I transform raw data into decisions that move the needle. With hands-on experience across
legal, healthcare, and consumer industries, I build end-to-end analytics
pipelines — from SQL data models to executive-ready dashboards — that make complex
business questions easy to answer. Currently earning my BBA in Business Analytics
at Florida International University while delivering real BI impact in industry.
Profiling 12,400 player sessions across RPG, Battle Royale, and Casual genres to surface engagement patterns, spend behavior, and churn signals with actionable business recommendations.
SQLExcelPower BIEDAChurn Analysis
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02 · Funnel Analysis
FitFlow Onboarding Funnel
Tracking 85,000 users from app download to paid subscription — pinpointing drop-off stages, comparing acquisition channel performance, and quantifying revenue recovery opportunities.
SQLConversion AnalysisRetentionLTV
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03 · Full-Stack BI Dashboard
StyleSound Sales Dashboard
Production-ready BI layer for a fashion & music merchandise brand — SQL data model through interactive executive dashboard covering $4.82M in revenue across categories, SKUs, and regions.
Built 5+ BI dashboards tracking sales, acquisition, and marketing KPIs — cut reporting time 30%
Analyzed 10,000+ record datasets, contributing to a 10% increase in company profit
Automated campaign reporting across 3 paid channels — reduced manual analysis time by 40%
IT Operations & Data Analyst
Pages Court Reporting
Oct 2024 – Nov 2025 · Miramar, FL
Built and optimized 3 data pipelines, reducing processing time by 35%
Supported 10+ client RFPs, contributing to higher contract renewal rate
Maintained 99% system uptime supporting 50+ court reporters and legal teams
Shipping and Receiving Clerk
CenterWell
May 2024 – Oct 2024 · Miramar, FL
Achieved 99%+ order accuracy across 200+ daily shipments
Reduced average packing time by 25% through process improvements
Contact
✉
Email
dilmcbean@gmail.com
📱
Phone
(954) 451-8643
📍
Location
Miramar, FL 33025
🎯
Seeking
Remote BI / Analytics Roles
Exploratory Data Analysis
Gaming Player Behavior Analysis
Profiling 12,400 player sessions across RPG, Battle Royale, and Casual genres to surface engagement patterns, spend behavior, and churn signals — translated into actionable business recommendations.
Dataset
12,400 player sessions
Period
FY 2024 · Q1–Q4
Tools
SQL · Excel · Power BI
Genres
RPG · Battle Royale · Casual
GenrePeriod
01Key Metrics
Total Players
12,400
↑ 18% YoY
Avg Session (min)
47.3
+6.2 from Q1
30-Day Retention
34.1%
RPG leads at 51%
Avg Revenue / User
$8.40
Top 5% = $142
02Session Distribution & Revenue by Genre
Distribution
Session Length by Genre
RPG has a long tail past 90 min. Casual peaks at 10–20 min and drops off sharply.
RPG
Battle Royale
Casual
Monetization
Revenue per User by Genre
RPG median 3.2× higher than Casual. BR has the highest 90th-percentile ceiling.
Median
90th percentile
03Retention & Churn Signals
Cohort Retention
Day-N Retention by Genre
RPG retains 2× better than Casual at Day 30. Battle Royale shows a sharp cliff at Day 14 — a matchmaking / skill-gap signal worth investigating.
RPG
Battle Royale
Casual
Churn by Spend Tier
Sessions Survived by Spend Tier
Non-spenders churn by session 4. High-value players persist past session 18 — spend is a leading indicator of long-term engagement.
No spend
Low ($1–$10)
High ($10+)
04Key Findings
2.8×
Quest-log players return more
RPG players with an active quest log return 2.8× more in week 1. Progression systems are the single strongest retention lever in the dataset.
61%
Top 5% drive most revenue
First purchase within 3 sessions is the strongest LTV predictor. Late buyers spend 40% less on average. Early conversion is everything.
Day 14
Battle Royale churn cliff
Sharp BR churn at Day 14 points to a skill-gap matchmaking issue. A comeback mechanic targeting sessions 10–16 is the recommended intervention.
Business Application
Who uses this and why
Game studios identifying which player segments to target for LiveOps campaigns
Product teams prioritizing investment — progression systems, matchmaking, or monetization prompts
Any user-based business asking: who stays, who spends, and when do we lose them
Executives needing a data-backed answer to "why is Day-30 retention declining?"
Tracking 85,000 users from app download to paid subscription — identifying exactly where users drop off, comparing performance across acquisition channels, and delivering recommendations with projected revenue impact.
Dataset
85,000 app downloads
Period
FY 2024 · Jan–Dec
Tools
SQL · Excel · Power BI
Focus
Conversion · Retention · LTV
Channel
01Summary KPIs
Downloads → Paid
4.2%
Industry avg: 3.8%
Biggest Drop-off Stage
Sign-Up
38% of users lost here
Trial → Paid Rate
26.8%
↑ +4.1 pts vs Q1
Median Days to Subscribe
9.4
from install date
02Conversion Funnel
Overall
Full Funnel — All Users · FY2024
85,000 installs entered the funnel. 3,570 converted to paid — a 4.2% overall rate with sign-up as the critical leakage point.
By Channel
Channel Comparison — All Channels
Organic converts 2× better than Paid Social at sign-up. Referral has the highest subscriber rate overall.
03Post-Conversion Retention
Retention Curve
Day-N Retention by Week-1 Workouts
3+ workouts in week 1 drives 68% Day-30 retention. Zero-workout users drop to single digits by Day 14.
3+ workouts
1–2 workouts
0 workouts
Time to Convert
Days from Install to First Payment
43% of subscribers convert within 7 days. A long tail extends past Day 30 — nurturing late converters is a growth lever.
% of subscribers
04Recommendations
Opportunity 01 · Signup Friction
~$340K ARR
Add social login at sign-up
38% drop-off is the #1 leakage point. A/B testing Google or Apple login vs the current email form is projected to recover 8–12% of lost users at current ARPU.
Opportunity 02 · First Workout
15–20% lift
Timed push at the 2-hour mark
Only 52% of signed-up users complete their first workout. A triggered push notification at 2 hours after signup matches results seen at comparable health apps.
Opportunity 03 · Trial Churn
Recovers Day 9–11
3-day trial extension test
31% of users stall at Days 9–11 without converting. Offering a 3-day extension to inactive trial users before the paywall fires could recapture high-intent churners.
Business Application
Who uses this and why
Product managers deciding where engineering resources deliver the highest conversion impact
Growth and marketing teams evaluating true ROI by channel — not just clicks, but paid subscribers
Leadership teams answering "why is trial conversion stalling?" with data, not guesses
Any SaaS or subscription app measuring onboarding health and identifying experiment priorities
A production-ready BI layer for a fashion and music merchandise brand — SQL data model through interactive executive dashboard. Revenue by category, SKU performance, regional breakdown, and return-rate monitoring.
Revenue Tracked
$4.82M · 68,400 orders
Period
FY 2024 · Jan–Dec
Tools
SQL · Excel · Power BI
Categories
Fashion · Music · Collabs
CategoryRegionPeriod
01Revenue Overview
Total Revenue
$4.82M
↑ 22% vs FY2023
Orders Processed
68,400
↑ 14% vs FY2023
Avg Order Value
$70.47
↑ 6.8% vs FY2023
Return Rate
8.2%
↑ +1.1 pts — flag
02Revenue Trend & Category Mix
Monthly Trend
Revenue by Category
Collab drops drive the Q4 spike. Fashion leads volume; Collabs punch above their SKU count in margin per unit.
Fashion
Music Merch
Collabs
Revenue Share
Category Mix FY2024
Fashion leads volume. Collabs overindex on margin relative to their SKU count.
03Revenue by Region
Regional Breakdown
Revenue by Region & Category
Southeast leads Fashion at $820K. Northeast overindexes on Music Merch. International is an emerging opportunity worth a dedicated campaign.
Fashion
Music Merch
Collabs
04Top SKU Performance
#
Product
Category
Units
Revenue
Margin
05SQL Data Model
06Key Findings
22%
YoY Revenue Growth
Fashion drives consistent MoM growth. Q4 spike is Collab-driven — artist partnerships generate disproportionate revenue vs SKU count.
80%
Highest Margin SKU
Enamel Pin Set at 80% margin and 22,400 units. Low COGS, high volume, strong festival season demand — most efficient SKU in the catalog.
8.2%
Return Rate — Flag
Above the 7.5% alert threshold and up 1.1 pts vs last year. Fashion — particularly Denim Jackets — is the primary driver. Sizing guide update recommended.
SE
Regional Leader
Southeast leads Fashion at $820K. Northeast overindexes on Music Merch. International is an emerging channel worth a dedicated campaign.
Business Application
Who uses this and why
Merchandising teams making weekly inventory and replenishment decisions by SKU and region
Sales directors tracking category performance against quarterly revenue targets
Finance teams monitoring AOV, margin, and return-rate trends for P&L reporting
Any retail or DTC brand needing a single source of truth across categories and geographies