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All Journal Excellent Jurnal Mirai Management Journal of Economic, Bussines and Accounting (COSTING) JURNAL PENDIDIKAN TAMBUSAI Journal on Education Jurnal Menara Ekonomi : Penelitian dan Kajian Ilmiah Bidang Ekonomi EKONOMI BISNIS Jurnal Sains dan Teknologi JATI (Jurnal Mahasiswa Teknik Informatika) Emerging Markets : Business and Management Studies Journal BUDGETING : Journal of Business, Management and Accounting Jurnal Fokus Manajemen Bisnis PENGARUH SISTEM KEUANGAN DAN KEBIJAKAN MONETER TERHADAP PERTUMBUHAN EKONOMI PADA ERA ORDE BARU DALAM PERSPEKTIF EKONOMI ISLAM Journal of Economics and Business UBS International Journal of Applied Finance and Business Studies Jurnal Ekonomika Dan Bisnis JRAP (Jurnal Riset Akuntansi dan Perpajakan) Jurnal Ekonomika Manajemen Dan Bisnis Indonesia Accounting Research Journal Applied Quantitative Analysis (AQA) Business and Investment Review Jurnal Informatika Ekonomi Bisnis Jurnal Media Akademik (JMA) Sainteks: Jurnal Sain dan Teknik International Journal of Management and Business Applied
Heikal, Jerry
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Religion Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Physics Social Sciences Other

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RFM Segmentation Analysis for Determine Online Marketing Strategy: the Soul Coffee Mate Case Study Aldyah, Tika; Sugino, Agus; Muzakkar, Milastri; Heikal, Jerry
BUDGETING : Journal of Business, Management and Accounting Vol 5 No 2 (2024): BUDGETING : Journal of Business, Management and Accounting
Publisher : Institut Penelitian Matematika Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/budgeting.v5i2.8728

Abstract

This study explores the impact of digital marketing on a cafe named The Soul, located in Jakarta. The cafe experienced a recent decline in sales attributed to factors such as new competitors, closures of nearby establishments, diminishing trends in working from cafes, and changes in customer demographics. Recognizing the need for a robust online marketing strategy, researchers aim to analyze customer segmentation using the RFM model, focusing on Recency, Frequency, and Monetary aspects of transactions over the past 11 months. The goal is to understand customer behavior, design effective online marketing strategies, and ultimately boost the company's revenue. The RFM analysis results identified eight customer segments for The Soul: Soulmate, Loyal Customers, Potential Loyalists, Promising, New User, At Risk, Can't Lose Them, and Hibernating. Proposed strategies include personalized promotions for different customer segments, such as monthly appreciation for "Soulmate" customers, discounts for loyal customers, and targeted communication for new or inactive users. These strategies aim to enhance customer engagement, loyalty, and satisfaction through digital promotions and personalized interactions. Keywords: Cafe Business, Customer Behavior, Customer Segmentation, Customer Loyalty, Digital Marketing, Online Promotion, RFM Model, Sales Analysis
Customer Segmentation Based on RFM Analysis as the Basis of Marketing Strategy Case Study of PT Pertiwi Agung Pharmaceutical Industry (LANDSON) Devi, Rizky Feliana; Siswanto, Fajar Hartanto; Azkia, Nayla; Heikal, Jerry
BUDGETING : Journal of Business, Management and Accounting Vol 5 No 2 (2024): BUDGETING : Journal of Business, Management and Accounting
Publisher : Institut Penelitian Matematika Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/budgeting.v5i2.8994

Abstract

The Pharmaceutical Company is a company that has quite large raw material import activities and has many benefits for society and institutions such as hospitals. Pharmaceutical companies play an important role in improving the quality of life of the human population in modern times because, in the field of marketing, pharmaceutical companies face increasing sales performance and profits, as well as maintaining customer loyalty. Pharmacy retail customers usually make drug purchases influenced by the selling price and suitability factors (suggestions) for certain drug brands. Based on these conditions, drug purchasing patterns for the Indonesian people become unpredictable, and it is difficult to increase sales and profits. One effort that pharmaceutical business players can make is to carry out sales promotions based on customer segmentation. Customer segmentation in pharmaceutical companies can be done using clustered data mining analysis methods, such as modified Recency Frequency Monetary (RFM). This method allows companies to group customers based on purchasing patterns of pharmaceutical products, thereby allowing companies to prioritize energy and resources to different segments. After the scoring and data processing process, the number of customers for each RFM Score is obtained, then the Monetary group is segmented which is divided into 4 (four) parts, namely Best Customers by quantity (36), Loyal Customers by quantity (188), Potential Customers by quantity (34) and Lost Customers by quantity (61). Then we continue to map it into only 3 (three) parts, namely Best Customers, Loyal Customers, and Potential Customers using blue as a sign to see the score range. From the results of dividing the 3 (three) group segmentations, the Loyal Customer Score segmentation is greater in quantity (188) so the blue color is darker than the others, which shows that the more customers spend their money. Of the 3 (three) customer segmentation sections, we put all of them into the Best Customer category, because they have introduced new products or products they have not purchased. By using RFM analysis, you can quickly find out customer targets that will be prioritized in carrying out marketing, campaigns, promotions, and rewards using digital channels and direct customer relations. Keywords: Farmasi Company, Group Segmentation, Recency Frequency Monetary (RFM).
Segmentation Strat Egy of Consumer Interest in Contemporary Coffee Shop Using RFM Model Passalaras, Raja Aulia; Daulay, Risma Yanti; Heikal, Jerry
BUDGETING : Journal of Business, Management and Accounting Vol 5 No 2 (2024): BUDGETING : Journal of Business, Management and Accounting
Publisher : Institut Penelitian Matematika Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/budgeting.v5i2.9030

Abstract

Increase customer loyalty by grouping customers into several groups and determining appropriate and effective marketing strategies for each group. Customer segmentation can be done through the clustering method. This research aims to analyze customer Recency, Frequency and Monetary (RFM) from the segmentation results of 5 contemporary coffee shops, namely Janji Jiwa, Kopi Kenangan, Tomoro Coffee, Fore Coffee, and Fami Cafe, from Persona Analysis Research and segmentation of consumer interest in contemporary coffee shops. Previously it could be summarized that cluster 3 or potential customers were the targets of the research by implementing appropriate marketing strategies based on the characteristics and profiles of the respondents. This cluster consists of 31 respondents, most of whom are women aged 24-28 years with a bachelor's degree and work as private employees in Jakarta. Interestingly, cluster 3 is divided between coffee fans and those who don't like coffee. Respondents in this study tended to prefer Tomoro Coffee, while those who didn't like coffee preferred Fami Cafe. In cluster 3, there are 29 people who are active Instagram users. The analysis used includes descriptive analysis where the data used is primary data. The data collection technique used is the observation method and distributing questionnaires to customers who are in cluster 3. The results of the research show that based on the results of the revenue segmentation analysis for the Tomoro Coffee and Fami Cafe coffee shops, it can be concluded that customers in cluster 3 consist of 3 segments. namely recency 3 which consists of 16 active customers who are very loyal and faithful. The strategy to retain these customers is by personalizing customer data to understand product/service shortcomings and knowing customer individual preference needs in order to attract them back. Keywords: Frequency, Monetary, Recency.
Customer Segmentation Using K-Means Clustering with SPSS Program in a Case Study of Consumer Interest in Current Coffee Shop Daulay, Risma Yanti; Passalaras, Raja Aulia; Heikal, Jerry
BUDGETING : Journal of Business, Management and Accounting Vol 5 No 2 (2024): BUDGETING : Journal of Business, Management and Accounting
Publisher : Institut Penelitian Matematika Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/budgeting.v5i2.9288

Abstract

This research aims to segment customers using the K-Means Clustering method in a case study of consumer interest in current coffee shops such as Kopi Kenangan, Kopi Janji Jiwa, Tomoro Coffee, Fore Coffee and Fami Cafe. Customer segmentation is useful for knowing consumer preferences such as interests, background, lifestyle, service quality and consumer characteristics that are useful for knowing the right persona and marketing strategy as well as the value proposition, especially at the Kopi Kenangan, Janji Jiwa, Tomoro Coffee, Fore Coffee and Fami Cafe coffee shops. The analytical method used in this research is data collection by distributing questionnaires to respondents with predetermined criteria in the Jakarta area and outside Jakarta. The questionnaire was distributed in November 2023. The survey results were analyzed using the K-Means Clustering method in the SPSS 23 program to group consumers based on certain attributes such as age, education, domicile, income, occupation, frequently used social media and most popular coffee shop preferences. such as coffee and non-coffee variants, price, service quality and recommending it to other people. Based on the results of K-Means Clustering data processing, there are 3 clusters, namely cluster 1 (Consumptive Customer), cluster 2 (Standard Young Customer), cluster 3 (Potential Customer) which can be concluded that cluster 3 is a potential cluster that is the target of this research, where cluster 3 consists of respondents who are coffee fans and don't like coffee but are very active in using social media and have made online food transactions and always dine in when making transactions at coffee shops. So it can be seen that cluster 3 can be explored and used as a marketing target by carrying out promotions on various social media applications by offering various choices of coffee and non-coffee variants in order to attract customers who don't like coffee so they are interested and want to try the products offered. . The value proposition for current coffee shops is to give the impression of Coffee Vibes and focus on providing a unique and unforgettable coffee experience by presenting innovative flavor variants and creative coffee presentations, exploring new flavors and offering premium quality that can be enjoyed by all groups and creating an atmosphere comfortable and Instagram- worthy to provide a pleasant customer experience that fits the rhythm of modern life. Keywords: Cluster Analysis, Current Coffee, Customer Segmentation, K-Means SPSS,.
Mendorong Pertumbuhan Pangsa Pasar B2B untuk Sektor Ride-Hailing Menggunakan Segmentasi Pasar Strategis Kamaratih F, Yositalida; Perdhana, Rizkita Bagus; Nugroho, Yusuf Wahyu; Heikal, Jerry
BUDGETING : Journal of Business, Management and Accounting Vol 5 No 2 (2024): BUDGETING : Journal of Business, Management and Accounting
Publisher : Institut Penelitian Matematika Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/budgeting.v5i2.9609

Abstract

The expansion of Indonesia's ride-hailing sector has been considerable, fueled by technological advancements and the widespread embrace of smartphones. Despite its swift growth, the industry faces difficulties concerning long-term viability, safety issues, and compliance with governmental regulations. Nevertheless, the integration of advanced technologies and strategic plans for service expansion into new regions presents significant opportunities. The competitive environment in Indonesia's ride-hailing market not only stimulates innovation but also shapes a lively and evolving market atmosphere. Originally designed for consumers, the ride-hailing sector has evolved into a versatile transportation solution for various business needs, including employee transportation and goods delivery. These services offer advantages for companies, such as enhanced operational efficiency and reduced logistics costs. Recognizing the diversity of the business market, segmentation becomes vital in comprehending customer needs. Through tailored marketing approaches, companies can deliver more pertinent solutions, boosting competitiveness and enlarging B2B market share. By using K-means clustering, it yields 5 clusters, namely cluster 1: Tech Innovators and Financial Players, cluster 2: Logistics Singular Focus, cluster 3: Logistics, Retail, and Automotive Synergy, cluster 4: Culinary, Logistics, and Travel Dynamics, cluster 5: Tech Titans, Healthcare Giants, and Financial Leaders. The analysis of user clusters on the B2B Ride Hailing Indonesia platform provides useful insights that guide strategic recommendations for improving service offerings, refining marketing strategies, and optimizing business operations. Targeting the millennial demographic through digital channels and influencers, examining marginal costs for high-traffic clusters to identify optimization opportunities, exploring expansion possibilities in clusters with growth potential, and tailoring business solutions for clusters with unique needs are among the recommendations. Keywords: K-means Clustering, Market Innovation, Market Share Expansion, Ride-Hailing Sector, Segmentation, Strategic Decisions.
Customer Segmentation of Pabuaran Store on Shopee E-Commerce Using RFM Model Analysis (Case Study of H&M Brand Sales Products) Arthanugraha, Adam; Azzuhri, Muhammad Basyar; Ramadhan, Yufiansyah Wahyu; Heikal, Jerry
BUDGETING : Journal of Business, Management and Accounting Vol 5 No 2 (2024): BUDGETING : Journal of Business, Management and Accounting
Publisher : Institut Penelitian Matematika Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/budgeting.v5i2.9724

Abstract

E-Commerce creates business activities that make it easier for people to be more effective because business transactions between sellers and buyers are not limited by space and time. Pabuaran store is one of the personal shopper service providers which started its business in 2019, the marketplace phenomenon in that year has increased massively and online business players in one of the marketplaces have also reached 7 (seven) million in 2019. Product graph seen in the Pabuaran Store's Services business tends to decline, this requires business actors to take strategic steps to maintain their existence in the business world. This is used encouraged service business owners to gain profits in the midst of the phenomenon that is occurring. In determining the variables, the general model used to group customers is the RFM (Recency, Frequency, Monetary) Model, which groups customers based on the time interval of the customer's last visit, frequency of visits, and the amount of value issued as company royalties(1). The recency value can determine the time span since the customer's last transaction. The frequency value can indicate how many transactions each customer conducts with the company. Additionally, the monetary value can reveal the amount of expenditure made by each customer in each transaction with Pabuaran Store on Shopee. The three segments have different campaign strategies. For Segment 1, a reactivation campaign is implemented, such as conducting live videos on Shopee. In Segment 2, a broadcast retention message is delivered to customers who have previously purchased products from Pabuaran Store. As for Segment 3, where loyal customers are identified, a loyalty point system is introduced to keep these customers engaged. Keywords: E-Commerce, Frequency, Monetary, Personal shopper, Recency.
Food Stall Owners’ Strategies in Response to Rice Price Surges: a Grounded Theory Analysis Pratiwi, Pratiwi; Humaira, Putri Syifa; Putri, Annisa Nurwanda; Heikal, Jerry
BUDGETING : Journal of Business, Management and Accounting Vol 6 No 1 (2024): BUDGETING : Journal of Business, Management and Accounting
Publisher : Institut Penelitian Matematika Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/budgeting.v6i1.10395

Abstract

The contemporary rice price surges are affected by several factors including natural, production-related, and even political factors. Food stall business owners affected by these surges need to make optimal decisions and strategies to ensure their business survival. This research aimed at identifying the strategies adopted by the affected food stall business owners. The study used a qualitative method with a Grounded Theory approach by conducting interviews with 6 respondents consisting of food stall owners in Jakarta. Based on the findings, 22 codes forming 5 categories were identified. This identification concluded 3 strategical themes: Price Adjustments, Substitution, and Omni-Channel totalling 24 points. The most common strategy applied by the participants is Price Adjustments as common as 21 points. This strategy is adopted to mitigate the impact of rising rice prices and maintain operations by focusing on price adjustments (13 points), portion sizes (5 points), and rice quality (3 points). Keywords: Food Stall Business Strategy, Grounded Theory, Rice Price Surges.
FMCG Industry Customer Segmentation for Cosmetic Product at PT. Paragon Technology and Innovation Sri Nugroho, Amanda; Saputro, Andi; Suhardi, Fitra Alghifari; Heikal, Jerry
Syntax Idea Vol 6 No 2 (2024): Syntax Idea
Publisher : Ridwan Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46799/syntax-idea.v6i2.3014

Abstract

Paragon Technology and Innovation is an Indonesian company within the Fast-Moving Consumer Goods (FMCG) industry that produces cosmetic products. PT Paragon is currently planning to develop new product and need understand further what product shall be prioritized to develop. Customer segmentation could be applied to give initial guide to determine which product to develop first. It is a process that divides customers into groups to find out the characteristics, behavior, or needs of customers for a product. Customer segmentation needs to be conducted because it can be used as supporting data to find out customer characteristics and determine effective and efficient marketing strategies for the company. Segmentation is one of the strategies to face business competition, to retain customers, and to assist management in developing marketing strategies to increase sales and company growth. The purpose of this study is to categorize the customers of PT Paragon Technology and Innovation products based on their characteristics. To determine customer segmentation can be done using the K-Means clustering algorithm. The clustering process is carried out by grouping PT Paragon's customer transaction history data in 2023 in November based on four categories, namely Face Care, Hair Care, Body Wash, and Make Up. The data is analyzed using IBM SPSS to determine the characteristics of each cluster. The population and samples used in the research were 79 and 52. The number of clusters used was 5, namely cluster 1 or struggling man customers, cluster 2 or beauty enthusiast customers, cluster 3 or forever young customers, cluster 4 or beauty careless casual customers and cluster 5 or luxtomer. The main cluster that provides shared value for PT Paragon Technology and Innovation consumers is cluster 2 or beauty enthusiast customers. The main category for shared value is the make up category, referring to the result, we recommend PT Paragon to mainly developa product following that category.
Segmentation, targeting and positioning analysis using k-means clustering model: A case study of the laptop market in Indonesia Saputra, Tubagus Chandra; Fadhilah, Savira Maghfiratul; Mangkuto, Shidiq Umar; Heikal, Jerry
International Journal of Applied Finance and Business Studies Vol. 12 No. 2 (2024): September: Applied Finance and Business Studies
Publisher : Trigin Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijafibs.v12i2.313

Abstract

In Indonesia's rapidly evolving laptop market, understanding consumer preferences is crucial for maintaining competitiveness. This study employs the K-Means Clustering algorithm to segment the laptop market based on variables such as age, income, expenditure, laptop price, main usage, and selection criteria. Data were collected from 271 respondents in the Jabodetabek area through an online survey. The analysis identified six distinct customer clusters: Edu-Tech Enthusiasts, Executive Civil Servants, Gov-Corp Society, Steady State Officials, Corporate Climbers, and Emerging Entrepreneurs. Each cluster exhibits unique characteristics and preferences, including preferred brands and price ranges. The findings emphasize the importance of targeted marketing strategies tailored to the specific needs of each segment. By leveraging these insights, laptop producers can optimize product offerings, pricing strategies, and promotional campaigns to enhance market share, customer loyalty, and profitability in Indonesia's competitive laptop industry.
The Reassessment of CAPM Relative Accuracy Comparative Study with Actual Price Movement in Indonesian (2019-2022) Fajarini, Nurfahma; Heikal, Jerry
International Journal of Management and Business Applied Vol. 3 No. 1 (2024)
Publisher : Asosiasi Dosen Peneliti Ilmu Ekonomi dan Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54099/ijmba.v3i1.743

Abstract

This study aims to investigate the accuracy of the Capital Asset Pricing Model (CAPM) in predicting stock returns on the Indonesian Stock Exchange (IDX) during the period of 2019 to 2022. The objectives of the research are to benefit the individuals and communities such as enhancing individuals’ decision-making in predicting stock returns, advancing community understanding of financial markets, and contributing new investment insight for societal benefits. The sample comprises 45 selected stocks out of more than 700 stocks, using K-Means Clustering to ensure a diverse and representative dataset. The study compared CAPM’s predictions with the Moving Average (MA) method. Findings show CAPM’s decisions align 87% with MA-analyzed price movements, underscoring CAPM’s effectiveness and the value of using multiple methods for financial predictions. While CAPM proves robust during economic recovery, further analysis is needed for optimal investment strategies. This study’s results challenge some arguments against CAPM’s accuracy.
Co-Authors Aldyah, Tika Alghifari Suhardi, Fitra Ali Wafa Amelia, Dona Andi Saputro Arda, Edvidel Ardiansyah, Giri Teguh Arthanugraha, Adam Awalludin Awalludin, Awalludin Ayu Pradina, Dinda Azkia, Nayla Azwar , Tasrika Azwar, Meiriza Azwar, Tasrika Azzuhri, Muhammad Basyar Chandra, Jon Hendra Saputra Chitra, Jimmi Darma Tenaya, I Putu Risky Daswirman, Daswirman Daulay, Risma Yanti Desmalina, Desmalina Devi, Rizky Feliana Dilla Sistesya Dwi Ramadona, Dasatry Elfira, Renti Fadhilah, Savira Maghfiratul Fahrizal, Rama Rizqullah Fajarini, Nurfahma Ferli, Isfan Fitria, Yossa Gandhi, Ayu Gelvi, Gelvi Givianty, Vasya Theodora Gusmeri, Gusmeri Hanifeliza, Rury Harsemarozi, Harsemarozi Hasibuan, Muhammad Satir Humaira, Putri Syifa Julian, Ahmad Kamaratih F, Yositalida Kettipusem, Sri Polya Kevry Ramdany Kurniawan, Haby Kurniawati, Yuni Mangkuto, Shidiq Umar Muzakkar, Milastri Nazmi, Fittria Ningsih, Andria Nisa, Khairi Nugrahmi, Lidya Nugroho, Septiadi Nugroho, Yusuf Wahyu Oki, Helzulmita Oktarini, Dwi Indah Pangestuti, Intan Passalaras, Raja Aulia Perdhana, Rizkita Bagus Pradina, Dinda Ayu Pratiwi Pratiwi Putra, Rahmad Yunendri Putri, Annisa Nurwanda Putri, Juandela Herina Putri, Maya Seruni Rahmawati, Nurmalinda Ramadhan, Andi Ramadhan, Yufiansyah Wahyu Safangati, Ainun Santosa, Suhari Saputra, Tubagus Chandra Saumananda Suroso, Nurinda Siswanto, Fajar Hartanto Sri Nugroho, Amanda Sugino, Agus Suhardi, Fitra Alghifari Syafer, Erdimen Syarrah, Ira Siti Syawaldi Afwan, Ahmad Waskita, Raden Maart Adi Wicaksono, Muhammad Haston Samudra Zulfahmi, Muhammad Riko Yohansyah