AI and e-commerce are fantastic partners
Did you know that AI and e-commerce are developing by influencing each other greatly! First, AI technology has brought about a revolutionary change in the field of e-commerce. It has advanced the shopping environment by enabling personalized customer experience, customized services, high-precision search results, and efficient operation. Now, customers feel frustrated that they cannot get recommendations based on their interests at shopping malls that are not equipped with AI technology, they have to wait for a long time for responses, and they have to endure inaccurate search results.
E-commerce provided AI with abundant data and played an important role in learning AI models. It generated vast amounts of customer data, sales data, and behavioral data to improve model performance such as recommendation engines and natural language processing. Learning high-quality data is essential for AI models. Only when you give high-quality data can you improve accuracy, reliability, and generalization ability. E-commerce serves as an excellent test bed for improving AI models and commercializing new AI technologies. Various new technologies are being tried in e-commerce and their performance can be verified. Eventually, AI and e-commerce are creating a synergy effect as a fantastic pair.
AI technologies that support e-commerce these days
What AI technologies are being used in e-commerce shopping malls that provide customers with a better shopping experience?
1) AI-based personalization recommendations
This means that AI analyzes customers' tastes and interests and recommends customized products. For example, a shopping mall equipped with PLATEER's AI personalized Martech solution, groobee, can make strategic personalization recommendations by using 28 types of AI recommendation algorithms that are the most provided by the industry! As in the 'Top 10 Most Popular Products', a statistical-based algorithm can be recommended to first-time visitors, and 'Purchase Pattern-like Products' or 'Customer Profiling-based Recommended Products' can be exposed to member visitors with a purchasing history. When marketers set their own goals (click/order) to increase AI efficiency, they automatically optimize the most effective recommendation algorithm for achieving them. |
2) New AI Targeting
Shopping malls that are making good use of AI analyze every customer's behavior with AI. With "groobee," a Martech solution that can be installed anywhere with its own mall in the form of SaaS, AI can target by using segments automatically classified into big data analysis and machine learning technology. The groobee automatically classifies 10 segments under RFM (1). Marketers can select specific segments such as "Customers to Care about" and "Customers Concerned about Departure" and send campaign messages. The groobee also automatically calculates the probability of purchase for all customers and predicts the probability of purchase by probability section. By exposing secret discount coupons to groups of customers with a high probability of purchase from 80 to 100%, you can conduct on-site campaigns without wasting any money. Recently, Groobee launched a "Taste Analysis Segment" function. When AI analyzes customers' tastes in the past month to create groups such as "Vacation Look," "Minimalist," and "Interseasonal Coat Style," and LLM models analyze detailed characteristics for those groups, marketers can select and target segments. |
3) Natural language processing and image analysis using AI
With AI technology, it is possible to derive results with high accuracy understanding the context of search terms and to recommend products based on image. The AI Chatbot function of X2BEE AI, an e-commerce AI recently launched by D2C e-commerce platform X2BEE, provides customized answers by understanding the context when customers ask natural language questions. In addition to handling simple and repetitive questions, it can also quickly handle customer services such as order details, delivery, product change, cancellation, exchange, return, and coupon confirmation. The groobee is an image-based similar product recommendation algorithm that helps early shopping malls recommend products. It is an image analysis AI function that can be useful when there is not enough customer behavior data accumulated or there is not enough customer behavior data on new products. By analyzing various attributes such as the type, color, and shape of a product image, it is possible to recommend products with high image similarity to the product style that customers prefer. |
4) AI-Based Customer Behavior Analysis
The integrated CS function of 'X2BEE AI' supports customer consultation work based on customer behavior analysis. It analyzes various behavioral data such as customer's order, inquiry, and product evaluation registration in the shopping mall and provides customer response guides and recommended answers. While the efficiency of consultation work is improved, customer satisfaction can be increased through customized responses. 'AI Demand Forecast' provided by 'X2BEE AI' helps efficient inventory management by predicting sales volume and inventory. After analyzing customer purchase data in the past with machine learning and deep learning technology, it predicts sales, inventory, and quantity by product/category. By predicting customer demand in advance, stable management such as inventory management and sales KPI setting can be achieved. |
5) AI-based product search technology
The 'AI Search' feature of 'X2BEE AI' is an e-commerce-specific search engine that demonstrates incredible search capabilities. Effective search is possible by selecting a variety of search options on the right side of the search box. First, the semantic search function derives the right product from a search using different keywords or ambiguous words for each person. Even if a customer searches by image, the image search function displays similar product results. In addition, the hybrid search function, which combines keyword search and semantic search, provides more accurate results by understanding the customer's search intention more closely.
The groobee also recently launched a "generative search service." It provides optimal search results by analyzing search terms entered in natural language with LLM. It derives highly accurate recommendation results for specific requirements such as "a black padded jacket that is good to wear in the winter." In this process, you can check more advanced search results by applying filtering such as color, price, and gender! |
What is the e-commerce shopping mall in the near future and the future?
💡 Personalization based on customer mood and circumstances
AI will analyze customer behavior data and preferences more precisely, providing more customized recommendations with each shopping experience, as well as exposing recommendations or messages that are appropriate for the current customer's mood or situation.
💡 Chatbot that breaks the boundaries of offline/online
If AI chatbots are applied with voice recognition technology, an interactive shopping environment can be created in which customers ask questions and get information directly from chatbots. Online shopping for customers will become more intuitive and convenient if they talk to chatbots in natural language, as if talking to employees in offline stores.
💡 Advanced predictive analysis
AI will go beyond analyzing customer behavior data in e-commerce shopping malls to help companies make better decisions by analyzing market trends and trends. So, I think we will be able to extract the insights needed for marketing strategies, product lineups, and pricing policies in the near future, although we are currently at the level of getting insights for demand forecasting and inventory management.
💡 Integrating the omnibus experience
Online shopping mall visits and offline shopping experience can be integrated. By linking customer experience in stores with online shopping mall visit data, AI will be able to track customers' purchasing journey and help them enjoy a consistent shopping experience everywhere.
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