Buying shoes is going to look very different, very soon. Two of the main drivers behind this transformation are likely to be machine learning (ML) and artificial intelligence (AI), cutting-edge technologies that are already disrupting industries all over the map. The footwear industry is no exception to this. Today, we’ll be taking a look at exactly how machine and deep learning is revolutionizing online footwear retail.
Machine learning vs. artificial intelligence
First up, a very brief primer in ML and AI. There’s a lot of debate around this but, strictly, machine learning is a subset of artificial intelligence. AI is an area of study that concerns itself with machines that can make intelligent-seeming decisions. Machine learning means teaching a computer to make decisions using large amounts of data. This is of particular interest in business, as modern life produces so much data that it’s hard to actually use the data - there’s just too much of it. We get around this by teaching computers to process the data (using ML) and humans end up only seeing the most relevant conclusions that have been extracted.
So, how can these technological advances be applied in an area of day-to-day life that so often wastes time and causes customer frustration: shoe shopping?
How Machine learning is Being used In the footwear Industry
1. Size-free shopping
It’s no secret - our technology, ShoeSize.me, is based on machine learning and is part of an innovation wave disrupting the footwear industry. Sizing is the biggest conversion killer in online shoe sales, so helping customers easily identify the right shoe size significantly improves conversion - which is a win for shoe vendors and customers alike. We utilise machine learning to understand how each individual shoe relates in millions of others in terms of size and fit. This enables our recommendation engine to provide the most accurate size recommendations in the industry.
Other, more archaic ways of determining shoe sizing simply won’t work for online retail. Foot scanning is complicated and nearly impossible to implement online, while size charts - despite their long history - have proven to be inaccurate and even downright dangerous to online conversion rates. It‘s in this context that our “size-free shopping” ushers in a new and powerful way to ensure your customers are happy with the fit of shoes they buy online.
2. Optimized marketing campaigns
In our data-based world, few business areas are not embracing machine learning to improve results. Marketing is one area that benefits hugely from this technology, using massive amounts of generated data to predict consumer behavior and then refine marketing campaigns as a result of that data. As you might expect, more closely tailored marketing campaigns mean in higher conversions and more sales.
Machine learning has already been used to great effect in many businesses. As more marketers are discovering the power it can unlock, we’ll see more of them applying the tech in their own campaigns. New Balance is one shoe retailer that has already applied machine learning and artificial intelligence to their marketing campaigns. They don’t want to find stylish people - they’re using the tech to find those who actually buck trends. It’s a mash-up of marketing and data, and it’s already gaining New Balance the exact kind of public attention they want.
3. Sales, trends & return forecasting
Less glamorous than marketing but just as beneficial to customers, machine learning is being put to good use in the area of stock and inventory management. Right now, online-only footwear retailers can see return levels of up to 70%. This causes a logistical nightmare for stock management and demand forecasting but, by using machine learning data to automatically predict inventory behavior, businesses can reduce the impact these returns have. Inventory management tools that benefit from ML superpowers can also extract trends and forecasts that retailers can use to make decisions about future products.
Crocs, major footwear players, have addressed the other perennial problem that affects brands behind the scenes. That’s the tsunami of data generated by modern businesses, and the difficulty of collecting it within a single source of data “truth” that enables them to take advantage of it. This in-depth article shows how the megabrand uses machine learning (and other types of artificial intelligence) to extract usable data from multiple business areas and present it to human employees in a manageable, centralized way. In this way, machine learning can help avoid information silos that might have previously prevented marketing departments. For example, it can take advantage of the information gathered by inventory management.
4. Personalized content and marketing
Even the quickest glance at modern retail will show you that personalization is still one of the biggest trends around. It can take various forms - retailers have learned that customers love a personal touch (even if it is entirely guided by artificial intelligence) and that personalization improves the effectiveness of marketing campaigns. Currently, around 25% of purchases are customized in one way or another and that number is only set to grow.
However, one of the main risks of personalization has been that customers become overwhelmed with the many (often irrelevant) customization options on offer. When machine learning is incorporated the quality, rather than quantity, of these options can be improved. That means customers are shown only choices that are truly likely to appeal to them as individuals.
5. Improved shoe classification
From one megaretailer to another - over in Zappos, they’ve been using machine learning to make the “behind the scenes” aspects of selling shoes more agile. More precisely, they’ve been using tech to improve shoe classification. Improving things like classification, which would manifest as better search or filtering results for customers, is a major win for user experience, which is a key factor in online conversion rates.
It’s worth bearing in mind that there are two types of personalization in retail. Firstly, you’ve got the customer-facing options, as we discussed above in point #4. You also have the under-the-hood personalization, which is what we’re talking about here - using deep learning, a subset of machine learning, to improve search and filtering features, or to enable Amazon-style reactive pricing, where prices are automatically raised when availability is low, and lowered when it’s high.
Machine learning is already proving to be one of the most powerful forces retail has seen for some time. It’s already heavily used in the footwear sector, solving all kinds of headache-inducing retail problems, from user experience to marketing and stock management to website design. As the technology is refined, and as its popularity grows, it’s likely to become even more important in retail as a whole and footwear in specific.
To see the full impact of machine learning on the footwear market, simply explore solutions like ShoeSize.Me. Our technology brings the highly technical benefits of machine learning to the market in a user-friendly and accessible way. By harnessing the power of machine learning, companies can see conversion rates improve by 9% on average, and return rates drop by around 13%. Our clients are also pleased to see increased average orders and an overall boost in total footwear revenues by an average of between 4-6%. Want to see what that looks like in a real life example? Download our Lloyds case study here and see for yourself!