Today, fashion is a big, booming industry, and with the advent of online shopping, and seasonal discounts, people tend to follow “whatever is in”, be it heavily cushioned thick-soled running shoes or mismatched earrings. But, trends that pick up, no more hail from the Paris or Milan fashion walk. They can originate anywhere, ranging from an outrageous pop video, or a movie that broke the box-office. Almost no-one can predict from before, what will be a hit in the world of fashion, or can they? More and more fashion and blog-sites come up every day, containing tons of data about latest fashion, accessories, and more. Even data about online sales or popular products in fashion-based websites can be extracted.
Why do we need data to boost fashion anyway?
Currently, many companies like Myntra, are hiring more data scientists than designers. The aim? To know what will be a “hit” in the industry, before it becomes a “hit”. Why is that so important? Well, fashion is a costly affair and to make it cheaper, one has to do some cost-cutting, and the best way to do it is to reduce unsold stocks. Clothing and accessories industry has one of the highest unsold stock quantities. Thus, decreasing unsold stocks will help keep costs in a limit, and also make fashion more accessible.
Steps to integrate data into fashion-
So, the aim is to build models and predict fashion trends. But how to go about doing that? What are the steps? Well, roughly-
- Mine data from Fashion-based websites.
- Extract data about Fashion – products being sold on e-commerce sites.
- Arrange the data in a meaningful format so that a data modelling algorithm can be run on it.
- Make changes to the algorithm, based on reinforced learning to make it smarter, by trying to incorporate changes such that the algorithm will learn from its mistakes.
You see, it all begins with getting the data- mining raw data, cleaning it, and restructuring it, so that it can be used by your business or consumed easily. Only then, can you create your pie-charts and bar graphs as well as your machine learning models, as and when required.
How to go from talking about it to actually doing it?
Although web scraping can be done for obtaining fashion-related data, by writing your own programs, you might end up having to write separate scripts for each and every website you try to scrape, since each will be storing data in different formats and structures. It is better to approach a service provider, well known for its data scraping capabilities- because scraping involves a lot of know-how about security and protocols, along with the basics of web scraping. Also, websites keep changing the way they store data, or the way they protect data, with time. So, it is better that you allow an entire team to look after the web scraping needs to boost your fashion business, instead of trying to make it a fast and small affair. This is because in today’s world, the company that has mined the most data, and can read it correctly, will be able to understand what the customer actually wants and emerge the clear winner in that sector.
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