AI Optimized Dynamic Marketing Website for Korean Tourism Agency

Project Stats

Required Services

The Client

For this project our client was a marketing agency working with the Korean Tourism Office. They wanted a dynamic website with AI controlled and optimized content. Their goal was to attract web users using pay-per-click (PPC) advertising campaigns and then monitor their activity. Our advanced Machine Learning (ML) technology would then analyze user interactions and make content adjustments as necessary, aiming to retain web users for longer.

The Project

In order to satisfy the requirements of our client, we split the project into three parts: microsite, admin portal, and an Artificial Intelligence (AI)/ML engine.

AI/ML Engine

The Machine Learning (ML) featured on our client’s project needed to be able to capture user data and information, then make predictions about their future interactions and behavior. This would allow our custom algorithms to determine what content is most likely to attract specific user interest and increase site dwell time. Ultimately, the goal of the AI/ML engine was to improve Key Performance Indicators (KPIs), such as bounce rate and traffic, while refining the platform's User Experience (UX).

Technical Difficulties

One of the biggest challenges with this project was developing an AI/ML engine capable of collecting large volumes of precise user interactions and data. Once collected, the data needed to be analyzed by AI and transformed into tangible algorithmic steps able to appropriately update site content.

The Solution

In order to create an AI/ML engine that predicted user behavior and interactions with a minimal margin of error, we elected to deploy a bandits algorithm and a supervised ML algorithm.

Bandits Algorithm

Bandit algorithms are a set of algorithmic steps designed to perform under uncertain conditions. This made them the obvious choice for our clients project, as web users can be unpredictable, making forecasting their behavior and content preferences difficult. The bandit algorithm allowed us to analyze user interactions by averages, using both new and existing online data. This algorithm is used during the exploration phase, primarily deployed to collect and understand user data.

Supervised ML Algorithm

Supervised ML algorithms are used to define and label datasets while training other algorithms to accurately classify data. Once the data is classified it can be used to help predict outcomes, in this case, user interactions and behavior. Together with the bandits algorithm, the supervised ML algorithm provided our client’s platform with a way of exactly projecting future user actions. As a result, we were able to solve the issues associated with creating an effective AI/ML engine.

Final Results

After working with the team at Idea Maker, our client now has a platform that satisfies all their requirements. They are now able to direct web users to their dynamically updated and AI driven website. Here are just a handful of features we implemented on their platform.