Recently, in the sports industry, a vast amount of data is being collected, including ticket purchase data, merchandise purchase data, fan club membership data, and even performance data during matches. However, the use of this data is still often limited, and it has not yet been fully utilized to its maximum potential. The reasons for this include the data being managed in a decentralized manner and the lack of a specialized environment to integrate and leverage this data.

Morph provides the optimal solution to address these challenges. By integrating, analyzing, and visualizing data, it can transform strategy development in the sports industry. In this article, we will explain how Morph makes this possible and how it can offer new value to the sports industry.

Common Challenges Caused by Data Fragmentation

In today's world, the importance of data utilization is widely emphasized across all industries, yet there are several common challenges in effectively harnessing it. First and foremost, data is often managed across multiple systems in many companies. Customer Relationship Management (CRM) systems, accounting systems, and marketing tools, for example, store data in different locations, making it difficult to handle this data in an integrated way. As a result, companies are forced to rely on partial information from each system, which makes it challenging to make decisions based on the overall picture.

Additionally, a significant challenge is the lack of the necessary environment and personnel for data analysis. Advanced analysis, such as statistical analysis and machine learning, requires personnel with specialized skills. However, securing such talent is not easy. Furthermore, with the wide variety of analysis tools available, without knowledge of tool selection and operation, it is difficult to fully leverage them.

Given this background, many companies understand the potential of data utilization but have not yet realized it. This is particularly true in the sports industry, where specific challenges, as discussed below, exacerbate the situation.

Impact of Uncontrollable Variables on Sales in the Sports Industry

The challenges in data utilization faced by the sports industry, in addition to the common challenges seen across industries, have unique difficulties. These challenges stem from the fact that the business model in sports is greatly influenced by uncontrollable variables, such as team performance and weather conditions, which cannot be managed on-site. For example, if a team's performance declines, it leads to a decrease in attendance, directly affecting ticket sales and related product revenues. However, it is impossible to fully predict the performance over the course of a season, and such uncertainties have a significant impact on business strategies.

In this context, developing effective strategies requires combining multiple data sources to perform more advanced analysis. For instance, by considering variables like weather, opponents, and team performance, it is necessary to predict attendance and product sales, and based on that, implement dynamic pricing and promotions. Achieving this kind of analysis requires more than just BI tools; it necessitates a highly customizable and flexible analytical platform.

Another challenge in the sports industry is that the data is vast and interconnected. For example, by combining fan purchasing behavior data with game performance data, it becomes possible to analyze how a team's performance affects fan purchasing intent. However, to integrate and leverage such complex data, advanced skills and expertise are required.

Ideal Data Utilization with Morph

Morph provides optimal solutions to address the above challenges through the construction of an integrated data analysis platform and practical support. In addition to offering tools, we provide a wide range of support, including data consulting services and engineering resources.

By utilizing these services, sports teams can efficiently overcome data utilization challenges and maximize the value of their data. Below, we will introduce several ways in which these solutions can be specifically applied.

Utilizing Dashboards

General BI tools are often used for easy analysis and quick visualization of data. However, since BI tools rely on the GUI provided by the tool, it can be difficult to implement specific customizations or unique metrics.

On the other hand, Morph creates dashboards entirely based on code, allowing it to meet advanced needs like these. For example, it is possible to reflect industry-specific requirements, complex data models, or design UI tailored to business processes, enabling the customization of fully original dashboards. This approach allows for the creation of dashboards that combine visual appeal with practicality, which can significantly improve the quality of decision-making.

Deepening Customer Segments with RFM Analysis

By combining customer data with sales data for goods and tickets, RFM analysis (Recency, Frequency, Monetary Analysis) is conducted to analyze purchasing trends and behavior patterns in detail. This allows us to cluster customers with similar characteristics, enabling the creation of targeted strategies for each segment.

Moreover, by classifying this cluster by gender or age group, we can deepen our customer segmentation and clarify the specific needs and values for each target group.

For example, the following insights can be gained:

• For male customers in their 20s, those with a high purchase frequency tend to have a high repeat purchase rate for recently bought products, so email marketing based on past purchase history would be effective.
• High-spending family customers tend to purchase less frequently, so proposing discount coupons or product bundles with benefits would be effective to increase their purchase frequency.

Such analysis enables personalized marketing for each customer, leading to the maximization of LTV (customer lifetime value).

Correlation Analysis Between Watching Behavior and Purchasing Behavior

By integrating customer viewing behavior data with purchasing behavior data, we can visualize their correlation. This analysis enables the club to better understand each customer's preferences and needs.

Based on this analysis, the following insights can be obtained:

• Customers with low attendance frequency tend to have higher merchandise purchase amounts, so targeting merchandise buyers who have never attended a game with ticket promotions can be effective.
• The customer group with the highest engagement relative to attendance frequency is the teenage and young adult demographic who purchase uniforms, so SNS-based uniform promotions or ticket promotions for merchandise buyers can be effective.

This kind of analysis is key to linking the match experience with the purchase experience seamlessly, serving as an important tool for improving customer satisfaction and expanding sales simultaneously.

The First Step Toward a Data-Driven Future

To address the complex challenges faced by the sports industry and deepen connections with fans, a perspective on data utilization is essential. Morph helps integrate distributed data and clarify previously obscure insights, thereby innovating the club's decision-making process. Even with uncontrollable elements like team performance and weather, implementing data-driven strategies can minimize fluctuations in revenue and aim for stable growth.

Customer segment analysis and visualization of viewing behavior provide a foundation for clubs to implement personalized marketing tailored to each fan. As a result, not only will fan satisfaction improve, but maximizing LTV will also strengthen the long-term revenue base.

In the future sports industry, where market conditions and competition are becoming more intense, data utilization will become increasingly important. Introducing Morph will serve as the starting point for clubs to leverage data, and the outcomes will be the driving force to shape the future.