Data Science

LodgIQ uses state of the art BigData Analytics and AI / Machine Learning based algorithms to forecast demand and price hotel rooms.

The Current Situation: Cumbersome, Manual and Arcane!

The field of revenue management is fraught with friction, inefficiencies and inconsistencies due to faulty data, poor system communication and arcane tools. Beyond system intelligence issues, revenue managers face the challenge of a seemingly endless stream of dynamic data that impacts demand—ranging from historical trends and market fluctuations to impactful events, varying flight patterns and even minute factors like changing weather. Unsurprisingly, determining optimal room rates while managing these technical complications and balancing a near limitless array of data points can be daunting for even the most seasoned revenue manager.

Access to clean data

A vast majority of legacy hospitality systems are dated and limited in their interaction capabilities, making the transfer of clean data challenging from the onset. Big Data has become increasingly significant.

Granular Market Demand Statistics

Hotels are directly influenced by the overall performance of their market. If demand is high then a hotel can optimize price. If demand is low they need to stimulate demand via promotions, partnerships and more to drive bookings. There are currently no tools in the industry that provide hotel strategic commercial teams with day-to-day market demand insights.

Real-time Market Demand shifts

Changes in weather, airlift and events can alter demand at a moment’s notice, but no system current exists in this space that can provide real-time alerts or automatically shift rates inline with demand.

With over 100 years of collective experience in revenue management and data science, at LodgIQ we empathize with hoteliers on the ever-increasing amount of diverse data from disparate systems, time-consuming manual analysis, and challenges required to continuously and confidently optimize room revenue. Through a paradigm shift away from traditional and rigid revenue management, LodgIQ is helping the way hoteliers approach revenue-related decisions. 

This problem needs a systematic approach which involves a daily cycle of diagnostic, predictive and prescriptive analytics.
Prepare >
Predict >
Evaluate >
Prescribe >
Package and Present >
Continuous Improvement
Step 1


Data analysis and visualization is used to cleanse Hospitality and Travel Industry Data in a Big Data environment to identify patterns and anomalies in pricing and demand by segment, day of week, month of year, days before arrival, same day last year, etc.

Data is ingested, transformed and aggregated in real time on a BIG data stack in a cloud environment.

State-of-the-art cloud engineering is performed using Cloud Services like EC2, S3, IAM and big data tools like EMR and Athena. This ensures scalability to thousands of hotels while maintaining sub-second response time. Care is taken to sift through large swathes of data to surface only the relevant pieces in an accurate and timely fashion. AI robots sift through Terabytes of data and identify demand or price movements and notify the user with actionable intelligence.

Step 2


Unique pipeline process consists of the following steps

Ingestion of data on a daily basis at a Market, Property and Compset level. LodgIQ has a state of the art machine learning pipeline to train, test and tune models.

LodgIQ is the only company in the travel industry that uses predictive analytics to forecast day by day occupancy, ADR, RevPAR and demand every day for a 365 day future horizon.

Step 3


We track and tune models for Forecasting and Pricing on a daily basis to measure accuracy using evaluation metrics like MAPE (mean absolute percentage error) and MAE (mean absolute error) between predicted and actualized values. These evaluation metrics are tracked across several dimensions: day of the week (DOW), days before arrival (DBA) and segments (transient, group or corporate).


Step 4


LodgIQ  Pricing engine is a third generation pricing algorithm.  The guiding design principles were:

  • Sound mathematical foundation – no adhoc rules!
  • Flexibility and configurability
  • Minimum data requirements
  • Interpretability  – Being able to explain the reasoning in business friendly terms
  • Extensibility – pricing by room_type,  by length-of-stay and by segment


Pricing engine is based on creating two separate models:

  1. A model for total demand that is inelastic, that is, not dependent on price.  This may depend on other features such as days-before-arrival, day-of-the-week, month-of-the-year, events, etc. This is the total number of people interested in staying.
  2. A model for willingness to pay This is the distribution of the random variable W, the willingness-to-pay, defined as the maximum price that a random customer is willing to pay for a night’s stay.  We will assume W usually has a log-normal distribution.
Pricing Conceptual View












At a given price point p, for a given vector of features (asof-date, stay-date combination, rooms OTB, etc.) the number of rooms sold is the number of customers in the demand pool that are willing to pay the price and the total demand D.

What-IF experimentation tool to interactive identify optimized price



Step 5

Package and Present

The results of these sophisticated algorithms are packaged as API and presented to the applications.







The cycle begins with and ends with continuous improvement!

Machine Learning Pipeline

State of the art pipeline to train, evaluate and tune a spectrum of models.  








Demand Forecasting

Modern AI/ML models to forecast KPIs (Occupancy,  RevPAR, ADR, Revenue). Both at market and individual hotel level.








Event Impact

Accurately estimate event impact (on demand and on price) for specific event days and also for dynamic ranges of shoulder dates.








Accuracy Tracking

Continuous tracking of accuracy (forecasted vs actualized)
Across KPIs (Occ, RevPAR, ADR) 
Across dimensions (DOW, MOY, DBA, Segment)








Algorithmic Price Optimization

Modeling demand and price elasticity using a sophisticated willingness-to-pay based objective function to maximize revenue.









A modern API based integration of key components (forecast and pricing) inside your applications.









I've tested various Revenue Management Systems (RMS), but none have impressed me as much as LodgiQ

Chief Commercial Officer

Washington DC


LodgIQ defines the NEW ERA in revenue management

Chief Commercial Officer

New York, NY


Finally a system that is easy to understand without unnecessary data and noise

Revenue Manager


4.7 stars on Hotel Tech Report
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