Learn how AI-empowered Generative Adversarial Networks can help you grow your eCommerce business.
Generative Adversarial Networks (GANs) are Neural Networks that take random noise as input and generate outputs (e.g. a picture of a human face) that appear to be a sample from the distribution of the training set (e.g. set of other human faces).
Generative modeling involves using a model to generate new examples that plausibly come from an existing distribution of samples, such as generating new photographs that are similar but specifically different from a dataset of existing photographs.
GANs today dominate over all other generative models. Let’s see why:
- Data labeling is an expensive task. GANs are unsupervised, so no labeled data is required to train them.
- GANs currently generate the sharpest images. Adversarial training makes this possible. Blurry images produced by Mean Squared Error stand no chance before a GAN.
- Both the networks in GAN can be trained using only backpropagation.
Generative Adversarial Networks (or simply GANs) are an approach to generative modeling by using deep learning methods. Such methods are, for example, convolutional neural networks. Generative modeling is a learning task in machine learning that is unsupervised. It involves discovering and learning patterns automatically in input data. It is done in such a way where the model can generate or output new examples that can be drawn from the original dataset.
GANs use two neural networks, setting them against one another (that’s why we call them “adversarial”). Opposing networks create new, synthetic data that resembles actual data. One neural network generates data instances (generator). Then another (discriminator) tests them for authenticity.
It is the discriminator that determines whether the data it reviews is from the actual training dataset. The discriminator rates the quality of the results on a scale of 0 to 1. Low scores make the generator correct the data and resubmit it to the discriminator. The cycle repeats until the GAN creates data with the same statistics as the training set.
For example, GAN can be trained on images to generate new ones that have realistic characteristics and look authentic to observers. Here GANs generate images of people who do not exist. In this article, we will focus on three applications of GANs in eCommerce.
1. Creation of Fashion Models With Custom Outfits
Sometimes being a fashion model is not that easy. Being good-looking is one thing, but presenting an outfit is a different thing. When presenting an outfit, it should be in the best light possible which requires a lot of patience. You will most likely have to change a lot of poses and locations. However, data science solutions today have focused more on machine-learning. With that being said, a lot of these researchers have set their goals on fashion models.
GANs can produce high-resolution images, customize outfits, and poses depending on a fashion model. They can reproduce the same fashion model in a variety of body types and outfits. GANs can create models that fit into the brand’s image and resemble the target audience. With GANs, fashion brands can even create their own “artificial” social media influencers.
A new research paper from Berlin-based unicorn fashion and technology company Zalando uses generative adversarial networks (GANs) to produce high-resolution images of virtual fashion models ready to model clothes of any style.
The researchers set out to create an AI system capable of transferring customizable outfits and body poses from one fashion model to another. They used an architecture based mostly on StyleGAN, a technique introduced by NVIDIA in 2018 that enables intuitive, scale-specific generational control.
The researchers built a proprietary image dataset with about 380K images in 1024x768 pixel resolution. Each image has a fashion model holding a pose and wearing an outfit comprising up to six pieces of clothing and accessories. A deep pose estimator extracts 16 key pose points from each pose.
It can help fashion brands and e-commerce a lot since these outfits are crucial to keeping their shopper’s experience positive. It helps their customers see all the possible outfits and things they can buy and also simplifies everything in the store. It can also help shoppers have a more personalized experience in the future based on the outfits they checked.
2. Enhanced Product Descriptions and Personalized Customer Interactions
NLP and AI in eCommerce have gone a long way. Just by applying personalization, chances are, you will start to experience revenue growth of up to 15%. There are only a handful of business strategies that can provide that growth. But when you take a look at personalization, you see that the whole idea starts, but also ends, with customer experience. If the customer feels appreciated and understood, they will most likely come back to your services again. For that reason, it is crucial to have an automated personalized customer experience - it can truly transform your business.
To understand personalization better, you can picture it as one-on-one marketing. In other words, it focuses on giving the customers what they want and what they are searching for. It ignores the old fashion ways where it lists all the products to all of the customers. More importantly, it focuses on what the target audience wants.
Personalization focuses on every customer and tries to meet everyone’s requirements as well as preferences. To be more exact, it means jumping into market research and getting any data you can acclaim on the customers. You can not apply personalization if you do not know your customers or if you do not know what they want or need. In those cases, learning algorithms can be of great assistance.
We have all experienced personalized greetings in marketing. For example, every marketing e-mail that you open has your first name in it. That is one way of personalization. It is also crucial to remember that we live in a time of empowered customers. It means that customers will most likely leave businesses that do not have high levels of personalization.
Usually, 33% of customers will leave a business because they think it does not have enough personalization efforts. In this example, we can see how important personalization is. Not only does it bring new customers, but it also helps with keeping your old customers happy. It is also safe to say that personalization is crucial in the process of business growth. The lack of it can bring harm to your business in the future.
Implementing NLP to improve the interactions between humans and machines presents retailers with exciting opportunities to capture the elements of in-store shopping that many customers desire.
While we’re yet to scrape the surface of how NLP applications could change the way we shop, some of the world’s largest online retailers are already exploring how they can combine AI-enabled tech to revolutionize online experiences.
Additionally, organizations that typically fall outside of the retail sector are investing in NLP technologies to ride the wave of e-commerce growth and tailor their services to help shoppers make better purchasing decisions.
GANs can generate marketing texts and reword the phasing of a product description to include user-defined keywords. GANs-produced texts are becoming more difficult to distinguish from human-written content. GANs can convert text to images and generate examples of products that match textual descriptions. It can offer the user a visual guide. The user can refine the text query until they find the right product. Machine interactions (for example, Chatbots) appear more natural as GANs learn how to respond in a more natural form.
3. Creation of Personalized Products
Personalization is one of the ways to match the proper digital content to customers. Companies use personalization to properly present their products. They do it in a way so it aligns with the customers’ preferences. It helps the brands stay relevant and also become more valuable to customers.
Ever listened to your favorite song and had images in your head? Imagine having customized products, for example, headsets or phone covers that remind you of your favorite music? GANs can convert audio into images. Check out these examples of what they produce. If GANs can create art from sound, we can apply the generated technique to the product to make it unique.
Consumers always look for custom products, and the demand for them will only increase. So we expect GANs to become extensively adopted in e-Commerce. GANs will take the online shopping experience in e-Commerce to a new level and help retailers engage and keep their customers. Besides what we have mentioned, GANs can also generate 3D objects and cartoon characters. GANs have capabilities for video prediction, production and alteration of video content, image-to-image translation, and much more.
Nowadays, the most used technique for personalization is deep learning. Usually, personalization platforms will offer out-of-the-box modeling. But not a single platform can provide everything - and no platform has a deep learning option. The best results will come out of a combination of machine learning and deep learning. It creates powerful flexibility that can be integrated across the existing platforms.
Users mostly know what they want. No matter if they are looking for clothing, events, festivals, or a market. Because of that, it is crucial to understand their behavior. That way, you can present them with the most relevant results. For example, let's say a customer is looking for a shirt. They start a conversation with a chatbot about a shirt - they should see all the available shirts. They should also view the best shirt results based on their shopping history and the description that they gave.
While the customer is browsing through an online store, they ask the chatbot for a product. Maybe the chatbot has not been programmed with this specific item. The chatbot finds this question too complex, so it sends the question to a human employee. However, for other products that are in-store, chatbots can be extremely helpful and even might give suggestions for the customers’ next purchase.
Based on what the customer is searching for, the chatbot can recommend a personalized list of suggestions that they might find helpful. Giving recommendations to the customer of what they want to buy will make them feel more understood. This way, the chatbot is also gaining more data. It is usually data on what the customer wants and how to understand their behavior.
eCommerce has changed the idea of how the market works in general and how business functions. The most impactful change is that these new technologies can accurately assume the buyers’ journey and predict their next shopping stop. It also became of great importance to understand the consumers’ behavior. By doing so, companies can have a better insight into what their customers want.
With all these novelties, data exchanges have become larger. A lot more image, video, and audio files will be transferred as a result of interaction between the consumer and the site. However, the consumer will have a better image in their head of what a site can offer.
So that’s where the future is at, with products having a personalized touch of music that you like. Isn’t the very thought of it so heart-pleasing? Well, the day is not so far when you can create and own products based on the music you love.
Expect a lot more from GANs in the years to come. Thanks to the superior demand for custom products that online shoppers crave. And merchants have to do whatever it takes to create fantastic shopping experiences and more amazing personalized products, like using GAN to win over and retain customers.
While GAN is definitely the future of product customization, make sure your online store is already offering custom products right away to increase conversions and sales.
The biggest advantage of machine learning in eCommerce is that we never have to program them. We can do some improvement and implement it, but that way we are telling it how to do certain things.
The more accurate and advanced GANs become, the more benefit businesses can get from them. The development of generative adversarial networks is easily traced thanks to new GAN apps.