There’s no doubt that artificial intelligence (AI) will fundamentally change the world over the next few decades. What many do not realize, however, is that in some fields, it has already become a large part of the status quo. One such example is e-commerce (ECom).

At DeeplogiX, we are working with big data purposefully searching for new and better ways to empower (ECom) marketplace merchants and customers through the boundless abilities of AI.

Below are a few examples of how we are leveraging various applications of AI, specifically machine learning, to advance our ECom business.

1. Chatbots
   • It’s an increasingly popular application for conversational interactions between an ECom service and its customers. Chatbots can be applied to the wide range of interactive communications both in B2C and B2B domains. It also provides the ability to navigate the customer to their destination smoothly.
   • We are working on chatbots with AI technologies such as NLP, machine learning techniques, and others.
2. Predicting sales
   • Chatbots are obviously functioning as part of the UX for customers. However, other things are running behind the scenes. We leverage “supervised” machine learning to predict product sales. Supervised machine learning is a form of AI in which “the machine,” or algorithm, is given sample data from the past that helps train it to process the data of the future.
3. Marketing to the new groups
   • We also make use of so-called unsupervised learning algorithms when segmenting customer groups for marketing campaigns. Traditionally, marketers have defined market segments in ways that appeared to make sense to them by age or gender for example. So, supervised learning techniques have basically been utilized for that. But AI is also demonstrating that those are not always the most effective approaches. In some recent cases, the past defined features and the past training data hindered accurate marketing when customers’ behavior or profiles, change or evolve. Then an unsupervised learning algorithm, working from raw real-time data only, might identify alternative means of segmentation, such as online behavior or preferences, that can serve as a more accurate predictor of interests or tastes.
4. Classifying products
   • There are numerous products available in E-commerce space, this can make categorizing a challenge. To solve the problem, we utilize a semi-supervised learning algorithm, which repeatedly resamples data until the algorithm learns how to process it in the most efficient way.
5. Analyzing ratings and reviews
   • Understanding user ratings and reviews is important, but it is also time-consuming. Applying a combination of NLP techniques and structural machine learning algorithms, a method commonly used in the study of the structure and formation of words (morphology), we can efficiently collect and analyze product review text, both positive or negative. In addition, structural machine learning can help us mine valuable data from product explanations and reviews.
6. Improving recommendation and search
   • Recommendation and search are typical applications of EC. However, we can advance these through our use of reinforcement learning algorithms to process data on customer reactions in response to products they are shown, for example, whether users clicked on a product when it was served to them in search results or in a recommendation. Similar to an A/B test, reinforcement learning algorithms notice how much “reward” (positive reaction from users) is obtained when different products are displayed in response to certain circumstances (a particular search query, or a user’s browsing history, for example). Combining knowledge of past customer reactions in response to particular circumstances, the algorithm can determine the most efficient course of action when those circumstances reoccur. And with each action and reaction, the algorithm becomes smarter.
7. Image recognition
   • Deep learning algorithms can be effectively used for the purpose of image recognition. Inspired by the structure and function of the brain, deep-learning algorithms develop the ability to recognize an object in a photo and then automatically categorize it, making it easier for users to post products for sale. Besides, it also works for finding fraudulent products.
8. Creative AI
   • This is a kind of new concept. However, it’s gaining more and more momentum. Creative AI means AI applications that are able to do non-routine work based on expert knowledge and which generate valuable content. Works of art generated by AI are a good and easy-to-understand example, like novels, paintings, music, film, and so on. It is not only art – but journalism is also in the scope of use of creative AI. You can see many cases of applying creative AI to services.

E-commerce may not be the first thing that comes to mind when people hear the words “artificial intelligence,” but there is no denying the impact it is already having on the way we buy and sell online. Through the “superhuman” abilities of machine learning, we hope to continue empowering both customers and merchants in our ECommerce businesses.