Sales Behavior Research Articles

Implementation of Business Intelligence for Analysis Data of Drug Sales Using Exploratory Data Analysis (EDA) and Visualization Data Using Looker Studio

This study aims to analyze pharmaceutical sales performance by leveraging big data and advanced information technology tools, providing insights for improved business strategies. Using the Exploratory Data Analysis (EDA) method, the study processes raw sales data through spreadsheet applications to identify key patterns and trends. The findings are then visualized with Looker Studio on an intelligent dashboard tailored to the needs of the marketing team. The dashboard enables quick, data-driven decision-making by displaying performance metrics and trends relevant to sales and marketing strategies. The results reveal critical insights into sales behavior, including high-performing products, regional sales variations, and temporal sales trends. The analysis equips the marketing team with actionable data to refine their strategies, prioritize product focus, and adjust marketing efforts in response to identified patterns. In conclusion, the use of EDA and data visualization provides a structured approach to big data interpretation, allowing the company to optimize business and marketing performance. The study underscores the potential of data-driven dashboards to enhance strategic planning, suggesting broader applications in various data-intensive business environments.

Automated pricing and replenishment forecasting for vegetable items

Firstly, the thesis extensively analysed and processed the vegetable sales data of a fresh food superstore. It integrated the product information and sales data of each vegetable category, and carried out data cleaning and analysis through Excel tools, including sorting out the outliers, listing the sales volume of each of the six categories, using pivot tables for statistical analysis, and establishing line graphs, heat maps, scatter plots, and other visual displays of the data characteristics and correlations. Secondly, the thesis investigated the relationship between sales volume and pricing, fitted the correlation between total sales volume and pricing using a random forest regression model, and predicted the daily replenishment volume for the coming week using an LSTM time series model. It also fitted quantitative fitting formulas for metrics for each vegetable category, classified profits into normal sales volume profits and discounted sales volume profits, and ultimately maximised profits through dynamic programming models and particle swarm optimisation algorithms. Finally, the thesis considered the impact of other factors such as weather, holidays, seasons and market environment on sales volume with specific data collection and analyses that help guide better pricing strategies. The findings will contribute to a better understanding of vegetable sales behaviour and optimise superstore operational strategies.

A demand forecasting system of product categories defined by their time series using a hybrid approach of ensemble learning with feature engineering

AbstractRetail companies face major problems in the estimation of their product’s future demand due to the high diversity of sales behavior that each good presents. Different forecasting models are implemented to meet the demand requirements for efficient inventory management. However, in most of the proposed works, a single model approach is applied to forecast all products, ignoring that some methods are better adapted for certain features of the demand time series of each product. The proposed forecasting system addresses this problem, by implementing a two-phase methodology that initially clusters the products with the application of an unsupervised learning approach using the extracted demand features of each good, and then, implements a second phase where, after a feature engineering process, a set of different forecasting methods are evaluated to identify those with best performs for each cluster. Finally, ensemble machine learning models are implemented using the top-performing models of each cluster to carry out the demand estimation. The results indicate that the proposed forecasting system improves the demand estimation over the single forecasting approaches when evaluating the R2, MSE, and MASE quality measures.

Improving Machine Learning Predictive Capacity for Supply Chain Optimization through Domain Adversarial Neural Networks

In today’s dynamic business environment, the accurate prediction of sales orders plays a critical role in optimizing Supply Chain Management (SCM) and enhancing operational efficiency. In a rapidly changing, Fast-Moving Consumer Goods (FMCG) business, it is essential to analyze the sales of the products and accordingly plan the supply. Due to low data volume and complexity, traditional forecasting methods struggle to capture intricate patterns. Domain Adversarial Neural Networks (DANNs) offer a promising solution by integrating transfer learning techniques to improve prediction accuracy across diverse datasets. This study presents a new sales order prediction framework that combines DANN-based feature extraction and various machine learning models. The DANN method generalizes the data, maintaining the data behavior’s originality. The approach addresses challenges like limited data availability and high variability in sales behavior. Using the transfer learning approach, the DANN model is trained on the training data, and this pre-trained DANN model extracts relevant features from unknown products. In contrast, Machine Learning (ML) algorithms are used to build predictive models based on it. The hyperparameter tuning of ensemble models such as Decision Tree (DT) and Random Forest (RF) is also performed. Models like the DT and RF Regressor perform better than Linear Regression and Support Vector Regressor. Notably, even without hyperparameter tuning, the Extreme Gradient Boost (XGBoost) Regressor model outperforms all the other models. This comprehensive analysis highlights the comparative benefits of various models and establishes the superiority of XGBoost in predicting sales orders effectively.

Ethical Sales Behavior Influencing Trust, Loyalty, Green Experience, and Satisfaction in UAE Public Entrepreneur Firms

Ethical Sales Behavior Influencing Trust, Loyalty, Green Experience, and Satisfaction in UAE Public Entrepreneur Firms

The Impacts of Ethical Sales Behaviour on Customer Loyalty in the Life Insurance Industry in Pakistan

The life insurance industry in Pakistan is highly competitive, with customer loyalty playing a crucial role in long-term success. Ethical sales behaviour emerges as a key differentiator, potentially fostering trust and driving positive customer experiences. This research investigates the relationship among ethical sales behaviour, customer trust in company and trust in sales agent, which directly affect customer loyalty in the life insurance industry. The quantitative survey targets customers of five major life insurance companies, ensuring a representative sample across diverse demographics. The questionnaires were sent to all companies customer on a random basis. The questionnaires are distributed with the help of office employee of the companies.The finding of the study shows that there is a direct and positive relation between sales agent ethical sales behaviour and customer loyalty. Furthermore, the trust of the customer in the sales agent and trust of the customer in a company has direct positive mutual relation and customer trust both affect customer loyalty. By demonstrating the tangible benefits of ethical sales behaviour, the study can encourage companies to implement and prioritize ethical practices. The identified key drivers of customer loyalty can guide training programs and performance evaluations for insurance agents, ultimately fostering a culture of ethical conduct within the industry.

Digital Shopper Dynamics: A Comprehensive Analysis of Online Buying Behaviour in the Retail Landscape of Bangalore City

The study navigates the evolving patterns of consumer Behaviour in the contemporary digital era, emphasizing the city-specific dynamics of Bangalore. The literature review establishes the global influence of the digital age on retail, creating a foundation for the investigation. Objectives center on understanding online buying factors, evaluating digital marketing effectiveness, and exploring technological impacts. Employing a robust methodology, including descriptive research and random sampling, demographic details of 450 respondents are analyzed, reflecting a diverse representation crucial for tailored insights in the local market. Correlation analysis unveils a positive link between ethical sales behaviour, customer trust, and commitment. However, the weak correlation with customer loyalty suggests the necessity for a holistic loyalty approach. Suggestions highlight the significance of ethical sales training, exploration of additional loyalty drivers, and customer-centric strategies. The research contributes vital insights for businesses and policymakers, guiding them in optimizing strategies in the dynamic realm of online retail in Bangalore City.

THE INFLUENCE OF ETHICAL SALES BEHAVIOR, BRAND EQUITY, AND CUSTOMER EXPERIENCE ON KETJE COFFEE CUSTOMER LOYALTY IN BANDAR LAMPUNG

The objective of this study is to examine the influence of ethical sales behavior, brand equity, and customer experience on customer loyalty at Ketje Coffee in Bandar Lampung. The research population consists of customers of Ketje Coffee in Bandar Lampung, with a sample size of 100 customers. The analysis method employed in this study is multiple linear regression analysis. The research findings indicate that ethical sales behavior, brand equity, and customer experience all have a positive and significant impact on customer loyalty. This implies that ethical sales behavior, brand equity, and customer experience play a crucial role in shaping customer loyalty. The results of this research can assist Ketje Coffee in enhancing ethical sales behavior, strengthening brand equity, and improving customer experience, thereby increasing the company's competitiveness and fostering better customer loyalty. In the current era of heightened business competition, this study provides a platform for Ketje Coffee to enhance customer loyalty and encourage customers to choose Ketje Coffee for their consumption needs.

LR-GA Algorithm Based Study on Vegetable Replenishment and Pricing Decision Making

The sales volume and sales price of vegetable products fluctuate greatly due to factors such as origin, variety and freshness period. Therefore, rational replenishment and pricing decisions are particularly important for supermarkets. Therefore, this paper proposes a replenishment and pricing strategy model for vegetable products based on linear regression-genetic algorithm. In this paper, all vegetable commodities are first classified into four categories using K-mean cluster analysis, and the sales performance of the four categories is observed to understand their sales behaviors and provide references for sales strategies. Then, the cost, price and profit margin of each vegetable commodity are calculated using the weighted method, and the linear regression equation between the sales volume of vegetable commodities and the weighted sales price is given. Finally, using the total profit formula considering the loss rate of each item as the objective function and the linear regression equation between the total sales volume of each vegetable item and the weighted sales price as the constraints, the optimization search is carried out by using linear regression and Genetic Algorithm (LR-GA) to find out the sales price and the sales volume under the maximum profit so as to realize the sales strategy.

Impact of the Spanish smoke-free laws on cigarette sales by brands, 2000-2021: Evidence from a club convergence approach.

In January 2006, the Spanish government enacted a tobacco control law that banned the advertising, promotion and sponsorship of tobacco. In January 2011, further legislation on this matter was adopted to provide a more restrictive specification of the ban. In this study, we analyze the effect produced on cigarette sales by these two prohibitions. We address this problem using a cluster time-series analysis to test whether the sales of cigarettes by brands have been homogenized with the prohibition of advertising, promotion, and sponsorship. The data source used was the official data on legal sales of cigarettes by brands in Spain, from January 2005 to December 2021 (excluding the Canary Islands and the Autonomous Communities of the cities of Ceuta and Melilla). To achieve our objective, we used log(t) test statistics to check if there is global convergence in the three selected periods according to the regulatory changes that have occurred in Spain (2005-2021, 2005-2010 and 2011-2021). Second, once absolute convergence is rejected, we applied a clustering algorithm to test for the existence of subgroup convergence. The cigarette brands that have been marketed during the period 2005-2021 (n=40), can only be grouped into three groups according to the behavior of their sales. When we focus on the period 2005-2010 (n=74), cigarette brands are grouped into five groups according to their sales behavior. Finally, the cigarette brands marketed during the period 2011-2021 (n=67) are grouped into three groups according to the temporal evolution of their sales. These results suggest a greater homogenization of cigarette sales after the application of the law of January 2011. Act 42/2010 (total ban on tobacco advertising, promotion, and sponsorship actions) was associated with greater homogenization of cigarette sales than the application of Act 28/2005 (partial ban). This finding supports what is established in the previous literature that indicates that Act 42/2010 provided a more restrictive specification of the ban than Act 28/2005.

Bi-objective sales planning using machine learning for industrial valves

Accurate prediction and forecasting of industrial products’ consumption, enables up-to-date and efficient supply, replacement of worn-out items, and prevention of resource wastage. Planning and forecasting the usage of industrial products can help often based on historical years’ performance and environmental factors, using either traditional methods or smart systems. However, the instability of some sales behavior in certain products and the lack of previous data for new products or sales offices can create problems in intelligent systems. In this paper, we present a hybrid and bi-objective model in the form of a business intelligence system that first fits an appropriate function to the products, providing a new estimated combination for the type and sales amount of all products, while taking into account the profit margin. This new intelligent system allows for flexible planning for the company, generating a special scenario for each new input strategy. Furthermore, using machine learning and based on similarity measurements and the company’s previous data, we predict the sales behavior for new products and sales offices in their first year of operation. Finally, the model announces the sales trend of each product in different time periods, separately for each sales office, taking into account the previous two stages. The current investigation outlines the integration of the proposed model into the business intelligence system of Mirab Valves Company, a reputable manufacturer of industrial valves, and its subsequent effective application as an exemplar. The model’s efficacy in forecasting sales of new products and sales offices is established at 79% and 92%, respectively.

Perception of and legal compliance with the law prohibiting cigarette sales to minors in Thailand following the raising of the minimum selling age to 20 years: a cross-sectional survey

ObjectivesThe minimum age for cigarette sales in Thailand was increased from 18 to 20 years, as stipulated in the Tobacco Products Control Act B.E. 2017. This study investigated tobacco retailers’...

Product Sales Incentive Spillovers to the Lending Market: Evidence from Subprime Auto Loan Defaults

This paper shows how convex incentives in vertical contracts between manufacturers and retailers can induce sales behavior with costs to consumers. We examine this problem in the automotive sector, where manufacturers commonly motivate new vehicle sales through dealer incentive programs with large discrete bonuses determined by monthly sales targets. Using subprime car loans from over 3,500 dealerships, we document high default rates on new car loans originated at the end of the month—the period when dealerships attempt to secure target-based bonuses by intensifying efforts to sell new cars. We provide evidence consistent with the observed higher default rates resulting from customers purchasing new vehicles at month end. New car purchases stretch borrower budgets and expose borrowers to rapid depreciation, which consigns the borrower with negative equity through much of the loan term. Our results imply that the quartile of customers with the highest payment-to-income ratio see default rates increase from 13.6% to 19.7% on the last day of the month. Although consumers bear high costs from increased defaults, we find no evidence that lenders that purchase the loans are hurt by the default increase. Our results demonstrate how the behaviors induced by convex incentive schemes for sales are borne by customers. This paper was accepted by David Simchi-Levi, business strategy. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4935 .

Analysis of the Problems of the Fan Economy and The Solutions

The fan economy is a relatively new phenomenon with a lack of regulatory oversight, and it in China has brought about various social problems, particularly regarding irrational consumption. With the rise of non-physical live stream sales and the bundling of products and derivatives, the quality of products has become uneven, and fake and inferior products have entered the market. This has led to consumption alienation, where fans blindly purchase products based on their affection for the artist. Also, this has damaged consumer trust and led to a decline in overall consumption. The industrys inadequate quality control practices constitute one of the primary factors contributing to this problem. Thus, this study analyzes the problems of the fan economy and gives suggestions. To address these issues, the brand party, the artist party, the platform, and relevant departments should work together to restrain each other and ensure that artists take legal responsibility for the products they promote. Platforms should also implement strict screening procedures for brand products and impose severe punishment measures to protect the rights and interests of consumers. Additionally, welfare should be independent of sales behaviour, and excessive bundling behaviour for derivative products should be rejected. Real-time monitoring and positive guidance should be implemented for minors to ensure responsible consumption. Overall, improving quality control and implementing responsible consumption practices are necessary for the fan economy to grow and thrive in China. By taking a collaborative and proactive approach, stakeholders can work together to ensure that the fan economy benefits artists and consumers.

The impact of emotional intelligence (EQ) in the B2B exchange processes: Increasing salespeople’s adaptive selling behaviour (ASB)

In today’s globalised markets, the business-to-business (B2B) exchange processes have become much more complex, as a result adaptive selling behaviour (ASB) has become essential to sales success within B2B organisations. This study has explored the phenomenon by examining whether emotional intelligence (EQ) has an impact on salespeople’s adaptive selling behaviour. Scholars have suggested that individuals possessing high EQ traits are more likely to be successful in adaptive selling, however there are limited studies available to address the gap in research. The research question is as follows, RQ1: To what extent does EQ have an impact on salespeople’s ASB? An exploratory qualitative approach, which consisted of a semi-structured interview, complemented with the Schutte Self-Report Emotional Intelligence Test (SSEIT) and a case study performance assessment, was conducted on 8 salespeople, employed in a B2B telemarketing company. The results of the thematic analysis have identified consistent themes within the interview, indicating that higher EQ scores could potentially enhance sales behaviour adaptability. 6 themes were identified as being reflective of the ‘Analytical’ adaptation criteria, 6 themes were identified for ‘Driver’ adaptation, 6 themes were identified for ‘Amiable’ adaptation and 8 themes were identified for ‘Expressive’ adaptation. These results highlight practical implications of incorporating EQ into employee selection, performance management and leadership training policies, within the current workforce.

Disclosing sales compensation and its impacts on misleading sales behaviors: some observations from Taiwan’s life insurance salespeople

PurposeThe purpose of this paper is to explore how sales compensation disclosure, salespeople’s perception of corporate social responsibility (CSR) toward customers (i.e. customer-focused CSR), regulatory knowledge and coworkers’ ethical behavior may influence life insurance salespeople’s moral intensity and intentions to engage in misleading sales behaviors.Design/methodology/approachThe hypotheses are analyzed using partial least squares (PLS) regression with the data gathered from full-time life insurance salespeople in Taiwan.FindingsThe main findings indicate that disclosing sales compensations will alter the ethical decision-making process of life insurance salespeople. The findings further point out that customer-focused CSR is an important variable affecting moral intensity and ethical intentions.Originality/valueThere has not been any research on the effects of compensation disclosure on moral intensity and misleading sales behavior. The literature gap has led to a poor understanding of the relationship between the compensation disclosure policy and ethical sales behavior. Moreover, previous studies indicate that specific factors (such as moral intensity and ethical intention) are directly associated, while the research shows that as long as a regulatory policy (e.g. the policy of compensation disclosure) changes, the correlation between these variables may shift from significant to nonsignificant (or vice versa). The results are interesting enough to warrant more research, and they also show that the direct link between variables mentioned in previous research is not always stable or universal.

Shedding Light on the Adverse Spillover Effects of Work-Family Conflict on Unethical Sales Behaviors at Work: A Daily Diary Study.

Despite the antecedents of unethical sales behavior (USB) have been well studied, these literatures primarily focus on the work domain and neglect the spillover effects of the home domain. Drawing on ego depletion theory as an overarching theoretical framework, this research investigates why and how salespersons' work-family conflict (WFC) at home triggers next day's USB at work. This study used daily diary data collected from 99 salespeople in two weeks to test the proposed hypotheses. The multilevel path analysis indicates that evening's WFC positively affects next afternoon's USB via increased next morning's ego depletion (ED). Furthermore, service climate was found to moderate this indirect relationship, such that the indirect relationship becomes weaker under high service climate. To the best of my knowledge, this study is one of the first to reveal that salespersons' daily WFC may serve as a role conflict event and lead to next day's USB at work, which provides a fine-grained understanding of spillover effects of daily WFC through a daily diary study.

The role of gender in sales behaviour: Evidence from institutional financial brokerage

We study the role of gender in sales behaviour using 336,401 daily institutional broker transactions over two years. Female brokers appear more efficient at generating revenue than males. Their more cautious sales behaviour sees them execute fewer transactions and sell lower risk financial products to more conservative clients. Directionally supportive of literature that records higher confidence levels, trading frequency and risk taking among males, we show how female brokers contribute to more diversified and successful sales behaviour. Our findings are relevant to gender unequal financial services industry and other quantitative domains that tend to overvalue male relative to female skills.

The Effect of Sales-Oriented, Customer-Oriented and Seller Performance on Venon Brand Sales Performance in Indonesia

This study explores how individual sales abilities affect sales-oriented and customer-oriented sales behavior. The population in this study is the B2B sales of the hair care industry. This research uses quantitative. Research methods to determine the Validity and reliability of each statement used in the questionnaire, this study tested the factor analysis method. To determine the level of significance and interrelationships between each variable used, the structural equation model (SEM) analysis method was used. Data was collected using an online questionnaire survey method via Google Forms. Except for the Competitive Intensity variable. ISC has a positive and significant effect on SO, and SO can significantly affect SP. Individual Sales Capability and Customer-Oriented Selling Behavior have a significant influence but have a negative direction. CO has a significant influence but has a negative direction on SP. Competitive intensity does not moderate the relationship between SO and Sales Performance. Furthermore, CI moderated the relationship between CO and SP. This research shows the importance of onboarding on customers in sales performance. Customer oriented sales behavior can play an important role in improving sales performance, especially in situations where market competition is getting tougher.

Can salespeople use social media to enhance brand awareness and sales performance? The role of manager empowerment and creativity

PurposeAlthough research analyzing the consequences of salesperson social media use in driving sales behaviors and performance outcomes has proliferated in the recent past, there are significant research gaps in the domain. Grounded in task-technology fit theory, this paper aims to propose a conceptual framework that integrates between previously disjointed areas of research and analyzes the relationships between salesperson social media use, brand awareness, creativity, manager empowerment and company performance.Design/methodology/approachSurvey responses were collected from a multi-industry sample of 158 business-to-business salespeople. Structural relationships were tested using partial least squares structural equation modeling.FindingsThe analysis shows that salesperson social media use positively affects brand awareness. The relationship between social media and brand awareness is magnified with the increase in salesperson creativity. Findings also show that manager empowerment increases salesperson creativity. Finally, brand awareness positively affects company performance.Practical implicationsSales organizations should focus on developing digital strategies, especially focusing on salesperson social media use to enhance company’s brand awareness, which in turn increases company performance. Moreover, sales managers should also follow empowering leader behaviors to enhance creativity.Originality/valueThe authors amalgamate salesperson social media use literature and branding literature by proposing salesperson social media use’s positive effects on brand awareness. This study also expands the knowledge by exploring the moderating effect of individual-level variables such as salesperson creativity on driving the effects of salesperson social media use.