Hotel Chain Leverages AI for 56% Revenue Surge

A leading US hotel chain struggled to fill rooms while maximizing revenue. Traditional marketing, based on customer segmentation, had limitations: ineffective targeting, suboptimal offer pricing, and limited room tier optimization. They partnered with an AI marketing firm to develop a system that predicts customer needs and preferences. This resulted in a 56% revenue increase, doubled marketing ROI to 1,000%, and the system was built in just four weeks.

Problem Statement:

The leading US hotel chain faced a significant challenge: consistently filling their rooms while maximizing revenue. While they utilized traditional marketing techniques based on customer segmentation, these methods proved insufficient due to several limitations:

●Ineffective Targeting: Traditional segmentation often failed to identify the most receptive customers for specific room offers, leading to missed opportunities and underutilized rooms.
● Suboptimal Offer Pricing: Traditional methods struggled to determine the optimal discount level for each customer, potentially offering excessive discounts to price-sensitive customers or missing out on higher-paying guests.
● Limited Room Tier Optimization: Traditional approaches couldn’t effectively predict which room tier a customer was most likely to book, potentially leading to suboptimal room assignments and lost revenue.

Results:

☑️ Hyper-Personalized AI Marketing Offers
☑️ 56% Revenue Increase vs. Traditional Marketing
☑️ Doubled Marketing ROI to 1000%
☑️ 4-Week System Development

AI Solution:

The hotel chain partnered with Kortical, an AI marketing firm to tackle low occupancy and maximize revenue. Kortical built a system that predicts customer room needs, preferences, and ideal offers to boost revenue.

Building the AI System (4 Weeks):

The solution was a three-tier system:

1. Customer Propensity Model: This model, similar to those used in retail, predicted which customers were most likely to need a room at a specific time.
2. Room Tier Prediction Model: This model, applied only to high-propensity customers, predicted the likelihood of booking a particular room tier based on an offered discount.
3. Offer Optimizer: This final piece combined probabilities from both models and calculated the “expected value” for each room tier and discount combination. The offer with the highest expected value was chosen for each customer.

Kortical’s platform played a crucial role in this process:

● Data Cleaning: Kortical’s data prep functionality addressed missing data issues, ensuring model accuracy.
● AutoML: Kortical’s Automated Machine Learning (AutoML) generated and evaluated thousands of candidate models, allowing for efficient selection of the best performers.
● Rapid Prototyping: Using Kortical’s Google Sheets plugin, the hotel chain quickly created a prototype app to utilize model predictions without extensive development.

An 8-week A/B test showed the AI system’s effectiveness: revenue increased by 56% and marketing ROI doubled compared to traditional methods.

Reference:

1. AI-powered Marketing: Increasing Revenue by 56% Through Hyper-personalized Offers

Industry: Hospitality
Vendor: Kortical.io
Client: Leading US hotel chain