Academic literature on the topic 'Optimize google products'
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Journal articles on the topic "Optimize google products"
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Indriyani, Ellisa. "OPTIMIZING STUDENTS’ BLOG BASED ONLINE BUSINESS BY UTILIZING GOOGLE KEYWORD PLANNER AND GOOGLE TRENDS." UAD TEFL International Conference 1 (November 20, 2017): 192. http://dx.doi.org/10.12928/utic.v1.167.2017.
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This paper aims to describe the search engine optimization of students’ blog based business by utilizing Google Keyword Planner and Google Trends. Students’ blog based business is one of activities employing content writing in Business English Writing Class. It is a blog based project. As a matter of fact, most of the blog based project ended up underutilized. Producing content writing is not that simple, besides the writing quality, students have to make sure that the writing product can be searched online and indexed by Google. Thus, not only having an excellent writing skill and qualified products, students also have to learn how to optimize their content writing. This study employs a descriptive qualitative method in term of case study. Subjects are Business English Writing Class students in English Education Department of Sebelas Maret University. The findings reveal that: 1) the students’ content writing have good quality, good optimization and resulted in the real transactional activities; 2) the students acknowledge that they have to conduct a mini research to figure out what is the most appropriate keyword elaborated in their writing used in promoting their products; 3) the students learn that blog based writing is not only substituting from paper based writing into internet based writing.
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Santoso, Agung, Dwi Lesno Panglipursari, and Abu Amar Fikri. "Strategi Pemasaran Menggunakan Iklan Digital Marketing Berbasis SEO (Search Engine Optimization)." IQTISHADequity jurnal MANAJEMEN 6, no. 2 (2024): 162–67. https://doi.org/10.51804/iej.v6i2.16412.
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Digital marketing advertising is a form of marketing that a company or brand uses to promote products using digital media or the internet. It aims to provide information and influence the public to buy and use its products. One of the digital marketing techniques used is SEO. SEO preparation is the first step before conducting an SEO process. It involves preparing the rules for using keywords used from both online ads, social media, and external links on each media. So when visitors want to search for information, they type keywords into Google. The SEO process will work according to the rules consisting of title, description and tags then it will be directly optimized. Optimization is divided into 2, namely on-page optimization, namely how to optimize content to increase visibility and improve performance on a website page. Off-page optimization is a way to optimize a website and improve performance from outside the website page
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Ti, Haowei, and Ding Ma. "Research on Google Search and Amazon Search Based on the Comparison of Algorithms." International Journal of Economics & Management Sciences 11, no. 3 (2022): 4. https://doi.org/10.37421/ 2162-6359.2022.11.626.
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Search engine optimization, which can optimize the search display position of content and expose the target content to readers faster, is a great tool for marketing. For foreign trade practitioners, the traffic entrances of the two major search engines, Amazon and Google, determine the exposure of products. Amazon drives consumers to click and search with shopping as the demand and has a clearer consumption intention, while Google is a bigger information plaza than Amazon, leading people to various websites.
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Elnashar, Abdelrazek, Hongwei Zeng, Bingfang Wu, et al. "Downscaling TRMM Monthly Precipitation Using Google Earth Engine and Google Cloud Computing." Remote Sensing 12, no. 23 (2020): 3860. http://dx.doi.org/10.3390/rs12233860.
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Accurate precipitation data at high spatiotemporal resolution are critical for land and water management at the basin scale. We proposed a downscaling framework for Tropical Rainfall Measuring Mission (TRMM) precipitation products through integrating Google Earth Engine (GEE) and Google Colaboratory (Colab). Three machine learning methods, including Gradient Boosting Regressor (GBR), Support Vector Regressor (SVR), and Artificial Neural Network (ANN) were compared in the framework. Three vegetation indices (Normalized Difference Vegetation Index, NDVI; Enhanced Vegetation Index, EVI; Leaf Area Index, LAI), topography, and geolocation are selected as geospatial predictors to perform the downscaling. This framework can automatically optimize the models’ parameters, estimate features’ importance, and downscale the TRMM product to 1 km. The spatial downscaling of TRMM from 25 km to 1 km was achieved by using the relationships between annual precipitations and annually-averaged vegetation index. The monthly precipitation maps derived from the annual downscaled precipitation by disaggregation. According to validation in the Great Mekong upstream region, the ANN yielded the best performance when simulating the annual TRMM precipitation. The most sensitive vegetation index for downscaling TRMM was LAI, followed by EVI. Compared with existing downscaling methods, the proposed framework for downscaling TRMM can be performed online for any given region using a wide range of machine learning tools and environmental variables to generate a precipitation product with high spatiotemporal resolution.
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Sihotang, Hengki Tamando. "OPTIMASI RISET KEYWORD DENGAN TEKNIK ALLINTITLLE PADA MESIN PENCARI GOOGLE." Journal Of Computer Networks, Architecture and High Performance Computing 1, no. 1 (2018): 1–5. http://dx.doi.org/10.47709/cnapc.v1i1.1.
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Online information needs have evolved in the real direction. These needs include the latest information, government services, and commercial products. The research question is how to describe and optimize keyword research with the allintitle technique on the google search engine. The development method used in this research is the prototype method because it is considered able to be evaluated directly on the user. The system testing is done for 3 months by placing keywords on several websites on Google. The conclusion that can be taken is to use the allintitle technique, the search results for the web are easier to find. And this web-based allintitle technique can overcome the challenges of captcha verification from the Google search engine.
 
 Keywords: Allintitle, Google's Search Engine, Keyword competition.
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Rovira, Cristòfol, Lluís Codina, and Carlos Lopezosa. "Language Bias in the Google Scholar Ranking Algorithm." Future Internet 13, no. 2 (2021): 31. http://dx.doi.org/10.3390/fi13020031.
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The visibility of academic articles or conference papers depends on their being easily found in academic search engines, above all in Google Scholar. To enhance this visibility, search engine optimization (SEO) has been applied in recent years to academic search engines in order to optimize documents and, thereby, ensure they are better ranked in search pages (i.e., academic search engine optimization or ASEO). To achieve this degree of optimization, we first need to further our understanding of Google Scholar’s relevance ranking algorithm, so that, based on this knowledge, we can highlight or improve those characteristics that academic documents already present and which are taken into account by the algorithm. This study seeks to advance our knowledge in this line of research by determining whether the language in which a document is published is a positioning factor in the Google Scholar relevance ranking algorithm. Here, we employ a reverse engineering research methodology based on a statistical analysis that uses Spearman’s correlation coefficient. The results obtained point to a bias in multilingual searches conducted in Google Scholar with documents published in languages other than in English being systematically relegated to positions that make them virtually invisible. This finding has important repercussions, both for conducting searches and for optimizing positioning in Google Scholar, being especially critical for articles on subjects that are expressed in the same way in English and other languages, the case, for example, of trademarks, chemical compounds, industrial products, acronyms, drugs, diseases, etc.
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Novia, Cahyuni, Siti Aida, Dewi Aminatus Zahro, and Alfiatul Lailiyah. "PKM Pendampingan Literasi Digital dan Optimalisasi Digital Marketing sebagai Media Promosi Keripik Pisang." GUYUB: Journal of Community Engagement 4, no. 3 (2023): 286–304. http://dx.doi.org/10.33650/guyub.v4i3.7068.
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Many SMEs in Indonesia still experience difficulties in utilizing digital technology optimally. This was also experienced by Ibu Nur's Banana Chips UKM. So far, Ibu Nur's Banana Chips UKM has never carried out promotions using digital media because the owner does not know about digital technology that can be used as a digital marketing tool, so sales of Ibu Nur's Banana Chips often decline. This community service aims to provide counseling, training and assistance regarding what digital media can be used to promote Ibu Nur's Banana Chips SME products and to find out how to effectively promote products on various social media to support increasing sales of Banana Chips products. The method used in this service activity is lectures and training on the use of social media applications as a means of promoting Ibu Nur's Banana Chips products. The solution to this activity is to carry out digital literacy in Ibu Nur's Banana Chips UKM to increase knowledge about digital technology and optimize digital marketing using Google Maps and social media (Instagram, Facebook and WhatsApp business) as promotional tools. As a result of this service, SME owners can understand digital technology and optimize the use of social media platforms as a promotional tool there by increasing product sales.
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Purbaningsih, Yuli, Budi Sulistiyo Nugroho, Yogi Nurfauzi, Rainier Hendrik Sitaniapessy, and Hadenan Towpek. "Identification of Digital Marketing Improvement Strategies for MSME Products and Services Post-Covid-19 Pandemic in Indonesia." Jurnal Manajemen Industri dan Logistik 6, no. 2 (2022): 284–96. http://dx.doi.org/10.30988/jmil.v6i2.1045.
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The strategy to improve digital marketing for MSME products and services is a critical study. Therefore, the authors are interested in reviewing scientific evidence of increasing digital era MSME marketing to increase understanding. Finally, we can conclude that the findings include several strategies to increase the promotion of MSME products and including MSME managers preparing products and services that have future value and serving customers better, making the best buyer journeys, understand when and how to fix any shortcomings, develop periodic promotions, optimize website promotions, develop internet promotions, always place website pages easy to find, review items, create a business Google account, participate, participate, influencers, then right-click, are virtual entertainment. It is hoped that this study will become a vital force in increasing the understanding of digital marketing strategies in MSME businesses.
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Pratiwi, Galuh Eka, and Adi Nugroho. "IMPLEMENTASI METODE RANDOM FOREST UNTUK KLASIFIKASI PENJUALAN PRODUK SABUN PALING LARIS." Jurnal Teknik Informasi dan Komputer (Tekinkom) 7, no. 2 (2024): 541. https://doi.org/10.37600/tekinkom.v7i2.1610.
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This research aims to analyze soap product sales data in a supermarket to understand sales patterns and categorize products based on their sales levels. Considering the multitude of soap products, the supermarket finds it difficult to conduct research on the best-selling soap products. The data used includes 4,694 soap product sales data from January 2022 to December 2023, with variables such as type, brand, price, and quantity sold. In this study, the Random Forest method is used to classify soap products into four categories: not popular, less popular, popular, and most popular. The process of data analysis and processing was carried out using Google Colaboratory with the Python programming language. Based on the evaluation results, the produced model has an accuracy of 94.6%, which indicates that this method is effective in classifying products based on sales levels. The results of this research are expected to help supermarkets optimize inventory management and design more targeted marketing strategies to increase profits. With this classification, supermarkets can focus more on products that have the potential to contribute greater profits.
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Uci Nurkholifah, Indria Sukma Sektiyaningsih, and Rochayati Febriarhamadini. "The Influence of Social Media, Product Quality and Brand Image on the Purchase Decision of Stone Island Brand Products." International Journal of Applied and Advanced Multidisciplinary Research 3, no. 5 (2025): 331–44. https://doi.org/10.59890/ijaamr.v3i5.17.
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This study aims to identify the influence of social media, product quality, and brand image on purchasing decisions for Stone Island brand products. This research used a descriptive quantitative method with a causal associative approach to examine cause-and-effect relationships. Data were collected through an online questionnaire distributed via Google Forms to 98 respondents who use Stone Island products. The data analysis included validity tests, reliability tests, classical assumption tests, t-tests, and f-tests, with the help of SPSS version 25 software. The results showed that, partially, social media has a positive and significant influence on purchasing decisions, while product quality and brand image do not have a positive and significant impact. However, simultaneously, social media, product quality, and brand image positively and significantly affect purchasing decisions for Stone Island products. These findings provide valuable insights for companies to optimize social media use to enhance consumer purchasing decisions
Book chapters on the topic "Optimize google products"
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Bansal, Rohit, and Nishita Pruthi. "Avatar-Based Influencer Marketing." In Influencer Marketing Applications Within the Metaverse. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-8898-0.ch005.
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In the digital era, the presence of consumers on social media platforms is increasing at a higher pace than ever before. It pushes marketers to employ new ways to endorse their products on these social networking platforms. One such digital marketing trend is the use of avatar-based influencer marketing. Virtual influencers are computer-generated influencers who have a large social media following particularly on picture-based platforms such as Instagram. Avatar marketing has gained popularity as a brand endorsement strategy. Despite the increase in popularity, there are very few studies that study the effectiveness of avatar-based influencer marketing. This chapter aims at demystifying the benefits and risks of employing virtual influencers as a marketing gimmick. Data were collected from various secondary sources, such as journals, books, theses, reports and websites using different databases like Scopus, Web of Science, Google Scholar etc. Findings revealed that avatar marketing, if utilized strategically, has great potential to optimize digital marketing strategy.
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Semerádová, Tereza, and Petr Weinlich. "Reaching Your Customers Using Facebook and Google Dynamic Ads." In Research Anthology on Strategies for Using Social Media as a Service and Tool in Business. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-9020-1.ch030.
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Dynamic Product Ads (DPAs) and Dynamic Search Ads (DSAs) represent a type of online advertising primarily used for remarketing purposes. However, more and more advertisers can use DPA for acquisition campaigns as well. Dynamic ads are an integral part of the marketing strategies of all large e-shops and companies that have a product or service catalog. Dynamic advertising reduces the time and effort put into the creation of online advertisements and provides the highest level of personalized targeting possible. With a product catalog and a properly set pixel or tracking code, it is possible to create individually customized ads within seconds. This chapter tests the effectiveness of DPAs and DSAs within the context of the two greatest advertising platforms, Facebook and Google ads, and compares their performance against the manually optimized ad sets.
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Semerádová, Tereza, and Petr Weinlich. "Reaching Your Customers Using Facebook and Google Dynamic Ads." In Impacts of Online Advertising on Business Performance. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1618-8.ch007.
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Dynamic Product Ads (DPAs) and Dynamic Search Ads (DSAs) represent a type of online advertising primarily used for remarketing purposes. However, more and more advertisers can use DPA for acquisition campaigns as well. Dynamic ads are an integral part of the marketing strategies of all large e-shops and companies that have a product or service catalog. Dynamic advertising reduces the time and effort put into the creation of online advertisements and provides the highest level of personalized targeting possible. With a product catalog and a properly set pixel or tracking code, it is possible to create individually customized ads within seconds. This chapter tests the effectiveness of DPAs and DSAs within the context of the two greatest advertising platforms, Facebook and Google ads, and compares their performance against the manually optimized ad sets.
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Thakkar, Axita, Bijal Zaveri, and Tsegaye Mathewos. "EcoTech Harmony." In Advances in Logistics, Operations, and Management Science. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-2346-5.ch009.
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“EcoTech Harmony” explores the fusion of artificial intelligence (AI) and sustainable resource management in the circular economy, resonating in a world grappling with technology and environmental crises. Industry leaders like Apple, IKEA, and Google showcase a commitment to reshaping business practices for sustainability, leveraging cutting-edge AI to optimize resource efficiency and promote the circular economy. The study, done globally, examines Indian companies aligning with sustainability guidelines, including Tata Group and Infosys. Tesla's AI-driven role in the electric vehicle revolution is highlighted, emphasizing energy optimization. Consumer product giants like Patagonia, Adidas, and tech titans Microsoft and Philips showcase how AI contributes to waste reduction and environmental stewardship. The study aims to guide companies worldwide in transitioning to a sustainable future, fostering a harmonious synergy between business, technology, and the environment.
Conference papers on the topic "Optimize google products"
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Koizumi, Kosei, Yusaku Okada, and Chikori Ino. "A system for evaluating the status of future human error prevention activities in manufacturing sites based on information noticed during daily work." In 16th International Conference on Applied Human Factors and Ergonomics (AHFE 2025). AHFE International, 2025. https://doi.org/10.54941/ahfe1006567.
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In factories such as aircraft manufacturing, where the number of products manufactured is so small that the defective product rate must be reduced to zero, a great many human error countermeasures are taken. However, the burden on workers due to too many countermeasures has exceeded the limit, and there is an urgent need to optimize the management of error countermeasures as a whole. To that end,(1) Collection of detailed human factor information on problematic events such as nonconformance events(2) Collection of human factor information during normal operations(3) Structural analysis of human factors(4) Propose guidelines for human error countermeasures based on the analysis results and present multiple specific measures(4) Propose guidelines for human error countermeasures based on the results of the analysis and present multiple specific countermeasures.In this study, with regard to (2), we attempted to examine a method for collecting human factor information from on-site conversations, based on the activation of constructive communication at the work site. We called “hints that lead to constructive communication” “positive words,” and designed a system to evaluate this information from multiple perspectives. In the collection phase, the system uses Google Form, which allows workers to input information at any time from their own information terminals, and simple summaries are fed back to the site as a preliminary report the next morning.In the evaluation phase,Phase I: Establishment of a safety management systemPhase 2: Strategic safety management: Leadership, followership, and information gathering/sharing system in the field.Third step: Reputation management: educational curriculum for the next generation, development of generalist staff with diverse knowledge.Fourth step: Strategic management: Future production including social contribution, regional contribution, investment in the future, and fostering future value.We have developed a system that can present the satisfaction level of on-site communication regarding the four levels of communication, as well as advice on communication content that should be activated in the future. We have developed a generative AI that has been specially trained using past literature data on safety management, reputation management, and brand management, and this generative AI can provide “advice on communication content” in response to on-site data. The prototype application was tested at an actual factory. The prototype application is now being tested at an actual factory, and improvements are being made in order to deploy it in activities aimed at preventing nonconformance events.
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Quiroga Quiroga, Óscar, Homero Ortega, and Mateo Rueda Rodríguez. "GPTE3T: integrando la Inteligencia Artificial para optimizar el sistema de mediciones de resultados de aprendizaje en la E3T." In Ingeniería: una transición hacia el futuro. Asociación Colombiana de Facultades de Ingeniería - ACOFI, 2024. http://dx.doi.org/10.26507/paper.3817.
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El uso de la Inteligencia Artificial (IA) tiene hoy la capacidad de apoyar diversos procesos empresariales, médicos, educativos, entre otros. En el caso del sistema de mediciones de SO en la Escuela de Ingenierías Eléctrica, Electrónica y de Telecomunicaciones (E3T), el sistema de mediciones presenta varias complejidades. Por un lado, se debe soportar dos programas de formación de pregrado (Ingeniería Eléctrica e Ingeniería Electrónica) y por otro lado, se tiene una alta participación de profesores temporales de cátedra. Semestre a semestre ocurren diversos cambios de profesores - versus asignaturas, la información es cambiante, etc. GPTE3T es una solución que usa la API de GPT, pero es capaz de entrenarse con los documentos que se producen en la E3T para los procesos de formación, de mediciones de SO y mejora continua. Gracias a ello, tiene la capacidad para responder las preguntas que hacen los actores sobre estos procesos con una calidad y rapidez similar a Chat GPT. GPTE3T puede ser visto como una solución cliente servidor alojada completamente en la nube para brindar un conjunto de servicios como: el servicio que permite subir los archivos de entrenamiento a un repositorio en la nube; el servicio que inicia el entrenamiento cuando detecta que hay nuevos archivos en el repositorio para luego guardar los resultados de ese entrenamiento en una base de datos vectorial y especializada en lenguaje natural; el servicio que permite que diversos actores puedan interactuar con GPTE3T para obtener respuesta a diversas inquietudes. En este sentido, varios elementos componen la solución: por un lado, los diversos manuales o documentos que se han desarrollado y los que se pueden desarrollar con el tiempo; el script que facilita la subida de los documentos a un repositorio basado en Google Drive; el script que llama esos archivos y los lleva a la API GPT; la información vectorial que se genera en ese proceso y que se guarda en la base de datos Pinecone; un chat en una página web que sirve de intermediarios entre GPTE3T y las personas interesadas en interactuar con este.