Portfolio: Data Analyst, Data Visualization, Data Storytelling
Darrell Wolfe, Topos Creative, LLC
I simplify complicated geeky things & rebuild systems of operation for efficiency and automation.
Data Analyst & Business Intelligence | Power BI & Tableau | SQL, Excel, Power Query, Power Pivot
I worked in banking, sales, and call centers for 15 years, including Wells Fargo, Idaho Central Credit Union (ICCU), and Alliance Data (now Bread Financial). Throughout my career, regardless of the position, I found myself the resident Excel expert for every team.
When I transitioned to the Assessor's Office at Kootenai County, the same held true. However, it was then that I realized (1) I really do enjoy Excel and Database Deep Diving; and (2) I had a skillset that was unique. I thought "everyone knows Excel, right?"
While I chased down this passion, I learned about Data Analytics as a profession, and started to make my pivot. Eventually, I pivoted to Data Analyst for the county, and started my work on the Google Data Analytics Certification.
I enjoy finding new data puzzles to solve, developing new skills when needed, and chasing down the answers. My final years at Wells Fargo, as a Technical Writer and Business Documentation specialist, also helped me learn how important it is to document your work processes and finding. Case in point, check out my Learn_R user guide and my capstone project: Cyclistic Membership Campaign | A Coursera Capstone Case-Study, Track_1,Case_study_1, Cyclistic-bike-share-analysis.
Connect with me work on:
My Project Highlights
In this fictitious example used as a Case Study, Lily Moreno (Director of Marketing) has asked my marketing analytics team to help analyze historical data for a marketing campaign. The Cyclistic finance analysts have already concluded that members are more profitable than casual riders and the Moreno is convinced that the company’s future success depends on maximizing memberships.
Moreno is asking three questions of her teams to “guide the future marketing program”:
- How do annual members and casual riders use Cyclistic bikes differently?
- Why would casual riders buy Cyclistic annual memberships?
- How can Cyclistic use digital media to influence casual riders to become members?
- Moreno has assigned me the first question to answer: “How do annual members and casual riders use Cyclistic bikes differently?”
She’s asked for a detailed report clearly showing my findings and recommendations for point one.
Marketing Team Goal: Design marketing strategies aimed at converting casual riders into annual members. In order to do that, however, the marketing analyst team needs to better understand how annual members and casual riders differ, why casual riders would buy a membership, and how digital media could affect their marketing tactics. Moreno and her team are interested in analyzing the Cyclistic historical bike trip data to identify trends.
Fictitious Board Presentation:
The following would be presented in Tableau (Link Here), starting from "The Business Task" and working right.
Ladies and Gentlemen of the Board, the Finance Analysts have identified that annual members are more profitable than casual riders, our marketing team has taken the next step to analyze our historical data in order to develop a marketing strategy to maximize memberships.
Director Moreno has identified three key components:
- Investigating how annual members and casual riders utilize Cyclistic bikes differently.
- Unearthing the reasons that might motivate casual riders to invest in Cyclistic annual memberships.
- Exploring innovative ways to leverage digital media, aiming to convert casual riders into committed members.
With these actions, we intend to shape marketing strategies that resonate with our riders' preferences and patterns, increasing memberships enhancing profitability.
My task today is to address question number 1.
"How do members and casual riders use Cyclistic differently?"
Summarizing the findings
The data for Cyclistic shows that there were over 8 million individual rides between January 2022 and July 2023, centering in in Chicago IL. Of that total, 60.50% were Member rides, riding 4.9 million rides over casual user's 3.2 million rides.
Distance and time
Casual riders go farther given individual ride instances; however, Members ride farther in total miles cumulatively.
Members mostly use stations inside the city but in affluent areas; whereas, casual riders use them near tourist locations, specifically near the water in Chicago.
Date and time
Members and casual riders follow a similar pattern of a slow increase in the morning, maxing at mid-day and decrease into the evening. However, Members are significantly more active than casual members between 6a and 8a.
Members ride more often on weekdays vs Casual riders more often on weekends.
Members ride more often than Casual riders but for shorter distances, and their highest usage is in
Summer(June, July, and August), with a taper effect on either side, in Spring (May) and in Fall (September). That being said, members do continue to use the service in winter at a higher rate than casual members.
Extrapolating from these findings:
If Casual riders ride longer rides but Member riders make up far more total distance, then Member riders are riding far more often but for shorter distances.
This means, we can target casual riders who fit the pattern of frequent shorter rides as a prime target for the campaign.
We should focus the ad campaign on the inner city regions, specifically in higher income areas.
Later, if we find this method of targeting casual riders who fit member patterns effective, we could expand the campaign to target new station areas that fit these patterns, or even develop new stations not currently used by members, but this would be a premature investment at this stage.
2. CO2 (kt) Worldwide (*Coursera Practice Data, Wolfe, Darrell)
During the Data Analytics courses, I had the opportunity to play with the CO2 dataset. I found it fascinating that data can be used to tell a story; however, it can be used to mislead or paint a misleading picture.
On one hand, CO2 Kilotons per Capita would make it seem the USA was doing less damage than some other nations.
However, when the data is sliced by CO2 Kilotons per Country, it is clear that the US is producing more total CO2 than any other country.
These tell different stories, and it helps to see these side-by-side.
Tableau Public can be used as a tool, not just as as data visualization "report". The Assessor's Office needed a tool that property owners could use to self-serve lookup their property and see if their Homeowner's Exemption and/or Timber Exemption were in place. We first built the tool in Power BI but later decided to transition to Tableau Public. The Tableau Public tool is then embedded in the Assessor's Office website, to make it feel cohesively a part of the website (as opposed to a taking the user to a separate site). A property owners can use the county GIS site to look up their AIN and use this tool.
Examples: 175217 (no/no), 107763(yes/no), 147362 (no/yes)
