Machine learning allows companies to get to know their users in an intimate and non-evasive manner. This technology is being utilized by various industries – the healthcare system, security and of course, apparel retail.
What is User Experience?
According to the digital marketing experts at Smashing magazine “user experience is how a person feels when interfacing with a system. The system could be a website, a web application or desktop software and, in modern contexts, is generally denoted by some form of human-computer interaction (HCI).”
As technologies and methodologies become more sophisticated, the website has evolved into a more interactive experience. An apparel’s online success depends largely on how its users perceive their engagement with it. Questions shoppers ask themselves when browsing an online store include – “Is navigation straightforward on this site?”, “Are the images a good representative of the product?”, and “How easy is it to find what I need?”
Whether the shopper answers these user-based questions positively or negatively affects if they end up becoming regular users or to put it in retail language – loyal customers.
What is Machine Learning?
It’s important to distinguish between artificial intelligence and machine learning.
Artificial intelligence is intelligence demonstrated by machines that have the ability to execute certain tasks by imitating human cognition.
On the other hand, machine learning is a branch or subset of artificial intelligence that enables companies (e.g. apparel retailers) to predict the behavior of their users. By doing this, machine learning can be used to improve performance through experience over a period of time.
Another way to look at it according to tech expert, Atman Rathod is whilst AI is “about the ability of the computer to take better and faster decisions imitating human logic, Machine learning is more about the ability of computers to learn about users and customers by analyzing their interactions and behavior.”
How Machine Learning can Improve User Experience in Apparel E-tail
1. Give Relevant Product Recommendations
In a bid to improve the user experience for their shoppers, fashion e-tailer Zalando turned to machine learning. More specifically, through its digital outfit recommendation tool – Algorithmic Fashion Companion (AFC) – users can receive outfit recommendations in real-time.
The algorithm’s recommendations are based on items shoppers have bought, shown interest in or/and chosen to add to their ‘wish list’. To ensure the recommendations are up to date with current fashion trends, Zalando’s stylists personally provide adjustments to the algorithm.
By acting as a virtual style assistant, AFC enhances an online shopper’s user experience because the tool whittles down Zalando’s extensive product selection and suggests outfits. This saves time, offers inspiration, and stops shoppers from feeling overwhelmed by a seemingly never-ending product selection. Also, getting timely outfit suggestions is likely to make shoppers feel more understood by the Zalando brand and there’s a lower chance that the user will leave the platform or search for a competitor’s products.
For Zalando, being able to curate outfits at scale for millions of customers has driven 40% larger basket sizes – an increase in sales that facilitates their bottom line.
2. Offers Personalization
TechStyle Fashion Group which consists of brands like ShoeDazzle and Fabletics use machine learning to integrate online and retail shopping experiences.
TechStyle stores offer introductory customer quizzes to determine the personal style of their customers. Machine learning plays a huge role in making these predictions, as the technology clusters shoppers together who have matching attributes like purchase information and shopping behavior.
TechStyle brands also utilize machine learning to group similar products based on different attributes, such as color and fabric, the tool is then able to pair customer clusters with product clusters to show users the most relevant information.
These customer quizzes act as a personalization tool, for example, as MarTech Advisor reported “on a typical Fabletics quiz, shoppers are asked where they like to work out, what their favorite type of workout is, their body type and their favorite color palettes, all to inform what products will be displayed to them and recommended. Therefore, a customer who prefers boxing in a gym but loves blue will receive completely different recommendations than a customer who loves doing yoga at home but also loves blue.”
3. Provides the Perfect Fit
At Fit Analytics our machine learning and AI-powered platform offer solutions to online apparel e-tailers to help them boost their overall performance which includes user experience.
Fit Finder is our foundational size and fit solution – it draws on machine learning algorithms and the industry’s largest database which allows the intuitive tool to take the guesswork out of shopping online.
The size advisor helps shoppers find their fit by asking a series of size-related questions. It then compares the shopper to a similar cluster of users. By assessing the sales data of the cluster of shoppers, Fit Finder is able to recommend the right size.
Contrary to conventional wisdom, we have found that our medium-length sequence of questions allows for a more robust sizing experience for the customer. It also reduces shopper frustration, builds trust, and fosters long-term loyalty. The reason being, with each question asked, the user is able to engage with the brand in a way that bridges the gap between an in-store and online experience.
An advanced machine learning framework is used to develop Fit Finder’s sizing prediction, this makes the solution smarter with every recommendation it makes.
Machine Learning Conclusion
Savvy apparel and footwear retailers know that investing in machine learning technologies is the way to remain relevant in a saturated and competitive marketplace.
In an industry where the customer is king, more e-tailers are investing in machine learning algorithms to understand their customer’s fast-changing needs and expectations. This allows them to offer shoppers various choices and recommendations that improve their user experience on their platform.
Our machine learning technologies continue to help retailers enhance the experience shoppers have on their platform, whilst offering a sophisticated approach to size recommendation.
Image credits: Luis Rocha and Pers Nickety
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