Jun 25, 2024

How Krafton used AI for Customer Support and reduced training costs by 15%

With AI impacting every field, gaming companies like Krafton are also utilizing AI to make its customer support more efficient. Learn How

AI for Customer Support, Customer Care AI, How are companies using AI, how PubG uses AI

Generative AI is the wave of the hour and every company wants to be at the forefront of it, Gaming leaders like Krafton are not behind. This parent company for games like PUBG has been making waves with its innovative approach to customer support.

Why AI Customer Support? What’s the need?

To provide exceptional, cost-efficient customer support across multiple brands and languages, the company implemented an integrated support system that automates manual tasks. This strategic move has led to significant improvements in operational efficiency, data visibility, and customer satisfaction, resulting in a 15% reduction in support costs.

The company’s customer support team, known as Player’s Support, is dedicated to user retention. Their goal is to alleviate user discomfort, enhance player satisfaction, and devise customer care strategies through user analysis. The team handles approximately one million inquiries per year, supporting 13 languages for players in various locations.

In 2021, the company recognized the need for an integrated management system that could efficiently serve multiple locations and brands in the form of a bot. They faced challenges in supporting multiple languages, and the number of tickets handled by agents was inconsistent, making it difficult to plan and optimize agent time and costs.

To prevent the Player’s Support team from being overwhelmed, the company decided to use a complete customer service solution. They leveraged several features for ticket management, including prepared responses or actions (macros) that enabled agents to resolve tickets quickly. Automation helped agents run processes within specific time frames.

The Impact

This approach has yielded impressive results. The company has seen an 18% reduction in ticket processing time, a 15% reduction in agent costs, and a 28% reduction in inquiries with the help of an automated response system.

Decision pointsOpen-Source LLMClose-Source LLM
AccessibilityThe code behind the LLM is freely available for anyone to inspect, modify, and use. This fosters collaboration and innovation.The underlying code is proprietary and not accessible to the public. Users rely on the terms and conditions set by the developer.
CustomizationLLMs can be customized and adapted for specific tasks or applications. Developers can fine-tune the models and experiment with new techniques.Customization options are typically limited. Users might have some options to adjust parameters, but are restricted to the functionalities provided by the developer.
Community & DevelopmentBenefit from a thriving community of developers and researchers who contribute to improvements, bug fixes, and feature enhancements.Development is controlled by the owning company, with limited external contributions.
SupportSupport may come from the community, but users may need to rely on in-house expertise for troubleshooting and maintenance.Typically comes with dedicated support from the developer, offering professional assistance and guidance.
CostGenerally free to use, with minimal costs for running the model on your own infrastructure, & may require investment in technical expertise for customization and maintenance.May involve licensing fees, pay-per-use models or require cloud-based access with associated costs.
Transparency & BiasGreater transparency as the training data and methods are open to scrutiny, potentially reducing bias.Limited transparency makes it harder to identify and address potential biases within the model.
IPCode and potentially training data are publicly accessible, can be used as a foundation for building new models.Code and training data are considered trade secrets, no external contributions
SecurityTraining data might be accessible, raising privacy concerns if it contains sensitive information & Security relies on the communityThe codebase is not publicly accessible, control over the training data and stricter privacy measures & Security depends on the vendor's commitment
ScalabilityUsers might need to invest in their own infrastructure to train and run very large models & require leveraging community experts resourcesCompanies often have access to significant resources for training and scaling their models and can be offered as cloud-based services
Deployment & Integration ComplexityOffers greater flexibility for customization and integration into specific workflows but often requires more technical knowledgeTypically designed for ease of deployment and integration with minimal technical setup. Customization options might be limited to functionalities offered by the vendor.
10 ponits you need to evaluate for your Enterprise Usecases

At Fluid AI, we stand at the forefront of this AI revolution, helping organizations kickstart their AI journey in enhanced Customer Support with AI tech. If you’re seeking a solution for your organization, look no further. We’re committed to making your organization future-ready, just like we’ve done for many others.

Take the first step towards this exciting journey by booking a free demo call with us today. Let’s explore the possibilities together and unlock the full potential of AI for your organization. Remember, the future belongs to those who prepare for it today.

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