AI/ML in the Fashion Retail Space Part 1
The cumulative value of the fashion industry contributes to a cool 2% of the global GDP. That’s a high percentage for a single industry. Innovation and a daring attitude have always been a hallmark of fashion, and fashion retail is no exception to the rule. AI/ML are revolutionizing fashion retail and the fashion industry at large, and this multi-part blog series by AICorespot explores the application of AI/ML in the Fashion Space.
AI/ML have left fashion retail infinitely better off, and the numbers speak for themselves. The deployment of AI/ML technologies within the fashion industry has experienced a very positive response. Approximately half (44%) of retailers who have failed to implement AI/ML into their business framework are currently standing face-to-face with bankruptcy.
As an outcome of this, global spend on AI/ML within the fashion space is predicted to reach 7.3 billion US$ annually by the current year.
The effect of emergent tech in the fashion industry
Assisted by the proliferation of big data, client personalization, and other services in fashion are no longer possible without the deployment of AI and ML technologies. Reports by McKinsey state that the top 20% of international fashion brands are raking in 144% of the industry’s profits. The implication here is that for a company in the fashion space, it must be in the top 20% to be a profitable endeavour. Driven by this requirement, the fashion industry and its dominant brands and making investments in AI/ML technologies to retain relevance in a ruthless market space.
Let’s look at some of the use cases of AI and ML within the fashion industry, and how it is enhancing fashion retail.
Trends in the Fashion space that retailers will be required to be mindful of in the coming year
Post COVID-19 spending trends
We’ve taken a head first dive into the era of remote work. Lounge wear is at peak popularity (for obvious reasons) and there is reason to believe that customers have begun scrutinizing their closets, with an intention of moving from quantity to quality. This obviously makes inventory management more critical than ever before – retailers might no longer witness products flying of their shelves at light speed. The nature of demand has shifted.
There has been a hue and cry about digital transformation in the years immediately preceding the COVID-19 pandemic, and the global scenario has essentially made transformation an unavoidable reality. This trend is not likely to change. Qubit’s July 2020 survey revealed that 50% of consumers carry out >75% of their shopping on the internet. Companies might have a tighter grip on customer lifecycles, but will also be required to make sure that the web experience feels identical to the brick-and-mortar experience, there is no room for compromise.
Social Media’s Influence
Social media utilization has been gradually expanding throughout the planet over the previous decade and has become a vital component of the internet shopping and clothing trends tracking experience. As a matter of fact, approximately 3/4ths, or 74%, of customers utilize social media to make purchasing decisions. Increased social media utilization implies that more data is available to know your clients, on top of the requirement for more advertising channels.
Dwindling Customer Loyalty
With additional avenues to discover new trends and brands on the internet, and increased sensitivity to pricing, client loyalty is tanking. Latest McKinsey reports found out that 3/4ths of American customers switched brands during the pandemic. Fashion retail is required to exert themselves harder to entice and retain new and existing clients, and several are looking into differing marketing and pricing techniques to do so.
ML, the Swiss-army knife
This are just some of the ways in which ML can be utilized within the fashion space. These are some of the crucial strategies.
Recommendation engines are a brilliant utility for producing customized marketing for retail clients, based on historical preferences, and forecasted future behaviours. For instance, it is probable that clients who buy a specific white t-shirt are also inclined to buy a pair of jeans that goes well with it. In this scenario, the specific retailer in question might wish to suggest that a client who has either looked at or has bought the white t-shirt also adds the pair of jeans to their cart, by stating “shoppers who purchased this white t-shirt also bought this pair of jeans.”
This strategy facilitates fashion retailers to personalize suggestions for individual users, instead of harnessing more manual or rules-based strategies to target clothing adverts going by demographic considerations. This implies that retailer’s adverts are a lot more probable to be efficient in leading to a sale, so retailers can be more effective with their advert and marketing budgets and curb returns by suggesting items that users are more probable to be satisfied with.
Enhanced Client Support
Chatbots and Virtual Assistant technology has made it accessible for retailers to resolve simple client questions – regarding topics like return procedures and store hours – quickly and effectively on the internet. Chatbots and Virtual Assistants can also be utilized to impart style tips, which is a brilliant way for retailers to gather additional data about clients to enhance marketing strategies and inform general trend analyses.
Chatbots are made a reality by leveraging Natural Language Processing (NLP), a subdomain of AI that facilitates computers to process and comprehend human language by translating it into a numerical format. Owing to latest developments in deep learning strategies to NLP, chatbots have evolved considerably in the previous couple of years and will persist in their evolution and their capacity to understand and process human language. While not ideal, they’re an amazing solution for retailers seeking to hasten client response times in addition to freeing professional resources for more strategic activities.
Social media has become an increasingly critical aspect behind retail purchase decisions in the previous few years. Retailers can engage in analysis of social media activity (posts, comments, and likes), blog post subjects, and search engine patterns in order to predict client preference.
A particular hashtag witnessing increased usage (of a trending fashion accessory), or an increase in images posted online of that specific accessory (detected by image recognition), it’s a natural extension to make the presumption that this is an upcoming trend. Fashion specialists can leverage this type of data to make more educated decisions regarding new products, instead of requiring to depend strictly on a much more manual strategy of scoping out fashion events and fashion-related publications.
Industry juggernaut H&M has been increasingly reliant on big data, which includes social media data, to predict latest trends more precisely, and several other retailers are beginning to follow.
Analysing social media data for trend prediction can harness a broad array of ML strategies which includes NLP, deep learning in image recognition, and traditional learning strategies for forecasting.
One of the most typical problems that fashion retailers encounter is with regards to inventory management. If you make one too many orders of a specific item, you’ll be left with unpurchased stock that you’ve wasted resources producing. You’ll be required to discount drastically to get that product off the shelves before it falls out of fashion. On the other side of things, if you order too sparingly, you’ll miss out on prospective sales and damage the retail experience for clients, as it’s not a pleasing experience to identify an article of clothing you like, just to find out it’s not available in your size.
Inventory optimization is mainly a demand prediction issue. We are required to forecast how much of every SKU will be sold in a specific timeframe and then stock warehouses in an appropriate manner. There is an array of strategies to predicting demand. One typical strategy utilizes time series analysis to undertake analysis of fashion trends with any kind of temporal element. Social media in addition to other alternate data sources, like store receipts or foot traffic data can also absolutely be leveraged to improve SKU-level predictions.
As clients persist onwards in their current paradigm shift from brick-and-mortar based stores to web-based shopping, price comparison and contrasting becomes simpler and pricing optimization has become critical in assisting retailers keep up the pace with the competition. Pricing optimization facilitates retail businesses to establish ideal pricing and discounts/markdowns for products to increase sales speed and frequency.
Historically, retailers usually utilized broad pricing markdowns at specific times of the year or harnessed market search to inform price decisions, but ML facilitates a much more advanced strategy. This basically consists of dual steps: to start with, prediction of sales, and second, optimization of prices.
In prediction of sales, we’ll wish to forecast the quantity of sales for every SKU and store, provided historical trend patterns, as we would with regards to inventory optimization. After we’ve forecasted sales numbers over a particular time frame, we can then work towards optimization of prices by toggling trade-offs between margin and stock while maintaining their adherence to specific legal and business limitations, like maximum amount of discount for each category. There will be a trade-off between giving out massive discounts to shift items from store shelves prior to them going out of style or production, and making sure that discounts aren’t so high that they have a detrimental effect on the bottom line.
This brings us to the conclusion of the first part of AI-ML in the Fashion Space. We looked at specific applications and use cases within fashion for AI/ML, we will dive deeper into the topic in the next part, and look at how some other emergent technologies are influencing the retail space in the new normal.