Machine Learning Hotel Ad Targeting System
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The hotel industry faces challenges in effectively targeting potential guests through online advertising, as existing methods are costly and do not sufficiently promote specific hotels, leading to reduced direct bookings and increased commission costs with third-party booking platforms.
Innovation Solution
A computer-implemented method using a machine learning algorithm to create targeted advertising campaigns based on demographics, geographic location, and hotel amenities, allowing for automated information delivery to user devices, such as search engines, to directly promote hotels and reduce reliance on online travel agencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hotels use third-party booking platforms for advertising, then hotel visibility is improved, but commission costs increase and direct bookings decrease
Solution Approach 1:
The patent introduces a machine learning-based advertising system as an intermediary between hotels and potential guests. This system processes hotel information, guest preferences, and search queries to automatically generate and deliver targeted advertisements through search engines, replacing the need for third-party booking platforms while maintaining effective connectivity.
Solution Approach 2:
The system enables hotels to self-serve by automatically generating and managing their own advertising campaigns. The machine learning algorithm autonomously processes hotel data, identifies target audiences, optimizes ad delivery timing and channels, and tracks performance metrics, eliminating dependency on external booking platforms.
2Quantity of substance
If hotels increase advertising spend on online platforms, then more potential guests are reached, but advertising costs increase
Solution Approach 1:
The patent applies local quality by delivering advertisements selectively to specific users based on their location, search behavior, and preferences rather than broadcasting to all users uniformly. The machine learning system analyzes individual user profiles and delivers ads only to relevant potential guests, optimizing reach while minimizing wasted advertising spend.
Solution Approach 2:
The system dynamically changes advertising parameters such as delivery timing, channel selection, and message content based on real-time analysis of user behavior and contextual data. This adaptive approach optimizes the effectiveness of each advertising dollar spent by adjusting parameters to match user preferences and search patterns.
3Productivity
If automated advertising systems target specific audiences, then direct bookings increase, but system complexity increases
Solution Approach 1:
The patent implements a universal advertising system that performs multiple functions: data collection, audience segmentation, ad generation, delivery optimization, and performance tracking. This multi-functional system consolidates what would otherwise require multiple separate tools and processes, managing complexity while enhancing direct booking capabilities.
Solution Approach 2:
The system incorporates continuous feedback loops where advertising performance data is collected, analyzed, and used to refine targeting algorithms and optimization strategies. This feedback mechanism enables the system to automatically learn and improve its targeting accuracy over time, increasing direct bookings while the complexity is managed through automated learning rather than manual configuration.
Data Source
AI summary
Provided are methods and apparatus for scheduling automated information delivery to a user device. In examples, provided are computer-implemented methods for scheduling automated information delivery to a user device. At least a portion of the methods can be performed by a computing device including a processor. The methods can include (i) receiving, automatically via an application programming interface executed by the processor, information describing characteristics of potential hotel guests, characteristics of a hotel, and user input describing characteristics of an advertising campaign, (ii) creating, using a machine learning algorithm, advertising campaign information including instructions configured to direct an Internet search website to automatically display advertisements for the hotel that accompany search results, and (iii) sending the instructions to a server device configured automatically cause the advertisements for the hotel to be displayed on the user device. Various other methods, systems, and computer-readable media are also disclosed.