How did Covid-19 impact on Fashion Industry?
The pandemic has caused the deepest recession since WWII, hitting demand and supply simultaneously with a different force depending on the industry (Hans‐Helmut Kotz, 2020).
With so many industries and individuals impacted by Covid-19, it would be downright strange if the fashion world didn’t find itself among them. You see how brutal it was by reading just-style’s Timeline — How Covid-19 is impacting the global apparel industry.
Today, I’d like to see how Covid-19 impacted the Fashion industry with an actual dataset.
The dataset is the eCommerce purchase records from a medium-size jewelry retailer. The dataset contains 113,302 e-commerce purchase records from November 2018 to November 2020. As it contains all the records before and after the covid-19, I could check how real-world sales fluctuated during the Covid-19.
Here’s the plot before I actually divide them on before/ after corona.
I can see some sales boost in holiday seasons, especially in summer and winter. But, this data doesn’t seem very juicy and less consistent with real-world sales changes by months(Usually there’s a bigger boost on holiday seasons and it’s irregular that the sales in February hit that high). I found it because this plot mixed up before&after Covid-19 even though they’re including very different aspects.
So, let’s see the different aspects of before/after Covid-19 sales.
See how different they are!
A general pattern could be found in monthly sales prior to Corona, such as a high sales boom at end of the year, due to all the annual sales, parties, plans, and holiday gifts. It is also general that sales slightly increase in July or August because of the mid-season sale and holiday trips. (Fyi, Sales in the first half appear to be low because this record does not include sales prior to May 2018).
But The fluctuation after the Covid-19 seemed very irregular for me. It seemed like this must’ve affected Covid situation. I wanted to compare this fluctuation with the actual number of Covid cases, but I didn’t know the reason where this data exactly based on. However, I still had a very small clue that this marketing company is based in Northern America, so I started to compare Canada & USA’s annual Covid cases per month with this fluctuation.
And look what I found. The fluctuation showed perfect symmetry with the Canadian Covid-19 case.
Wow. Can you believe this?
When there are higher cases, sales get lower immediately.
When there are lower cases, sales increase.
I view this tendency as a reason that fashion is a way of communication to express oneself to the outer world, but as there are fewer opportunities to go out, people reduced their spendings on fashion. Higher cases made people stay inside, which reduced the sales consequently.
The crisis hit worse on middle-size fashion brands. Polarization hasn’t been any worse than this, that some Luxury brands enjoyed an unprecedented boom. Christian Dior’s sales and operating profit in Korea surged 75.8% and 137.1% each. Valentino Korea’s sales also increased 367.1% compared to 2019. Besides, there’s been a new pattern that few customers have sprinted to some popular luxury stores, such as Chanel, immediately after the department stores open.
The middle size fashion brands should find their own way to survive in this polarization. Still, there are silver linings. This crisis may also have accelerated responses which can lead to positive outcomes, such as the increased power of e-Commerce. Indeed, many fashion companies have taken time during the crisis to focus on e-commerce, which provides broader consumer groups and a range of sales.
Soon, I’ll come up with my own strategy for this middle-size jewelry retail shop, including full EDA, insights, and machine-learning project drew from the insights.
You can see the full code from HERE.
Thanks for reading! Hit the heart if you enjoyed it, and comment if you have any inquiries about fashion data analysis, ideas, or questions! Hope you have a great day. :)
