Specific Customer Segments Research Articles

Data Visualization of Bike Store Sales in Europe

This paper presents a comprehensive analysis of bike sales and customer demographics in Europe based on a dataset obtained from Kaggle.com. The dataset covers the period from 2011 to 2016 and includes information on customer age, gender, product categories, order quantities, unit costs, unit prices, profit, and revenue. The analysis utilizes data visualization techniques to identify key trends, including the top-selling products, customer purchasing power across different age groups and customer genders, and product profit margins. The findings suggest opportunities for retailers to target specific customer segments and product categories to maximize profits. Additionally, the report highlights the growing popularity of cycling as a sport and a healthy lifestyle choice, indicating a potential for increased demand in the bike retail market. Overall, the insights provided in this report aim to assist retailer chains in developing effective strategies to capitalize on the opportunities presented by the European bike sales market.

Unmanned smart hotel: applications and examples

PurposeIn the tourism sector, fully unmanned and partially unmanned hotel models serving customer segments from different income groups are increasing. Analyzing examples of unmanned hotels worldwide and their practices is crucial for understanding the automation systems used, the smart technologies employed, and the opportunities and challenges these hotels present, as well as for gaining insights into their impacts on the tourism sector.Design/methodology/approachThe data used in this research were obtained from secondary sources. One of the qualitative research methods, document analysis, was used for the analysis of these sources. The content analysis technique was used in the analysis of the data. A seven-stage systematic review process was used in the research. This seven-stage review process consists of the following stages: (1) determining the review objectives and formulating research questions; (2) identifying search terms and selection criteria; (3) conducting a search for unmanned hotel applications before clarifying exclusion and inclusion criteria; (4) evaluating the quality and relevance of unmanned hotel applications; (5) identifying content analysis review variables; (6) conducting content analysis; and (7) analyzing and reporting the findings.FindingsIn traditional hotel management, the innovations brought by digitalization and automation are transforming the guest experience and increasing operational efficiency. Unmanned smart hotels are equipped with various technological solutions, such as voice-controlled AI assistants, smart room control systems, AI-based concierge services, and robotic room service. These hotels are redefining roles and expectations within traditional hotel management, while simultaneously reducing costs and enhancing efficiency. Analyses indicate that unmanned smart hotels particularly appeal to specific customer segments, such as business travelers, and are becoming increasingly popular. These hotels offer advantages such as allowing guests to perform self-check-in, control their rooms, and receive necessary services via robots.Research limitations/implicationsThe universe of the research consists of all currently operating unmanned hotels worldwide. As a result of the research, 18 examples of unmanned smart hotels were identified. Hotels within the same chain with identical applications and processes were considered as a single example. Therefore, the research sample consists of 18 hotels.Originality/valueBy integrating these technological advancements, the hospitality and tourism industries can mitigate the impact of staff shortages, maintain high service standards, and improve operational efficiency. This approach allows businesses to adapt to changing workforce dynamics while continuing to deliver exceptional guest experiences. In conclusion, the significance and impact of unmanned smart hotels in the travel industry are growing. These hotels have the potential to shape the role of technology in the hospitality sector and influence future trends. Therefore, the adoption and development of unmanned smart hotels are important considerations for hotel operators and industry experts.

Customer Classification by Past Purchase Data Analysis

Customer categorization is an essential strategy for companies seeking to maximize their advertising spend. Businesses can boost client involvement and sales rates of conversion substantially by identifying specific customer segments, tailoring products or services to their preferences, minimizing the hassle of irrelevant advertisements, and increasing customer satisfaction, resulting in improved long-term interactions with clients. This paper presents a classification model that uses Keras and support vector machine stacked classification passed on to a meta-learner to predict the customer segment, and RFM analysis is performed to identify the customer segment. This focused strategy lowers marketing costs and boosts income, increasing the business's efficiency. Temporal mining helps us predict the next purchase of a customer using a time series model.

Marketing Strategies used by the Fashion Industry in the Digital Age

In today's digital age, the fashion industry adapts to evolving consumer preferences through strategic marketing approaches. Social media platforms like Instagram, Facebook, and TikTok are pivotal in reaching a wider audience and fostering personalized engagement. Collaborating with influencers and using e-commerce platforms, brands enhance their visibility and cater to the growing trend of online shopping, especially via mobile devices. They invest in user-friendly websites and apps, employing data analytics and AI for personalized marketing. Content marketing plays a crucial role in brand building, utilizing blogs, videos, and collaborations with influencers to showcase products and values. Email marketing remains effective, offering personalized promotions and targeting specific customer segments based on demographics and purchase history. Overall, the fashion industry's embrace of social media, ommerce, content marketing, and personalized campaigns enables them to thrive amidst digital transformations and connect meaningfully with tech-savvy consumers.

Discrete-Time Survival Models with Neural Networks for Age–Period–Cohort Analysis of Credit Risk

Survival models have become popular for credit risk estimation. Most current credit risk survival models use an underlying linear model. This is beneficial in terms of interpretability but is restrictive for real-life applications since it cannot discover hidden nonlinearities and interactions within the data. This study uses discrete-time survival models with embedded neural networks as estimators of time to default. This provides flexibility to express nonlinearities and interactions between variables and hence allows for models with better overall model fit. Additionally, the neural networks are used to estimate age–period–cohort (APC) models so that default risk can be decomposed into time components for loan age (maturity), origination (vintage), and environment (e.g., economic, operational, and social effects). These can be built as general models or as local APC models for specific customer segments. The local APC models reveal special conditions for different customer groups. The corresponding APC identification problem is solved by a combination of regularization and fitting the decomposed environment time risk component to macroeconomic data since the environmental risk is expected to have a strong relationship with macroeconomic conditions. Our approach is shown to be effective when tested on a large publicly available US mortgage dataset. This novel framework can be adapted by practitioners in the financial industry to improve modeling, estimation, and assessment of credit risk.

Customer Preferences for Delivery Service Attributes in Attended Home Delivery

Retailers face increasing competitive pressure to determine how best to deliver products purchased online to the end customer. Grocery retailers often require attended home delivery where the customer must be present to receive the delivery. For attended home delivery to function, the retailer and customer must agree on a delivery time slot that works for both parties. Using online data from a grocery retailer, we observe customer preferences for three delivery service attributes associated with each time slot: speed, precision, and timing. We define speed as the expected time between the placement of an order and its delivery, precision as the duration of the offered time slot, and timing as the availability of choices across times of the day and days of the week. We show that customers not only value speed as an attribute of delivery service but that precision and timing are also key drivers of the customer’s time slot selection process. We also observe substantial customer heterogeneity in the willingness of customers to pay for time slots. Customers that differ in their loyalty to the retailer, basket value, basket size, and basket composition exhibit distinct differences in their willingness to pay. We show that retailers with the capability to tailor their time slot offerings to specific customer segments have the potential to generate approximately 9% more shipping revenue than those who cannot. Our findings inform practitioners seeking to design competitive fulfillment strategies and academics modeling customer behavior in the attended home delivery context. This paper was accepted by Vishal Gaur, operations management. Funding: This work was supported by the Fundação para a Ciência e a Tecnologia [PTDC/EME-SIS/31908/2017]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2020.01274 .

Penerapan Bisnis Digital Untuk Pemasaran Bola Ubi Kopong Diperkotaan

The use of digital business is increasingly crucial in facing the era of globalization and technological advancements. This article discusses the implementation of digital business strategies to market a unique product, "Hollow Sweet Potato Balls," in urban areas. The research method employed is a case study, focusing on online marketing, social media, and e-commerce strategies to enhance visibility and product sales in the urban market.By leveraging these digital tools, the hollow sweet potato ball business can expand its market reach and enhance competitiveness in the competitive urban environment. The transformation into a digital business provides a better understanding for SMEs of consumer preferences and behaviors, enabling them to reach more specific customer segments. The use of social media platforms such as Facebook and Instagram also aids SMEs in marketing strategies and consumer interaction.Additionally, delivery services like GrabFood assist SMEs in adapting to the increasing demand for delivery services. In conclusion, SMEs need to adopt digital technology to adapt and thrive amidst the ongoing changes in the business landscape.

Customer Churn Analysis

Abstract: Customer churn analysis is a critical process for businesses to understand the reasons why customers stop using their products or services. It involves examining customer data and identifying patterns that can indicate potential churn. By analyzing this data, businesses can take proactive measures to retaincustomers, such as improving the quality of their offerings or targeting specific customer segments. Ultimately, effective customer churn analysis can help businesses reduce customer churn and increase profitability.

Confucian Virtues: A Cornerstone for Student’s Leadership Modelling

The study employs a quantitative approach, utilizing a descriptive research design through surveys to gather data. The researcher seeks to answer specific questions regarding the profile of respondents (age, sex, civil status, and frequency of store visits), the importance of customer loyalty in terms of promotion, place, price, and product, and whether a significant relationship exists between customer loyalty and respondent profiles. Data analysis indicates that most respondents are female, married, visit the same convenience store 1-3 times a week, with an average age of 40.68. The respondents generally view selected convenience stores positively, appreciating their spacious area, competitive pricing, and quality products, but show a neutral perception towards promotions. While most aspects of customer loyalty do not show significant relationships with respondent profiles, there is a notable correlation between civil status and the perception of pricing. Female customers, particularly those who are married, tend to rate the pricing of convenience stores more favorably. The study's findings suggest that convenience stores cater to the needs of married females, who find them helpful for home-related purchases and time-saving. The stores are well-structured, offering a wide range of products at competitive prices, resembling regular supermarkets in many aspects. Overall, this research sheds light on the importance of customer loyalty for the success of convenience stores and highlights the need for tailored strategies to retain and attract specific customer segments. By understanding customer preferences and aligning services accordingly, convenience stores can strengthen their position in the competitive market and foster sustainable growth.

WHO ARE ONLINE GROCERY SHOPPERS?

The acceleration of the digitalisation of grocery shopping is an important trend that shows that this way of sourcing groceries is increasingly accepted by customers. Uncovering, understanding, and describing the differences between online grocery shoppers is interesting from a scientific point of view and a practical one. Correctly targeting a specific customer segment increases the very effectiveness of marketing communication by spending the cost of communicating with those correctly targeted customers that are valuable to the company. Therefore, this paper explores the behaviour of customers when shopping online and tries to find similarities in this behaviour. The aim of the paper is to generate customer segments of online grocery shoppers that provide a more comprehensive insight by reflecting on their shopping behaviour, personality traits and characteristics, loyalty, overall satisfaction with online grocery shopping in the current retailer, and frequency of social media usage. An online questionnaire survey was conducted with a panel of respondents from the IPSOS research agency to obtain primary data. Data were analysed using factor and cluster analysis. These analyses resulted in the creation of a segmentation that identified five main segments of online grocery shoppers. The constructed combined segmentation divides shoppers into five segments: quality-oriented shoppers (18.9%), influential utilitarians (21.7%), loyal traditionalists (16.4%), satisfied conditional loyalists (14.9%), and movable eco-sympathizers (28.1%). Then these category types are characterised in terms of their most salient characteristics. The results of this study show the variables that influence customers in their decision-making process. Outcomes increase knowledge about online grocery shopping behaviour, motives, and purchase requirements. These are also beneficial for grocery retailers for better targeting or fostering loyalty.

How to Use the Six-Step Digital Ethnography Framework to Develop Buyer Personas: The Case of Fan Fit

One of the key features of digital marketing is customer centricity, which can be applied to the domain of health. This is expressed through the ability to target specific customer segments with relevant content using appropriate channels and having data to track and understand each interaction. In order to do this, marketers create buyer personas based on a wide spectrum of quantitative and qualitative data. Digital ethnography is another established method for studying web-based communities. However, for practitioners, the complexity, rigor, and time associated with ethnographical work are sometimes out of reach. This paper responds to the gaps in the practically focused method of using social media for digital ethnography to develop buyer personas. This paper aims to demonstrate how digital ethnography can be used as a way to create and refine buyer personas. Using a case study of the Fan Fit smartphone app, which aimed to increase physical activity, a digital ethnography was applied to create a better understanding of customers and to create and refine buyer personas. We propose two buyer personas, and we develop a 6-step digital ethnography framework designed for the development of buyer personas. The key contribution of this work is the proposal of a 6-step digital ethnography framework designed for the development of buyer personas. We highlight that the 6-step digital ethnography could be a robust tool for practitioners and academicians to analyze digital communications for the process of creating and updating data-driven buyer personas to create deeper insights into digital and health marketing efforts.

Optimizing SAP Pricing Strategies with Vendavo and PROS Integration

In today's fast-paced and competitive business environment, effective pricing strategies are crucial for sustaining profitability and market share. SAP, a widely used enterprise resource planning (ERP) system, provides fundamental pricing functionalities. However, the increasing complexity of markets requires more advanced, data-driven pricing solutions. Vendavo and PROS, two leading pricing optimization platforms, offer organizations enhanced capabilities such as real-time data analytics, artificial intelligence (AI)-driven price optimization, and precise margin management. Integrating these tools with SAP can provide businesses with a more agile and dynamic approach to pricing, allowing them to respond swiftly to market fluctuations and customer demand. Vendavo focuses on strategic price management by enabling businesses to set, monitor, and adjust prices based on market conditions and competitive data. Meanwhile, PROS uses AI and machine learning to forecast demand and optimize pricing strategies, helping organizations tailor prices for specific customer segments. Together, these solutions offer a powerful combination when integrated into SAP, providing a seamless and comprehensive approach to pricing. This integration delivers numerous benefits, including more accurate pricing, automated processes that reduce errors, and the ability to make data-driven decisions faster. As businesses face mounting competition and shifting market demands, the combination of SAP, Vendavo, and PROS offers a strategic advantage that enhances profitability and operational efficiency. By leveraging this integrated approach, organizations can ensure consistent and optimized pricing strategies, positioning themselves for success in a rapidly changing business landscape

Ethnocentrism and the selection of white wine by young Australian consumers

ABSTRACT We investigate consumer preferences for domestically produced white wines compared to foreign wines in Australia. A discrete choice experiment was used to analyze young Australian consumers’ preferences regarding white wine and a latent class model was estimated to take preference heterogeneity into account. We examine the relationship between consumers’ ethnocentrism and preferences for wine from a specific origin. Highly ethnocentric Australian wine consumers are found to strongly prefer Australian wines and to a lesser extent wine from New Zealand compared to otherwise identical French wine, confirming a country-of-origin effect. Carefully considering the information to include on wine labels helps to stimulate consumer demand in certain market segments. Producers can use information regarding consumer preferences for specific wine characteristics to put an attractive and profitable product on the market. For instance, Australian wines might have a domestic advantage over similar foreign wines, if this ‘country of origin’ is brought to the consumers’ attention. Hence, labeling wine as ‘Made in Australia’ is likely to increase the willingness to buy wines amongst a significant subgroup of Australian consumers aged 18–38. Adding other specific labels such as gold or silver medals to the bottle can also attract specific customer segments.

Systemizing and Automating the Concept Development Process Based on Product Configuration and User Feedback: Case Study on Automating the Design Process of Creating Concepts for a Kitchen Stand Mixer

Optimizing the development and evaluation of concepts during the design of consumer products through, e.g., topology optimization, often excludes areas associated with user needs (e.g., usability). This paper reports on an exploratory study of developing a system which can create new product concepts based on users’ preferences, requirements, and a clearly defined product structure. The 3D model of the product (structured using top-down design and resilient modeling) was integrated with calculations of the performance of the user needs (performance indicators). Different designs were developed based on design of experiment analyses and optimization analyses of the 3D model of the product (a kitchen stand mixer). The outcome of the analyses was a range of concepts which scored differently in the performance indicators. The best designs (based on Pareto front) were evaluated by six potential customers. Half of the participants preferred the same design, suggesting that this tool can be used to develop a design which a specific customer segment prefers. The process of creating the model and using it for customer interviews contributed a set of qualitative findings to the literature on combining parametric design, customization, and systematic design with user needs.

“I can get no e-satisfaction”. What analytics say? Evidence using satisfaction data from e-commerce

This study mines customer satisfaction (CS) segments using almost 270 thousand responses from a CS survey which ran in 140 e-commerce stores of a European country. To achieve this, it develops and applies a business analytics (BA)-informed framework. Then, it presents examples of how one e-commerce store exploited the extracted CS segments to build automated marketing actions for its customers, ranging from social media sharing strategies for the satisfied segments, to discounts for the less satisfied. This study contributes to customer satisfaction and segmentation literature. The extracted insights can be utilized to support decision making, ranging from targeted advertising for specific customer segments, to benchmarking for companies in similar industries.

Branding “Halal”: application of a certain religious emotion to attract a specific customer segment

Purpose The main purpose of this conceptual paper is to identify and explain the technique of branding “Halal” as a marketing strategy to reach and capture a certain market segment (niche), particularly Muslims. Design/methodology/approach The approach of this paper is descriptive based on literature survey as well as the author’s own opinion, experience and judgment. Findings In many cases, particularly in food market, the term “Halal” is being used to attract the Muslim customers’ religious feelings rather than providing them cost-effective value products. Research limitations/implications The paper is expected to contribute to the theoretical base, as it focuses on the use of religious sentiments in marketing that is worth further research conduction. Practical implications The arguments of this paper can be used by the producers of “Halal” products in including certain features that might satisfy the demands of religious Muslims regarding food products. Social implications This paper can be contributive for the greater needs regarding societal and Islamic values. Originality/value This paper is one of the initial attempts to highlight the religious feelings to attract a specific market segment.

ROPO and Reverse ROPO Effect in Gender-Generation Characteristics

Objective - The primary goal of the paper is to identify which purchasing channels (online or offline), Slovak consumers use to search for, research and purchase products as well as to evaluate the association between the selected elements of the purchasing process with respect to gender and generational characteristics of customers. Findings - This paper proves the existence of a significant association between the place customers search for information about products (on the Internet or in brick-and-mortar stores) and the place where they make the actual purchase. The paper also reveals that in terms of the common gender and generation characteristics and the overall purchasing process there is a significant association in most of the analyzed cases. Novelty - Although online shopping is considered convenient, cheaper and more time-efficient than shopping in brick-and-mortar stores, consumer behavior in the e-commerce sector may differ in terms of gender and generational characteristics. It is therefore important for marketers to know this behavior and tailor their business strategies to specific customer segments. Type of Paper: Empirical. JEL Classification: M31, M37. Keywords: Customer Behaviour; ROPO; Purchase Channel; Association Analysis; Gender; Generation Reference to this paper should be made as follows: Fedorko, R; Bačík, R; Rigelský, M; Oleárová, M. (2021). ROPO and Reverse ROPO Effect in Gender-Generation Characteristics, Journal of Management and Marketing Review, 6(1) 24 – 35. https://doi.org/10.35609/jmmr.2021.6.1(3)

USING MACHINE LEARNING METHODS TO ASSESS RISKS WHEN IMPLEMENTING A NEW CREDIT PRODUCT

The article discusses the issues of assessing the creditworthiness of individuals using credit scoring. This rating system is an effective approach to determining the level of risk for a specific customer segment. This is especially true of the situation when a credit institution launches a new credit product. The main idea proposed in the article is that new customer scoring cards are created on the basis of existing cards by mathematical data processing. The novelty of the method lies in the fact that the scoring is done based on a dedicated subset of customer data stored in the corporate storage. The approach helps to make a decision on granting a loan and can be recommended for use in lending

Predictive Data Mining Model for Electronic Customer Relationship Management Intelligence

The rising adoption of e-CRM strategies in marketing and customer relationship management has necessitated to more needs especially where a specific customer segment is targeted and the services are personalized. This paper presents a distributed data mining model using access-control architecture in a bid to realize the needs for an online CRM that intends to deliver web content to a specific group of customers. This hybrid model utilizes the integration of the mobile agent and client server technologies that could easily be updated from the already existing web platforms. The model allows the management team to derive insights from the operations of the system since it focuses on e-personalization and web intelligence hence presenting a better approach for decision support among organizations. To achieve this, a software approach made of access-control functions, data mining algorithms, customer-profiling capability, dynamic web page creation, and a rule-based system is utilized.

Winning strategies for customer loyalty in the sharing economy: A mixed-methods study

Loyalty is key for sharing economy (SE) platforms, as their success depends on gaining and retaining a critical mass of users. Using a sequential design of quantitative (exploratory factor analysis) and mixed methods (qualitative comparative analysis), a sample of 208 users of SE platforms was analysed. The results indicate that loyalty is achieved at the intersection of website (app) organisation, platform responsiveness and reliability, and customer interaction with the peer service provider. Three strategies are proposed to achieve loyalty, each comprising two out of the three mentioned antecedents. Sensitivity analysis of the loyalty antecedents suggests specific customer segments (millennials versus baby boomers and males versus females).