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【2021 Application Example】 Eastern Home Shopping Implements OneID AI Traffic Monetization Service, Cost-Effectiveness Up to 2 Times

How to integrate consumer data from various group companies to create advertising synergy and enhance the conversion rate of e-commerce guided orders is probably what every cross-industry business owner dreams of. No problem, this can be achieved gradually through AI!

Eastern Home Shopping is affiliated with the Eastern International Group, which includes East International, Eastern News Cloud, Eastern Insurance Representatives, Eastern Natural Beauty, Eastern Global Marketing, Eastern Pet Cloud, HerEastern, Focus Media, Hong Kong Strawberry Net, and Bear Mom's Vegetable Market, among other companies. With cross-industry and cross-domain relationships within the group, and independent operations of membership systems in each unit, consumer data could not be exchanged within the group, making it difficult to uphold Eastern Group's promise to 'place customers in a godly position'.

Eastern Group's companies cover a wide range of industries, with large and dispersed member databases.

▲ Eastern Group’s companies cover a wide range of industries, with large and scattered member databases.

The Eastern Group boasts significant member traffic and has applied AI news recommendation algorithms and other related technologies across various venues. However, the independence of member systems in each unit of the Eastern Group prevents the exchange of consumer data within the group, lacking a comprehensive basis for consumer behavior analysis. This results in the inability to enhance the precision of personalized services and marketing strategies.

When analyzing the challenges and trends of the current retail market, Eastern Group remarked that in response to changing consumer demands, non-traditional business models are emerging, leading to the fragmentation of retail. Various emerging business models provide services or products catering to their niche markets, leading consumers to rely less on traditional retail models.

Retail fragmentation, becoming more apparent in emerging countries, rapidly develops new forms of retail such as high-growth flash sale eCommerce, which threatens traditional B2B2C eCommerce platforms. These emerging business models quickly divide traditional retail spaces and could revolutionize existing market rules. The retail market is expected to continue evolving towards segmentation.

The rapid integration of AI applications in the new retail industry to meet highly competitive markets

Under the trend of merging physical and digital realms, the line between offline retailers and online e-commerce is increasingly blurred. Offline retailers are setting up brand official websites and developing brand apps, investing in e-commerce platforms, while e-commerce operators are starting to established offline physical experience stores, enhancing touchpoints with customers. Both are exploring consumer data profiles through offline-online integration, based on AI technologies like machine learning, deep learning, computer vision, language processing, mobility control, and decision-making technologies to actively integrate intelligent retail AI applications, shaping the new retail industry.

Additionally, Google Chrome claimed in 2021 that it would disable 3rd party cookie functionality within two years, causing retail companies to lose the ability to track personalization via Cookies and understand user behavior across different times, locations, and ads. This will prevent cross-device, cross-platform tracking, forcing companies to transform and face big challenges in traffic advertising sales.

Therefore, the Eastern Group decided to implement the 'OneID AI Traffic Monetization Service Validation Plan', establishing an exclusive data alliance for the Eastern Group, using 'Unified ID' for cross-industry, cross-service data exchange. Transforming from collecting personalized data of related companies to analyzing common behavioral characteristics of consumers across industries, segmenting them to obtain users with similar behaviors, and providing interesting content. Additionally, utilizing first-party data and AI technologies to improve ad click-through rates, enhancing the advertising value and e-commerce guided order conversion rates.

This AI technology project is co-developed by Eastern and ASUS computers, encompassing major development tasks such as project planning, system architecture design, system environment setup, algorithm development, algorithm model validation, and system verification. The employed technologies include a big data parallel processing framework, natural language processing, user recommendation embedding systems, similarity search, search engine indexing, and click rate prediction. This project aims to develop a comprehensive data collection, processing, and integration platform 'Data Middleware', collecting various data sources, focusing on users as the basic unit, forming structured data tables, and calculating user tags for precise characterization of each user. Subsequently, this data is utilized for precise AI advertisement placements.
 

Eastern Data Middleware architecture diagram.

▲Eastern Data Middleware structure diagram

Eastern Home Shopping introduces OneID AI Traffic Monetization Service, predicting cost-effectiveness to be up to 2 times

Eastern stated that this project primarily applies 'user behavior data' and 'AI technology', with user behavior data provided by the Eastern Group and AI technology being co-developed by company and ASUS teams, covering systems such as AD Serve, precise audience estimation system, AI automatic optimization system, advertising efficacy system, and user profiling system. The customer data and traffic of AI technology co-developed with ASUS remain independent and not interconnected.

According to estimates, this development project's total cost-effectiveness could reach 200%, expected to precisely capture the user's digital trajectories, behavior, and profiles, potentially resulting in significant growth in customer lifetime value (LTV), effectively integrating Eastern's online and offline services, enhancing membership service content, and substantially increasing corporate value.

In the future, as the Eastern Group continues to expand into international markets, it currently targets Mainland China as the primary promotion market, extending the entire service module with Eastern Global’s operational model to the global Chinese market while ensuring compliance with GDPA, merging it with Strawberry Net to provide Eastern's new retail services with the advantages of big data and AI globally.

Eastern Group will expand its technology to the global market through Strawberry Net.

▲Eastern Group will expand its services and technology to the global market through Strawberry Net.

「Translated content is generated by ChatGPT and is for reference only. Translation date:2024-05-19」

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Using Plant Growth Chambers as an Example - Standardizing Electronic Device Procedures Based on Imaging

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solution for gradual upgrades of plant factories Most importantly, the system injects a sense of technology and modernity into agricultural production, helping to attract the younger generation to the field and injecting new vitality into the industry Looking ahead, HaiBoTe's smart agriculture system has broad application prospects and expansion potential In addition to plant factories, this system can also be applied to traditional greenhouse cultivation, urban agriculture, and even home gardening In the field of aquaculture, similar technology can be used to monitor and optimize the breeding environments for fish or shrimp In the food processing industry, similar intelligent monitoring and forecasting systems can be used to optimize production processes and enhance food safety Even in the pharmaceutical industry, this type of precise environmental management system could be applied to drug research and production processes To further promote this system, HaiBoTe could adopt a multifaceted strategy Firstly, they could collaborate with agricultural colleges and research institutions to establish demonstration bases, allowing more people to experience the benefits of smart agriculture firsthand Secondly, they could develop customized solutions tailored to different scales and types of agricultural production, expanding the applicability of their products Furthermore, they could raise awareness and acceptance of smart agriculture within the industry by hosting forums, online seminars, and sharing success stories Lastly, they could explore collaborations with government departments to integrate this system into policies supporting the modernization and sustainable development of agriculture, thereby promoting the widespread adoption of smart agriculture on a larger scale Through these efforts, HaiBoTe not only can expand its market share but also make a significant contribution to the sustainable development of global agriculture, truly realizing the vision of 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【導入案例】化身大型AIOT科技遊樂場 海科館華麗轉身好吸睛
Transforming into a Large-Scale AIoT Technology Playground: The Spectacular Makeover of the National Museum of Marine Science & Technology

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CCTV Intelligent Video Search System

Search for a specific person, find someone with a suitcase entering the factory in Gao'an area Color features of the person and the object confirmed, person in blue and black top, suitcase in black color, throughCCTV the intelligent video search system, by setting object and color retrieval conditions, it can successfully locate three video clips containing the target subject This greatly aids operational staff in finding the target items, and through this system, search speed can far surpass manual effort6fold Pain Points The CSE-Kaohsiung Plant is densely equippedCCTVto monitor every corner of the plant area, but when an incidenthappens, it's impossible within a limited time throughCCTVvideo playback to find the incident, the implications and risks behind this are self-evident Many areas that are usually unmanned can easily become security blind spots Thus, how to monitor a vast plant area more intelligently and effectively is one of the crucial aspects of building a smart plant for 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searching Problem Scenario Object Detection The data source for object detection comprises two parts Open-source datasetsOIDv4and AES Kaohsiung PlantCCTVImage files For these files, search for usable data, specificallyOIDv4image files For these files, extract the defined nine major categories of objects for training data among them, two object categories, knives and gasoline barrels, were not found inOIDv4found usable data for knives and gasoline barrels, while the remaining seven categories of objects are available fromOIDv4useful training data found for the remaining seven categories of objects, all marked Regarding the Kaohsiung PlantCCTVimage files, select some frames Frame of the footage, and manually annotate the objects to be_detected for training and testing data Nine Major Objects Color Recognition The data source for color recognition is divided into two partsInternet image screenshots, and Kaohsiung PlantCCTVimage files Currently, no publicly available open-source datasets specifically for color recognition applications have been found, so images are collected from the web Search the web for images of the defined nine major object categories, save the images after separating the objects from the background, keeping only the object sections, and mark the images according to color Additionally, for the Kaohsiung PlantCCTVimage files, use the already-markedbounding boxextractCCTVimage files from variousFramesections of objects identified by color, and finally, visually identifiable images are marked according to color Each object category has its specific color definition, depending on the usual colors seen in these objects in real life Dynamic Ignore during Training FromOIDv4during the training of the object detection pilot model, since each image in this dataset is only marked for a single category, but the image may contain other desired detection categories unmarked For such cases, dynamic ignore techniques will be employed during training to avoid 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Translation date:2024-12-12」