Search

After the pandemic we experienced, companies have felt the need to reinvent themselves and adapt to the present moment. The Internet and social networks have developed and increased their activity substantially. Users spend more time on social networks, shop more online, and feel more than ever a need for information and to view content. The main objective of this research is to define and implement a content marketing strategy for the social networks, through a quarterly content plan in the marketing services company Naive. In the first part of the research, presented in this paper, the work consisted of designing and implementing a questionnaire, obtaining a sample of 200 respondents to assess their perceptions and habits regarding social networks and the content offered on social networks, to study the results. The results obtained and analysis done will be used to develop a content strategy for Naive, which include studying the specific objectives for the company's different social networks, the actions to be developed and the content to be implemented. [ABSTRACT FROM AUTHOR]

Thousands of lives have been infected by the Coronavirus since the outbreak began; pandemics affected people's lives and had a significant impact on marketing and marketing strategy; businesses and markets had to rethink and regulate the process to adapt to the new norm in this context. The research reveals how the organization utilized corporate social responsibility as a marketing strategy during the Covid 19 pandemic. The study also emphasizes that pandemics presented an opportunity for corporate social responsibility (CSR) and marketing strategies to ensure honest pricing as a top priority, product safety, and positive shopping experiences for consumers. Second, the research examines consumer decision-making and marketing challenges during COVID-19 within the context of how businesses adopt the marketing strategy. Finally, the study discusses marketing philosophies and their future implications. [ABSTRACT FROM AUTHOR]

In today's data driven world, competitive business tends to collect more information of their customer to know about their preferences, choices, behaviours etc. To survive among competitors and grow exponentially they provide customer with customized ads and services, based on the data they have of their customers. Collection, pre-processing and segmentation of data in traditional way is bit hassle to handle as data are in enormous amount. To tackle this problem, machine learning techniques are being utilized. Businesses can optimise operations and strategies for marketing strategies through customer segmentation using RFM analysis and K-means clustering. Businesses can categorise customers effectively by using K-means clustering and RFM to analyse the recency, frequency, and monetary value of each customer. By using this strategy, businesses can better understand customer behaviour, personalise marketing campaigns, improve product recommendations, and raise customer satisfaction. The study shows a useful application to assist companies run more efficiently in today's cutthroat business environment. [ABSTRACT FROM AUTHOR]

The cosmetic business's year-on-year rise has resulted in fierce competition in the market. With the correct marketing strategy in place, the company will be able to compete in an increasingly competitive market. PT X is one of the small and medium enterprises engaged in the cosmetic industry. The SWOT and QSPM methodologies were used in this study to find a viable alternative marketing plan for PT. X. The study of IFAS and EFAS on the Cartesian diagram is located in quadrant III, and the technique utilized is to adjust the strategy, according to the findings. The Weakness-Opportunity strategy with a TAS score of 7,090 was picked in the QSPM matrix analysis. This implies that PT. X should implement a market penetration strategy in order to boost market share through increased marketing activities. Market penetration is achieved by enhancing the company's internal skills, allowing them to recognize and capitalize on possibilities in the Cosmetics Industry, allowing them to outperform the competition. And at least survive in the competitive cosmetic industry. [ABSTRACT FROM AUTHOR]

The development of technology-made startup companies in the e-commerce sector in Indonesia is experiencing reasonably rapid growth. E-commerce is a business process that connects companies, sellers, and consumers through electronic transactions and the exchange of goods, services, and information. There is a lot of e-commerce in Indonesia, so it has to take the right strategy to compete with competitors. E-commerce companies have involved social media to support marketing achievement goals, one of which is using social media Twitter. Twitter user-generated content or User Generated Content (UGC) can be used by companies as a source for obtaining market-related information and designing marketing strategy plans. This study aims to identify customer engagement formed as part of the implementation of Social Customer Relationship Management (SCRM) carried out by the company using the Social Network Analysis (SNA) method. And analyze UGC regarding e-commerce Tokopedia, Shopee, and Bukalapak using the topic modeling method. The results showed that Shopee formed a more optimal customer engagement through the implementation of SCRM by the company and the most talked about modeling topics were Hallyu or the Korean wave, giveaways, and also promos. [ABSTRACT FROM AUTHOR]

A new home industry selling food product that try to enter the market amidst of high competition in the business may have many challenges and limitations. Thus, it requires innovative strategic target marketing to survive the competition. The purpose of this study was to determine the internal & external conditions of such a business, to obtain alternative innovative strategies that suit the business conditions, to priorities the strategy to be implemented and to design a target marketing system that aligned with the chosen strategy. The result of the IFE & EFE Matrix analysis recommends the management to change strategy. Thus, Blue Ocean Strategy is used to generate alternative of strategies. Next, the best strategy was chosen using the Quantitative Strategic Planning Matrix. The result is the strategy to build customer relationships with specific customer by focusing the product variant followed by interesting packaging. Those become the basis to design the customer acquisition concept developed using the Sales Funnel Framework as part of the strategic target marketing. Consequentially, although providing fast customer service and fast provision-delivery are still the required strategies to manage decent quality standard, it become less stressful to achieve. Lastly, deciding clear and aligned tagline become a positioning that strengthen the marketing strategy. [ABSTRACT FROM AUTHOR]

Promotion is one way that can be used to increase the number of registrants at a university. The number of registrants for a university has greatly increased in the student segmentation process carried out to determine the target area for promotion. A good segmentation process can produce accurate information that can be used as a basic promotion or marketing strategy. This study develops a model based on the KMeans algorithm for the segmentation process based on the number of registrants and the citizen population (school-age) in a city. This algorithm was chosen because it can group data that has similarities into a cluster. Elbow method is used to determine the cluster optimum number based on Within Cluster Sum of Squared Errors (WSS) calculations and inertia is used for calculations to visualize data in a plot or graph. The results of this study are in the form of a model that can interpret clusters, namely clusters with high population but have a small number of registrants, cluster with high population and have a high number of registrants (promotional targets), cluster with a low population but have high number of registrants, and a cluster with low population but less registrants. With the interpretation of the model a university can determine the target city for promotion based on the targeted cluster. [ABSTRACT FROM AUTHOR]

This study aims to formulate a product bundling strategy for IndiHome products using the Apriori Algorithm, one of the association rule algorithms in data mining techniques. This study used 62,504 sales transaction data for IndiHome products at Telkom Jakarta Timur in 2021 to be processed through data mining techniques with the association method. The association method generates association rules based on the relationship between item. The evaluation of the results of this algorithm is based on the value of support, confidence level, and lift ratio. The results of this study reveal that the Apriori algorithm can form association rules that are applied in sales strategies to optimize products. Based on the result, this study finds that the association rules formed can be implemented into a product bundling strategy in terms of product mix strategy and sales channel strategy. Furthermore, this product bundle also shows the effectiveness of each channel location, so this application can be used to justify a marketing strategy that should be implemented. In addition, the sales force channel is one of the most effective and strategic selections that can offer attractive product package programs at competitive prices that can increase customer purchasing power. Thus, the company can increase product sales, especially achieve daily and monthly targets and optimize potential returns. [ABSTRACT FROM AUTHOR]

Selling is a business or concrete step made to move a product, whether products or services, from the manufacturer to the target consumer. The primary goal of sales is to generate profit or profit from the items or goods produced by good management producers. In practice, sales cannot be carried out without the participation of actors such as agents, dealers, and marketing employees. Marketing is a critical component of increasing revenue. The Nave Bayes approach was used in this study to determine the best marketing plan for increasing profits. Naive Bayes is a statistical classification that can be used to predict the likelihood of class membership. Using four criteria from the study's findings, 22 items were discovered in the unsold category. And the accuracy results are 100% while using the rapidminer software. It is intended that this research will be valuable to store owners in developing the best marketing plan. When working with big volumes of data, it is advisable to employ rapidminer with the naive Bayes approach. As a result, the acquired outcomes are more ideal. [ABSTRACT FROM AUTHOR]

Social networks are communication platforms that enable the connectivity of users from various geographic areas and distinct ethnic and cultural backgrounds, hence contributing to the development of a strengthened digital social structure. The significance of social networks for companies and stakeholders has garnered considerable interest from scholars and researchers across diverse disciplines, prompting exploration of numerous aspects pertaining to social networks. Moreover, a multitude of scholarly investigations have been conducted on the utilization of social media as a platform for marketing purposes. Additionally, various researchers have delved into different facets of the advantages and disadvantages associated with this phenomenon. This study aims to examine the advancements of social networks throughout many diverse fields, considering their applications and patterns of progression over time. [ABSTRACT FROM AUTHOR]

Today, companies' green competitiveness is developing under the influence of a wide set of economic, social, environmental, corporate and marketing determinants. The purpose of the paper is to justify the role of marketing determinants in developing companies' green competitiveness. The provided analysis of the semantic core of information-commercial and scientific analytics on search queries related to marketing determinants of companies' green competitiveness using Google Analytics and Google Trends allowed identifying the following groups of marketing determinants of its provision: determinants of sustainable strategic development (green marketing strategies, green supply chains, green logistics, green pricing); media information determinants (green networks, green platforms, green digital tools, green web tools); and determinants of targeted green marketing (green brand, green advertising, green promotion). The systematic combination of correlation analysis tools, Godrick-Prescott smoothing and filtering method of time series of information-commercial and scientific analytics on search queries on the issues of green competitiveness of enterprises allowed identifying three different vector trends: increasing the level of information and commercial interest in the field of companies' green competitiveness (at the request of the Internet users) is accompanied by increasing interest of the scientific community in both trend and cyclical components (positive and statistically significant correlation); an uneven growth of interest in the issues of companies' green competitiveness on the part of the Internet users and the scientific community (negative and statistically significant correlation); the object of dominant interest on the part of the Internet users and researchers are such marketing determinants of companies' green competitiveness as green brand and green promotion (results of the correlation analysis by cyclical and trend components). [ABSTRACT FROM AUTHOR]