Customer Demographics Research Articles

Big Data Analytics for Energy Efficiency

Industries and utilities explore more and more case studies and expert advice concerning the possibilities of intelligent business administration and Big Data analytics. The movement to integrate and meaningfully interpret huge tanks of data points is reaching beyond cutting edge utilities into the mainstream. This project aims at exploring the application of Big Data analytics in the energy sector. Within project’s framework, already developed and operational datasets will be combined with supplemental data from publicly available sources, including prior energy efficiency program participation, enrollment in other energy programs and services, geographic data, customer equipment profiles, demographics, and psychographic customer segmentation categories. There are significant technical, budgetary and project management challenges in undertaking the development and integration of Big Data analytics. There are also organizational challenges in managing change, making decisions in a cooperative framework, and grounding project goals and strategies in the customer experience. The ultimate goal of the project is to develop a reliable database for a step closer to the energy efficiency direction.

Joint Age Estimation and Gender Classification of Asian Faces Using Wide ResNet.

Two key facial features, age and gender, have been widely explored. Companies and organizations have investigated in related applications in several fields including insurance, retails, marketing, etc. It would bring tremendous benefit, which allow companies to easily identify their customer demographics. Several approaches have been proposed with remarkable results. However, because of the lack of open and multi-ethnic datasets, most modern age and gender estimating models were trained solely based on white people with Western facial features, and thus fall short with non-Caucasian people. In this paper, we developed an applicable Wide ResNet model to estimate the age and the gender of Asian faces. The model was trained with a newly improved Asian face database. The experiments have shown promising results, as it can match the performance of Microsoft’s how-old API estimator in a specific dataset.

Need of Business Analytics and Prediction Modeling in Retail Marketing in Indian Context

In the digital era, Indian Retail market growing fast than ever and creating stiff competitive business environment. So, analyzing customer behavior, buying pattern and the ability to customize the products to meet demand targeted customer in time has become more important. Therefore, analyzing, diagnosing and channelizing customer data for the benefits of customer as well as the business growth is important to survive in industry for long run. International retail players are already using effective customer analytics systems or big data analytics software’s for each and every stage of the retail process starting from market study of current trends, customer purchasing behavior, sales and demand forecasting through predictive analytics, product optimization, offers and promotions and many more. After all, identifying the targeted customers interested in a specific product line by the analysis of purchase history, customer demographics and finding out effective medium to reach them through Omni-channel marketing strategies is the core of customer analytics. This paper is an outcome of a descriptive research on the retail industry in India and the application of customer analytics to shape the retail industry’s business strategy.

Insurance Customers' Expectations for Sharing Health Data: Qualitative Survey Study.

BackgroundInsurance organizations are essential stakeholders in health care ecosystems. For addressing future health care needs, insurance companies require access to health data to deliver preventative and proactive digital health services to customers. However, extant research is limited in examining the conditions that incentivize health data sharing.ObjectiveThis study aimed to (1) identify the expectations of insurance customers when sharing health data, (2) determine the perceived intrinsic value of health data, and (3) explore the conditions that aid in incentivizing health data sharing in the relationship between an insurance organization and its customer.MethodsA Web-based survey was distributed to randomly selected customers from a Finnish insurance organization through email. A single open-text answer was used for a qualitative data analysis through inductive coding, followed by a thematic analysis. Furthermore, the 4 constructs of commitment, power, reciprocity, and trust from the social exchange theory (SET) were applied as a framework.ResultsFrom the 5000 customers invited to participate, we received 452 surveys (response rate: 9.0%). Customer characteristics were found to reflect customer demographics. Of the 452 surveys, 48 (10.6%) open-text responses were skipped by the customer, 57 (12.6%) customers had no expectations from sharing health data, and 44 (9.7%) customers preferred to abstain from a data sharing relationship. Using the SET framework, we found that customers expected different conditions to be fulfilled by their insurance provider based on the commitment, power, reciprocity, and trust constructs. Of the 452 customers who completed the surveys, 64 (14.2%) customers required that the insurance organization meets their data treatment expectations (commitment). Overall, 4.9% (22/452) of customers were concerned about their health data being used against them to profile their health, to increase insurance prices, or to deny health insurance claims (power). A total of 28.5% (129/452) of customers expected some form of benefit, such as personalized digital health services, and 29.9% (135/452) of customers expected finance-related compensation (reciprocity). Furthermore, 7.5% (34/452) of customers expected some form of empathy from the insurance organization through enhanced transparency or an emotional connection (trust).ConclusionsTo aid in the design and development of digital health services, insurance organizations need to address the customers’ expectations when sharing their health data. We established the expectations of customers in the social exchange of health data and explored the perceived values of data as intangible goods. Actions by the insurance organization should aim to increase trust through a culture of transparency, commitment to treat health data in a prescribed manner, provide reciprocal benefits through digital health services that customers deem valuable, and assuage fears of health data being used to prevent providing insurance coverage or increase costs.

A STUDY OF COMPARING CONCEPTUAL AND PERFORMANCE OF K-MEANS AND FUZZY C MEANS ALGORITHMS (CLUSTERING METHOD OF DATA MINING) OF CONSUMER SEGMENTATION

Consumers an important asset in a company that should be maintained properly especially potential customers. Tight competition requires companies to focus on the needs of the customer wants. Consumer segmentation is one of the processes carried out in the marketing strategy. To support the grouping process results consumers or consumer segmentation data mining is the support of a very important role. Based on mapping studies on data mining in support of consumer segmentation obtained two algorithms are often used for consumer segmentation include a K-Means Clustering and Fuzzy C-Means clustering. The attributes used for mining in customer segmentation processes are customer data, products, demographics, consumer behavior, transactions, RFMDC, RFM (Recency, Frequency Monetary) and LTV (Life Time Value). And it is important to combine the clustering algorithm to algorithm Classification, Association, and CPV to get the potential value of each cluster.

Housing Instability in the Lafayette Community and Beyond

This paper addresses the issue of housing instability low-income families face and the ways the community can help. The issue is prevalent in the Lafayette community and beyond. Habitat for Humanity is a nonprofit organization working to raise awareness of housing instability in our community, specifically through the ReStore of Lafayette. The ReStore is run solely on the community’s donations and volunteer work. All proceeds from the store go to building affordable houses for low-income families. My objectives in the research I conducted were to gain a better understanding of how the ReStore goes about eliminating housing instability in the United States and other countries. To do this, I posed this question: How can my volunteering hours at the ReStore help improve the marketing/advertising of their resources and address the housing instability many people face in the Lafayette community and beyond? Through primary and secondary research, I was able to come up with an answer that would be feasible for Purdue students and the Lafayette community. Journaling my thoughts and observations after each volunteer session helped me better reflect on my work and contribution to the housing instability issue of Lafayette. The activity theory is a concept about the flow of community service that I drew upon. This theory helped me visualize the interconnectedness between the community and the Restore. In my results section, I was informed about the ReStore’s current advertising methods and customer demographics. Being an active volunteer is the first step to effective community service. The more volunteers there are, the more awareness on the issue. Moving forward, I feel that more individuals should be aware of housing instability from a young age. More widespread awareness of the housing instability many people face in our community will benefit everyone in the long run.

The influence of innovation stimulus, facilitating factors and support mechanisms on innovation performance in Brazilian small textile firms

The ability of the organisation to develop new products is crucial in rapidly changing business environments, and thus, customer demographics, consumers' expectations and buying behaviour play an important role. Hence, exploring the antecedents of innovation performance becomes more important. This study analyses the relationships among innovation stimulus, facilitating factors and support mechanisms for innovative performance in the textile industry of Santa Catarina, Brazil. The sample included 128 respondents, who were all employees of five small and medium-sized textile firms; data were analysed using structural equation modelling. We found that innovation stimulus, facilitating factors and support mechanisms are correlated, while support mechanisms have no influence on innovation performance. The results also highlight a concern regarding the cost and time spent on developing innovation projects. This research contributes to innovation performance literature by analysing innovation antecedents including visions of innovation as both a process, represented by efficiency, and a result, represented by efficacy.

Leveraging fine-grained transaction data for customer life event predictions

This real-world study with a large European financial services provider combines aggregated customer data including customer demographics, behavior and contact with the firm, with fine-grained transaction data to predict four different customer life events: moving, birth of a child, new relationship, and end of a relationship. The fine-grained transaction data—approximately 60 million debit transactions involving around 132,000 customers to >1.5 million different counterparties over a one-year period—reveal a pseudo-social network that supports the derivation of behavioral similarity measures. To advance decision support systems literature, this study validates the proposed customer life event prediction model in a real-world setting in the financial services industry; compares models that rely on aggregated data, fine-grained transaction data, and their combination; and extends existing methods to incorporate fine-grained data that preserve recency, frequency, and monetary value information of the transactions. The results show that the proposed model predicts life events significantly better than random guessing, especially with the combination of fine-grained transaction and aggregated data. Incorporating recency, frequency, and monetary value information of fine-grained transaction data also significantly improves performance compared with models based on binary logs. Fine-grained transaction data accounts for the largest part of the total variable importance, for all but one of the life events.

PSX-30 Influence of cut, breed type and supplementation on consumer ratings of pasture-raised lamb

Abstract Consumer ratings and influence of cut, breed type and feed supplementation on sensory attributes of lamb meat were investigated. Lamb cuts (leg, loin, rib and foreshank) were obtained from purebred hair sheep (PB; n = 18; Barbados Blackbelly or St. Croix) and crossbred wool × hair sheep ram lambs (CB; n = 17; Dorset × Barbados Blackbelly or Dorset × St. Croix) rotationally grazed for 77 d in late spring on tall fescue only (PS) or supplemented with soy hull (SH) or corn gluten (CG) at 2.0% of BW (DM basis) at individual feeding stations. Lambs were harvested at 10 mo of age. Customers (n = 170) of a local food hub randomly received two different cuts of lamb and were asked to complete a survey rating each cut for qualitative characteristics (1=extremely undesirable to 9=extremely desirable) before and after cooking. Customer demographics and diet habits were also recorded. Responses were analyzed using PROC NPAR1WAY and GLM procedures of SAS; survey response rate was 66% (113/170). Cut of lamb did not influence attributes. Breed type affected pre-cooking aroma (CB > PB; P < 0.05); tended to affect pre-cooking quality (CB > PB; P = 0.07); and affected after-cooking texture (PB > CB; P < 0.01). Diet affected (P < 0.05) pre-cooking texture (SH > CG > PS; P < 0.02) and tended to influence after cooking juiciness (PS < SH = CG; P = 0.09). Before-cooking attributes were influenced by a breed x diet interaction (P < 0.05) with CB x PS and PB x CG consistently receiving lower scores for color, texture, appearance, quality and ease of preparation. After-cooking aroma was lower (P < 0.05) for CB x SH and PB x PS compared to PB x CG lambs only. Results indicate that breed type and supplementation, but not type of cut, selectively influenced consumer sensory ratings of pasture raised hair sheep lamb.

Data Warehouse Testing: A Step Towards using Business Intelligence in Banking

In the modern era, technology plays a vital role by contributing in the development of business by providing various tools and techniques to enhance the business decision-making process. Data warehouse is an important entity that contributes to the decision-making process, which can be seen in the literature available over the years. Data warehouse provides the basis for quality analysis of available data by deriving accurate information from data. Like many other industries, banking sector is also facing challenges due to various reasons like large over-dues, non-performing assets, changing customer demographics, matching customer expectation levels, increased competition from financial technological companies and banking competitors, etc. Thus data warehouse system serves to be the best solution for the banks to overcome challenges as data warehouse system integrates all the data at one place and provides a consolidated view of the past transactions which can be used for report generation and performing analytical analysis in order to help the management to maximize business performance. In addition to making strategic decisions, data warehouse also assist in helping the banks to improve customer retention, optimize discounting, market segmentation, business performance, customer deposits, etc. Thus, a data warehouse system provides a solution to all data management problem and generate patterns and reports for analytical end users for enhancing decision making processes. In this research paper, we shall be representing a bank model that focuses on the loan department of a bank data warehouse and shall be explaining how business intelligence plays a role in improving the loan analysis for the banks. Loan analysis may include summarizing the classified loans, analyzing loans within and exceeding the threshold limits of Loan-To-Value (LTV) ratio, analysis of loan rejections and other analysis pertaining to loans.

Effect of guest demographics on perception of hotel room price

Purpose A review of past scholarly work on pricing issues in hospitality has revealed a lack of focus on customers’ demographic profiles. However, research in other disciplines reveals that understanding price perception differences among groups of customers with different demographics, including culture, is an important consideration when offering pricing strategies. The purpose of this paper is to contribute to the body of pricing research by exploring the effect of hotel guests’ demographics on their perception of hotel room prices. Design/methodology/approach Through Qualtrics, data were collected from 414 respondents who stayed at a mid-scale hotel within the past 24 months. The respondents’ perceived value (PV), perceived fairness (PF) and willingness to pay (WTP) for hotel rooms were examined with MANOVA and ANOVA tests to determine the effects of customer demographics on these variables. Findings Age, gender and marital status showed a significant effect on PV while age, gender and culture significantly affected PF. However, none of these variables significantly affected WTP. The culture of origin and the culture raised-in influenced PV, PF and WTP similarly. Originality/value This study reconciles several divergent results from previous studies and extends the scope of others by introducing different scenarios to each of the three dependent variables. To the best of the authors’ knowledge, it is also the first research study on this subject to evaluate more than two cultures and their effects on the independent variables.

An Assessment of Service Quality Dimensions Conducted at Oyo Hotel, Bangalore

OYO, an online hotel booking service organization is getting popularity all over India for their innovative and technology based services. Customers are becoming tech-savvy and are increasingly using Internet for booking hotels online. It is important to know the factors that are determining the consumer’s changing behavior. The purpose of the paper is to understand the factors influencing customer’s decision to book OYO rooms. An exploratory study using purposive sampling method was carried out. Chi-Square analysis revealed that customer demographics, specifically age, education and occupation play a significant role on customer usage frequency. The study aids in giving an additional insight to understand consumer behavior in online hotel booking services and to understand the need for assessment of service quality to deliver the expected service.

Building customer loyalty in retail banking: a serial-mediation approach

PurposeThe purpose of this paper is to advance research on the relationship between customer experience and customer loyalty by exploring the serially mediating roles of brand equity and customer satisfaction and the moderating roles of age, gender, education and family income in the retail banking industry.Design/methodology/approachA total of 500 responses of retail banking customers were used to test the model using the partial least squares structural equation modeling approach. Advanced statistical techniques, such as importance-performance map analysis and a joint application of FIMIX-PLS and PLS-POS, were used to gain new insights.FindingsThe study highlighted that the relationship between customer experience and loyalty is serially mediated by brand equity and customer satisfaction. Age, gender and education were found to be significant moderators in the customer experience–loyalty relationship. Age and gender were found to be significant moderators in the brand equity–loyalty relationship.Practical implicationsThe study strongly suggests that practitioners not only focus on delivering exceptional customer experiences but also on providing leverage brand equity and satisfaction to build customer loyalty. Practitioners should focus on training their front-line employees to improve the quality of their behavior and relations with customers and thereby build customer loyalty.Originality/valueTo the authors’ knowledge, this is the first study to explore the mediating role of several variables sequentially and the moderating role of customer demographics in the customer experience–customer loyalty relationship.

Proposition of Machine Learning Driven Personalized Marketing Approach for E-Commerce

The ubiquity of digital devices and Internet has formed a constantly connected online environment which led to the extensive adoption of e-commerce. However, the active participation of growing number of stakeholders intensifies the highly competitive landscape of the dynamic e-commerce market and the scarcity of trust in e-commerce business impede the generation of consistent sales growth. The obstruction necessitates the implementation of innovative marketing strategies to enhance the relationships with customers to develop customer loyalty. Therefore, a machine learning driven personalized marketing approach is proposed to facilitate the implementation of personalized marketing in which there are 2 significant sequential elements namely, the development of personalized marketing contents and delivery of the contents to prospective customers. Cluster analysis is employed to perform customer segmentation to discover customer segments due to the capability of the analysis to identify similarities in customer preferences in which the discovered customer segments are used to construct personalized marketing contents. In addition, artificial neural network is employed to predict prospective customers due to the capability of artificial neural network to comprehend complex relationships between customer demographics and buying behaviour in which the prediction facilitates the delivery of the constructed personalized marketing contents to potential repeat customer to optimize the marketing initiative. The combination of cluster analysis and artificial neural network empowers the construction of an efficacious marketing pipeline which enhances the competency of e-commerce businesses.

Perceived fairness of revenue management practices in casual and fine-dining restaurants

PurposeRestaurants typically have small profit margins and with the pressure of increasing food and labor costs, management is looking to revenue as a way to maintain and drive profits. One technique to increase revenue is through revenue management practices, but management needs to be aware of their customers’ reactions to these practices prior to implementation. The paper aims to discuss this issue.Design/methodology/approachThis study utilizes linear regression to determine the impact of select restaurant revenue management practices, customers’ familiarity with revenue management in general and in restaurants specifically, and customers’ demographics on perceived fairness of revenue management practices in casual and fine-dining restaurants.FindingsResults indicate that customers find certain restaurant revenue management practices, such as charging premium prices on certain days of the week, fair in both casual and fine-dining restaurants, while others are not in either. Non-refundable reservation fees were found to be fair for fine-dining establishments only. Increased familiarity with restaurant revenue management was associated with higher perceptions of fairness for both casual and fine dining. Age was the only demographic studied that affected perceived fairness.Originality/valueThis study is the only known study to simultaneously evaluate the impact of price and duration restaurant revenue management techniques in combination with customer demographics and revenue management familiarity on consumer perceptions of fairness.

Fixed vs. Flexible Pricing in a Competitive Market

We study the selection and dynamics of two popular pricing policies—fixed price and flexible price—in competitive markets. Our paper extends previous work in marketing, for example, Desai and Purohit (2004) by focusing on decentralized markets with a dynamic and fully competitive framework while also considering possible noneconomic aspects of bargaining. We construct and analyze a competitive search model, which allows us to endogenize the expected demand depending on pricing rules and posted prices. Our analysis reveals that fixed and flexible pricing policies generally coexist in the same marketplace, and each policy comes with its own list price and customer demographics. More specifically, if customers dislike haggling, then fixed pricing emerges as the unique equilibrium, but if customers get some additional satisfaction from the bargaining process, then both policies are offered, and the unique equilibrium exhibits full segmentation: haggler customers avoid fixed-price firms and exclusively shop at flexible firms, whereas nonhaggler customers do the opposite. We also find that prices increase in customer satisfaction, implying that sellers take advantage of the positive utility enjoyed by hagglers in the form of higher prices. Finally, considering the presence of seasonal cycles in most markets, we analyze a scenario in which market demand goes through periodic ups and downs and find that equilibrium prices remain mostly stable despite significant fluctuations in demand. This finding suggests a plausible competition-based explanation for the stability of prices. The online appendix is available at https://doi.org/10.1287/mnsc.2017.2922 . This paper was accepted by J. Miguel Villas-Boas, marketing.

Antecedents and Consequences of Customer Satisfaction: Do They Differ Across Online and Offline Purchases?

Retailers seek to utilize both online and offline purchase channels strategically to satisfy customers and thrive in the marketplace. Unfortunately, current multichannel research is deficient in answering what drives customers’ satisfaction, and consequently their loyalty, differently when customers purchase online versus at a physical store. This gap in knowledge can be a significant concern for retailers due to the negative impact of having dissatisfied customers on their bottom lines. Using a version of the American Customer Satisfaction Index (ACSI) model, we demonstrate several important purchase-channel differences in the antecedents of customer satisfaction and its subsequent effect on customer loyalty. Specifically, we show that when retail customers buy electronic goods online they view purchase value as a significant attribute in rating satisfaction, and are more satisfaction-sensitive when making repurchase decisions than when they purchase offline. On the other hand, the overall quality of the purchase experience and customer expectations are stronger drivers of customer satisfaction in the offline purchases. We provide evidence that these differences between the channels generally persist across customer demographics (gender, age, and education) and broader product categories, and we also discuss the specific contexts where they do not. Our work offers actionable guidance to retailers seeking to enhance customer satisfaction and loyalty across both the online and offline channels.

Demand cycles and market segmentation in bicycle sharing

Consumers often display unique habitual behaviors, and knowledge of these behaviors is of great value in prediction of future demand. We investigated consumer behavior in bicycle sharing in Beijing, where demand prediction is critical for cost-effective rebalancing of bicycle locations (putting bikes where and when they will be rented) and supply (number of bicycles). We created baseline statistical demand models, borrowing methods from economics, signal processing and animal tracking to find consumption cycles of 7, 12, 24 h and 7-days. Lorenz curves of bicycle demand revealed significant stratification of consumer behavior and a long-tail of infrequent demand. To overcome the limits of traditional statistical models, we developed a deep-learning model to incorporate (1) weather and air quality, (2) time-series of demand, and (3) geographical location of demand. Customer segmentation was added at a later stage, to explore potential for improvement with customer demographics. Our final machine learning model with tuned hyperparameters yielded around 50% improvement in predictions over a discrete wavelet transform model, and 80–90% improvement in predictions over a naïve model the reflects some current industry practice. We assessed causality in the deep-learning model, finding that location and air quality had the strongest causal impact on demand. The extreme market segmentation of customer demand, and our relatively short time span of data combined to make it difficult to find sufficient data on all customers for a model fit based on segmentation. We reduced our model data to only the 10 most frequent to see whether such segmentation improves our model's predictive success. These results, though limited, suggest that customer behavior within market segments is more stable than across all customers, as was expected.

The role of demographic and motivational factors on mobile commerce usage activities in South Africa

Background: Many studies have examined the mobile commerce phenomenon and most of these have focused on understanding the organisational and environmental factors that affect its adoption. Few have examined how an individual’s characteristics and their perceived motivational appetite affect their mobile commerce usage and specifically the activities they engage in. Objectives: This study examines the role of demographics (individuals’ characteristics) and motivational factors on mobile commerce usage activities from the South African perspective. Method: An objective approach and a positivistic stance were followed. The research model and instrument from earlier studies by Chong (2013a) were contextualised to suit this study. Data collection was done through an online questionnaire, 88 responses were received and 81 used for data analysis. Results: The findings show that age is significantly related to mobile commerce transactions, and one’s education status influences the following mobile commerce usage activities: transactions and location-based services. One’s gender influences how one uses mobile commerce. A significant relationship between all three motivational variables and mobile commerce usage activities is evident. Conclusion: The findings provide the financial services industry and providers of mobile commerce offerings with a better understanding of the relationships between customer demographics and the mobile commerce activities they engage in. An additional variable of customer income has been identified for future studies to better understand the relationship.

Editorial for special section “Gendering cosmopolitanisms: Recognition, belonging and difference,” womens' studies international forum

We study two hostess pubs on Joo Chiat Road, Singapore - a “Filipina pub” and a “Vietnam pub”. We compare and contrast the customer demographics of each establishment, and the resultant culture, which is a function of the ethnic-group of the hostesses, the demographics of the customers, the policies and ethos of the owner/manager, and the pricing model whereby hostesses get financially rewarded. Regarding pricing model, the Filipina pub relies upon “ladies drinks” whereas the Vietnam pub fosters a tipping system. Whilst the ladies drinks model signifies a hyper-capitalist business environment, the tipping model is feudal, honour-based and debt-based. We use as a theoretical lens the concept of “cosmopolitanism” and ask whether a “grounded” “cosmopolitan ethics” is even possible in an industry which is attractive to customers largely because of its structural, racial, and gendered differences and inequalities.