Inside Ceramic Souls: A Data Analysis of Studio Activity
When the owner of Ceramic Souls, Julia Ducayet, approached me about solving some of their data headaches, I was happy to help a local Nashville business. This project was the first of a few I did for the local studio. My objective was to cross-reference to find membership and student patterns throughout their first year of business.
Ceramic Souls Studio, Nashville
About Ceramic Souls
Let me introduce you to Ceramic Souls, an imaginative 24/7 hour pottery studio headquartered in the vibrant East Nashville. Their network of over 50 members and 300 students fosters happiness, creativity, and passion.
Ceramic Souls’ mission is clear: to create a warm, creative connection for potters. Their business includes various classes, from beginner to intermediate, in handbuilding and wheel pottery. Additionally, Ceramic Souls offers membership tiers that cover a member’s allotted studio time and shelf storage. Since their inception in 2025, Ceramic Souls has been enthusiastic about providing Nashville locals with creative output built on a compassionate community, from beginners to seasoned artists.
The Challenge
Ceramic Souls’ finances already identified higher profitability through the peak summer and fall months.
However, Julia, owner of Ceramic Souls, had operational data on member and student activity but lacked a clear way to understand participation patterns, productivity levels, and use of studio resources. Her goal was to transform raw actual data into insights that could reveal engagement trends and inform studio management decisions.
This is where I step in, helping Ceramic Souls answer these questions to allow better business decisions.
Data Source
The data used in this analysis is from the customer relationship management (CRM) platform Kilnfire. This CRM records member and student data, including the pieces produced, production date, membership type, and the names of members and students.
Data Exploration
Step-by-step exploration of the data:
Step 1: Dataset Preparation & Cleaning Process
Before diving deep into the data, rigorous cleaning and preparation were necessary. Julia provided me with the data in a large Excel file with over 30 columns. I had to analyze each to decide what would be useful to me.
Raw Studio Dataset
Key Data Issues
•Inconsistent fields
•Missing values
•Unstructured notes
Cleaned Dataset
Step 1.1: Identifying Relevant Variables
I reviewed the raw dataset to find the fields required for analysis. Non-essential columns such as “Piece Price” and “Pickup ID” were removed to simplify the dataset and focus on the variables relevant to production activity: Full Name, Created date, and Quantity produced.
Step 1.2: Classifying Member vs. Student Activity
The original dataset did not distinguish between members and students. Using a reference list of member and student names, I applied an IF(COUNTIF()) logic to classify each record as either Member or Student. This allowed analysis of participation patterns across the two groups.
Step 1.3: Assigning Membership Tiers
Members belonged to different membership tiers based on payment levels. To incorporate this information into the dataset, I used XLOOKUP to match each member’s name with their corresponding membership type. This filtering allowed further analysis of studio activity across membership tiers.
Step 1.4: Structuring the Dataset for Analysis
After cleaning and classifying the records, I converted the dataset into an Excel table. Structuring the data in this format allowed efficient sorting, filtering, and aggregation of studio activity, and prepared the dataset for visualization and analysis.
Step 1.5: Data Validation
After structuring the dataset, I performed validation checks to ensure the cleaning process preserved accurate totals. I verified that quantities, record counts, and classifications aligned with the original dataset before beginning analyses.
Step 2: Customer Activity Exploration
With the dataset cleaned and ready, I began exploring the customer behavior to uncover insights.
Step 2.1: Customer Activity Analysis
I began by visualizing the distribution of customer types. Understanding the balance between students’ and Members’ piece quantities is an important starting point.
Studio production is driven by students, rather than members, accounting for roughly 72% of total pieces produced.
Step 2.3: Production by Membership Tier
To better understand what members contributed to the business, I organized the pieces by membership type. The numbers atop the bars indicate the number of members of the specific membership type.
Members in the Clay Connoisseur – Founding Member and Clay Connoisseur tiers produce significantly more pieces per person than other membership types. Those tiers attract the most engaged studio users.
Step 2.4: Production by Member
I continued by examining the number of pieces per member. Unsurprisingly, the two most active members are associated with the two highest-producing tiers.
Identifying these high-engagement members helped highlight the studio’s core user base and provided insights into member retention and loyalty.
Step 2.5: Student Participation Trends
Examining the frequency and quantity of students, I noticed they followed a similar trend. Busier months, like November and July, and the in-between months produced high numbers of students. Occasionally, the quantity exceeds the number of students for that month.
Step 2.2: Production by Month
To gain a broad perspective, I analyzed the overall trend in the distribution of member and student pieces over the first year of business.
Production activity peaks during mid-summer and early fall, suggesting seasonal increases in studio participation. Both members and students follow a similar pattern, though students consistently produce more work.
Building an Interactive Tableau Dashboard
Now, let’s explore the process of remodeling these insights into an interactive Tableau dashboard:
Step 1: Data Connection
I began by connecting to the cleaned, processed, and analyzed data, which was exported to the Tableau server as a CSV file.
Step 2: Selecting Visualizations
Next, I selected the most effective visuals to answer the questions and convey key insights from the analysis.
Step 3: Creating the Dashboard
Once I had selected and edited the individual visualizations, I started building the dashboard. I arranged each visualization on the dashboard canvas to ensure the narrative is coherent.
Step 4: Publishing
After completing the dashboard, I published it to Tableau Public to make it accessible to internet users.
Step 5: Sharing the Dashboard
Now, I’m excited to share the Tableau Dashboard with you. You can interact with it by clicking the following link: Ceramic Souls Executive Overview Dashboard.
Key Insights
The overall data exploration journey created valuable insights into customer behavior and patterns. Here are the key findings:
1. Students Drive Most Studio Activity – Students account for approximately 73% of total production. The fact that Ceramic Souls’ class offerings are valuable, both for attracting customers and sustaining studio activity.
2. Production Is Concentrated Among a Few Members – The studio may benefit from adjusting the payment plan of the highly concentrated membership tiers among the most concentrated members
3. Production Fluctuates by Month – Studio production saw a jump in the late-summer and early-fall months. These fluctuations may be influenced by: seasonal demand, class schedules, holidays, or travel patterns. Understanding these trends can help the studio schedule class load, staffing, and kiln usage more efficiently during peak periods.
4. Strong Overall Studio Output – Ceramic Souls shows strong overall production activity. 17,830 pieces produced in under a year, reflecting consistent use of the studio by members and students. This level of activity suggests that the studio has built a healthy and active community of potters.