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On the heels of the devastating economic effects of the COVID-19 pandemic, the fashion industry is beginning to pick up the pieces and build a path forward. Machine learning and AI had already been gaining momentum within the industry and, in order to keep up in today’s increasingly competitive climate, it’s crucial for fashion retailers to take a look inwards to identify potential efficiency gaps in order to remain competitive.
Some of the key themes that fashion retailers will need to keep in mind in the next year include:
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:
Recommendation engines are an excellent tool for generating personalized marketing for retail customers, based on past preferences and predicted future behavior. For example, perhaps customers who purchase a particular black shirt are also very likely to purchase a jean jacket that goes well with it. In this case, that retailer might want to recommend that a customer who has either looked at or purchased the black shirt also checks out the jean jacket by saying “customers who bought this black shirt also purchased this jean jacket.”
This sort of approach allows fashion retailers to customize recommendations for individual users, rather than using more blanket manual or rules-based approaches to target clothing advertisements based on demographics. This means that retailers’ advertisements are much more likely to be effective in leading to a sale, so retailers can be more efficient with their advertising budgets and also reduce returns by recommending items that users are more likely to be satisfied with.
There are a handful of ways to build recommendation engines and the appropriate approach relies on the nature of your data and business goals. Check out this blog post for detail on how this all actually works under the hood.
Chatbot technology has made it easy for retailers to answer simple customer questions — around topics such as return processes and store hours — very quickly and efficiently online. Chatbots can also be used to provide style advice, which is an excellent way for retailers to collect more information about customers to improve marketing tactics and inform general trend analyses.
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 approaches to NLP, chatbots have developed substantially in the last couple of years and 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.
Social media has become an increasingly crucial factor in driving retail purchase decisions in the last few years. Retailers can analyze Social media activity (posts, comments, and likes), blog post topics, and search engine patterns in order to forecast consumer preferences.
For example, if the hashtag #tie-dye is gaining popularity or image recognition technology shows that people are posting more and more images of tie-dye clothing, it might be safe to assume that this is a new upcoming trend.
