The Business Problem

Cyclistic's marketing team needed to understand how casual riders differ from annual members, since converting casual riders into members was identified as the fastest path to future growth — but no one had modeled the behavioral gap between the two groups.

Technical Solution / Architecture

I processed and cleaned over 5 million individual ride records in R, standardizing timestamps, station data, and ride types across twelve months of raw CSV exports. I then built a star-schema data model in Power BI, connecting ride facts to date, station, and rider-type dimension tables, and layered on DAX measures for ride duration, frequency, and time-of-day segmentation.

Measurable Project Impact

• Casual riders rode 20–25% longer per trip than annual members, revealing a leisure-driven usage pattern distinct from commuter behavior.
• 60% of casual rides occurred on weekends, pinpointing exactly when and where to target conversion campaigns.
• Recommendations projected a 10–15% increase in membership revenue if acted on by the marketing team.

The Business Problem

Bellabeat, a wellness-tech company, needed to understand how users actually engage with fitness tracking — activity, calories, and sleep — to make its wellness campaigns land with the right audience instead of a generic one-size-fits-all push.

Technical Solution / Architecture

I queried and cleaned 940,000+ Fitbit activity records in SQL, joining daily activity, hourly intensity, and sleep tables into a unified analysis base. I ran exploratory analysis in Excel to surface engagement and sleep-quality segments, then built Tableau dashboards to visualize the patterns for a non-technical marketing audience.

Measurable Project Impact

• Active users burned 30–40% more calories than the low-activity segment, confirming a clear high-value user group.
• 45% of users showed inconsistent activity and poor sleep, identifying a specific at-risk segment for targeted wellness content.
• Dashboards drove a 10–20% lift in user engagement when paired with targeted wellness initiatives.

The Business Problem

Vaccination reporting was a manual, repetitive process — data had to be pulled, cleaned, and re-loaded by hand every reporting cycle, slowing down visibility into regional trends and key performance indicators.

Technical Solution / Architecture

I designed a cloud-native pipeline using Amazon S3 as the raw data landing zone, AWS Glue for automated Python-based ETL transformation, PostgreSQL on Amazon RDS as the structured data store, and AWS QuickSight for the final reporting layer — removing every manual hand-off in the process.

Measurable Project Impact

• Cut manual processing time by 80% by fully automating extraction, transformation, and loading.
• Improved data accuracy by removing manual re-entry points from the reporting cycle.
• Delivered real-time QuickSight dashboards giving stakeholders live visibility into vaccination trends and regional KPIs.

The Business Problem

Tailwind Traders' leadership team was tracking sales performance across multiple countries through disconnected spreadsheets, making it slow and error-prone to compare revenue, margin, and growth trends across regions in one view.

Technical Solution / Architecture

I built the data model from raw transactional tables in Power Query, structured it for reliable multi-country roll-ups, and wrote DAX measures covering revenue, margin, and period-over-period growth. The final report was designed for an executive audience — every visual answers a specific leadership question at a glance.

Measurable Project Impact

• $866.97K in net revenue tracked and broken down across regions and product lines in a single dashboard.
• Replaced manual spreadsheet reporting with one live, leadership-ready view.
• Faster decision cycles for regional performance reviews, with drill-down from company-wide to country-level in a few clicks.

The Business Problem

Celestial Airlines was receiving a high volume of dissatisfied customer reviews. As the Data Analyst, my task was to uncover the root causes behind the dissatisfaction.

Technical Solution / Architecture

I reviewed overall metrics — average ratings, average delays, and customer-based breakdowns — then analyzed each airline service using key visuals in Power BI to pinpoint satisfaction drivers. I also examined the relationship between delays, flight distance, and satisfaction rates, using Power Query for data prep and PowerPoint to package findings for stakeholders.

Measurable Project Impact

• Over 57% of customers were dissatisfied with the airline's services, establishing the scale of the problem.
• Online services identified as the primary bottleneck dragging down overall satisfaction.
• Satisfaction drops as delay time increases but rises with longer flight distance, and business travelers reported higher satisfaction than other segments.
• Set a measurable target — an average rating of 3.6+ needed to move the airline into a higher satisfaction bracket.

The Business Problem

Designed an end-to-end sales analytics solution for a global electronics retailer to uncover inefficiencies, hidden profit drivers, and operational gaps across product categories, geographic locations, customer segments, and order fulfillment.

Technical Solution / Architecture

I built a star schema data model in Power BI, structuring the sales table as the central fact table connected to product, customer, store, and date dimensions. I applied feature engineering and exploratory data analysis (EDA) to extract patterns not visible in the raw data, then analyzed revenue and profitability by category, customer demographics and retention, and delivery performance to identify operational bottlenecks.

Measurable Project Impact

• Music, Movies & Audio Books generate a 61% profit margin despite only $3.1M in revenue — flagged as an under-leveraged growth category.
• 61% of buyers are returning customers, pointing to strong retention.
• Customers aged 50+ drive $30M in revenue, outperforming all other age groups combined.
• The US leads on both revenue (~$30M) and customer volume (~6,900 customers) as the top-performing market.
• Identified a major data quality issue: 79.06% of orders are missing delivery status, and store counts are uniformly recorded as "67" per country — flagging both a reporting bottleneck and a likely data integrity error, and surfaced as the most pressing operational inefficiency to investigate next.

The Business Problem

Long-term personal music streaming history sat as raw, unstructured data with no visibility into listening trends, preferences, or usage patterns over time. The goal was to turn that raw history into an interactive dashboard capable of surfacing meaningful insight across more than a decade of activity.

Technical Solution / Architecture

I cleaned and structured a dataset of ~150,000 streaming records spanning 2013–2024, covering track, artist, album, platform, play duration, shuffle/skip status, and timestamps. Play duration was converted from milliseconds into minutes and hours for readability, and I built calculated DAX measures to power year-over-year trend analysis, ranking charts, and platform breakdowns — all wrapped in an interactive, filterable dashboard with a clean UI inspired by Spotify's own design language.

Measurable Project Impact

• Processed ~149K tracks totaling over 5,000 hours of listening time.
• Surfaced year-wise listening trends across an 11-year span (2013–2024).
• Identified leading artists, top albums, and most-played tracks by listening share through dedicated ranking charts.
• Broke down platform usage across Android, iOS, and Web with interactive filters for dynamic exploration.

The Business Problem

Using the UK Train Rides dataset, the goal was to transform raw ticketing and journey records into an interactive dashboard capable of surfacing trends in revenue, ticket sales, delays, and customer behavior to support smarter operational decisions.

Technical Solution / Architecture

I structured and modeled over 60,000 records covering ticket sales, revenue, delays, and payment data. DAX-driven KPIs were built to track total revenue and monthly sales trends, alongside interactive slicers letting users dynamically explore delay causes, payment preferences, and refund patterns by ticket type and purchase channel.

Measurable Project Impact

• Tracked total revenue and ticket sales trends over time with month-over-month visualizations.
• Broke down delay causes — including weather, technical faults, and staffing — to identify operational bottlenecks.
• Analyzed customer payment preferences and refund patterns segmented by ticket type and purchase channel.
• Delivered actionable KPIs via interactive slicers, supporting smoother, data-driven operational decisions.

The Business Problem

Dubai's real estate data was fragmented across off-plan sales, secondary transactions, rental listings, community data, and metro-proximity information, making it difficult to get a consolidated view of market performance or compare investment opportunities consistently. The business needed a centralized analytical solution to identify which market segments offer the strongest price and rental-yield potential, what factors most influence pricing, which developers and communities are driving the highest transaction activity, and where high-growth, high-yield opportunities exist compared with traditional prime locations.

Technical Solution / Architecture

I designed and developed an end-to-end BI solution integrating five disparate data sources totaling over 93,000 property listings into a star-schema data model, connected through shared Community and Date dimensions to support scalable, cross-market analysis. I engineered key performance metrics — including gross rental yield, metro-proximity segmentation, and normalized price-per-square-foot — to enable consistent comparison across property types and market segments, then built a four-page executive dashboard in Power BI with DAX-driven KPIs.

Measurable Project Impact

• Tracked $71B in cumulative market activity across the dashboard — $51B in secondary transactions and $20B in off-plan sales.
• Identified freehold ownership as the primary price driver, commanding a 97% premium — a larger influence on price than metro accessibility.
• Surfaced high-growth, high-yield communities outperforming traditional prime locations, informing investment strategy recommendations.
• Delivered a scalable KPI framework (gross rental yield, normalized price-per-sqft, metro-proximity segmentation) enabling consistent comparison across market segments.

The Business Problem

Airbnb's global listing portfolio (279,713 listings, 5.37M reviews) is mature and scaled, yet averages just 62.88/100 in overall guest satisfaction. The goal was to determine which markets, room types, and listing attributes most strongly predict guest satisfaction, and whether the platform's scale was actually translating into a better guest experience.

Technical Solution / Architecture

I cleaned and standardized two raw datasets in MySQL — handling NULLs, standardizing 662 messy host_location values into consistent country names, and engineering an amenity_count field. I then built a Power BI semantic model centered on one fact table related to a date dimension, plus a standalone feature-engineering measures table, and ran a Key Influencers analysis to isolate the statistical drivers of review score.

Measurable Project Impact

• Cleanliness (+13.34 pts), accuracy (+13.3 pts), and communication (+10.98 pts) emerged as the strongest statistical drivers of review score — well ahead of price or location.
• Entire-place listings dominate the platform (~65%), with the highest prices in Cape Town ($2,405) and Bangkok ($2,078).
• Scale hasn't translated into satisfaction: satisfaction tracks with amenity count and service quality, not price tier or listing volume.

The Business Problem

Built an end-to-end BI dashboard analyzing repeat customer support calls for a Google Fiber case study, including a mockup dashboard built before finalizing the report, to identify which markets and problem types drove the highest repeat-contact volume and where first-contact-resolution training should be prioritized.

Technical Solution / Architecture

I combined and cleaned 3 raw market datasets (450 rows, 11 columns) in BigQuery — handling nulls, duplicates, and inconsistent problem-type labels — into an analysis-ready table (1,350 rows, 9 columns). Date fields were transformed into Year, Month, Quarter, and Week dimensions using Power Query, feeding a single flat-table data model (one fact table, contact-level grain) behind an interactive Power BI dashboard with market, problem-type, and time-period filters. The engagement also included a full stakeholder document set: Stakeholder Requirements, Follow-up Questions, Project Requirements, and Strategy documents.

Measurable Project Impact

• Market 1 accounted for ~63% of all repeat calls (12,647 of 20,240 total).
• Internet & Wi-Fi issues drove ~51% of repeat contacts (10.3K of the total), identified as the top problem type.
• Recommended prioritizing first-contact-resolution training in Market 1 as the highest-impact intervention point.