How eCommerce Shops are Leveraging Machine Learning to Resolve Sizing Issues

There is no doubt that internet access and adoption have been on the increase; this factor has also enabled online transactions. eCommerce shops are leveraging this shift in buyers’ attitudes to make big gains and relegate brick and mortar to the background.

What the marketing world witnessed in April 2020, was an unprecedented rise of eCommerce retail sales in North America by triple digits. And, Statista reports that in the same 2020, more than two billion people bought their products from eCommerce stores, with global e-retail sales of over $ 4.2 trillion.

While this is big news for eCommerce stores, an unfortunate incident is that the amount of returns purchasers make may go over a trillion dollars a year if care is not taken. The main reason for this is that most eCommerce shops have not been able to create size charts, so we have sizing problems plaguing e-commerce stores in a lot of ways.

The basis of sizing issues eCommerce stores face is that they are outlets and usually get their products from different manufacturers across the globe. Any apparel that has the size “medium” by one brand may even go for “small” from another brand; if a customer purchases such apparel, there could be problems of proper fitting and this can lead to a return.

This was not the issue when people made their own clothes or had them tailor-made. What goes mostly for customers today is ready-to-wear clothes, which again they mostly shop online.

Shoppers don’t have the opportunity of trying out apparel before purchasing, unlike what we saw with brick and mortar. If as a customer, you do not shop exclusively from one brand, you may erroneously believe your body configuration has changed.

This is the problem shoppers face with the absence of a standardized apparel size system; the numbers on the labels are arbitrary; they are not realistic. The only option left for shoppers is to make returns.

The impact of returns on eCommerce stores

While shoppers are frustrated with the sizing issues they encounter, eCommerce stores are not finding it easy, too. In the first place, they lose huge sums of money when customers make returns.

Since it’s obvious the problem did not emanate from the customers, the cost of shipping lies on the eCommerce store, and when this comes in volumes, you can imagine how much it will turn out to be. Another significant impact of customers’ returns is the organization’s product review rating.

Customers today have access to social media and other platforms to relay their frustrations and sentiments. Negative reviews from customers can seriously damage any organization; when your customers have a bad impression about your services and products, the most likely thing that will happen to your brand is churning.

Knowing that both time and effort go into the acquisition of a new customer, you must do what is necessary to avoid customer churn and remain relevant in the market.

Mitigating sizing problems

How eCommerce Shops are Leveraging Machine Learning to Resolve Sizing Issues

As the problems associated with sizing are becoming more worrisome for eCommerce brands, it has got to the situation where drastic measures must be put in place to reduce returns and their negative impact. eCommerce stores are now collaborating with professionals in the sizing business to mitigate this problem.

A few years back, nobody knew anything about size charts, and that was even not necessary since people were not into the ready-to-wear vogue. With the relative disappearance of brick and mortar and the boost in online shopping, size charts are becoming very necessary to eliminate returns.

Your size chart, which characteristically is depicted by the bust, waist, hip, and height, is a document that mirrors the measurements for your size range within your brand. At times, we may need to go for other measurements such as inseam for bottoms.

A lot of funny things go on in the fashion industry; what you have as a size 8 dress today used to be a size 16 dress some years back. One thing that is majorly responsible for this is vanity sizing.

Brands tend to size down people’s body measurements to encourage customers to purchase; this brings a lot of confusion into the whole process of standardization. You can also add the fact that different countries have different systems to the utter confusion.

But organizations such as Kiwi Sizing are stepping in to correct this anomaly, and eCommerce stores are pairing up with such organizations to give customers a better experience and reduce waste. Every eCommerce store should necessarily have size charts, but this can be problematic if you are selling products from different vendors.

In that situation, you’ll need to create enormous amounts of size charts. What Kiwi Sizing has done is to deploy Big Data, machine learning, and AI to develop a sophisticated Sizing Image AI feature.

What you need to do is simply upload your size chart image, with the aid of automation, the content is read promptly, and size charts are created within the app in seconds. Your next duty is to update and add any additional content.

Usually, the prompt you will be asked to enter could be your age, gender, height, and weight. It’s from this information that a full 3D avatar is generated to help in creating your size chart.

Machine learning and AI are then deployed to compare your input with its database of a lot of body measurements of 3D body scans. With the aid of predictive analytics, a product that fits your body size is recommended.

The interesting thing about this eCommerce plugin is that everything works out within seconds. Where eCommerce shops will find very valuable is the handy size recommender tool and the ability of the eCommerce plugin to convert between international sizes. This is very useful when dealing with customers from other countries.

Conclusion

Two important things AI and machine learning are bringing into the fashion industry and the issues of sizing are that the rate of returns will be drastically reduced or completely eliminated, and the other is that customer experience is greatly enhanced. In this win-win situation, the software 3D scanner captures the body proportions, the machine learning algorithms go on to analyze the collected data to ensure that the customer gets fitting apparel.

The customer is satisfied and the eCommerce store does not lose revenue due to returns.

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Author: Efrat Vulfsons

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