Rising Stars Muay Thai: Culminating Experience Project

Final Client Package — IBM 6530 — Cal Poly Pomona

Jarrod Griffin

2026-04-30

The Story

The Problem → What We Found → What To Do

RSMT runs great events. We built the analytics infrastructure from scratch — website, tracking, data pipeline — then analyzed two full event cycles to answer one question: who is our audience, how do we track them, and how do we reach more of them?

Zero Digital Foundation

RSMT had no way to measure what was working.

0 Dedicated website before this project

0 Analytics tracking or customer data

0 Data-driven campaigns or channel measurement

Four revenue streams operating in the dark:

🎟 Live TicketsPrimary revenue — untracked funnel

📺 Pay-Per-ViewDigital revenue — no click tracking

🥊 Gym MembershipsLong-term LTV — no conversion path

🛍 MerchandiseAncillary revenue — invisible to analytics

What We Built

The analytics pipeline — deployed from scratch:

Instagram / Paid Ads / Organic Search
          │
          ▼
  www.risingstarsmuaythai.com
  (Google Tag Manager + GA4)
          │
          ▼  BigQuery export — event stream
  data/raw/ga4_events.parquet
          │
          ▼  DuckDB SQL · R statistical models
  Session models · Funnel analysis · Logistic regression

RSMT 14: Jan 31, 2026 · RSMT 15: Apr 18, 2026 · This analysis: Apr 30, 2026

📊 Live Dashboard: guileless-medovik-60d7d7.netlify.app

What we measured:

3,843 Tracked sessions across two event cycles

6 California venue markets · ACS 2023 drive-time analysis

12 RSMT 15 Instagram posts scored for engagement

Six Assessment Objectives

AO1 — Website Optimization Bhamini Optimize RSMT’s website to capture user data and increase conversions — addressing the foundational gap of no way to track behavior or guide users toward tickets, PPV, or memberships.

AO2 — Audience Segmentation Lydia Evaluate potential markets to identify where RSMT can scale events most effectively — prioritizing the market with the best audience size, purchasing power, and demographic alignment.

AO3 — Social Media Engagement Christian Examine engagement data across digital platforms to identify which content generates reactions, clicks, and purchase intent — understanding what to post, how often, and which styles perform best.

AO4 — Customer Journey Mapping Jarrod Establish GTM + GA4 tracking to map the customer journey from initial social media touchpoints to ticket or membership purchase — quantifying drop-off at key friction points.

AO5 — Event-to-Membership Metrics Juan Implement web analytics and integrated channel data to map the journey from initial touchpoint through membership purchase — identifying friction points and enabling data-driven interventions.

AO6 — Fighter Engagement Karima Analyze fighter Instagram posts using likes, comments, and an engagement score to evaluate performance across matchups — optimizing promotional strategy based on measurable engagement rather than assumptions.

What the Data Revealed

Finding 1: The Wrong Traffic Is Winning AO1 · AO3 · AO4

Paid Social drives the most sessions to the site — but organic visitors engage 8× more and convert at higher rates.

Paid Social = most sessions, lowest engagement (8.5%). Organic Search = fewer sessions, highest engagement (69.7%). Converted users browse 4.5 pages on average vs 1.8 for non-converters — deeper engagement drives purchase, not traffic volume.

SPEAKER: Jarrod · ~2.5 min

“Here is the first surprise. On the left — sessions by channel. Paid Social is the biggest driver. That’s the channel RSMT is actively spending on. But look at engagement rate on the right. Paid Social sits at 8.5%. Organic Search — the channel RSMT does nothing to actively capture — is at 70%. More than eight times higher. And when we look at converting users versus non-converters, buyers browse an average of 4.5 pages compared to 1.8 for non-buyers. Traffic volume doesn’t predict a sale. Depth of engagement does. Right now RSMT is investing in the channel that does the least conversion work.”

Key numbers to hit: Paid Social 8.5% / Organic Search 70% / 4.5 vs 1.8 pages

→ Transition: “But raw engagement rates don’t tell us which channel actually deserves credit for conversions. For that we need attribution modeling.”

Attribution: Which Channels Actually Drive Conversions? AO4

Five attribution models agree: Organic Search and Direct punch far above their session-volume weight.

Markov Chain & Removal Effect models credit Organic Search at 2–3× its raw session share — it closes journeys that Paid Social starts. Paid Social’s credit drops in data-driven models: it generates awareness, not conversions. Budget implication: shift marginal spend toward SEO content.

SPEAKER: Jarrod · ~2 min

“Now here’s the IBM 6530 contribution that goes beyond the descriptive analysis. We ran five attribution models on the actual user path data from BigQuery — asking the question: when we know the full sequence of channels a user touched before buying a ticket, which channel deserves the credit?

The heuristic models — first touch, last touch, linear — give you a range of answers. But the data-driven models, Markov Chain and the Removal Effect, both tell the same story. Organic Search is undervalued by last-touch metrics. When you remove Organic Search from all paths, overall conversion probability drops substantially. That’s the Markov removal effect. Paid Social gets a lot of raw credit in first-touch models because it’s often how users first discover the site — but it does less of the actual conversion work.

The practical implication: RSMT shouldn’t cut Paid Social, but the marginal next dollar should go to SEO content — fighter bios, event previews, local keyword pages — because that’s the channel closing sales.”

Key numbers to emphasize: Markov chain reranking of channels vs. last-touch. Organic Search disproportionate contribution.

→ Transition: “Knowing which channels drive conversions is one piece. The other is knowing how fast buyers decide — which is our next finding.”

Finding 2: Buyers Decide in One Session AO4 · AO5 · AO1

Each additional page viewed multiplies conversion odds by 10.31×. And the homepage loses visitors in 6 seconds.

78.3% Complete the full journey — first visit to buy-link click — within a single session

90.6% Convert on the same calendar day as their very first touchpoint

10.31× Conversion odds per additional page viewed (logistic regression, McFadden R² = 0.274)

The homepage gets 1,212 views but holds attention for just ~6 seconds — only 175 visitors reach the event page, and 123 reach tickets. Yet the homepage drives 83% of all final conversion clicks. Content pages build intent; the homepage must close it. Every friction point in the first visit is a permanently lost sale.

SPEAKER: Juan · ~2.5 min

“Finding two makes this urgent. The bar chart shows conversion rate climbing steeply with each additional page viewed — but here is the number that should change how RSMT thinks about marketing: 78.3% of buyers complete the entire journey — first visit to buy-link click — in a single session. And 90.6% convert on the same calendar day as their first touchpoint. There is almost never a second visit. Now — the homepage gets 1,212 views but holds attention for just six seconds. The event page holds it for over a minute. Yet the homepage still drives 83% of all final conversion clicks. It works as a closing page — but only for visitors who content pages have already warmed up. If the homepage loses someone in six seconds, that sale is gone permanently.”

Key numbers: 78.3% / 90.6% / 6 seconds / 1 min 03 sec / 83% / 1,212 views

→ Transition: “Finding three zooms out from the website to the market itself. Lydia.”

Finding 3: Sacramento Has a Ceiling AO2

Isochrone analysis across six California markets reveals a location gap.

12.96M Reachable audience within 60 min of Los Angeles

+309% More reachable people than Sacramento’s 3.1M baseline

51.1% Aged 18–54 in the LA catchment — the core ticket-buying demographic

$91,853 Median household income — supports premium pricing and larger venues

Click any bubble to see full market detail. Bubble size = reachable population within 60 minutes. Los Angeles isn’t just larger — income, age demographics, and census-tract coverage align closest to RSMT’s buyer profile. Full comparison in Appendix A.9.

Finding 4: Followers Don’t Predict Activation AO6

Koby Taylor (686 followers) outscored Vera Sokolova (2,450 followers) on every metric. Matchup excitement drives activation — not follower count.

510 Taylor vs Tiffer — rank #1 · Koby Taylor: 686 followers

502 Houston vs Hall — rank #2 · Darian Houston: 1,559 followers

253 Harris vs Sokolova — rank #8 · Vera Sokolova: 2,450 followers

The fighter with the most followers ranked 8th. The top performer had less than a third as many. Booking decisions based on follower count leave audience activation on the table — engagement score is the reliable signal.

Three Strategic Moves

Move 1: Fix the Funnel AO1 · AO3 · AO4 · AO5

The website must convert the traffic social media sends — and remove every friction point for the single-session buyer.

Add a “Buy Tickets” CTA above the fold78% of buyers convert in one session with no return visit. The button must be visible without scrolling on mobile — the moment intent fires, there must be zero steps between it and a click.

Surface event content on the homepageThe event page holds attention for 63× longer than the homepage (1 min vs 6 sec). Fighter card previews, event date, and venue directly on the homepage funnel more traffic toward the content that builds purchase intent.

Route all social links directly to the event pageInstagram visitors currently land on the homepage and leave at 8.5% engagement. Sending them directly to the event page — not the generic homepage — shortens the path for followers who are already curious.

Test mobile experience before every event cycle90.6% of converters act on the day they first visit. Page load speed, sticky button visibility, and tap-target size on mobile should be verified in the two weeks before each event goes on sale.

SPEAKER: Bhamini (~1 min) then Christian (~1 min)

BHAMINI: “Move one: fix the funnel. Four changes, all low-cost, all high-leverage. First — a Buy Tickets CTA above the fold on the homepage. 78% of buyers never return. If intent fires and the button isn’t visible on mobile without scrolling, the moment is gone. Second — surface event content on the homepage: fighter previews, event date, venue. Right now that content lives two clicks deep. It needs to be the first thing visitors see.”

→ hand to Christian

CHRISTIAN: “Third — route every social media link directly to the event page, not the homepage. Instagram visitors currently land on the homepage and bounce at 8.5% engagement — skip the detour. Fourth — test the mobile experience in the two weeks before each event goes on sale. 90% of converters act on day one, mostly on mobile.”

→ Transition: “Move two is about a first-mover opportunity. Jarrod.”

Move 2: Win on Organic Search AO4

Organic Search is RSMT’s highest-converting channel. It’s also the one RSMT does nothing to capture.

Publish SEO-optimized content 2–3 weeks before each eventSearch-intent visitors are actively searching for “Sacramento Muay Thai events.” They arrive with pre-formed motivation, engage at 71%, and convert at the highest rate of any channel. Capturing them organically reduces CPM costs and improves overall conversion efficiency.

Build fighter bios, event previews, and bout cards as indexed contentThis is the same rich content that drives 4.5 page views per converting user. Building it for SEO serves double duty: search ranking and on-site intent formation. One well-written fighter profile earns traffic indefinitely — a paid impression expires in 24 hours.

This is a first-mover opportunityNone of RSMT’s regional competitors — IKF Events, NAK Muay Challenge, or local MMA promotions — currently employ SEO-optimized content strategies. Consistent publishing before RSMT 16 and beyond would establish a measurable digital competitive advantage that is hard to replicate quickly.

SPEAKER: Jarrod · ~1.5 min

“Move two. Organic Search is RSMT’s highest-converting channel — and it’s the one RSMT does nothing to actively capture. We looked at the regional competition: IKF Events, NAK Muay Challenge, local MMA promotions. None of them are running SEO-optimized content strategies. None. Publishing fighter bios, event previews, and bout cards two to three weeks before each event captures people already searching for Sacramento Muay Thai — people who arrive with pre-formed intent and engage at 71%. A paid impression expires in 24 hours. A well-written fighter profile earns traffic indefinitely. This is unclaimed territory and a first-mover advantage that is genuinely hard to replicate quickly.”

→ Transition: “Move three: expand smart. Lydia and Karima.”

Move 3: Expand Smart AO2 · AO6

Los Angeles gives RSMT 4× the audience. Engagement scoring tells RSMT who to put on the card.

Expand to Los Angeles:

Stage one LA event — test before committing to a full calendar12.96M reachable people · +309% vs Sacramento · 51.1% aged 18–54 · $91,853 median income. The market can support premium pricing and larger venue capacity that Sacramento’s ceiling cannot.

Target digital campaigns within the 60-minute drive catchmentThe isochrone analysis identifies exactly where the audience clusters across 3,085 census tracts. Paid social and search campaigns should be geo-targeted to those areas — not broadcast broadly across the LA metro.

Book fighters by data, not assumptions:

Headline Taylor vs Tiffer and Houston vs Hall at the next eventEngagement scores of 510 and 502. Koby Taylor (686 followers) outperformed Vera Sokolova (2,450 followers) on every metric. Follower count is not a reliable predictor — matchup excitement and comment activity are.

Track engagement score every event cycle going forwardBuild a data-driven promotional roster over time. Weight comments 2× more than likes when scoring: someone who wrote a sentence is more invested than someone who double-tapped. Use this score as the promotional and booking criterion — not gut feel.

SPEAKER: Lydia (left column) then Karima (right column) · ~2.5 min total

LYDIA (~1.5 min): “Move three: expand smart. Los Angeles is the target. 12.96 million reachable people — 309% more than Sacramento. The recommendation is to stage one event in LA before committing to a full calendar. Test the market, validate ticket demand, then scale. And when running campaigns, geo-target the 3,085 census tracts within the 60-minute drive catchment. Not a broad metro spray — a precision campaign in the neighborhoods the isochrone analysis already identifies.”

KARIMA (~1 min): “On the fighter side — use the engagement score, not gut feel. Headline Taylor vs Tiffer and Houston vs Hall at the next event: scores of 510 and 502. And track this every event cycle going forward. One event of data is a snapshot. Three events is a pattern reliable enough to use as a booking criterion.”

→ Transition: “Four findings, three moves. Jarrod will bring it home.”

Three Things to Remember

1 Your buyers decide in one session. Fix the homepage CTA and mobile experience first — there is almost never a second visit.

2 Social media gets them there. Your website must convert them. Shift investment from volume (Paid Social) to intent (Organic Search and content).

3 Los Angeles is your growth market. Engagement scoring is your booking tool. Both are first-mover opportunities no regional competitor has claimed.

The overarching implication: RSMT has proven it can build an audience with high intent that acts quickly. The only remaining constraint is removing friction between discovery and purchase — and scaling into a market where that audience is four times larger.

SPEAKER: Jarrod · ~1.5 min · SLOW DOWN — pause after each number

“Before we go to questions — three things to walk away with.”

[pause] “One: your buyers decide in one session. Fix the homepage CTA and the mobile experience first — because there is almost never a second visit.”

[pause] “Two: social media gets them there. Your website must convert them. Shift investment from volume to intent — from Paid Social toward Organic Search and content.”

[pause] “Three: Los Angeles is your growth market. Engagement scoring is your booking tool. Both are first-mover opportunities no regional competitor has claimed.”

[beat] “RSMT has already proven it can build an audience with high intent that acts quickly. The only constraint left is removing friction between discovery and purchase — and scaling into a market four times larger than Sacramento.”

Thank You — Questions?

Key numbers

90.6% of converters act on day 1
78.3% complete the journey in 1 session
10.31× conversion odds per additional page viewed
83% of final conversion clicks come from the homepage
12.96M reachable audience in Los Angeles (+309%)
Organic Search: 71% engagement vs Paid Social: 14%
Taylor vs Tiffer: 510 · Houston vs Hall: 502

SPEAKER: Jarrod opens Q&A · all answer in their area

Jarrod: “Thank you. We’re happy to take questions — and we’ve prepared a full appendix with regression outputs, channel data, the full market table, and methodology if any of those come up.”

— Q&A PREP (own the answer for your AO) —

Q: “How do you define a conversion — is that an actual ticket sale?” A (Jarrod or Bhamini): Click intent to Eventbrite or Akaizo — the strongest signal measurable without a post-purchase pixel. All rates are lower bounds. Appendix A.3.

Q: “Why LA over Anaheim — Anaheim is slightly larger by population?” A (Lydia): Higher median income ($91,853 vs $90,893), more census tracts (3,085 vs 3,044), and stronger existing social media presence. Full comparison in Appendix A.9.

Q: “Why logistic regression specifically?” A (Jarrod): Binary outcome (converted or not), interpretable odds ratios, standard for click-behavior data. McFadden R² = 0.274. Full output in Appendix A.4.

Q: “Is two event cycles really enough data?” A (Jarrod): Directional, not causal — we say so explicitly. RSMT 16 is the first real test of whether patterns are systematic. Appendix A.12.

Q: “How does RSMT do SEO without a dedicated team?” A (Jarrod): Fighter bio templates + a content calendar = 2–3 posts per event cycle. Manageable with existing staff. The competitive window is now — before anyone else claims the space.

Appendix

Supporting detail — available for Q&A

📊 Dashboard: guileless-medovik-60d7d7.netlify.app

A.1 Data Sources · A.2 Technology Stack · A.3 Conversion Definition
A.4 Regression Full Output · A.5 Channel Performance Table · A.6 Page Funnel
A.7 Last-Touch Before Conversion · A.8 Journey Distribution
A.9 All Six Markets · A.10 All Fighter Rankings · A.11 Traffic Sources · A.12 Limitations

A.1 — Data Sources & Collection

Source	Dataset	Coverage	Key Variables
GA4 (event stream)	`ga4_events.parquet`	RSMT 13 launch → Apr 30, 2026	event_name · page_location · link_url · user_pseudo_id · ga_session_id · device_category · traffic_medium
GA4 Data API	`ga4_api_channel_acq.parquet`	All-time channel-level aggregates	sessionDefaultChannelGroup · sessions · engagementRate
Google BigQuery	Raw streaming export	Full event stream, lossless	Complete GA4 schema, exported via `bigrquery` R package
ACS 2023 Census	60-min isochrone (6 venues)	Census tract 5-yr estimates	population · median_income · pct_age_18–34 · pct_age_35–54 · census_tracts
Instagram (manual)	RSMT 15 promo posts	12 matchup posts	likes · comments · engagement_score = likes + 2×comments

Collection context: Google Tag Manager and GA4 were deployed from scratch in late 2025. RSMT had zero analytics infrastructure before this project. BigQuery streaming export began at site launch and is continuous.

A.2 — Technology Stack

Tracking & Ingestion

Tool	Purpose
Google Tag Manager	Event triggers, custom tag deployment
Google Analytics 4	Behavioral event collection
Google BigQuery	Cloud warehouse + raw export
`bigrquery` (R)	BigQuery API auth & pull

Storage & Query

Tool	Purpose
Apache Parquet + `arrow`	Columnar in-memory format
DuckDB + `DBI`	SQL over Parquet, no server needed

Analysis & Modeling

Package	Use
`dplyr`	Data wrangling (lazy eval over Arrow)
`ggplot2`	Publication-quality charts
`forcats`, `scales`	Factor ordering, number formatting
`stats::glm()`	Logistic regression (base R)

Reporting & Output

Tool	Purpose
Quarto	Reproducible documents & slides
RevealJS	HTML presentation engine
Posit Connect Cloud	Shiny app (AO2 census map)
Excel + Tableau	AO6 Instagram engagement scoring

A.3 — Conversion: How We Measured It

A session is counted as a conversion when it contains at least one of:

Page view → /go/tickets or /go/ppvRSMT redirect slugs. GA4 fires the page_view event before the external redirect fires, capturing the click-through moment. Both ticket and PPV paths are captured.

Outbound click → eventbrite.comLive ticket purchase platform. Tracked via GA4 outbound link event + GTM click trigger on the purchase button element.

Outbound click → akaizosports.livePay-Per-View streaming platform. Same GTM click-tracking mechanism as Eventbrite.

What this captures:

High intentA visitor who clicked to the purchase platform — the strongest measurable signal short of a post-purchase pixel

What this does NOT capture:

Confirmed transactions (no Eventbrite pixel)
Users who typed the URL directly after leaving
In-person or phone ticket sales

Key implication: All conversion rates here are lower bounds. True conversion rate is higher. A post-purchase pixel for RSMT 16 would close this gap and enable confirmed-purchase measurement.

A.4 — Logistic Regression: Full Model Output

Model: glm(conversion_flag ~ session_event_count + page_view_count + device_category + traffic_medium, family = binomial) · McFadden R²: 0.274 · N: 1,433 sessions

Predictor	Odds Ratio	95% CI	p-value
(Intercept)	0.044	0.026 – 0.076	< 0.001	***
session_event_count	0.488	0.426 – 0.56	< 0.001	***
page_view_count	10.306	7.078 – 15.008	< 0.001	***
device_categorymobile	0.778	0.48 – 1.26	0.3076
device_categorytablet	0.000	0 – Inf	0.977
traffic_mediumorganic	1.634	1.154 – 2.314	< 0.01	**
traffic_mediumpaid	0.270	0.166 – 0.439	< 0.001	***
traffic_mediumreferral	0.365	0.116 – 1.144	0.0838
traffic_mediumsocial	1.199	0.326 – 4.406	0.7845

Reference category: desktop device · cpc (paid social) medium. Intercept OR reflects baseline log-odds and is not standalone-interpretable. *** p<0.001 · ** p<0.01 · * p<0.05

A.5 — Channel Performance: Complete Table

All acquisition channels — sessions, % of total traffic, and engagement rate

Channel	Sessions	% of Total	Engagement Rate
Paid Social	2,006	52.2%	8.5%
Direct	1,027	26.7%	54.3%
Organic Search	585	15.2%	69.7%
Organic Social	208	5.4%	61.5%
Referral	17	0.4%	58.8%

Source: GA4 Data API (ga4_api_channel_acq.parquet). Unassigned and (other) excluded. Engagement rate = sessions containing ≥1 engaged event ÷ total sessions.

A.6 — Page Funnel: Every Number

Homepage → Event Page → Ticket Page — full metrics from GA4 Pages & Screens

Page	Views	Users	Avg. Engagement	Observation
Homepage	1,212	841	~6 seconds	Highest traffic; most visitors exit without a clear next action
Event Page	175	—	~1 min 03 sec	63× longer dwell than homepage — users who arrive here read the card
Ticket Page	123	—	—	Final step before Eventbrite redirect
PPV Page	122	—	—	Parallel purchase path (Akaizo)

Homepage first-step navigation (Path Exploration · 1,028 homepage sessions):

Immediate next page from homepage	Users
PPV page	122
Ticket page	101
Event page	78
Exit / bounce	remaining majority

Source: GA4 Pages & Screens report + Path Exploration tool. 30-day window ending Apr 30, 2026.

A.7 — Last Page Before Conversion (Full)

Page visited immediately before the buy-link click — all categories

Page Category (last before conversion click)	Conversions	% of Total
Home Page	187	82.7
Events Page	20	8.8
Dedicated Tickets/PPV Page	18	8.0
Fighter Profile	1	0.4

The homepage triggers the most final conversions despite being the page with the shortest dwell time. It functions as a closing page: content pages (fighters, fight card) build intent; the homepage must execute the purchase click. A sticky “Buy Tickets” CTA above the fold converts this already-warm traffic with zero additional friction.

A.8 — User Journey: Days & Sessions to Conversion

Converters beyond 14 days or 5 sessions represent a small tail and are excluded here for readability.

A.9 — All Six Markets: Full Comparison (AO2)

ACS 2023 · 60-minute drive-time isochrone · candidate venue addresses

Market	Census Tracts	Reachable Pop.	Median HH Income	Age 18–54	Pop Δ vs Sacramento
Sacramento (baseline)	711	3,166,025	$89,844	48.3%	—
San Jose	1,124	5,014,076	$147,459	50.7%	+58.4%
San Francisco	1,232	5,249,936	$143,095	50.1%	+65.8%
Riverside	1,658	8,207,709	$94,760	49.5%	+159.2%
Los Angeles	3,085	12,960,180	$91,853	51.1%	+309.4%
Anaheim	3,044	13,203,228	$90,893	51.3%	+316.9%

Why Los Angeles over Anaheim (Anaheim is marginally larger by population):

Higher median income ($91,853 vs $90,893) → stronger premium ticket potential
More census tracts (3,085 vs 3,044) → broader geographic audience spread
Higher existing social media presence (20 tracked website users vs 13 in Anaheim)
Venue: 1111 S Figueroa St (Crypto.com Arena area) vs 2695 E Katella Ave (Honda Center)

Source: ACS 2023 5-year estimates, Census Bureau. Drive-time isochrones generated at the census-tract level. Age 18–54 = pct_age_18–34 + pct_age_35–54.

A.10 — All 12 Fighter Matchups: Complete Rankings (AO6)

RSMT 15 Instagram promotional posts · Engagement Score = Likes + 2 × Comments

Rank	Matchup / Post	Likes	Comments	Engagement Score
1	Taylor vs Tiffer	428	41	510
2	Houston vs Hall	420	41	502
3	Phetamphone vs Handy	288	37	362
4	Sanchez vs Villanueva	268	10	288
5	Harris vs Sokolova	246	7	260
6	Jack (solo)	197	30	257
7	Logan (solo)	206	23	252
8	Rodriguez vs Sardi	206	14	234
9	Mayorga vs Licea	201	10	221
10	Bullie vs Hargrove	163	13	189
11	Carlos (solo)	135	24	183
12	Underwood vs Roberts	113	14	141

Follower context (top 3 by follower count): Carlos — 2,457 followers · Vera Sokolova — 2,450 followers · Darian Houston — 1,559 followers.
Koby Taylor (Taylor vs Tiffer, rank #1, score 510) has significantly fewer followers than Sokolova (rank #8, score 253). Follower count is not a reliable booking predictor — matchup excitement drives engagement.

A.11 — AO3: Traffic Sources + AO5: Conversion Timing

AO3 — Traffic Source Breakdown (total events)

Source	Events
google	1,946
(direct)	1,894
ig (Instagram in-app)	634
bing	71
yahoo	40
facebook.com	30
lm.facebook.com	30
duckduckgo	26
l.instagram.com	19
m.facebook.com	11
l.facebook.com	5
ecosia.org	4
instagram.com	4
chatgpt.com	2
linktr.ee	2

Total page views: 2,668 · Unique users: 1,095 · Avg: 2.44 pages/user

AO5 — High-Intent Conversion Timing

85.9% Same-day: reach a high-intent page (/events/) on the same calendar day as first visit

82.4% Single-session: reach a high-intent page within the same browsing session as first touch

AO5 Method: Conversion = first visit to /events/ page. First-touch date vs. conversion date across multiple months of data. Pattern is consistent across all months — not event-specific noise.

Implication: Direct social and email links to /events/ pages (not the homepage) shorten the path to high-intent behavior where purchase intent is already forming.

A.12 — Study Limitations & RSMT 16 Outlook

Known limitations:

Click intent ≠ confirmed purchaseWe measure redirect clicks to Eventbrite/Akaizo. The gap between click and confirmed transaction is unknown without a post-purchase pixel. All conversion rates are lower bounds.

Two event cycles = directional, not causalRSMT 14 (Jan 31) and RSMT 15 (Apr 18) establish behavioral patterns. Causal claims need more cycles and controlled variation — these findings should seed experiments, not replace them.

No demographic data on site visitorsGA4 does not surface age/gender in this export. Demographic findings (51% age 18–54) come from ACS Census at the market level — not from actual visitor profiles.

What RSMT 16 data adds:

Third cycle → trend validationThree data points confirm whether channel and funnel patterns are stable across events. RSMT 16 is the first real test of whether these findings are systematic — not event-specific.

Pre/post optimization comparisonIf homepage CTA and direct social links are implemented before RSMT 16, the data will directly measure impact — converting these recommendations from theoretical to evidenced.

Longitudinal fighter engagement baselineThree events of Instagram scoring data builds a fighter performance ranking over time — not a single-event snapshot. More reliable as a booking and promotion criterion.