Hi there! I'm Tiffany, a BI Analyst who loves turning data into compelling stories. I'm also a mom to two wonderful kids, a friend to many, and a partner to one amazing person – they're a constant reminder of the human side of everything we do.

My journey into data analysis started with a fascination for understanding people, which led me to study psychology. Now, I combine that understanding with my analytical skills to uncover meaningful insights from data. In my free time, I enjoy outdoor adventures, books, video games, and crafts.

I'm a lifelong learner with an insatiable curiosity and a passion for expanding my skillset. I believe that continuous learning is essential for growth and success, both personally and professionally. Throughout my career, I've actively sought opportunities to enhance my knowledge and expertise in areas such as business writing, communication, and ethical conduct. I'm also drawn to understanding the intricacies of business operations, including customer service, cultural diversity, and effective teamwork. This drive to learn and adapt has equipped me with a versatile toolkit and a strong foundation for navigating the ever-evolving business landscape.

Alongside my professional development, I'm driven by a strong desire to make a positive impact on the lives of others. I've dedicated time to various volunteer roles, including supporting hospice patients, mentoring students, and assisting with community health initiatives. These experiences have not only enriched my life but also instilled in me the value of empathy, compassion, and teamwork. I believe these qualities are essential for fostering positive relationships and contributing to a supportive and collaborative work environment.

Beyond The Bullet Points:

While a traditional resume can offer a snapshot of my professional life, it can't capture the full story. This page is dedicated to providing a glimpse into the experiences and passions that have shaped my career path, leading me to pursue my current goals.

From Diverse Experiences to a Focused Path:

My background is a tapestry of diverse roles. I've navigated the fast-paced world of food delivery as a DoorDash driver, maintaining a stellar 5-star rating while ensuring timely and efficient service. I've supported students with diverse learning needs as a Special Education Assistant, fostering a positive and productive learning environment. And yes, I've even donned the hat of a Security Officer, prioritizing safety and vigilance in various settings.

These experiences, while seemingly disparate, have instilled in me valuable skills – adaptability, communication, problem-solving, and a strong work ethic – that I carry into my current pursuits.

The Data Spark:

My true passion lies in the world of data. I'm fascinated by its ability to tell stories, uncover hidden trends, and drive informed decision-making. This fascination led me to pursue formal training in Business Intelligence:

• Business Intelligence Analyst Bootcamp: This intensive program provided me with a comprehensive foundation in data analysis, visualization, and storytelling, culminating in a formal certification as a Business Intelligence Analyst.
• CrewTracker Software Externship: I had the opportunity to apply my newfound skills in a real-world setting, contributing to a large-scale Power BI conversion project. This involved translating complex SQL queries and formulas into DAX, demonstrating my proficiency in data manipulation and analysis.
• Beats by Dre Externship: I conducted in-depth market research for Beats by Dre through the amazing company Extern, delivering actionable insights through dynamic dashboards and a comprehensive slide deck.

A Life Long Learner:

My commitment to continuous learning is evident in my academic pursuits:

• Bloomsburg University of Pennsylvania: I earned my Bachelor of Arts degree in Psychology, delving into the complexities of human behavior and cognition. I further honed my communication and teaching skills by serving as a Teaching Assistant for General Psychology.
• Kutztown University: Driven by a genuine interest in science, I pursued additional coursework in biology and allied health. This experience broadened my understanding of scientific principles and research methodologies.
• University of the People: Currently, I am in deep in pursuit of my MSIT! Actively enhancing my technical acumen, diving deeper into IT and management strategy, and bringing even more robust solutions to the data world. Continuously learning is my superpower!

Beyond the Classroom:

My pursuit of knowledge extends beyond traditional academia. I hold a TEFL certification, opening doors to potential opportunities in teaching English as a foreign language. I'm also a strong communicator, capable of conveying complex information clearly and concisely, a skill honed through various roles, including my launching role as a Tutor Assistant at TripleTen, where I guided hundreds of students through challenging BI concepts.

Looking Ahead:

I'm eager to leverage my skills and experience to contribute to a data-driven organization. If you're seeking a passionate and dedicated BI Analyst with a proven track record of success, I invite you to connect with me. Let's explore how my skills can help you achieve your business objectives.

• GitHub
• LinkedIn
• Instagram

Email

TifCPic@gmail.com

Phone

1(541)300-8146

Location

Remote or Local near Albany, OR, USA

From Catch-22 to a Master in Tech

The Catch-22 of the job market is all too familiar: you need experience to get a job, but you need a job to get experience. After a challenging search for an entry-level role, I found my way to Extern.com, a platform that promised to solve this very problem. What followed was a project that not only gave me a portfolio piece but also changed my career trajectory and cemented my love for tech.

This is the story of my Externship with a major tech company, working on a project with a strong focus on Market & Competitor Analysis for Beats by Dre.

Decoding the Wireless Speaker Market

My project began with a core business challenge: "What are the key factors driving consumer interest in high-fidelity wireless speakers, and how do evolving preferences influence this market?"

This wasn't just a theoretical exercise. It was a real-world problem for a global brand, and it was my job to find the answers. To tackle this, I needed to go beyond what I learned in my boot camp and dive into new skills and tools.

My Methodical Approach

This project was a true test of my skills, taking me from the foundational stages to a final, strategic deliverable.

1. Foundational Research & Planning

I started by getting into the mind of the customer. I used frameworks like AIDA (Attention, Interest, Desire, Action) to create a customer journey map and an ideal consumer profile. This step was crucial for ensuring that my analysis would be human-centric and focused on a real business problem.


2. Data Acquisition & Analysis

   With the groundwork laid, I dived into the data. I used Python to scrape and clean a massive dataset of nearly 5,000 Survey responses and Amazon reviews for key Beats by Dre competitors like JBL, Bose, Sony, and Marshall. This was my first time working with messy, real-world data, and I leveraged libraries like Pandas & Numpy to prepare it for analysis.

   I then moved on to Exploratory Data Analysis (EDA), using Matplotlib and Seaborn to visualize trends. I analyzed review lengths, ratings, and common keywords to find initial patterns.

   
3. Advanced Insights with AI

   The most exciting part of this project was applying advanced techniques and modern tools to gain a deeper understanding of the data. To get started, I imported a full suite of Python libraries for my analysis, including:

   • nltk (Natural Language Toolkit): A powerful platform for working with human language data, which was essential for preparing the text for analysis.
   • textblob: A library for performing sentiment analysis and other natural language processing (NLP) tasks.
   • wordcloud: To visually represent the most common words in customer reviews.
   • google.generativeai (Gemini API): An advanced AI tool used to perform qualitative analysis and summarize key takeaways from the sentiment analysis.

   I also leveraged AI tools like Gamma & Claude for a presentation slide-deck and dashboard creation. This felt like the "next tier" of learning, as I explored advanced prompting techniques to find hidden insights in open-ended survey responses and build a dynamic dashboard.

Actionable Insights for a Global Brand

The result of my work was a comprehensive analysis that led to clear, actionable recommendations. I delivered a presentation and an AI-driven dashboard that provided a data-backed roadmap for the company's product strategy.

Here are a few key findings from the analysis:

• Premium is Profitable: My analysis found that 20% of users were willing to pay over $200 for superior audio quality.
• Targeted Demographics: I identified that Gen Z prioritizes clarity, powerful bass, and loudness, while Millennials value balanced sound and brand reputation.
• The Path Forward: My recommendations included focusing product development on superior sound and design, a digital-first marketing strategy, and competitive pricing to secure market leadership.

This project was a powerful reminder that data analysis is not just about numbers, but about telling a compelling story that solves a business problem.

The Career Pivot to a Tech Passion

This externship was a pivotal moment in my career pivot. It transformed my love for data into a love for tech, demonstrating how the right tools and a structured approach can bridge the gap between education and real-world application. The skills I gained are invaluable.

My success in this project was the final confirmation I needed to take the next step: enrolling in a Master of Science in Information Technology program. I am excited to continue building on this foundation and applying my unique blend of business acumen and technical skills to new challenges.

For a deeper dive into this project and to see my work, explore these links:

• My GitHub Portfolio: Visit my GitHub repository for this project HERE
• Final Presentation: See the full presentation delivered to stakeholders HERE
• AI-Driven Dashboard: Explore the dynamic dashboard I created HERE

Resources for Your Career Journey

If you're looking to break the "Catch-22" and gain real-world experience, I highly recommend exploring externships and professional development platforms.

• Externships: I am living proof that externships with Extern.com can provide the experience you need. They give you an opportunity to work on real-world projects with leading companies and build a portfolio that stands out.
• Boot Camps: My foundation in data analytics came from the Business Intelligence Analyst boot camp at TripleTen. If you're looking to acquire the in-demand skills needed for a career in tech, check out their program. You can use my referral link My Discount Link for more information.

From Legacy Reports to Interactive Dashboards

Data tells a story, but sometimes that story gets lost in translation. During my first foray into the professional world as a BI Analyst, I had the opportunity to help a company, CrewTracker Software, rewrite their data narrative. They were wrestling with a common problem: an outdated reporting system that hindered their clients' ability to understand and act on critical information. My team and I tackled this by migrating over 170 Crystal Reports to modern, interactive Power BI dashboards. This experience not only solidified my BI skills but also highlighted the transformative power of effective data visualization.

The Crystal Reports Conundrum

CrewTracker Software provides solutions for managing field service operations. Like many established companies, they relied heavily on Crystal Reports, a legacy reporting tool. While functional, Crystal Reports can be cumbersome, especially for complex data analysis and interactive exploration. Imagine trying to piece together a dynamic, 360-degree view of your business from static, printed reports – that's the challenge CrewTracker's clients faced.

Our mission was clear - modernize CrewTracker's reporting system by converting these static reports into dynamic, user-friendly Power BI dashboards. This would empower their clients to:

• Effortlessly explore their data.
• Identify trends and patterns.
• Make data-driven decisions with confidence.

A Three-Pronged Approach to Transformation

To efficiently tackle this large-scale migration, we adopted a three-tiered approach:

1. First Responders: Extracting the Blueprint
• 5 of our 20 externs had the first task - to dissect the existing Crystal Reports and document their key elements. This step was crucial "reverse engineering," laying the groundwork for accurate replication in Power BI. This involved:
• Connecting to the CrewTracker workstation via Remote Desktop Connection.
• Capturing screenshots of the report layout and field details.
• Exporting PDF views of the reports.
• Most importantly, extracting the underlying SQL queries and formula fields.
• As a First Responder, I personally cataloged 91 Crystal Reports.
2. Power BI Team: Building the Interactive Future
• All 20 externs joined the Power BI team. We were responsible for the core conversion and modernization work:
• Translating the extracted SQL queries and formulas into Power BI.
• Designing interactive dashboards that went beyond the limitations of the original static reports.
• Creating merged reports, where appropriate, to consolidate information and provide a more holistic view.
• I successfully converted 3 Crystal Reports into a complex and interactive merged Power BI dashboard
3. Peer Reviewers: Ensuring Accuracy and Quality
• To maintain quality, each Power BI report underwent rigorous peer review. This involved:
• Verifying the accurate replication of the original report's layout and functionality.
• Confirming the accuracy of the SQL queries.
• Ensuring that all Crystal Report formula fields were correctly translated into Power BI DAX measures and calculated columns.
• Assessing the overall completeness and purpose of the converted report.
• I contributed to this stage by peer-reviewing 9 Power BI reports, ensuring they met the high standards of accuracy and usability.

Diving Deeper: The Call List Merge Report

One of the most challenging and rewarding tasks was creating a merged Power BI dashboard for the "Call List" reports. This involved:

• Consolidating SQL queries from three separate Crystal Reports into a single, optimized SQL Direct Query within Power BI.
• Designing a dashboard that accurately reflected the layout expectations of the original reports while leveraging Power BI's interactivity. This required careful consideration of how to best represent the data and enable user exploration.
• Translating complex Crystal Report formula fields into Power BI DAX measures and calculated columns. This was a critical step, as DAX provides powerful analytical capabilities but requires a different syntax and approach than Crystal Reports formulas. I had to ensure that the formatting of these DAX calculations allowed for flexibility and future use.

Key Insights

This project yielded significant insights into the power of modern BI tools:

• Enhanced Data Accessibility: Power BI dashboards provide a more intuitive and interactive way for users to explore data compared to static Crystal Reports.
• Improved Data Visualization: Interactive dashboards allow for dynamic filtering, drilling down into details, and visualizing trends, leading to better understanding and faster decision-making.
• Increased Efficiency: By consolidating reports and automating data refreshes, Power BI can streamline reporting processes and save time.

Results: Empowering CrewTracker's Clients

By modernizing its reporting system, CrewTracker Software is now better equipped to serve its clients. The transition to Power BI enables:

• Deeper Data Insights: Clients can now uncover hidden patterns and trends in their data, leading to more informed business strategies.
• Improved Decision-Making: Interactive dashboards provide the right information at the right time, empowering clients to make quicker and more effective decisions.
• Increased Client Satisfaction: A user-friendly and insightful reporting system enhances the overall client experience.

A Foundation for Growth

This externship was an invaluable experience, providing me with hands-on experience in a real-world BI project. I honed my skills in:

• Data extraction and analysis.
• Report design and development.
• Data visualization and dashboard creation.
• SQL and DAX.
• Teamwork and collaboration.

This project provided a strong foundation for my career as a BI Analyst, and I'm eager to leverage these skills to help other organizations unlock the power of their data.

Want to Learn More?

If you’re curious about Power BI or SQL, I’ve got some resources for you:

• TripleTen: An online coding bootcamp that enables people with busy lives to make the transition into tech. My Discount Link
• Analyst Builder: Self directed learning with guided video's. Now offering a Power BI course!

I hope this gives you a fun and insightful peek into my data adventure!

Hey everyone!

So, It's been one year since I wrapped up a super interesting project analyzing Zomato's customer base, and I wanted to share how I dove into the data and what I found. If you don't know, Zomato is like the go-to app in India for anyone who loves food.

As a budding BI Analyst, this project was a fantastic opportunity to flex my analytical muscles and uncover some tasty insights. This was the first project that I had complete control over from planning to presentation. My mission was to figure out who Zomato's customers are, how we can group them, and what makes them order all that delicious food.

Data Dive - Not as Scary as it Sounds!

First things first, I had to get my hands dirty with the data. Luckily, the files weren’t huge, so I could use good ol’ spreadsheets to clean things up. Think of it as prepping your ingredients before cooking – essential, but not the main course.

Then came the fun part: segmentation analysis. This is where we start grouping customers based on what they do and who they are. It's like sorting people into Hogwarts Houses to understand them better.

What I Found (The Fun Part)

Here's a little sneak peek into what I discovered:

• Zomato's crowd is on the younger side, mostly around 23 years old, and a lot of them are single guys.

• There's a good mix of genders, but there are significantly more customers who are single than married.

• Most customers have small family sizes (2-3), are educated, but here’s a twist – a lot of them are unemployed.
• Those who are employed tend to be earning less.

To visualize this, I created a dashboard with some cool charts and graphs. Because who doesn't love a good visual?

RFM Analysis - Or, How to Keep Customers Coming Back for More

I also used something called RFM analysis. It might sound technical, but it's actually pretty straightforward. It's all about:

• Recency: How recently someone placed an order.
• Frequency: How often they order.
• Monetary: How much they spend.

By looking at these factors, we can segment customers even further and figure out the best ways to keep them engaged. It's like a restaurant knowing its regulars and what they love!

So What? (Why This Matters)

All this data is super useful for Zomato because it helps them:

• Target their marketing: Instead of blasting everyone with the same ads, they can tailor their messages to different groups.
• Boost sales: By understanding customer behavior, they can offer the right promotions to the right people.
• Keep customers happy: This is the ultimate goal! By providing personalized experiences, Zomato can turn one-time buyers into loyal fans.

Why This Matters to You (Potential Employer or Future Coworker)

This project shows I can:

• Take raw data and turn it into something meaningful: I'm not just about numbers; I'm about insights.
• Communicate complex stuff in a way that anyone can understand: No jargon, just clear and concise info.
• Provide actionable recommendations: I don't just identify problems; I offer solutions.

I'm excited to keep growing as a BI Analyst and tackle even more interesting challenges. As someone who tutors BI Analyst skills at TripleTen, I am continuously honing these skills. If you're looking for someone who's passionate about data and eager to learn, let's chat!

Want to Learn More?

If you’re curious about TripleTen or data analysis, I’ve got a link that will lead you to a recruiter for further information and a DISCOUNT!

TripleTen: An online coding boot camp that enables people with busy lives to make the transition into tech. My Discount Link

I hope this gives you a fun and insightful peek into my data adventure!

Diving Deep into the Shopify App Store 🕵️‍♀️📊

In today's digital age, e-commerce platforms like Shopify have revolutionized how businesses operate. But with thousands of apps available in the Shopify App Store, how can developers ensure their app stands out and achieves success? 🤔 That's the question I tackled in my latest project for the TripleTen Business Intelligence Analytics Program.

My Mission: Uncover the Keys to App Store Success 🔑

This project challenged me to analyze data scraped from the Shopify App Store to understand the app landscape and identify the factors that contribute to an app's popularity and positive reception. I utilized my Power BI skills to transform raw data into interactive dashboards and visualizations, revealing key insights.

Data Wrangling and Exploration 🧹

Data Wrangling and Exploration 🧹 The project began with a raw Excel file containing information about various aspects of Shopify apps, including app details, categories, and user reviews. My first step was to familiarize myself with the data, clean any inconsistencies, and prepare it for analysis in Power BI. This involved joining different tables and ensuring data accuracy.

Visualizing the App Landscape 📊

With the data prepped, I harnessed the power of Power BI to create interactive dashboards. I focused on three key areas:

1. App Store Landscape: I used KPI cards and charts to visualize the overall distribution of apps, their categories, and their ratings. This provided a comprehensive overview of the app marketplace.
2. Review Analysis: I delved into the review data, using visualizations to understand user sentiment, identify trends in feedback, and analyze the impact of developer responses on app ratings.
3. Developer Performance: I analyzed app developers based on various metrics, such as average ratings, number of reviews, and responsiveness to user feedback. This allowed me to identify top-performing developers and understand their strategies.

Key Findings 🗝️

My analysis revealed several interesting insights:

• New apps tend to receive more ratings early on, suggesting the importance of initial impressions.
• Most apps are rated favorably, indicating a generally positive user experience in the Shopify App Store.
• Developer responsiveness significantly impacts app ratings, with higher ratings for apps where developers actively engage with user reviews.
• Reviews marked as helpful by other users tend to have higher ratings, highlighting the importance of community feedback.

Insights for App Developers 💡

Based on my findings, I was able to provide valuable recommendations for Shopify app developers:

• Prioritize early engagement with users to gather initial feedback and build a positive reputation.
• Actively respond to user reviews, demonstrating a commitment to customer satisfaction and continuous improvement.
• Focus on creating high-quality apps that address user needs and provide a positive experience.
• Encourage users to provide feedback and mark reviews as helpful to build community trust.

This project was a fantastic opportunity to apply my Power BI skills to a real-world dataset and gain insights into the dynamics of the Shopify App Store. By transforming raw data into compelling visualizations, I was able to uncover valuable insights and provide actionable recommendations for app developers.

Want to Learn More?

If you’re curious about Power BI or data analysis, I’ve got some resources for you:

• TripleTen: An online coding bootcamp that enables people with busy lives to make the transition into tech. My Discount Link
• Microsoft Learn: Self directed learning from the source company
• Alex The Analyst: Youtube Playlist. (Check out his other playlists too!)

I hope this gives you a fun and insightful peek into my data adventure!

Superstore's Return Problem

In my previous project, I acted as a data consultant for Superstore, analyzing their overall sales and profitability. One major issue I uncovered was an alarmingly high rate of product returns. So, I decided to take a closer look, focusing specifically on returns and using Tableau to tell a compelling data story for Superstore's CEO. This was my fifth project in the TripleTen Business Intelligence Analytics program, and it was all about honing my skills in creating impactful dashboards and presentations.

From Data to Narrative - The Power of Storytelling

This time, my goal wasn't just to present numbers; it was to craft a narrative that would resonate with the CEO and drive action. I wanted to answer the crucial questions: Why are customers returning so many products, and what can Superstore do about it?

I started with the same dataset as before—a spreadsheet containing detailed information on orders and returns. After joining the relevant data, I dove into the analysis, using Tableau to visualize the data from different angles.

Uncovering the Return Culprits

Through my visualizations, several key factors contributing to the high return rates emerged:

• Problem Customers: I discovered several customers with exceptionally high return rates, some even returning 100% of their purchases. This suggested potential issues with customer expectations, product suitability, or even potential fraud.
• Problem Products: Similarly, certain products were returned at an unusually high rate, indicating potential problems with product quality, descriptions, or sizing information.
• Location, Location, Location: Geographic analysis revealed that certain states, particularly Utah, had significantly higher return rates than others. This hinted at possible regional differences in customer preferences, shipping issues, or other location-specific factors.
• Seasonal Spikes: I also found that returns spiked in August and September, following a period of increased purchases. This suggested a connection between promotional periods or seasonal buying habits and returns.

Recommendations for a Return-Free Future (Almost!)

Based on these findings, I presented the CEO with the following recommendations, packaged in a clear and compelling Tableau story:

• Targeted Employee Training: I suggested providing additional training to the technology department to better assist customers in selecting the right products. This could involve improving product knowledge, providing better online resources, or offering personalized recommendations.
• Customer Outreach: For customers with 100% return rates, I recommended direct outreach to understand the reasons behind their returns. This feedback could provide valuable insights into systemic issues and inform necessary changes to the website, shipping processes, or other areas.
• Immediate Action on Problem Products: As a triage measure, I recommended immediately halting sales of the products with consistently high return rates. This would stop the bleeding and allow time for a more thorough investigation.
• Proactive Product Information Improvements: I suggested a focused effort to improve product information, including more detailed descriptions, accurate size guides, and encouraging customer reviews through social media collaborations. This would help customers make more informed purchasing decisions and reduce the likelihood of returns.
• Policy and Promotion Adjustments: Finally, I recommended considering stricter return policies or implementing return fees for purchases made in problematic states. I also suggested adjusting promotional strategies, perhaps shifting from discounts to value-added offers like free shipping, especially during peak seasons.

The Power of Data Storytelling

This project was a fantastic opportunity to showcase not just my data analysis skills but also my ability to communicate complex information effectively through data storytelling. By creating a compelling narrative with clear visualizations, I aimed to provide Superstore's CEO with the insights needed to make informed decisions and address their return problem.

Want to see the Data Story? You can explore the interactive dashboards and the full Tableau Story I created on Tableau Public by clicking the image below:

Want to Learn More?

If you’re curious about Tableau or data analysis, I’ve got some resources for you:

• TripleTen: An online coding bootcamp that enables people with busy lives to make the transition into tech. My Discount Link
• Tableau's Official Learning Resource: Self directed learning
• Andy Kriebel (VizWiz): YouTube Channel by a Tableau Zen Master

I hope this gives you a fun and insightful peek into my data adventure!

Superstore on the Brink - My Data-Driven Rescue Mission!

Picture this: a bustling store, shelves overflowing with products, but the cash registers are singing a sad, empty tune. That was the predicament facing Superstore in my latest project for the TripleTen Business Intelligence Analytics Program. I stepped into the role of a data-savvy consultant, armed with the power of Tableau to diagnose their financial woes and prescribe a data-driven cure.

From Spreadsheet Chaos to Visual Clarity

My adventure began with a massive Excel spreadsheet—a data goldmine, but in its raw form, it was a bit like a tangled ball of yarn. My first order of business was to untangle that yarn, joining different datasets and organizing the information into a usable format. Think of it as preparing the ingredients for a delicious data dish!

Tableau Time - Turning Numbers into Narratives

With the data prepped and ready, I unleashed the magic of Tableau, a fantastic data visualization tool. This is where the real fun began! I transformed rows and columns of numbers into engaging charts, graphs, and interactive dashboards that told a compelling story. Here's what I uncovered:

• Profit Hotspots and Cold Zones: I zoomed in on different product categories and regions to pinpoint where Superstore was making money and where they were losing it. It turned out that Office Supplies and Technology were the heroes in the West Region, while Furniture was dragging down profits in the Central Region. Some product subcategories, like Copiers, Phones, and Accessories, were consistent winners, while others, like Tables, Bookcases, and Supplies, were consistently underperforming. It was like discovering hidden treasure and uncovering hidden traps!
• Advertising Alchemy: I analyzed sales trends over time to identify the sweet spots for advertising. Turns out, timing is everything! I recommended focusing advertising efforts in Indiana during October (spooky good deals!), Vermont during November (pre-holiday shopping frenzy!), and Washington during March (spring cleaning bonanza!).
• The Return of the Returns: This was the biggest "uh-oh" moment of the analysis. Product returns were a major drain on profits, with some products seeing return rates as high as 100%! Yikes! I also identified some customers who seemed to be professional returners. Time to investigate!

My Data-Driven Prescription for Profitability

Based on my data-driven detective work, I presented Superstore with a set of recommendations to get them back in the black:

• Fuel the Fire: Keep investing in the profitable product categories and regions. Don't fix what ain't broken!
• Address the Duds: For the loss-generating areas, it's time for some serious soul-searching. Are the products priced wrong? Is there a quality issue? Should they be discontinued altogether? It's time to make some tough decisions.
• Smart Advertising, Not Just More Advertising: Targeted campaigns are key! By focusing on the right time and the right place, Superstore can get more bang for their advertising buck.
• Tame the Returns: High return rates are a red flag. Investigating the root causes and implementing strategies to reduce returns is essential. Maybe it's time for clearer product descriptions, better quality control, or a review of the return policy.

This project was a fantastic opportunity to see how data visualization can transform a struggling business. By turning raw data into clear and engaging visuals, I gave Superstore the insights they needed to chart a course toward a profitable future.

Want to see the visual evidence? You can explore the interactive dashboards I created on Tableau Public by clicking the image below:

Want to Learn More?

If you’re curious about Tableau or data analysis, I’ve got some resources for you:

• TripleTen: An online coding bootcamp that enables people with busy lives to make the transition into tech. My Discount Link
• Tableau's Official Learning Resource: Self directed learning
• Andy Kriebel (VizWiz): YouTube Channel by a Tableau Zen Master

I hope this gives you a fun and insightful peek into my data adventure!

Cracking the E-Commerce Code: My Adventure in Data Decoding

Ever wonder what goes on behind the scenes at your favorite online stores? How do they know what you like, what you might buy next, and why you sometimes abandon your shopping cart at the last minute (we've all been there!)? Well, in my latest project for the TripleTen Business Intelligence Analytics program, I got to play detective and uncover these secrets using the power of data analysis!

This wasn't just any project—it was a deep dive into the world of e-commerce, where I transformed raw website activity logs into juicy business insights. Think of it as turning a jumbled mess of puzzle pieces into a clear picture of customer behavior.

From Data Jungle to Data Playground

Imagine a massive spreadsheet filled with every click, every view, and every purchase on an e-commerce website. That was my starting point—a data jungle! My first task was to tame this wilderness, filtering, cleaning, and organizing the data into something usable. It was like going on a data safari, clearing paths and labeling everything along the way.

Building the Customer Journey: Funnels and Repeat Customers

With the data tamed, I built two key tools to understand the customer journey:

• The Conversion Funnel - The Buyer's Adventure: This funnel tracks the steps a customer takes from discovering a product to actually buying it. It's like following a breadcrumb trail! My analysis revealed a bit of a bottleneck: while many people added items to their carts (a respectable 29% conversion rate), only 10% actually completed the purchase. This was a big "aha!" moment, suggesting there might be issues with the checkout process or product pages that need fixing. Maybe the shipping costs were a surprise, or the checkout process was too complicated. Time to investigate!
• Retention Rates - Will They Be Back?: This metric tells you how many customers become repeat buyers. I focused on a cohort of customers who made their first purchase in September 2020. The results? Not so great. After just one month, only 13% were still buying, and by month four, it was down to a measly 3%. Ouch! This clearly showed a need for strategies to keep customers coming back for more.

My Data-Driven Detective Work: Recommendations for Success

Based on my data sleuthing, I presented the e-commerce company with some key recommendations:

• Conquer Cart Abandonment: Those abandoned carts are like lost treasure! By figuring out why people are leaving without buying, the company can make changes to the checkout process and win back those potential sales.
• Woo Repeat Customers: Happy customers come back for more! Strategies like personalized emails, loyalty programs, and top-notch customer support can turn one-time buyers into loyal fans.
• Ask the Customers!: Sometimes, the best way to find out what's wrong is to just ask! Gathering customer feedback through surveys or reviews can provide invaluable insights.
• Keep Analyzing!: Data analysis is an ongoing adventure. Regularly checking the data keeps the company on track and helps it spot new opportunities and challenges.

This project was a blast! I got to use my business intelligence skills to turn raw data into a compelling story and provide real, actionable advice for a business. It's like being a data superhero, using numbers to save the day!

Want to see the evidence?

You can check out a PDF with screenshots of all my work PDF HERE

Want to Learn More?

If you’re curious about business analysis, I’ve got a resource for you:

• TripleTen: An online coding boot camp that enables people with busy lives to make the transition into tech. My Discount Link

I hope this gives you a fun and insightful peek into my data adventure!

My SQL Adventure with the Zuber Database:

Ever wondered what makes a taxi driver's day? Is it a sunny Saturday or a rainy Tuesday? Which company reigns supreme in the Windy City? As part of my Business Intelligence Analytics training with TripleTen, I dove headfirst into a project that aimed to answer these very questions, using the magical powers of SQL!

Think of SQL as the secret language you use to chat with databases. It's how you ask them questions and get them to spill their secrets. In this case, our database was a treasure trove of Chicago taxi ride data – think of it as a digital diary of every trip, from pickup to drop-off.

Why did I embark on this data quest?

Imagine you're running a ride-sharing app. Wouldn't it be awesome to know what customers want, what affects their travel plans, and how your competitors are doing? That's exactly what this project was about! By digging into the data, we could uncover hidden patterns and trends that could help companies like Uber or Lyft make smarter decisions.

My Mission:

• Part 1: The Great Taxi Census: I used SQL to count taxi rides for different companies during specific periods. It was like conducting a census, but for taxis! I wanted to know who was the most popular kid on the block. By grouping and sorting the data, I could easily see which companies were racking up the rides. It was a bit like a taxi popularity contest!
• Part 2: Loop to O'Hare - The Weather Challenge: This part was all about figuring out if Mother Nature had a say in how long it took to get from the Loop to O'Hare. I played detective, finding the neighborhood IDs for these locations and then checking the weather records for each hour. I even created a simple weather rating system: "Good" for no rain or storms and "Bad" for those less-than-ideal conditions. Then, I pulled up all the Saturday rides between these two points, noting the weather and the ride duration. This allowed me to see if a rainy Saturday meant a longer trip to the airport.

The Tools of the Trade (aka SQL Commands):

I used a few key SQL commands that acted like my trusty tools:

• SELECT: To pick the specific information I wanted (like ride counts or weather conditions).
• WHERE: To filter the data and focus on specific criteria (like rides on Saturdays).
• GROUP BY: To organize the data into categories (like by company name).
• ORDER BY: To sort the results (like from most rides to least).

Click the image to see all of the query's

Why This Matters (Beyond Just Numbers):

This project wasn't just about crunching numbers. It was about using data to tell a story. By analyzing the data, we could understand:

• Customer Preferences: Which taxi companies are the most popular? When do people tend to travel?
• External Influences: How does the weather affect travel time?
• Competitive Landscape: How are different companies performing relative to each other?

Want to Learn More?

If you’re curious about SQL or data analysis, I’ve got some resources for you:

• TripleTen: An online coding bootcamp that enables people with busy lives to make the transition into tech. My Discount Link
• W3Schools: Self directed learning
• Khan Academy: Self directed learning

I hope this gives you a fun and insightful peek into my data adventure!