The future of commerce is AI. Artificial intelligence (AI) can figure out what we like and why. And now it can create those products for us.

Back in the day, people invented things with the intention of selling them to uninformed consumers, and they used to hire people to create catchy advertisements to promote them. 

Even though most people are aware that this is no longer the same way things are done, the true reality of how things work today is more surprising than many people realize.

In today’s commerce, we have Commerce AI, commonly known as ‘Cai’ model. By taking into account millions of online customer comments, this new technology can “read consumer minds” and design the right set of product features to provide you with exactly what buyers want.

This new technology enables companies to read the minds of consumers by rapidly ingesting millions of online customer feedback, and then designing the best set of product features to meet the needs of their customers.

Researchers from Stanford and MIT trained the Cai model over the last three years, using feedback data gathered from over half a billion people within 50k categories of products in nine different languages.

It resulted in a ground-breaking model: CAI understands and extracts the most useful information from the image, text, and video review of the customer feedback. Then, it develops new concepts of product that fulfills the needs and wants of the customer. 

Thousands of product categories with millions of products

Something faster as well as accurate was needed. In this regard, Cai model outperforms the BERT model, a powerful AI language model that appliances to a variety of tasks but demands considerable computing resources for fine tuning.  Cai has been trained specifically for understanding commerce and it takes less than an hour to do so, whereas BERT takes a few hours to achieve the same level of accuracy. 

One of Cai’s strengths and responsibilities include differentiating customer sentiment across a diverse range of product categories, and which is trickier than it seems. Say for example, when a microwave gets hot, it’s good, but when a phone gets hot, it’s bad. Or even worse, a thin jacket can be good, while another can be not.

Nevertheless, the Cai model works beyond just sentimental analysis. Additionally, it learns what other factors matter to the consumers when they buy a product, such as why they bought (or returned) a product, how they might use it, etc.

But Cai goes beyond sentiment analysis. It also learns what else matters to the consumers when they purchase a product, such as the reasons for purchasing (or returning) a product, the ways they might use a product, and so on. Cai is capable of determining this for any language with little or no human interventions. 

Getting the human out of the loop is another strength of Cai’s. A neural network model is based on labelled training data; however, the acquisition of these datasets can be extremely expensive and can be exploitative. Furthermore, the model does not scale across languages and domains. Initially, Cai receives some assistance from humans to learn the language, but later it is able to learn the language by itself. 

Cai also faces a difficult challenge in interpreting different product SKUs (individual product) data, as products are often referred to interchangeably by customers in their feedback. Further complicating the situation is the fact that frequently the same product is listed under different names in various locations within an online catalogue. As an example, it is difficult to distinguish between consumer feedback pertaining to a 32GB and 64GB phone if they appear together in the same product listing.

Despite the vast ocean of products and categories on the internet, Cai is able to accurately interpret customer intent and understand their minds.

Opening Up New Market Opportunities

With the Cai model, product brands can learn more about their customers that helps then to create better as well as more relevant products than before.

With AI for market intelligence, companies can identify market opportunities to develop the right products. Due to the exponential growth of unstructured feedback data every year and consumers increasing rely on this information for their purchase decisions, brands must utilize technology-driven solutions to determine what features should be included or excluded in their next product.

This is being adopted by top brands like Suzuki for their cars, Coca Cola for their soft drinks and Unilever for its product lines. Even progressive brands like Cisco and Midea are also using the Commerce AI platform for expanding their product offering by identifying new customer segments and opportunities.

If you are a large company offering consumer electronics and consumer packaged goods with many popular product SKUs, it’s likely that you have millions of customer reviews and comments on your products, your competitors’ products, and related products throughout the world

Every day, there are an increasing number of product feedbacks and conversations about these products. With simple tools and limited information, it is impossible for one to understand the environment for the launch of new products. There are still a lot of brands using the traditional method like traditional surveys, focusing on spreadsheets and groups that need manual review to understand the market. 

According to Neilson research, 85% of newly launched products fail. Perhaps Cai could be the solution.

Cai eCommerce Trends

Cai is shaping the eCommerce industry; due to this reason many big players are moving towards Cai. Here are a few areas in which AI-driven solutions are transforming the ecommerce landscape:

  •  An AI-enabled marketing email that delivers offers of products (or services) targeted to recipients’ interests. These email marketing tools not only read more human-like than automated software, but analyze user responses and are more tailored to the needs of each individual customer.
  • E-commerce platforms can manage supply chains more efficiently with AI-powered supply chain automation. Additionally, business decisions can be made regarding delivery schedules, vendors, and market demands. 
  • An AI-powered data analytics tool can offer a range of benefits to the eCommerce industry, including customer profiles, business intelligence, and analysis of online sales
  • Omnichannel solutions are using integrated AI solutions to create seamless and consistent customer experiences across online as well as brick-and-mortar stores. In Sephora, AI-based omnichannel solutions uses a mix of Natural Language Processing (NLP), Artificial Intelligence, and computer vision to bridge the gap between online and in-store customer experience.

Bottom Line

Customer experience in the retail will be dramatically impacted by AI in the retail industry. In addition to linking the online and offline shopping experience, it assists in personalizing the entire experience for the customers. Cai helps customers with product advice, and can adapt to their ‘Wishlist’. Additionally, it is more time-effective for customers and more cost-effective for businesses since it reduces the amount of time, they spend looking for products. Additionally, Cai aids in the gathering data about shoppers. This allows, for instance, for easy creation of special offers. Thus, Cai increases customer engagement by enabling the company to understand and create the right product to attract new customers while also increasing the company’s customer base. 

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