Customer Preferences Research Articles

AI-enhanced inventory and demand forecasting: Using AI to optimize inventory management and predict customer demand

The advent of artificial intelligence (AI) has ushered in a new era of efficiency and accuracy across various industries, with inventory management and demand forecasting being at the forefront of these advancements. Traditional inventory management techniques, often reliant on historical data and simple statistical models, fall short in addressing the dynamic and complex nature of contemporary markets (Chopra & Meindl, 2016). AI, with its advanced algorithms and machine learning capabilities, offers a transformative approach to these critical business functions. This paper explores the integration of AI technologies in optimizing inventory management and predicting customer demand. AI-enhanced inventory management involves the application of various AI technologies such as machine learning, natural language processing (NLP), computer vision, and robotics process automation (RPA) (Ivanov et al., 2017). Machine learning algorithms analyze vast amounts of historical data to identify patterns and trends, enabling more accurate predictions and adjustments in inventory levels. NLP processes unstructured data from sources like social media and customer reviews to provide deeper insights into market trends and customer preferences (Cambria & White, 2014). Computer vision technologies assist in real-time monitoring of inventory levels and identifying discrepancies through visual data, while RPA automates repetitive tasks like order processing and inventory tracking, thereby reducing human error and increasing efficiency (Aguirre & Rodriguez, 2017). This paper highlights significant improvements in forecast accuracy and inventory turnover rates achieved through AI implementation and discusses future implications for supply chain management.

Harnessing marketing analytics for enhanced decision-making and performance in SMEs

In the contemporary business landscape, small and medium-sized enterprises (SMEs) are increasingly leveraging marketing analytics to enhance decision-making and improve performance. This paper explores the transformative impact of marketing analytics on SMEs, emphasizing its role in driving data-driven decisions that optimize marketing strategies, enhance customer engagement, and boost overall business performance. Through the systematic collection and analysis of data, SMEs can gain deeper insights into customer behaviors, preferences, and trends, enabling more targeted and effective marketing campaigns. The implementation of marketing analytics facilitates the measurement of key performance indicators (KPIs) such as click-through rates, conversion rates, and return on investment (ROI), providing a robust framework for evaluating the effectiveness of marketing initiatives. By utilizing various types of analytics, including descriptive, predictive, and prescriptive analytics, SMEs can make informed decisions that not only respond to current market conditions but also anticipate future trends and opportunities. Moreover, the adoption of marketing analytics supports the personalization of customer experiences, allowing SMEs to deliver tailored content and offers that resonate with specific audience segments. This level of personalization fosters deeper customer relationships and increases the likelihood of conversions and customer loyalty. The continuous monitoring and analysis of marketing data enable SMEs to remain agile and responsive to changing market dynamics, ensuring that their marketing strategies remain relevant and effective. By embracing a data-driven approach, SMEs can minimize the risk of ineffective marketing efforts and allocate resources more efficiently, ultimately driving sustainable growth and competitive advantage. In conclusion, harnessing marketing analytics is crucial for SMEs aiming to enhance decision-making and performance. The ability to derive actionable insights from data empowers SMEs to optimize their marketing strategies, improve customer engagement, and achieve better business outcomes. As SMEs continue to navigate an increasingly data-driven world, the strategic use of marketing analytics will be pivotal in securing long-term success and growth.

Sustainable energy strategies among small and medium-scale enterprises: The mediating role of business associations

Small and medium-scale enterprises increasingly show greater interest in managing their environmental footprints in response to shifting customer preferences. Beyond the mixed results in the literature exploring environmental concerns from an organisational perspective, questions related to firm strategy and energy efficiency have yet to be definitively determined. This study investigated the mediating role of business associations in the relationship between firm strategy and energy efficiency among small and medium-scale enterprises. Using binary logit regression, data from over 20,000 firms across 39 countries were analysed. Our analysis demonstrates that business strategy positively impacts climate-friendly energy generation, energy management, and energy consumption improvements. Furthermore, business associations were found to significantly mediate these relationships. A comparative analysis revealed some differences across economic regions, with the impact of business strategy being more pronounced in the Middle East and Africa compared to other regions of the world. The present findings suggest that improving small and medium-scale enterprises’ affiliations to business associations is a promising approach to increasing their adoption of sustainable energy practices. The study findings further advance the understanding of the firm strategy-energy efficiency nexus, and have implications for policymaking in environmental performance enhancement.

Review of Capability of Methods for the Verification of Country of Origin for Food and Feed

Country of origin is defined as the country where food or feed is entirely grown, produced, or manufactured, or, if produced in more than one country, where it last underwent a substantial change. In the UK, EU-assimilated legislation states that indication of the country of origin is a mandatory labelling requirement for food and feed, including products such as meat, vegetables, eggs, honey and wine. The country of origin claim plays an important role for consumers who tend to relate certain country of origin labelling to superior quality or brand identity. Patriotism (or ethnocentrism) can also play a role in consumer food choice. In Europe, there are 3500 products with a specific geographical origin and their production methods are officially protected (Protected Designation of Origin = PDO; Protected Geographical Indication = PGI; Geographical Indication (for spirit drinks) = GI). These goods often carry a premium price. In addition to customer preference and sale price, country of origin claims are important to businesses when they seek to (i) monitor food miles (carbon footprint), (ii) ensure sustainable sourcing of, for example soy and palm oil (including new Regulation (EU) 2023/1115 on deforestation-free products), (iii) avoid trading of goods which are subject to sanctions, (iv) reassure consumers over concerns of farming and animal welfare standards, (v) avoid foods which are linked to exploitation of farm workers, enforced, or child labour. ‘Verification’ of geographical origin involves testing against a database to confirm that the data for a sample are consistent with those for that geographical location as claimed on a product label. Verification therefore does not involve testing a sample from an unknown location to unequivocally identify its provenance, as such methods are not available or are extremely limited in scope.

Efficient Algorithms for Range Mode Queries in the Big Data Era

The mode is a fundamental descriptive statistic in data analysis, signifying the most frequent element within a dataset. The range mode query (RMQ) problem expands upon this concept by preprocessing an array A containing n natural numbers. This allows for the swift determination of the mode within any subarray A[a..b], thus optimizing the computation of the mode for a multitude of range queries. The efficacy of this process bears considerable importance in data analytics and retrieval across diverse platforms, including but not limited to online shopping experiences and financial auditing systems. This study is dedicated to exploring and benchmarking different algorithms and data structures designed to tackle the RMQ problem. The goal is to not only address the theoretical aspects of RMQ but also to provide practical solutions that can be applied in real-world scenarios, such as the optimization of an online shopping platform’s understanding of customer preferences, enhancing the efficiency and effectiveness of data retrieval in large datasets.

Quality Matrix-Based Product Innovation in Birkinpet Pet Harness Apparel

This study employs the Quality Function Deployment (QFD) method to develop BirkinPet pet harness apparel, focusing on customer needs and competitive advantages. A House of Quality (HoQ) matrix was constructed using responses from 96 participants, evaluating the relationship between Customer Importance and Technical Importance to identify key technical priorities for immediate implementation in BirkinPet’s production process. The primary HoQ matrix results indicate that lightweight, comfort, and safety (481.5), design for various activities (416.1), and ergonomic and comfortable design (389.9) are the most important attributes to customers. The main technical priorities include ensuring the product is safe and strong (4.99) and providing a variety of sizes and options (4.86). The novelty lies in the comprehensive and structured application of QFD, emphasizing innovation in designing products that align with current market trends and customer preferences. By integrating comfort, safety, and flexible design, BirkinPet successfully creates a high-quality product that meets customer needs, enhancing customer satisfaction and loyalty while strengthening its competitive position in the pet accessories market.

Exploring Customer Preference to Develop an Effective Customer Relationship Management Strategy

Exploring Customer Preference to Develop an Effective Customer Relationship Management Strategy

Eksplorasi Pemetaan Pelanggan dan Prioritas Pengembangan Jaringan Internet : Pendekatan Sistem Informasi Geografis Berbasis Web dan AHP

ArsyaNet, a local internet service provider, strives to deliver fast and reliable internet access to its customers. This study aims to implement a web-based Geographic Information System (GIS) for customer mapping and network development prioritization using the Analytic Hierarchy Process (AHP) method at ArsyaNet. Leaflet is employed to map the distribution of ArsyaNet customers within the RT/RW network in the Pringgasela District. The main objective of this research is to accurately and efficiently map the distribution of internet customers to support optimal network development. By integrating AHP into the GIS, customer preferences and other relevant factors such as access speed, service quality, and infrastructure needs in specific areas will be considered in determining the priority locations for network development. The AHP method allows for the grouping of customers based on their preferences and determining locations by considering weights on each relevant aspect. Integrating Leaflet into the GIS enables accurate and interactive mapping results using a lighter map compared to the Google Maps API. This research is conducted to enhance the quality of internet services provided by ArsyaNet by better understanding the needs and preferences of customers in selecting internet service providers and their customer distribution.

Characterization of Meat Metabolites and Lipids in Shanghai Local Pig Breeds Revealed by LC-MS-Based Method.

The meat of local livestock breeds often has unique qualities and flavors. In this study, three Shanghai native pig breeds (MSZ, SWT, and SHB) exhibited better meat quality traits than globalized commercial pig breeds (DLY). Subsequently, metabolomic and lipidomic differences in the longissimus dorsi (L) and gluteus (T) muscles of the Shanghai native pig breeds and DLY pig breed were compared using liquid chromatography-mass spectrometry (LC-MS). The results demonstrated that the metabolites mainly consisted of (28.16%) lipids and lipid-like molecules, and (25.87%) organic acids and their derivatives were the two most dominant groups. Hundreds of differential expression metabolites were identified in every compared group, respectively. One-way ANOVA was applied to test the significance between multiple groups. Among the 20 most abundant differential metabolites, L-carnitine was significantly different in the muscles of the four pig breeds (p-value = 7.322 × 10-11). It was significantly higher in the L and T muscles of the two indigenous black pig breeds (MSZ and SWT) than in the DLY pigs (p-value < 0.001). Similarly, lipidomic analysis revealed the PA (18:0/18:2) was significantly more abundant in the muscle of these two black breeds than that in the DLY breed (p-value < 0.001). These specific metabolites and lipids might influence the meat quality and taste properties and lead to customer preferences. Therefore, this study provided insights into the characterization of meat metabolites and lipids in Shanghai native pig breeds.

Improving Company Performance using Financial Planning Approach Toward Fragrance Companies: A Case Study of Saja

The perfume industry has experienced significant shifts towards using natural oils and synthetic components, driven by changing customer preferences and increased awareness of natural products. Saja, a perfume company established in 2022, aims to capitalize on this growing trend by offering natural products. Despite its potential, the company faces financial challenges due to a lack of strategic financial planning. An analysis of Saja's financial performance from late 2023 to mid-2024 revealed inconsistent income and fluctuating cash flow, as identified through financial ratios and DuPont analysis. These issues highlight the company's low financial performance. To address these challenges, Saja plans to enhance its financial planning by optimizing budget allocations, increasing production cost efficiency, and expanding product and market segments to boost income and reduce expenses. The sustainability of this strategy was assessed using three economic and risk scenarios: realistic, optimistic, and pessimistic. The feasibility of the proposed strategy was measured through the Payback Period (PP) in future and present value, Internal Rate of Return (IRR), and Net Present Value (NPV). Results indicate that the proposed financial strategy is acceptable and feasible, suggesting that its implementation would improve Saja's financial performance.

Central Maintenance Spin-Off Creation towards Digitalization Focused on the Parent Company: A Case Study of Integrated Steel Corporation (ISC) in Indonesia

The steel industry plays a crucial role in national development. Major global steel producers such as the United States, Russia, and Japan have seen declines due to the rise of China as a dominant steel producer. Integrated Steel Corporation (ISC) in Indonesia has also faced declining performance, with its market capitalization dropping by 68.9% over the past eight years. The research primarily aimed to analyze customer preferences in the maintenance industry and determine positioning strategies. Additionally, it sought to identify the factors driving the formation of the Strategic Maintenance Services (SMS) spin-off from ISC and propose strategies for SMS to achieve a competitive advantage. Data were collected qualitatively through interviews and Focus Group Discussions (FGDs) with customers to understand their perspectives, SMS management to grasp core business competencies and strategy, and ISC management to understand the parenting strategy. The results show several internal dynamics within ISC drove the formation of the SMS spin-off: 1) the transformation of the parent company; 2) short circuit incident at the main ISC factory; and 3) the cost burden of maintenance support. External factors indicate rapid industrial growth and the company's strengthening as a group after experiencing losses for almost a decade. The proposed strategy for SMS involves leveraging ISC’s subsidiaries and joint venture companies for centralized service maintenance. SMS aims to offer customized one-stop maintenance solutions with high quality at competitive prices by enabling Digital Maintenance Information (DMI). The target markets include large industries in steel manufacturing, petrochemicals, flour mills, cement, and logistics.

Effect of Logo Change on Brand Attitude: A Case Study of Mahindra at Nepal Electricity Authority

Rebranding, mainly through logo changes, has gained traction in the automobile industry as a response to evolving customer preferences. However, the impact of such corporate actions on brand attitude remains a complex and underexplored area. This study delves into corporate rebranding nuances, focusing on logo change and its effects on consumer brand attitude. Drawing from a theoretical framework, the study hypothesised that logo appropriateness, familiarity, and attractiveness positively influenced brand attitude. This study employed a descriptive and explanatory research design, focusing on Mahindra drivers of the Nepal Electricity Authority (NEA) as the target population. Data were collected through a primary survey using a structured questionnaire, resultingin 152 responses. The results, essential to brand managers and marketing professionals, revealed a positive influence of logo appropriateness and attractiveness on brand attitude. In contrast, logo familiarity did not significantly impact it. Overall, the findings underscore the importance of a well-designed logo that aligns with the brand's core values. Such a logo can significantly enhance consumer perceptions and foster positive relationships. Therefore, brand managers should prioritise logo design that resonates with their identity and values, particularly during rebranding efforts, to maintain a strong brand image among consumers.

Developing and Validating a Perceived Digital Customer Value Scale in Restaurants

ABSTRACT Customers are switching to being “digital customers” when interacting with restaurants more digitally than traditionally. Therefore, the current study aims to create a multidimensional scale for measuring perceived digital customer value derived from ordering and payment functionalities in restaurants. Seven stages were taken in the development of the scale; the first was qualitative for item generation, and the other steps were quantitative for empirically establishing the scale’s psychometric qualities. Four hundred twenty-five customers filled out the questionnaires from two different types of restaurants (i.e. fast-food and casual dining restaurants) in Cairo, Egypt. This study found five dimensions of the perceived digital customer value scale: price, hedonic, experiential, transient, and service quality. This measurement helps restaurants comprehend digital customer behavior and change customer preferences and interactions in a digitalized environment. Accordingly, the study extends customer value theory and previous models within the hospitality industry, addresses gaps in the literature, and improves understanding of customer value in digital transformation. The developed multidimensional scale helps restaurant management understand their digital customers’ preferences and deliver value that meets their desires in a digital transformation.

Strategi Pemasaran untuk Meningkatkan Daya Saing Coffeeshop: Studi Kasus 110 Degreecafe

The research results show that the marketing strategy implemented has had a positive impact on the competitiveness of 110 degree café. Proper market segmentation, followed by product offerings that suit customer preferences, has increased customer loyalty. Strong brand positioning has differentiated 110 degree café from its competitors, while creative promotional efforts have attracted the attention of the target market. In addition, the use of social media as a communication and promotional tool has expanded reach and increased brand awareness. This research aims to analyze the effectiveness of the marketing strategy implemented by 110 Degree Café in Seturan Yogyakarta. Through a case study approach, primary data is collected through interviews with management and direct observation, while secondary data is obtained from related literature and information from the industry. Analysis is carried out through a theoretical framework involving marketing concepts, such as market segmentation, brand positioning, promotions, and social media marketing. This research makes an important contribution to the understanding of the importance of effective marketing strategies in increasing the competitiveness of coffee shops. Practical implications of this research include recommendations for improving marketing strategies, including increased focus on deeper market segmentation, product innovation, and more efficient use of social media.

Service pricing and charging strategy for video platforms considering consumer preferences

AbstractHeterogeneity of consumer preferences is a crucial feature of the platform economy, and service price and charging strategies crucially influence customer choice behavior. To enhance consumer experience and boost product value, video platforms offer value‐added services, also known as premium membership features. For instance, Youku's benefits of value‐added service include seamless video playback, high‐definition picture quality, and exclusive episodes. This study constructs a two‐stage Hotelling model from monopoly to duopoly competition. Considering the heterogeneity of consumer preferences for product quality and platform usage costs, we analyze the impact mechanism of the customer preferences heterogeneity and the service pricing of video platform enterprises. In addition, we analyze the Nash equilibrium strategies of competing enterprises in the competition scenario, and we consider two charging strategies: freemium and bundled sales. The results indicate that the consumer sensitivity factors significantly impact the optimal pricing of services and the profits of enterprises and that this impact varies with the heterogeneity of customer composition and the type of charging strategies. For the optimal solutions in a competition scenario, the proportion of quality‐sensitive customers is positive for the freemium model, whereas it is negative for the bundled sales model. Furthermore, we analyze the impact of heterogeneous customer composition on the choice of charging strategy utilizing numerical analysis. This study provides a theoretical basis and decision support for product development and value‐added service pricing that can be applied to video platform enterprises and other media platforms.

The LSTM-EMPG Model for Next Basket Recommendation in E-commerce

Personalized recommendations play a crucial role in the modern e-commerce landscape, enabling businesses to meet customers&amp;apos; evolving preferences and boost sales. As customer preferences change, businesses are realizing the importance of suggesting what customers might want to buy next. However, existing approaches face challenges in capturing sequential patterns in user behavior and accurately utilizing previous purchase information. These challenges can be addressed using Long Short-Term Memory Networks (LSTMs). Nevertheless, LSTMs alone may not fully capture users&amp;apos; repetitive purchase behavior or consider the exact timing of purchases. To account for these limitations, Probabilistic Models such as the Modified Poisson Gamma model (MPG) can be employed. The research reported in this paper proposes and investigates a new approach for the next basket recommendation based on the integration of LSTM with an enhanced Modified Poisson Gamma model to enhance next basket recommendation accuracy in e-commerce. The enhanced model (EMPG) includes a refinement of the MPG model to increase its predictive accuracy, and its recommendations are then integrated with an LSTM network to optimize the LSTM’s predictions. The proposed hybrid LSTM-EMPG model has been evaluated on the Instacart dataset and has produced superior results compared to the Multi-period LSTM, the GRU-based model. DREAM (RNN), and DREAM (LSTM) in terms of predictive accuracy, achieving a higher precision and recall.

The implementation of Importance Performance Analysis (IPA) to determine the quality of service towards the tourists’ expectations of Al-Fatih tour &amp; travel

Improving service quality to meet diverse tourist expectations. Importance-Performance Analysis (IPA) is an effective method for evaluating and enhancing service quality. Significant transformations in the tourism industry involve global mobility, technology, and changing customer preferences. Tourists now expect friendly personal interactions, fast service, and experiences tailored to their needs. Holistic and structured assessments are required to identify areas needing improvement and to allocate resources effectively. IPA helps BPW management set priorities and improvement strategies based on the differences between the importance and performance of service elements. This study aims to implement IPA in evaluating services at Al-Fatih Tour Travel, highlighting nine priority services that need enhancement to meet tourists' expectations. The results using the IPA Cartesian diagram show nine priority services to be improved in quality: (1) Environmental conditions, (2) Appearance, (3) Accuracy in fulfilling promises, (4) Booking and delivery services, (5) Fast and accurate service, (6) Readiness to provide service, (7) Providing a sense of security, (8) Knowledge and skills according to their field, (9) Treating tourists with full attention.

Co-creation in a duopoly considering the effect of product return

Co-creation has become popular in experience good markets as it can resolve the uncertainty of the mismatch between product attributes and customer preferences. This paper delves into an examination of how firms strategize product co-creation with their customers in a competitive duopoly context, all the while taking into account the potential product return stemming from mismatch uncertainty. Results show that when the launching cost is moderate and the customer’s participation cost is relatively small, an asymmetric equilibrium with one firm launching the project and the other not is reached; otherwise, both firms adopt co-creation or not, conditional on the launching cost. Besides, firms may fall into a prisoner’s dilemma in which both firms launch the project when the launching cost is moderate. Furthermore, it is shown that co-creation functions as an effective way for firms to escape the Bertrand trap and reap the benefits of the product’s high valuation. Additionally, there is a mutual influence between co-creation and product return. A higher product return cost incentivizes firms to embrace co-creation, but meanwhile, it makes them more susceptible to falling into a prisoner’s dilemma scenario. In a state of equilibrium, co-creation consistently diminishes the product return for the firm engaging in it. Particularly, the firm not providing co-creation can take a free ride on the competitor’s co-creation benefits in reducing product return when the return cost is relatively large. Lastly, the value of co-creation can be amplified through the implementation of a price discrimination strategy, particularly when the participation cost is relatively large.

Commercial techniques to improve customer service

The advance of digital platforms has transformed the shopping process, making it more accessible and practical. In this study, we seek to understand the preferences of customers of commercial premises of Malecón 2000 in the city of Guayaquil, both in their face-to-face and online shopping. The sample focused on customers between 20 and 40 years old, using a descriptive research with a quantitative approach through surveys in Google Forms. The studies surveyed highlight different aspects of customer service and commercial techniques, while the results obtained underline the importance of direct interaction with merchants. They also highlight the need to improve product diversity and the online shopping experience to maintain market relevance in an increasingly competitive retail environment.

Innovative digital marketing strategies for SMEs: Driving competitive advantage and sustainable growth

In the rapidly evolving digital landscape, small and medium-sized enterprises (SMEs) must adopt innovative digital marketing strategies to drive competitive advantage and sustainable growth. This review explores the critical components and benefits of leveraging digital marketing for SMEs, emphasizing how tailored strategies can enhance market presence, customer engagement, and business performance. SMEs face unique challenges in resource allocation and market competition, necessitating cost-effective and impactful marketing approaches. Innovative digital marketing strategies, including search engine optimization (SEO), social media marketing, content marketing, email campaigns, and data-driven analytics, provide SMEs with the tools to compete effectively against larger enterprises. By utilizing these strategies, SMEs can achieve greater visibility, attract a broader audience, and convert prospects into loyal customers. SEO and content marketing play a pivotal role in enhancing online visibility and organic traffic. By producing high-quality, relevant content and optimizing it for search engines, SMEs can improve their search rankings and attract targeted audiences. Social media platforms offer powerful channels for engaging with customers, building brand awareness, and fostering community. Through targeted advertisements and influencer partnerships, SMEs can reach specific demographics and drive engagement. Email marketing remains a cost-efficient strategy for maintaining customer relationships and promoting products or services. Personalized email campaigns can nurture leads, encourage repeat business, and build customer loyalty. Additionally, data-driven analytics enable SMEs to measure campaign performance, understand customer behavior, and make informed decisions to refine marketing efforts. The integration of innovative technologies such as artificial intelligence (AI) and machine learning further enhances digital marketing capabilities. AI-driven tools can automate tasks, provide insights into customer preferences, and optimize marketing strategies in real-time. For instance, chatbots can improve customer service, while predictive analytics can forecast trends and personalize marketing messages. In conclusion, innovative digital marketing strategies are essential for SMEs seeking to drive competitive advantage and sustainable growth. By embracing SEO, social media marketing, content marketing, email campaigns, and data-driven analytics, SMEs can maximize their marketing impact, enhance customer engagement, and achieve long-term business success. Leveraging these strategies allows SMEs to remain agile, responsive, and competitive in the dynamic digital marketplace. Keywords: Innovative, Digital Marketing, SMEs, Sustainable Growth, Competitive Advantage.