Introduction

As day by day Tesla Cars are getting popular because of being fully electrical cars causing less pollution and having advanced AI features such as Auto-Driving, I thought to dive deeper in this topic and understand what are the trends of the Tesla Cars. The research questions I asked to myself were,

1. What are the things users need to know before buying a car?

2.What is the information Showroom owners/Sales person needs to know while guiding the users to make their car selection process easier ?

Both these questions helped me to understand what type of information I should be focusing on and what all visualizations I should be delivering to users to help them in car buying process.

Data

The dataset I used for this visualization is from Kaggle which is Tesla Used cars The dataset consists of approximately 2000 observational rows and encompasses 14 columns, combining both quantitative and qualitative variables. The data had columns like car models, locations, color, past accident/damage history, price, odometer readings etc. Most of the columns were qualitative and hence it was little challenging to make graphs/ visualizations through them.

Methodology

I have started with analyzing the data. The data is related to used Tesla Cars of 4 different models. The idea behind this visualization was to help Tesla showrooms understand the trends, what they have in inventory at the same time it is useful for users as they can have comparison of various aspects of car.

To start with my visualization, I had to do certain basic filtration’s I have done these using open refine. Certain observational cells were meaning similar but named differently and hence I had to modify it a little bit. Once I was done with that, I exported my data into Tableau Public and started my visualizations on various tableau sheets. To make my visualizations effective, I needed to tweak certain parameters like filter, titles, colors and color themes etc. I had to look for certain tableau visualization tutorials to form diagram like donuts etc. Later I combined those visualization images on Figma to form a poster out of it.

To conduct my user research, I conducted 3 user interview before starting my visualization journey and two peer reviews within the visualization process. The recruitment criteria for users was simple which was they should be car owner/interested in cars, have recently bought a car or planning to buy one and hence exactly understand what a new car buyer looks at before buying any car. The individual of course have to be adults and should be living in USA.

The two users were the potential car buyers who had brought cars in past and planning to buy a new car as well as one was the car showroom owner. I asked the following questions, such as

1.Which aspects do you focus on while buying a car?

2.What would you like to see if you were buying a Tesla Car?

3.Have you anytime bought a second hand car or are you interested in buying one? What do you look in the second had car before buying it?

To showroom owner,

1.what information you need to guide users which car they should buy?

Summary of Key Insight From user research

The users wish to see visualizations which are easy to read and they could quickly understand.

1.Users wished to see simple diagram such as bar graphs or tree maps. Color coding or using distinct symbols within diagram were helpful to make it more readable as well as aesthetic.

2.Users wanted to know about the current trends before they make such huge investments at the same time they wanted to know about the availability of cars at the same time.

3.The Price aspect along with color of car, location, model of car and its previous history such as damage happed and Odometer readings i.e. distance traveled by car were few of the important factors users wish to see while purchasing the car.

4.The showroom owners also needs to know about the trends of used cars, how many cars they have in inventory and at which locations they are available. They wish to know about color and model preferences by users at the same time its price range and past performance in order to resale it to users.

Results

After talking to users and making various visualizations, I went ahead with simple diagrams by color coding and labeling them.

The important aspect about any car company is there car models and their prices. Users were keen to know this information and hence it a column graph was made to understand various used Tesla Car models from their inventory and its average price in USD.

From the column graph above, we can clearly understand that Model X would be the expensive one from all four models vs the Model 3 is the cheapest in given 4 different Tesla car models. Users can easily make decision related to which model they should aim for keeping in mind their own budget as well as Tesla showroom owners or sales website can also use this graph to guide users after knowing their budget.

As soon as we understand the car price ranges for various models, the next question arise is related to their performance. To understand their past performance I summed it up into 3 aspects, average distance traveled by the each car model and previous damage or accidents happed to them. At the same time I considered here the location aspect as well to understand the geographical challenges the car must have faced.

The above area diagram help users understand the general trends of accident history of various Tesla Car models. The color green shows no accident history/damage vs color red shows previously repaired car models. Most of Tesla models have vary rare accident cases but the model with almost null accident cases even though it has overall highest odometer reading(i.e. distance traveled by the car) was Model X and the model with most no of accident cases was Model S. To understand in which geographical region the car is performing the best, we also have location parameter at the bottom, which shows the Model X was most traveled in Illinois State, which adds that the car can perform well in snowy weather as well with minimum to no accident cases.

This also justifies the above column graph which showcase the Model X to be the most Expensive.

To understand certain other trends of Tesla Car Models. I have made a map of where Tesla Cars are maximum used and no wonder the California state tops the list.

The pie diagram below represents the similar information but the reason to use it is to understand proportionally which state is using more Tesla Cars.

The another important aspect for choosing a car that is identified by users is color and hence I have made a tree map to understand most popular color of Tesla model.

The following bar graph shows all four models with average price based on car color and no of available cars of that particular color. As reflected by earlier diagram, the popular color which was Pearl White has the highest Average price compared to others.

The following donut diagram represents most popular color in each model and available quantity.

To understand one more aspect was types of wheels provided by Tesla in each model type. This is shown by tree map and color coded by model. To understand the importance better I have also included average price in labels to make decision making process easier for users.

The following poster is final outcome of my project which would be useful for Tesla Showrooms of even to display on websites for users as well as sales person.

Reflections

I feel the overall visualization would be useful to both car buyers as well as showroom owners to understand various aspects. I could understand importance of color coding and grouping through the semester which I tried to reflect through this visualization. Certain aspects such as improving labeling and giving average prices where ever possible was the critic given to me by my peers.

I personally feel that the poster could have little better in terms of organization and representation of information.

References

“Discover | Tableau Public.” Accessed October 3, 2023. https://public.tableau.com/app/discover.

“Kaggle: Your Machine Learning and Data Science Community.” Accessed December 11, 2023. https://www.kaggle.com/.

The Tesla Used Cars Dataset https://www.kaggle.com/datasets/aravindrajpalepu/tesla-used-cars

Figma. “Figma.” Accessed December 11, 2023. https://www.figma.com.

Tesla. “Electric Cars, Solar & Clean Energy.” Accessed December 12, 2023. https://www.tesla.com/.