Hover over the visualizations for more information or click them to read feedback about the individual pieces at the bottom. For best experience, view in Tableau here.
At the end of each quarter, I complete an in-depth analysis of my capsule wardrobe. I’ve found this to be a helpful way to evaluate each piece to determine if it needs some TLC or if it even belongs in my closet anymore. Additionally, it’s a good way to identify trends and provide more insight into my habits and personal style evolution. This is the first time I’ve built data visualizations to review this data and also the first time I’ve used Net Promoter Score in this way.
What is Net Promoter Score or NPS?
It’s a common metric used to measure how likely a person would be to recommend a product, brand, experience. You’ve probably completed an NPS survey a few times in your life without even realizing it!
It looks something like this:
How likely would you be to recommend [a product, e.g. Gold Zipper] to a friend? Please respond on a scale of 0-10.
Why did you choose that response?
Once you have your survey data, classify the responses into 3 categories:
Detractors (0-6)
Passives (7-8)
Promoters (9-10)
It might seem a bit odd that people who responded to the survey with a 7 or 8 would be considered “Passive” because that seems pretty high on the scale. Actually what it means is that these people may have had a good experience, but not so good that they would actively recommend or “promote” it to their friends.
Once you have your data categorized, calculate the NPS:
% of Promoters - % of Detractors
The score can range between -100, which would mean that every response was between a 0 or 6 (e.g. a Detractor) and +100, meaning every response was between a 9 and 10 (e.g. a Promoter).
How I used NPS to analyze my Capsule Wardrobe data:
At the end of the quarter, I review each active piece in my Capsule Wardrobe on the following criteria:
Style
Fit
Condition
Net Promoter Score (e.g. “how likely would I be to recommend this piece to a friend?”)
Open Text Feedback
Retention Decision
Take a look at a quick example below:
You’ll notice that I also have a column called “csat" which stands for Customer Satisfaction and rates the overall satisfaction with a product on a scale of 1-5. This is typically good for a quick “thumbs-up/thumbs-down” review of an item. I’m not currently using this data, but do collect it just in case I want to use it in the future.
For the sake of this example, let’s assume that the above screenshot is my entire wardrobe, that would mean:
Total Reviews in Wardrobe: 18
Promoters (9-10): 16
Passives (7-8): 2
Detractors (0-6): 0
% of Promoters: 16/18 or 89%
% of Detractors: 0/18 or 0%
% of Promoters - % of Detractors = 89
NPS: 89
My favorite reason for using NPS is the ability to automatically sort open text, qualitative data. By doing this, it makes it easy to identify trends and produce actionable recommendations - even with your own wardrobe!
Click on the images above to see how you can view feedback on pieces grouped by NPS classification (e.g. Detractors, Passives, and Promoters).
What the data revealed:
Pieces that were purchased specifically for work, the gym or on sale are liked less.
There’s a clear divide between pieces added to my wardrobe for a specific purpose vs. pieces added because they were “cute”. There’s a misconception that capsule wardrobes need to be basic to be ultra efficient when in reality your capsule wardrobe needs to be sustainable to be ultra efficient. By sustainable, I mean that it needs to be something you’re not turning over season-to-season and something you can stick with, not just a fad that leads you relapsing to a overstuffed closet. By ultra efficient, I mean that pieces are being worn regularly, but not being over-used which leads to premature deterioration.
So, why the divide between pieces purchased with a functional vs. aesthetic purpose? Looking at the data, it comes down to timing. The visualization “NPS by Year Added” shows two significant dips, one in 2014 and one in 2017 which are the years I started working professionally and exercising regularly. At the time that I added these “functional" pieces to my wardrobe I didn’t know how to maintain my personal style for pieces needed for work and the gym. I had no idea, for example, that you could get a lightweight, breathable athletic turtleneck - I’d only seen tank tops and basic t-shirt styles. I’d also never seen someone with my style in a professional office setting and so instead I bought pieces that fit the setting and not me, which took up space and were hardly worn.
Recommendation: Get to you know style. Pay attention to what you’re comfortable in, what you’re drawn to, what fits for your day-to-day lifestyle and carry that over to every aspect of your clothed life. Not only will it result in you being more satisfied with what you wear, it will also make your closet more efficient. One of the early mistakes I made was over-wearing a few pieces to the point of pre-mature deterioration because my closet was too lean and I only liked a few of my pieces. Refining your closet to something you love isn’t vain, it’s practical and efficient.
Do you see any insights?
Explore the dashboard and see if you can find any interesting trends or insights and share them in the comments section below! If you’re interested in conducting a similar capsule wardrobe analysis or data visualizations - check out this video I made walking through my process, here.
What is Wardrobe Science?
This year, I'm tracking what I wear so I can build a dataset of real-world capsule wardrobe data to analyze. I was inspired to start this project after reading an article by Torrence Boone from Google, that tracked fashion trends by Google searches. Read his article here.
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