Watsons Taiwan, holding the leading position in physical chain drugstores in Taiwan, has continued to expand its digital transformation Since establishing Watsons' online store in 2014, apart from actively developing the e-commerce market, the company has significantly enhanced the online and offline OO omni-channel consumer experience by integrating Insider AI technology This integration utilizes extensive in-store sales data, consumer behavior analytics, and AI-driven personalized recommendations delivered at optimal times to increase conversion rates OO Online Plus Offline Boosts Customer Conversion Rate, Driving Business Growth Watsons Group, a global retail giant, has been deeply rooted in Taiwan for the past 30 years specializing in retail, store operation SOPs, and retail supply chain optimizations However, managing an e-commerce platform only began a few years ago Unlike the commonly discussed 'O2O' online to offline in retail, Watsons adopts 'OO', which is offline plus online Currently, about 20 of customers who order at Watsons' online store choose to pick up their goods at physical stores Proper service at these stores acts as a catalyst for converting online-originated customers into additional in-store revenues According to statistics, Watsons has nearly 6 million members with a substantial volume of transactions in physical retail outlets However, with over 12 million active app users and nearly 3 million app downloads, the level of member activation is still lacking By utilizing AI technology for data integration, such as providing optimized product recommendations through AI, Watsons could significantly enhance its customer conversion rate from offline to online consumption or guide online customers to in-store purchases, thereby driving business growth Homepage Personalized Recommendation Module Recommended for You Originally, Watsons used the e-commerce solution Hybris from the global system integrator SAP, which was more geared towards simple display and sales, lacking sufficient technical resources to handle enhancing the consumer experience Insider is a marketing technology martech company with offices in 25 cities globally, including a professional consultancy team in Taiwan that provides localized digital solutions Committed to optimizing digital marketing effectiveness with technology, Insider helps brands drive digital growth and is a partner to many domestic and global enterprises including Watsons, Carrefour, IKEA, Lenovo, Adidas, Sinyi Realty, and Singapore Airlines Insider has shown outstanding performance in improving customer conversion rates, repurchase rates, and advertising ROI through AI technology Watsons introduced Insider's AI algorithms primarily for enhancing customer experience, using AI's personalized and integrated marketing modules to elevate the customer interaction and improve e-commerce conversion rates Additionally, AI functionalities search for the right customers, expanding new customer groups and providing a superior shopping experience Page-specific Discount Code Copy Feature Recommended Based on Customer Behavior Insider has developed various technological modules that can be applied in different customer scenarios to enhance conversion rates Currently, Watsons' e-commerce websiteAPP utilizes different Insider modules, with some parts also tailored based on Watsons' unique attributes such as necessities repurchase, app navigation, and scratch card discounts, designing conversion kits or personalized recommendation modules for specific customer situations within Watsons Introduction of WebAPP Personalized Recommendation and Conversion Module Kits Effectively Increases Conversion Rates by 10 Watsons has already introduced the first four of the planned modules, with a full rollout of all five modules expected by 2021, aiming to enhance both online and offline cross-sales and thereby comprehensively improve Watsons’ overall e-commerce and retail performance 1 Web RecommendationConversion Suit 2 App RecommendationConversion Suit 3 InStory for eCommerce 4 Mobile App Template Store 5 Insider Architect Watsons has currently implemented the AT module, with completion expected by the end of 2021 Since partnering with Insider in 2020, Watsons has introduced WebAPP personalized recommendation and conversion module kits, effectively increasing transaction conversion rates by an average of over 10, with ROAS Return on Ad Spend averaging over 10 Watsons also hopes to integrate POS sales records into Insider's CDP Customer Data Platform to achieve a more optimized OO interaction mechanism and complete an all-channel consumer experience By combining Insider's AI technology, Watsons' self-operated official website, supplemented by extensive in-store sales data and member consumer behaviors, along with AI's personalized recommendations delivered at optimal points, the technology will significantly boost consumer transactions online and interactive opportunities in-store Utilizing new technologies in the competitive e-commerce sector allows Watsons to maintain a unique leadership position in the beautyhealth category in the consumers' minds「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

Although satellite remote sensing images can make all surface objects visible, it still requires a lot of time and manpower to be truly applied to the industry In order to effectively solve the problems that customers face in digesting huge amounts of image data and eliminate technical obstacles for cross-domain users to process satellite remote sensing images, ThinkTron has developed an "AI Analysis Cloud Service Platform for Remote Sensing Big Data" as a new beginning for cross-domain AI applications for spatial information In recent years, in response to the impact of industrial globalization, Taiwan's agriculture has continued to transition towards technology-based and higher quality, improving the yield and quality of crops by solving problems, such as microclimate impacts and pest and disease control The demand of agriculture on images has expanded endlessly to accurately grasp the growing environment of crops In the early years when UAVs unmanned aerial vehicles were not yet popular, manual field surveys were the most basic but most labor-intensive work With the emergence of UAV drones, aerial photography operations might not be difficult, but the range that can be photographed is limited Furthermore, surveying expertise is required to accurately capture spatial information At this time, the use of satellite remote sensing data may break away from the past imagination of using image data Taiwan Space Agency TASA ODC data warehouse services In the past ten years, with the breakthrough of modern satellite remote sensing application technology, Digital Earth has become a new trend in global data collection Countries have developed data cube image storage technology, and the development of smart agriculture has become one of the largest image users Determining planting distribution is the first step in understanding crop yields Free satellite remote sensing images, powerful data warehousing support, and the team's robust image recognition technology are important supports for accelerating agricultural transformation Using satellite remote sensing image data can accelerate the development of smart agriculture However, in the past, it was difficult to extract large-area crop distribution through satellite remote sensing images, not to mention the cost If you wanted to use free information, you had to visit all websites of international space agencies, look through the wide variety of satellite specifications, and carefully evaluate the sensor specifications, image resolution, and revisit cycle After finding suitable images, you had to look at them one by one to filter the ones you need Next is downloading dozens of images that are often several hundreds of Megabytes MB each, which might exceed the capacity of your computer Also, after overcoming image access and preparing data, you must then start to confirm the downloaded image products and which bands you want, because the image you see is not just an image file jpg or png, but rather complex multi-spectral information, attribute fields and coordinate information It takes a lot of effort just to confirm the correct information Facing GIS software packages with complex functions is the start of another trouble The complex image pre-processing process and the inflexible machine learning package greatly reduce the efficiency of analyzing data After finally getting the results of crop identification, you might find that the best time for using map information may have already passed The above-mentioned complex and time-consuming satellite image processing problems are precisely the pain points of the market ThinkTron expanded from traditional machine learning to modern deep learning applications, and developed an "AI Analysis Cloud Service Platform for Remote Sensing Big Data" under the GeoAI framework, breaking through the constraints of details in the spatial information for customers Differences between the process before and after introducing the AI analysis cloud service platform ThinkTron said that Taiwan's ODC Open Data Cube system has been completed and began providing services after years of efforts from the Taiwan Space Agency TASA, formally becoming aligned with international trends The powerful warehousing technology allows users to easily capture and use image data of a specific time and spatial range according to their needs The warehouse stores multiple satellite image resources from international space agencies, including the ESA's Sentinel-1 one image every 6 days, Sentinel-2 one image every 6 days, USGS's Landsat-7 one image every 16 days, Landsat-8 one image every 16 days, and the domestic Formosat-2 one image every day and Formosat-5 one image every 2 days ThinkTron develops satellite image recognition tools based on Python Breaking free from the limitations of GIS Geographic Information System software packages, ThinkTron integrated GDAL Geospatial Data Abstraction Library based on Python, and considered computing efficiency and parallel processing when developing all tools required for satellite image processing and image recognition modeling, including coordinate system and data format conversion, grid and vector data interaction, and data intra-difference and normalization All of the tools are designed with AI applications in mind, and some commonly used tools are packaged into an open source package under the name TronGisPy to benefit the technical community ThinkTron utilized the team's understanding of satellite remote sensing images and the collected tagged data crop distribution maps to preset the image recognition modeling process, the required training data specifications, and dataset definitions This is imported into the machine learning LightGBM or deep learning CNN framework that was completed in advance, and the entire training process to be performed in the Web GIS interface, providing users with partial flexibility to freely filter images, confirm spatial and temporal ranges, select models, and adjust hyperparameters In addition to the operation of training models, it also provides historical models to output identification results, and finally displays the identification results of crop distribution on the Web GIS map In fact, agriculture is not the only industry that needs satellite remote sensing applications AI applications of spatial information have also appeared in various fields as companies in different industries aim to enhance their global competitiveness For example, surveying and mapping companies that have a large amount of map data can use the AI analysis cloud service platform to store map data while also accelerating the efficiency of digital mapping Under the severe global climate change and the risk of strong earthquakes, there is a wide variety industrial insurance, agricultural insurance, financial insurance, or disaster insurance are all inseparable from spatial information The use of remote sensing image recognition to understand insurance targets has long been an international trend AI Analysis Cloud Service Architecture for Remote Sensing Big Data

Have you had your morning coffee yet Over the past decade, Taiwan has gradually formed a coffee drinking culture With the advancement of AI technology, autonomous coffee roasters can now rely on AI for precise location setting while also cultivating a loyal customer base Let's see how this is done According to the International Coffee Organization ICO, Taiwanese consume approximately 285 billion cups of coffee annually, with the coffee market in Taiwan estimated at 80 billion TWD, growing about 20 each year In recent years, the 'drinking coffee' culture in Taiwan has become synonymous with popularity, with coffee being the most frequently chosen daily beverage by 65 of the population Coffee enthusiasts, particularly the more avid ones, are willing to pay more for coffee beans that suit their tastes An increasing number of unmanned drink kiosks have also begun to appear in the Taiwanese beverage market Unmanned coffee beverage shops face difficulties in expanding quickly, primarily due to two major issues one is the appropriateness of customer flow and machine placement locations which still rely on manual analysis the second is penetrating the market of mid to high-end coffee lovers accurately AI resolves two major challenges for autonomous coffee roasters suitable placement and cultivating a loyal customer base To tackle these issues and help autonomous coffee roasters quickly break into the market, Raysharp Electronics intends to implement AI for people flow counting analysis and unfamiliar face recognition These technologies aim to calculate the crowd size at potential roaster locations and classify consumers by gender and age for more precise market analysis They also provide multiple choices for the roasting of raw coffee beans, offering a more customized service tailored to the needs and tastes of professional coffee aficionados with a pack of 'high-quality roasted beans' Since 2018, the rise of unmanned stores has been mainly due to owners wanting to reduce persistently rising rent and personnel costs However, the initial assessment of store locations still requires hourly labor expenses for manual estimation of customer flow, leading to possible miscalculations of both on-site consumers and passerby traffic These inaccuracies may prevent precise real-time analysis of customer flow, or even misguided estimations of operational efficacy after a trial run, thus missing the optimal timing for loss-preventing location retraction Raysharp Electronics introduces autonomous coffee roasters equipped with AI-based people counting analysis and facial recognition Raysharp Electronics combines AI people counting analysis and facial recognition with the coffee trend known as 'black gold', addressing the preferences of numerous coffee connoisseurs in Taiwan who enjoy personally selecting coffee beans at bulk stores and frequenting high-quality grinding cafes or chain coffee shops A new concept for the first autonomous coffee roaster offering choices based on the origin, variety, and roasting methods of coffee beans has emerged AI coffee roasters enhance customer loyalty and materials management efficiency by 20 For the advanced development of autonomous coffee roasters, Raysharp Electronics engineers have equipped the AI NVIDIA development platform on the basis of TCNNFacenet Through AI, tens of thousands of images related to gender and age are used for sample training, allowing even first-time coffee roasting customers to be easily classified using unfamiliar face recognition This gains consumer trust, enhances willingness to use, and allows for recording purchase information and future product recommendations, leading to consumer purchase behavior analysis This information helps owners tailor future material preparation based on consumer preferences for different coffee beans, reducing raw material transportation and storage issues, and improving material management efficiency by 20 Moreover, by placing these autonomous coffee roasters in high-traffic areas, owners can use cameras to capture the crowd and assess whether the machine location has an adequate customer base, quickly analyzing whether to reposition the machines, and more easily targeting the best locations for middle and high-end coffee lovers The unmanned coffee roaster features a professional roasting mode interface, providing options based on the origin and variety of coffee beans, their roasting methods light, medium, deep, and related temperature, wind speed, and timing settings If improvement needs arise during the process, engineers can adjust firmware parameters and also assist in integration with the owner's ordering system Staff members briefly describe the operation of the autonomous coffee roaster 'Black Gold' penetrates deeper into coffee shops, science parks, and commercial buildings through AI This autonomous coffee roaster targets coffee connoisseurs and can be placed in middle to high-end coffee shops to roast more customized coffee beans than those available in bulk stores Upon completing a batch of coffee beans, it immediately provides them to professional technicians within the coffee shops for grinding and manual brewing The remaining roasted beans can also be taken home for brewing and enjoyment It also adds value to coffee shops by better understanding consumer preferences for coffee beans and launching more customer-attracting drink promotions and appropriate inventory management In addition to coffee shops, the autonomous coffee roaster can also utilize AI-based people counting analysis to precisely set up near scientific parks and commercial buildings, offering high-quality coffee beans for office brewing to internal employees with high coffee consumption needs Furthermore, implementing a physical membership system can initiate coffee bean purchase promotions or periodic payment incentives, thus attracting new clients and cultivating existing customer loyalty and retention The operation interface of the smart autonomous coffee roaster「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」