MOSZCZYNSKI 12-23 (2)
E-commerce has become an inseparable part of the modern economy. The Internet gives businesses access to global markets and diverse international communities. It embodies globalization and operates in a perfectly competitive market environment.
On the other hand, the Internet allows for more effective penetration of local markets and reaching domestic customers. Using the right methods and tools has become the primary way to gain a competitive edge. The online market is no longer new or unfamiliar; it’s one of the main development directions for modern economies. Efficiency in this market reflects the effectiveness of both businesses and national economies. Someone might prefer to sell in the so-called “real world,” on a tangible domestic market. However, the digitization of the modern economy pushes us into online commerce, leaving less room for everything else. It’s safe to say that those who don’t get on board will be left behind. Using advanced tools in the digital economy is a necessity we cannot escape. Advanced digital tools, such as neural networks, are approaching the complexity of some models and algorithms used in online applications. Building and implementing neural networks is more demanding than typical data science modeling, requiring thorough and specialized expertise, particularly in machine learning parameter tuning. Neural networks are especially useful for analyzing and processing information from streamed data. Typical non-neural models can operate based on a trained configuration of parameters learned from historical data. In contrast, neural networks are better suited for learning and adjusting parameters based on real-time data streams. This feature of neural networks is particularly useful in the applications discussed below.
What Are the Key Applications of Neural Networks in E-commerce?
Offer Personalization
Personalization is one of the most crucial tools in e-commerce. For personalization, training a neural network requires a historical transaction set for each individual customer. Neural networks analyze past customer behavior, as some customers enjoy sports, others are interested in cars or DIY. The system can tailor offers to individual preferences. Another way of identifying customer preferences is by analyzing the pairs of products they purchase. The system identifies synergistic pairs, or items that complement each other based on their functional relationships. For example, if someone buys a drill, a machine learning algorithm may suggest drill bits and protective headphones. Neural networks can analyze customers in groups or segments. For instance, the system may detect that pregnant women search for specific items online, prioritizing safety and ergonomics over price. The algorithm identifies that these individuals are more responsive to suggestions in medical, pharmaceutical, or dietary supplement areas. In this case, neural networks don’t need individual transaction histories; they use general information about specific customer segment behaviors.
Creating Product Descriptions
Before making an online purchase, customers typically review product descriptions carefully. Often, descriptions or images do not reflect reality, are incomplete, or exaggerated, leading customers to distrust online content. They may prefer to visit a store, verify product features and quality firsthand, and ultimately purchase in-store. Neural networks are the only tool capable of identifying products from images. These algorithms can recognize items like jackets or pants in photos, distinguish their main features, style, and material quality, and determine size by comparing proportions. Through image analysis, neural networks can create coherent and reliable descriptions for customers, saving stores from hiring people to write descriptions. Descriptions generated by neural networks contain significantly fewer errors than those written by humans.
Fraud Detection
Cases of online fraud pose a serious problem for e-commerce. Customer uncertainty and distrust of the Internet result in significant financial losses due to lost revenue. In some cases, customers never receive their ordered products despite paying for them. Acts of fraud are often preceded by specific seller behaviors. Fraud occurs under certain circumstances and technical characteristics and appears at specific transaction times. Fraud leaves distinct traces and patterns. Neural networks can detect suspicious behavior in real-time, just before a transaction, and prevent fraud.
Sales Forecasting
Optimizing spending and capital frozen in inventory is essential in e-commerce, as well as order fulfillment time. Quick delivery is important to customers, and there’s intense competition in this area. Fast order fulfillment requires having the product in stock. Each stored item incurs fixed trade credit costs, storage, protection, and administrative expenses. To optimize inventory levels, we need a reliable sales forecast for the upcoming period. Based on sales history, neural networks can reliably forecast periods of high sales and estimate stock requirements for specific times. This information optimizes inventory, shortens product delivery times, and prevents unnecessary storage costs.
Customer Opinion Analysis
We live in an era of information and the freedom to share opinions and suggestions. Customers can provide valuable insights into our weaknesses and shortcomings. However, despite the abundance of feedback, it’s difficult to extract valuable conclusions. Each person has a unique way of expressing observations, with varying levels of emotion and bias. Feedback often includes feelings like regret, complaint, or admiration, each directly affecting the tone. We may encounter cynicism, sarcasm, or exaggeration of certain features or functionalities. Filtering emotions to obtain valuable, coherent information is a tedious process. Neural networks can help find valuable insights, particularly identifying real customer needs and weak points in our products or services. Reliable, coherent, and valuable feedback can help achieve a significant competitive advantage.
Chatbots in Customer Service
Neural network-based chatbots can provide 24/7 online customer service. While customers may sometimes find talking to chatbots frustrating, the availability and access to critical information at unusual times can be highly valued. Neural networks can easily provide such a level of service.
Price Optimization for End Customers
Price adjustment is crucial in commerce. Prices that are too high slow turnover, while those too low lead to revenue losses. In a business with thousands of products, implementing a sound pricing strategy is challenging. Adding promotions, discounts, and various marketing and loyalty activities, the potential losses from a poor pricing strategy become apparent. We must understand customers’ price sensitivity; price can be both a stimulating and deterring factor. Neural networks can efficiently optimize the pricing process.
Ad Personalization
Online, there’s a constant battle for user attention. The digital space is saturated with content and incentives. Users are accustomed to ignoring ads and avoiding engaging stimuli. However, ads occasionally meet specific customer needs. Spending on mass advertising isn’t worthwhile if only a small portion reaches the customer. A better approach is to send ads tailored to specific customers, containing information that appeals to them and encourages a particular response. To do this, we need to know our customers, their preferences, and needs, and understand what will serve as an effective incentive. As in previous cases, neural networks can be utilized here.
Optimizing Logistic Processes
Modern e-commerce logistics operates in a diverse environment of goods, deadlines, sizes, and sometimes peculiar customer expectations. Effectively managing logistics is extremely tedious and time-consuming. Systems based on neural networks can optimize correlated delivery routes, pickup times, loading processes, and even control warehouse unloading robots. Neural networks can also be applied to operate trucks or trains, enabling self-loading at logistics points.
Summary
Neural networks are undoubtedly an innovation and a cutting-edge tool among new technologies. Soon, most companies will use these algorithms to optimize their business. Since ancient times, development has focused on optimizing costs, time, quality, safety, and other aspects. It will be up to company managers to decide whether they take advantage of the opportunities neural networks offer.
Wojciech Moszczyński – A graduate in quantitative methods from Nicolaus Copernicus University in Toruń, specializing in econometrics, data science, and management accounting. He specializes in optimizing production and logistics processes. He conducts research in artificial intelligence development and application, actively promoting machine learning and data science in business environments.
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