LodgIQ uses state of the art BigData Analytics and AI and Machine Learning based algorithms to forecast demand and price hotel rooms.
LodgIQ uses multi-textured data feeds to create a proxy for demand. Data is ingested, wrangled and aggregated on a Big Data stack in a cloud environment. Machine Learning models are run on a daily basis to make accurate market-level and property-level forecasts and provide pricing recommendations. This is done for 100+ global markets and several hundred properties.
Models are trained and tuned on a continuous basis. Most of these model building and tuning tasks are extremely computationally intensive. Care is taken to bid for cloud compute instances, acquire, setup and run the models on cloud based compute instances and relinquish them as soon as the jobs are finished. All of these are accomplished in a programmatic fashion. This is necessary to run a spectrum of state of the art models from Tree Ensemble to Deep Learning models, accomplishing a high level of accuracy at a very affordable cost.
State of the art pipeline to train, evaluate and tune a spectrum of models.
Modern AI/ML models to forecast KPIs (Occupancy, RevPAR, ADR, Revenue). Both at market and individual hotel level.
Accurately estimate event impact (on demand and on price) for specific event days and also for dynamic ranges of shoulder dates.
Continuous tracking of accuracy (forecasted vs actualized)
Across KPIs (Occ, RevPAR, ADR)
Across dimensions (DOW, MOY, DBA, Segment)
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.