The traditional retail industry is being reinvented and modernized as more and more physical stores stimulate business by adopting state-of-the-art e-commerce platforms. . The recent rapid development and deployment of artificial intelligence technologies, such as machine learning, computer vision and reinforcement learning, led to the development of new products and e-commerce solutions for various scenarios and reinforced the retail value chain.
E-commerce market size and opportunities for IA
E-commerce activities are often enabled on a number of online sales platforms (for example, Alibaba's Taibao, Tmall, Amazon, and JD.com); or in the official online stores of the brand (Tesla, Nike, Casper, for example). With the latest advancements in AI and digital technologies, the operational costs of e-commerce have been reduced, allowing more retailers to perform e-commerce transformations. The global retail e-commerce market in 2018 is $ 2.8 billion and is expected to grow 75 percent to $ 4.9 trillion by 2021. China is the largest e-commerce market in the world, with the largest number of B2C sales and the largest number of consumers. According to an AliRearch () survey of more than 1,000 e-commerce retailers in China, more than 80% of marketers have adopted and often use artificial intelligence tools in their businesses.
AI Technologies boost e-commerce
Current IA-based e-business strategies are primarily based on computer vision, natural language processing, and reinforcement learning technologies.
Computer vision: By leveraging computer vision, retailers can create product lists with high-quality images, allowing customers to better understand the details of a product or service. Computer vision technology also offers insights in areas such as algorithm-based product poster design and product recommendations based on customer style preferences.
NLP: E-commerce platforms use NLP in their search and ranking algorithms for keyword analysis and attribute extraction from product descriptions to enhance the shopping experience with better matching products. NLP-based dynamic rating systems can also guide customers more efficiently to fashion products.
Learning by reinforcement: Many large e-commerce platforms use big-time and reinforcement learning technology to predict user behavior to optimize product rankings in search results and increase their conversion rates. e-commerce.
Implementations of AI in the e-commerce value chain
Product search: Product research is one of the most widely used and important features for e-commerce platforms. Customers can find products that match their interests through keywords, combining products based on NLP technologies and visual "image search", which leverages computer vision. E-commerce platforms also use booster learning technologies to optimize their ranking algorithms and provide better search results.
Custom product recommendation: In addition to research, e-commerce platforms also use machine learning and NLP techniques to engage consumers and make personalized product recommendations based on their buying patterns and browsing history.
Dynamic prices: Many e-commerce platforms use dynamic, data-based pricing tools and machine learning algorithms to make real-time price adjustments or predict future prices based on forecast supply and demand. the request.
Fraud risk management: E-commerce retailers use machine learning technologies to identify possible fraudulent credit card transactions to prevent and control risks in real time and to ensure the security of online payments.
Representative Use Cases for IA E-Commerce Implementation
Alibaba: Alibaba launched its own "Pailitao" image search engine in 2014. Since then, Pailitao has been widely implemented in the e-commerce platforms Alibaba "Taobao" and "AliExpres" to automatically return images visually similar to those uploaded by Alibaba. examples to coordinate clothes, accessories and other products.
Pinterest: In 2017, the US social media platform, Pinterest, has launched its visual search tool "Lens" as a search engine for ideas about its mobile application. The company has partnered with retail giant Target to integrate Pinterest's goal with target.com's e-commerce platform to help customers find items of interest through images.
Point correction: As a personal style service provider, Stitch Fix provides highly customized clothing recommendations with style algorithms developed using machine learning technology, customer preferences, and brand data. purchases historic.
Amazon: Amazon has long used dynamic pricing algorithms for product sales in its e-commerce platform, often adjusting product prices based on factors such as inventory and demand trends.
Limitations of the application of AI in electronic commerce
Cold start problemNote: Due to the scarcity of data, retailers operating a new business on an e-commerce platform may not be able to take advantage of advanced features based on artificial intelligence, such as referral system and dynamic pricing, which are based on big data and analysis .
Problem of evolution of the algorithm: Enhanced learning technology can address performance issues on e-commerce platforms. Algorithms often have difficulty solving design problems and can tackle challenges effectively by searching for very large decision spaces.
Long Tail Effect: Ecommerce recommendation algorithms can present only a small number of the most popular items to customers and do not recommend rare "long tail" products that may be more attractive to niche consumers, as these products can for example. lack of sufficient classification data.
Future Trends for IA Applications in E-Commerce
Personalization based on data: Personalized search algorithms and product recommendation based on each user's information (social behavior, profession, preferences, etc.) will become increasingly common in e-commerce platforms.
Man in the tie: Operational models combining artificial intelligence and human knowledge will become increasingly widespread. For example, Stitch Fix has adopted a hybrid approach that keeps humans informed about product recommendations, for example.
How to connect offline channels with online sales in e-commerce: More and more retailers, especially fashion traders, provide "image search" services to enable consumers to locate and purchase an item online, such as a fashion show or in a magazine.
location: Tingting Cao | editorMichael Sarazen