. No matter your experience in revenue management, you are ultimately in the driver’s seat. LodgIQ simply elevates your ability to act on revenue opportunities in a simple manner, with optimal forecasting and room pricing.