The Evolving E-commerce Landscape: Harnessing IoT for Precision Marketing
The rapid proliferation of the internet has fundamentally transformed the landscape of business models, shifting the focus from mere consumer demand to autonomous customer sales, strategized sales experiences, and the integration of Internet of Things (IoT) platforms. The surge in online shopping, further accelerated by the impact of the COVID-19 pandemic, has propelled the growth of the e-commerce sector, particularly in China, where a diverse range of e-commerce enterprises have emerged.
IoT technology has found widespread adoption across various domains, including logistics, medical care, and national power grids, and has been recognized as one of the five emerging strategic industries at the national level. Key IoT technologies, such as Radio Frequency Identification (RFID), Global Positioning System (GPS), and Geographic Information System (GIS), have been extensively utilized in the aquatic product cold chain logistics, offering unparalleled advantages in achieving location tracking, source tracing, and electronic operations during the processing, transportation, storage, and sales of aquatic products.
The collection and sharing of data and information throughout the logistics process represent an incomparable advantage over other information technologies, facilitating the realization of systematic and intelligent management of aquatic product cold chain logistics. From a marketing industry chain perspective, the use of digital technology for product production through IoT enables cross-field integration and expansion of digital content coverage. Based on the collected user data, precise positioning can be achieved, and various forms of promotional activities can be offered. In this process, information about the user is continuously tracked in real-time through diverse intelligent devices and fed back to the manufacturer, establishing a new cycle.
However, the utilization of the e-commerce model in the marketing of fresh products still lacks innovation in terms of marketing cost control and brand management. Furthermore, the market has transitioned into a new era of retail, centered around the customer experience, which has led to shifts in consumer psychology and behavior, characterized by personalization and diversification in purchasing. Traditional e-commerce marketing falls short in effectively leveraging user data and accurately analyzing and targeting specific users, rendering it ill-suited for the current e-commerce marketing landscape.
Precision Marketing: Integrating IoT Technology for Enhanced E-commerce Strategies
In this manuscript, IoT technology is employed in the marketing system of fresh products. Through IoT identification, the product identification code is realized, enabling the retrieval of traceability information linked to the identified object. Furthermore, based on the gathered consumption information, an enhanced version of the k-means algorithm is utilized to cluster the product information and user consumption behavior, thereby augmenting the stability and accuracy of the clustering process.
The IoT identification possesses the ability to uniquely identify a target object on a global scale. It empowers the user to effectively manage, process, and exchange pertinent information through the marker data. Existing research has demonstrated the feasibility of attaining food traceability and monitoring the cold chain by leveraging RFID smart tags with temperature and relative humidity sensing capabilities in intercontinental fresh fish logistics chains. Additionally, the utilization of sensor technology, RFID, and other crucial IoT technologies has culminated in the development of an innovative intelligent warehouse management system, offering a range of benefits such as efficient product information collection, intelligent processing of incoming and outgoing goods, enhanced warehouse management efficiency, reduction in error rates, corporate costs, and employee workload.
In the IoT, object identification is utilized for physical or logical objects sensed. It enables the management and control of identified objects, and the retrieval of object-related information. This is typically achieved through Ecode, Handle, and OID identification systems. Existing studies have proposed IoT identification management schemes tailored to the characteristics of user-centric identification management, as well as comprehensive schemes for IoT identification management, comprising a standard identification information model, a user-centric management architecture, and a multi-layer verification mechanism.
However, a significant portion of these studies predominantly concentrate on the logistics aspect of the industry chain, without due consideration to harnessing customer consumption information through IoT technologies. Given the addressability of IoT devices, it is plausible to integrate IoT-based digital marketing, thus fostering cooperative interconnectivity on a larger scale. Through this, organizations can collaboratively explore and share each other’s marketing channels, thereby expanding their reach and optimizing their marketing strategies.
Enhancing Clustering Accuracy: Improving the K-means Algorithm
In the contemporary era of constantly evolving and diverse consumer consumption patterns, it is imperative to leverage consumer patterns for analysis, design relevant algorithms, and perform pattern analysis to formulate optimal strategies that meet consumers’ individualized and reasonable needs, in line with their expectations for novel patterns.
The clustering algorithm, such as k-means, exhibits proficiency in classifying and analyzing customer value. However, it is important to acknowledge certain limitations that can impact the accuracy of its classification outcomes. One limitation lies in the algorithm’s insensitivity towards ring data or irregularly distributed data. Such data patterns may not conform to the assumptions of the algorithm, leading to potential misclassifications. Additionally, ensuring a balanced volume of data samples for each category is crucial. Insufficient data samples for a specific category can inadvertently cause its merging with another category, distorting the true representation of the consumer groups. Moreover, the manual setting of the k value, which determines the number of clusters, introduces the possibility of deviation or an execution paradox in evaluating the clustering effect.
To address these challenges, it is crucial to incorporate measures that weigh the similarity of data points and optimize the initial clustering center selection strategy within the algorithm. By carefully considering the similarities between consumer profiles and fine-tuning the selection of initial cluster centers, the algorithm can potentially overcome the aforementioned limitations and enhance the accuracy and reliability of the clustering outcomes. This approach aims to ensure a more comprehensive and nuanced understanding of the consumer groups, enabling personalized and targeted marketing strategies in the context of fresh produce e-commerce.
Proposed System Architecture and Experimental Evaluation
A proposed system, based on IoT technology, for product identification is presented and its specific structure is illustrated. The scheme is designed to be compatible with the OID coding methods of other identification systems, thereby enabling unique product identification and eliminating any ambiguity in traceability objects. Incorporation of GPS/GIS technology during transportation guarantees timely and secure delivery of products to consumers. Additionally, through the logistics information platform, consumers can gain access to pertinent information about the aquatic products in circulation, including their source, production and processing procedures, transportation and distribution, and after-sales service.
The sales link in the cold chain logistics of fresh products serves as the terminal link and is the only link that directly connects with the consumers. The manner in which data and information regarding the aquatic products are processed in this link can have a direct impact on the sales volume and traceability of these products. The system employed in this link involves the use of RFID technology for reading the product information during the stocking and sales process, temperature sensors to monitor temperature changes, a video monitoring system to record the sales process, and 3G and 4G communication technology to transmit information regarding the sales of fresh products.
To ensure the seamless delivery of fresh products to the supermarket, it is essential to verify the alignment between the transported products and the supermarket’s order records. To accomplish this, a handheld RFID reader is employed to extract information from the RFID tag affixed to the aquatic products, and this information is amalgamated with product sales data and integrated into the RFID tag. Once the correctness of the information is verified, the product is then handed over, stored, and placed on the shelf. To facilitate this process, compact fixed read-write devices are installed on the supermarket shelves, capable of automatically retrieving pertinent information when the aquatic products are either added to or removed from the shelves.
The proposed system incorporates an enhanced version of the k-means algorithm to cluster the product information and user consumption behavior, thereby improving the stability and accuracy of the clustering process. The initial clustering center of the k-means algorithm is improved using a density-based approach combined with the maximum and minimum distance methodology. Additionally, the calculation of the similarity metric is enhanced by weighing the distance between samples and considering the information entropy of the features.
The effectiveness of the improved k-means algorithm is evaluated through a series of experiments, and the results are compared to the traditional k-means clustering approach. The findings demonstrate that the enhanced algorithm consistently outperforms the traditional method, with a substantial reduction of 20.6% in the average false recognition rate across various values of k. Moreover, the proposed system exhibits exceptional concurrent processing capability, with a processing time of merely 2 ms, indicating its suitability for real-time marketing and e-commerce applications.
Practical Implementation and Performance Evaluation
The practicality of the proposed system is verified through actual operational efficiency testing and analysis of the results. The improved blockchain is initially deployed on six virtual machines, and the testing process is arranged within the blockchain network through the writing of shell script commands and sending transaction requests using a multi-threaded approach. The system latency and throughput are derived from several tests, showcasing the system’s stable throughput during read operations and low latency performance, which are critical requirements for many marketing and e-commerce applications.
Furthermore, the investigation into the impact of personalized user and consumer similarity analysis on the performance and output of three IoT e-commerce companies reveals the notable advantages of the algorithmic model in comparison to the conventional data analysis model. The findings indicate that the incorporation of personalized user and consumer similarity analysis techniques within the IoT e-commerce domain leads to enhanced marketing results, heightened customer engagement, elevated conversion rates, and augmented customer satisfaction.
The empirical evaluation of the proposed system’s performance demonstrates its effectiveness in addressing the persistent challenges encountered in the development process of e-commerce marketing. By integrating clustering algorithms and IoT technology, the system enables the representation of product information and user consumption, thereby enhancing the operational efficiency of all facets of the industrial chain and promoting information sharing among enterprises at every node. The system’s stability, low latency, and high concurrent processing capacity provide valuable assistance to e-commerce enterprises in managing the ever-expanding data influx and empowering managers to devise specific measures in customer service, channel development, supply expansion, efficiency management, and other areas based on the results of comprehensive big data analysis.
Conclusion and Future Considerations
The advent of IoT technology has propelled the expansion of the e-commerce industry, and the integration of clustering algorithms and IoT technology can effectively address the persistent challenges encountered in the development process of e-commerce marketing. The proposed system, which establishes an IoT-based consumer behavior analysis system, represents a pivotal platform for e-commerce enterprises to achieve precision marketing.
The experimental results showcase the system’s stability, low latency, and high concurrent processing capacity, providing valuable assistance to e-commerce enterprises in managing the ever-expanding data influx and enabling managers to devise specific measures in customer service, channel development, supply expansion, efficiency management, and other areas based on the results of comprehensive big data analysis.
While the methodology employed in this work offers several practical advantages, it is important to acknowledge its limitations. These limitations may include a lack of detailed information regarding the scalability and generalizability of the proposed system, as well as the absence of a discussion on testing results and system performance in the context of large-scale datasets or diverse e-commerce scenarios. Furthermore, the article does not delve into potential ethical considerations, data privacy concerns, or the potential biases that may arise during the classification process.
As the e-commerce landscape continues to evolve, the integration of IoT technology and advanced data analysis techniques, such as the enhanced k-means algorithm, will play a crucial role in empowering e-commerce enterprises to deliver personalized and targeted marketing strategies. By harnessing the power of IoT and leveraging the insights gained from user behavior analysis, e-commerce businesses can establish a more comprehensive understanding of their customer base, optimize their marketing efforts, and ultimately enhance the overall customer experience.
The findings presented in this article contribute to the growing body of literature in the field of IoT e-commerce and emphasize the importance of incorporating personalized user and consumer similarity analysis for driving more impactful and personalized marketing strategies. These insights offer valuable implications for businesses operating in the e-commerce domain, highlighting the significance of embracing algorithmic models to stay competitive and responsive in the ever-changing digital marketplace.
