The e-commerce AI market is expected to grow significantly by 2032. According to forecasts, the size of this market will increase to $45.72 billion, according to The percentage of e-commerce companies making AI a top priority is 84%. This trend suggests that commerce The importance and value of artificial intelligence.
Since 2020, the eCommerce market has been growing, with sales exceeding $4.2 trillion. To be a leader in this ever-changing commerce landscape, it's important to understand the latest trends and stay ahead of the curve.
In this article, I'll introduce the technology, use cases, advantages, and limitations of AI in the commerce industry.
Why commerce AI matters
What is possible for the commerce industry with AI?
Retail and e-commerce companies aim to leverage AI to increase sales, improve customer experience, and increase operational efficiency. They do this by adopting technologies like chatbots to improve the customer experience and leveraging marketing operations AI to automatically generate product images and descriptions to deliver productive content. They also use predictive analytics to optimize operational processes such as purchasing, pricing, and inventory management to increase operational efficiency.
These companies pursue a strategy to grow by indirectly influencing revenue rather than directly growing revenue. They focus on improving the customer experience and increasing operational efficiency to contribute to revenue growth both directly and indirectly.
This approach, coupled with the adoption of professional AI solutions and data analytics, helps them gain deeper insights and predictive power, optimize business processes, and develop creative marketing strategies. Ultimately, this is indirectly having a positive impact on revenue growth and business success. To summarize where commerce companies are leveraging AI, here are some of the areas where they are using it
- Personalized product recommendations
It's getting easier to collect and process customer data about their online shopping experience. Artificial intelligence can recommend favorite items based on previous purchases. Commerce companies can analyze the data they collect and provide consumers with personalized experience and implement marketing campaigns.
- Pricing optimization
AI can provide personalized price optimization by analyzing a customer's purchase history, preferences, behavior patterns, and more. Dynamic pricing, which adjusts prices in real-time to account for market trends, competition, inventory status, and more, can also be effective.
- Improve customer service
Artificial intelligence will play a big role in improving customer service. Chatbots leveraging natural language processing (NLP) technology will provide 24/7 customer support, while AI recommendation systems will enhance the user experience (UX) with personalized product recommendations.
- Customer Segmentation
Harnessing the power of analyzing large amounts of data can greatly contribute to customer segmentation. By analyzing and understanding each person's purchase history, search patterns, reactions, and more, you can create personalized marketing strategies and offers.
- Smart delivery
When AI and the Internet of Things (IoT) combine, smart logistics systems become a reality. Processes ranging from real-time inventory management to shipping optimization will be automated to help you save money and increase efficiency.
- Sales and demand forecasting
Artificial intelligence will play an important role in forecasting demand for items you sell. By comprehensively analyzing various factors such as seasonality, economic conditions, and changes in consumer behavior, it will be possible to make more accurate and faster forecasts, which will greatly help with inventory management and strategy.
Seargest - What will the shopping experience look like in the future?
The word "sugest" is a combination of Search and Suggest. An AI technology that finds and recommends products or content based on an individual's data and preferences. For example, Amazon's algorithmic technology, known as Siri, can help you type in a search query, correct typos and translate to get the most relevant results. It can also anticipate the context of your search and provide you with a variety of results that you might be interested in.
Through Air Search, NAVER is enhancing web search results through Smart Block, Knowledge Interactive, Omniscience, and video scene navigation. In particular, Smart Block is a feature that reflects the searcher's intentions and tastes to show the best customized search results for each user. In other words, NAVER aims to transform the user experience by displaying ultra-personalized content based on AI's understanding of the user's search intentions. In addition, NAVER launched the "Poyu" tab, an AI shopping curation space that gathers users' interests and tastes using its AI-based product recommendation technology. The idea was to maximize dwell time by showing users items that match their interests and tastes in real time from NAVER Shopping's DB of 1 billion products.
Artificial Intelligence in Commerce
- Recommendation Systems
Recommendation systems are technologies that analyze customer preferences and behavioral data to make personalized recommendations for products or services. Typical algorithms used include Collaborative Filtering, Content-Based Filtering, and Hybrid Filtering.
- Image Recognition
Image recognition technology is used to automatically analyze and understand product images offered on commerce sites. Deep learning algorithms such as Convolutional Neural Networks (CNN) are commonly used to categorize products, identify detailed attributes, spot trends, and more.
- Natural Language Processing (NLP)
NLP is a technology that understands and processes text data, and is used on commerce sites to analyze text data such as customer reviews and inquiries to extract information, analyze sentiment, and build question answering systems. Representative NLP models include Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformers (GPT).
- Dynamic Pricing
Dynamic repricing is a technology that adjusts prices in real-time to account for demand and competition. It uses artificial intelligence algorithms to help you set optimal prices by considering economic indicators, demand forecasts, and competitor pricing information.
- Virtual Personal Shopping Assistants
A service where AI interacts with shoppers to provide a personalized shopping experience. It understands the buyer's needs and preferences through a voice or text chat interface and provides personalized suggestions and recommendations.
Commerce AI application examples
Amazon has been aggressively hiring AI talent lately, and the industry's interest is growing. With more than 2 billion visitors per month, the company is poised to become the leading AI-powered e-commerce company. Here's how Amazon is using AI to drive sales and gain market share.
- Product recommendations based on artificial intelligence
Every time a customer purchases something on Amazon or adds an item to their cart, they can receive a list of recommended purchases, tagged with things like "Recommended for you" and "What other customers bought". This acts as a powerful nudge to encourage customers to buy. Amazon's product recommendations feature uses artificial intelligence to provide ultra-personalized suggestions.
- Voice shopping with Alexa
Rather than tapping on the screen, Alexa uses voice Helps you find and buy products with just a prompt. This technology has the advantage of making online shopping easier and faster for users. Alexa can also provide users with purchase reminders or shopping recommendations.
Amazon has delivered another round of sales growth powered by chatbots. The AI model provides quick answers to simple questions that customers want to know. They also allow for real-time interaction with anyone who visits the store. In addition to customers who have already made a purchase, chatbots are deployed to help potential customers with their shopping, offering catalogs, answering questions, and directing them to relevant websites.
- Sales forecasting and inventory management
Amazon buys a huge amount of goods for customers in 185 countries. With a market share of more than 37%, Amazon's size has put a lot of pressure on maintaining excess inventory, so they've turned to AI to help predict product demand. Image recognition and Deep learning, natural language processing, and more to strategically leverage product predictions.
- Optimize warehouse and shipping
With the help of artificial intelligence, warehouse workers can place items that arrive from manufacturers on warehouse shelves and record their location on a computer. By streamlining the repetitive and manual task of scanning items, they were able to reduce work hours and improve accuracy.
NAVER's proprietary recommendation reason modeling technology and hyperclobas provide a sophisticated understanding of users' search queries and pioneer AI commerce experiences.
NAVER's recommendation reason modeling technology is incorporated into the "personalization block". This technology analyzes the connections between a user's personal shopping history and their search queries to provide personalized results for purposeful shoppers. As a result, users can find products that perfectly match their preferences and needs.
NAVER has also launched a 'AI Cue Sheet Helper' to help merchants organize their draft Shopping Live scripts. The AI Cue Sheet Helper helps merchants organize the generates draft scripts that help merchants find the most effective ways to organize their messages to convey important information, leading to tangible results like increased sales.
As such, NAVER is at the forefront of the use of AI in commerce in the ways described above, and continues to lead the way by focusing on user-centric services and using AI to solve problems that merchants face.
Under the slogan "Shopify Magic," Shopify has a number of AI-enabled features to make merchants more efficient. A key feature is Sidekick, an AI commerce assistant that helps merchants make smarter decisions and be more productive.
'Sidekicks' provide a range of support, from answering business questions to improving creative processes, improving the quality of online products, and streamlining business procedures. This frees merchants from complex and time-consuming tasks and allows them to focus on important decisions.
Also, one of the "Shoppify magic" features is to generate personalized FAQs and responses based on the keywords you type in, along with blog posts and emails with interesting content. This is a great help in customer service and marketing efforts.
When it comes to email creation specifically, it not only generates subject lines and internal content for you, but it also recommends the best time to send them out, which is a huge help in building your marketing strategy.
By utilizing AI-powered services to reduce the workload of sellers and provide innovative solutions in various areas such as data analysis and decision support for business growth, Shopify has established itself as a leading model for the use of AI in commerce.
Limitations of commerce AI and how to overcome them
Commerce AI is not only specialized in providing more personalized and customized services, but it is also significantly improving business performance by analyzing psychological satisfaction and purchasing patterns. However, there are still some limitations that are being discussed.
One of the limitations of commerce AI is the lack of data. Below, we'll discuss different perspectives on this and its impact.
- Lack of quality data: AI requires sufficient and accurate data. However, in the real world, it is difficult to obtain precise and extensive data about customers' purchase history, behavior patterns, preferences, and more. This is due to a number of factors, including customer privacy policies and the difficulty of collecting and managing data. This can cause AI to make predictions and recommendations based on incomplete or biased information, resulting in inaccurate results.
- Lack of temporal data: In commerce, AI needs real-time or up-to-date data to reflect customers' changing preferences and trends. However, tracking and updating everything in real time is resource- and technology-intensive.
- Lack of diversity: Sometimes it's difficult to get a sufficient amount of diverse data because users don't have the same tastes and patterns. In this case, AI is likely to deliver optimized or generalized results for only a subset of users, which can detract from the individual user experience.
- Structural issues: Unstructured material or complex forms of Information processing is also a big challenge for AI. While this information can contain important insights, processing and analyzing it is limited by traditional methods and requires the development of additional algorithms and techniques.
Bias and interpretability
AI reflects the characteristics of the data it learns from, so if the training data is biased toward some users, products, or situations, the AI's recommendations or predictions may also be biased toward those areas. For example, an AI that has been trained primarily on the buying patterns of younger generations may not accurately reflect the tastes or needs of older users. This means that the AI may reflect imbalances in society caused by factors such as gender, age, and geography, potentially raising ethical concerns.
AI algorithms can also be subject to bias due to their own characteristics. For example, some recommendation systems can lead to the "long tail" phenomenon, where they continue to recommend popular products that are frequently purchased, reducing diversity. 강화학습과 같은 방법론에서는 Over-optimization can lead to "overshooting," which can cause undesirable results for users.
Some AI models can be difficult to interpret their results, especially deep learning models, whose decision-making process is opaque and whose internal behavior can be difficult to explain. While AI should work fairly for both sellers and buyers, it should also be fully transparent about how it works and the rationale behind its decisions. However, many current AI systems are designed to be "black boxes," making it difficult to see their internal logic from the outside.
As the use of artificial intelligence in commerce increases, its limitations include Ethical considerations are becoming increasingly important. Commerce AI collects and analyzes a lot of personal information about users, including their purchase history, browsing behavior, and product preferences. While this data can help improve the user experience, it can also raise privacy and data breach concerns. This can damage user trust and lead to legal issues, so proper data management and security measures are required.
As already mentioned, AI has the potential to reflect biases inherent in the training data. This can lead to inaccurate recommendations or even discriminatory results for some users or groups.
How to overcome
While the limitations of commerce AI seem clear, as described above, there are also solutions that are being actively discussed. Below, we'll discuss how to overcome the limitations of commerce AI.
- Insufficient data: It's important to build a data pipeline to systematically collect and manage customer data. It also requires constant monitoring of the model's performance and ongoing training and updating to respond to new data and technological advances.
- Bias: Build a balanced dataset with a diverse group of consumers, and follow fairness and diversity-related Evaluate metrics to adjust the training data so that the model is not biased.
- Interpretability: The decision process of an AI model should be structured in an explainable way, and techniques and methods should be used to verify the model's behavior.
- Ethical considerations: Strict adherence to privacy policies, and monitoring and action for algorithmic bias or adverse predictions.
The bottom line: AI technology is going to be a game-changer for commerce and marketing, and you need to be able to build and apply it well.
AI can act on its own, it's up to humans to utilize it
Recently, many companies have been looking to apply generative AI to commerce. It's no exaggeration to say that generative AI chatbots like ChatGPT are changing the paradigm of search. The global generative AI market is booming, and local companies are rushing to develop AI technologies and services.
As a result, AI applied to the commerce industry has evolved into AI that can discover and solve problems on its own based on its intelligence and reasoning capabilities. When applied to our shopping experience, behavioral AI will be able to analyze customers and make suggestions on its own without being asked. This will allow for more free-flowing communication between customers and AI. Both search and shopping ads will become hyper-personalized. We also expect to see the introduction of "short-term memory" that remembers previous conversations, leading to chatbots that have conversations rather than one-off questions and answers.
However, not every commerce industry that has embraced AI has been successful. One entrepreneur used a free trial of Shopify to open a commerce with AI. From images to products, he actively utilized Midjourney and ChatGPT to create webpages, make t-shirts, and start selling them. He even introduced paid ads, but none of them actually converted into purchases. They were capitalizing on evolving technology trends, but they didn't succeed because they basically had no insight into their product and market.
So running an AI-powered commerce industry isn't just about having the right people or technology to utilize AI. It's about building a data-driven AI models, but more importantly, it's important to work with a partner that has a deep understanding of how the commerce industry operates.
- Artificial intelligence in eCommerce - Everything you need to know. - Core dna
- Reshaping E-Commerce: The Influence Of AI-Generated Content
- NAVER Commerce Evolves with AI Technology..."Users and Sales Embrace"
- 'Shopify' Takes on 'Amazon' with AI Solutions
- Marketing is AI's dream, are you ready?
- AI Readiness Report 2023 | Scale AI