Technology & Business

Applications Of Machine Learning In The Fashion Industry

Oct 20

10 min read

Following the devastating economic effects of the COVID-19 pandemic, the fashion industry is starting to pick up the pieces and chart a new course. Machine learning and artificial intelligence (AI) have already gained traction in the industry, and in order to compete in today’s increasingly competitive environment, fashion retailers must look inwards to identify potential efficiency gaps.

New spending habits in a post-COVID-19 world.

Digital Transformation.

Impact of Social Media on Purchase Decisions.

Decreasing customer loyalty.

Consumer loyalty is declining as there are more online resources for finding new brands and as consumers are more prices sensitive. According to a recent study, 75% of Asian-European consumers switched brands during the pandemic. To draw and keep customers, fashion retailers will need to work even harder. To that end, many are experimenting with various pricing and marketing strategies.

Machine learning can be used in quite a handful of ways within the fashion industry. Some of the key methods, which we will explore in more detail below, include:

Customized recommendations

Improved customer support

Trend forecasting

Inventory optimization

Price optimization

Customized Recommendations

Based on past preferences and anticipated future behavior, recommendation engines are an excellent tool for creating personalized marketing for retail customers. Customers who buy a specific black shirt, for instance, may also be very likely to buy a jean jacket to go with it. In this situation, the retailer might want to advise a customer who has examined or purchased the black shirt to look at the jean jacket by stating that “customers who bought this black shirt also purchased this jean jacket.”

Instead of using more general manual or rules-based approaches to target clothing advertisements based on demographics, this strategy enables fashion retailers to customize recommendations for specific users. This means that advertisements from retailers are much more likely to result in a sale, allowing them to spend their advertising budgets more effectively. They can also decrease returns by suggesting products that customers are more likely to be pleased with.

There are several ways to construct recommendation engines, and the best strategy depends on the characteristics of your data and your business objectives.

Improved Customer Support

Retailers can now quickly and effectively respond online to common customer inquiries about things like return policies and store hours thanks to chatbot technology. Additionally, style advice can be given by chatbots, which is a great way for retailers to learn more about their customers and enhance marketing strategies and trend analyses in general.

Chatbots are made possible by using Natural Language Processing (NLP) a subfield of AI that enables computers to process and understand human language by converting it into a numerical format. Thanks to recent developments in Deep learning approaching NLP chatbots have developed substantially in the last couple of years. They will continue to evolve in their ability to understand and process human language. While not perfect, they’re a great solution for retailers looking to speed up customer response times as well as free up professional resources for more strategic tasks Click here to view our new AI chatbot models if you want to learn more about chatbots.

Trent Forecasting

In the last few years, social media has grown in importance as a factor influencing retail purchase decisions. Retailers can forecast consumer preferences by looking at search engine trends, blog post topics, and social media activity (posts, comments, and likes). For example, if the hashtag #tie-dye is trending or image recognition software shows that people are posting more and more images of tie-dye clothing, it may be safe to assume that this is a new upcoming trend. Instead of relying solely on a much more manual approach to assessing fashion shows and fashion magazines, fashion experts can use this type of data to make more informed decisions about new products. H&M has relied heavily on big data, including social media data, to better predict upcoming trends, and many other retailers are beginning to follow suit, pun intended.

Social media data analysis for trend forecasting can benefit from a variety of ML techniques, including NLP, deep learning for image recognition, and traditional supervised learning methods for prediction.

Inventory Optimization

Inventory management is one of the most common problems that fashion retailers face. If you order too many of a particular item, you will have unsold inventory that you have either wasted money producing or will need to drastically discount to get off the shelves before it goes out of style. On the other hand, if you order too little, you’ll miss out on potential sales and hurt your customers’ retail experience, as it’s an unpleasant customer experience to find an item of clothing you like only to discover it’s out of stock in your size.

Inventory optimization is primarily a problem of demand forecasting. We must forecast how much of each SKU will be sold in a given time period and then stock warehouses accordingly. Social media data, like store receipts or foot traffic data, can absolutely be used to improve SKU-level forecasting.

Price Optimization

Price comparison is becoming simpler as consumers switch from in-store to online shopping, and price optimization has become essential for retailers to stay competitive. Retailers can maximize profits by setting the best prices and markdowns for products to move them off the shelves as quickly as possible.

In the past, retailers frequently used seasonal broad price markdowns or market research to guide their pricing decisions, but machine learning enables a much more sophisticated approach. This essentially entails two steps: the first is sales forecasting, and the second is price optimization.

In inventory optimization, we focused to forecast the number of sales for each SKU and store based on historical trends. We can then optimize prices by juggling tradeoffs between stock and margin while adhering to specific legal and business constraints. There will always be a trade-off between offering steep discounts to get products off the shelf before they go out of style and making sure that those discounts don’t go too far and negatively affect profits.

What to Do From Here?

Examine your current procedures critically and look for areas that could be improved. Are you observing a lot of unsold inventory at the end of the season or an expanding social media presence that is untapped in terms of data? It might be time to make an investment in AI to protect your brand’s future and keep up with the sharper competition.

Go Further on AI in Retail

Uncover additional use cases for the retail space as well as retail-specific challenges (i.e., estimating ROI, data collection and management, data governance) and how to effectively navigate them.

Let’s stick with BzAnalytics as the best method for implementing chatbots and AI to build your own e-commerce application and inventory management system. Visit us for more. WWW.BZANALYTICS.AI