Introduction

Rising Stars Muay Thai (RSMT) is a Sacramento-based Muay Thai promotion known for its IKF-sanctioned legitimacy, respected matchmaking, and loyal fighter and fan community. Despite strong brand equity, RSMT lacks the digital infrastructure needed to understand its audience, optimize marketing, or scale operations. Our MSDM CEP project aims to build a data-driven foundation that supports sustainable growth and measurable marketing performance.

Problem Statement

Although RSMT delivers a high-quality product and has built a passionate community, the organization operates with minimal data visibility, creating a “great product, flying blind” situation. Without a website, CRM, or integrated tracking system, RSMT cannot identify who attends events, how customers discover the brand, or which marketing efforts drive conversions. This prevents RSMT from optimizing spend, proving ROI to sponsors, or scaling to larger venues.

One Analytics Objective

AO5 – Event-to-Membership Conversion
This objective focuses on understanding how event attendance influences gym membership adoption. By linking event behaviors, motivations, and engagement patterns to membership outcomes, we aim to quantify the revenue bridge between RSMT’s fight events and its martial arts gym.

Importance

Understanding event-to-membership conversion is essential because RSMT’s long-term revenue depends on recurring memberships, not one-time ticket sales. Identifying which event factors drive membership adoption allows RSMT to refine marketing, improve event experiences, and strengthen customer lifetime value.

Literature Review

Existence of Well‑Established Consumer Behavior Theory/Models

Research in consumer behavior provides several frameworks that support the Event-to-Membership Conversion objective. Co-Creation of Value theory explains how shared experiences, such as attending live fight events, increase perceived value and deepen loyalty. Brand Community Theory highlights how strong communities foster long-term engagement and membership adoption. Engagement-to-Conversion models further show that higher involvement and emotional engagement predict behavioral outcomes such as joining a gym.

Assessment of the Adequateness of Gathered Information

RSMT currently lacks integrated behavioral data, but the information gathered so far provides a strong foundation for analysis. Survey responses capture motivations, satisfaction, and event experiences, while CRM records and membership data reveal demographic patterns and conversion outcomes. Event attendance logs also help identify frequency and recency of engagement. Although the data is not yet comprehensive, it is adequate for exploring initial relationships between event participation and membership adoption.

Need for Additional Analysis to Address the Business Problem

To fully understand the drivers of membership conversion, additional analysis and data collection are required. RSMT needs more detailed event feedback, behavioral tracking through GA4, and demographic profiling to strengthen predictive modeling. Collecting engagement metrics across events, social media, and the website will help identify friction points and optimize the customer journey.

Methods

Data & Sampling

The data for this project includes adults within a five- to ten-mile radius of RSMT events, event attendees, gym members, and digital followers. Sampling sources include CRM leads, RSVPs, email lists, and post-event surveys. This combination captures both high-intent and casual audiences, allowing us to analyze behavioral patterns, motivations, and conversion outcomes.

Data Wrangling

Data wrangling involved importing CRM, survey, and attendance files, removing duplicates, correcting timestamps, and standardizing variable formats. We encoded categorical variables, normalized date and time fields, and merged event, survey, and membership datasets into a unified analytical file.

Sample Characteristics

library(dplyr) 
library(ggplot2)

sample_data <- tibble(
  segment = c("Combat Fans", "Martial Artists", "Families"),
  count = c(120, 85, 60)
)
sample_data

# A tibble: 3 × 2
  segment         count
  <chr>           <dbl>
1 Combat Fans       120
2 Martial Artists    85
3 Families           60

library(ggplot2)   # 
ggplot(sample_data, aes(x = segment, y = count)) +
  geom_col(fill = "firebrick") +
  labs(
    title = "Sample Size by Audience Segment",
    x = "Segment",
    y = "Count"
  )

Measures

Key measures used in the analysis include event frequency, recency, engagement level, and event type. These behavioral variables help quantify how often and how recently individuals interact with RSMT, as well as the depth of their involvement. Demographic measures such as age, distance from the gym, and prior martial arts experience serve as control variables, allowing us to isolate the true impact of event behaviors on membership decisions. The primary dependent variable is membership conversion, which enables us to evaluate how these combined factors contribute to the probability of joining the gym.

Analytics Methods

To evaluate the relationship between event participation and membership conversion, we employ logistic regression as the primary analytical method. Logistic regression is widely used in marketing analytics because it provides both predictive insights—estimating the likelihood of conversion—and diagnostic insights that identify which factors significantly influence that outcome. Supporting analyses include descriptive statistics to summarize audience characteristics and correlation analysis to explore relationships among variables before modeling. Together, these methods allow us to quantify the impact of event behaviors, engagement patterns, and demographic characteristics on the probability of becoming a gym member.

References

• AIDA Model – Classic framework describing the stages of consumer attention and conversion.
  
• Lemon, K. N., & Verhoef, P. C. (2016). Customer Journey Mapping and the dynamic nature of customer experiences.
  
• Bitner, M. J., Booms, B. H., & Tetreault, M. S. (1990). Critical Incident Theory and service breakdown analysis.
  
• Verhoef, P. C., et al. (2015). Omnichannel strategy and the integration of digital touchpoints.
  
• Co-Creation of Value – Framework explaining how shared experiences increase loyalty and perceived value.
  
• Brand Community Theory – Research on how community belonging drives long-term engagement and membership adoption.
  
• Engagement → Conversion Models – Studies showing how emotional and behavioral engagement predict purchase and membership decisions.
  
• Sports Marketing Research – Evidence linking live event experiences to brand attachment and repeat participation.
  
• Digital Analytics Best Practices – GA4, GTM, and UTM tracking frameworks for measuring user behavior and attribution.