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Dash, Mihir
- Measuring the Efficiency of Marketing Efforts in the Indian Pharmaceutical Industry Using Data Envelopment Analysis
Abstract Views :282 |
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Authors
Mihir Dash
1,
Arunabhas Bose
2,
Samik Shome
3,
Shamim Mondal
3,
Dennis J. Rajakumar
3,
Ramanna Shetty
3,
Madhumita G. Majumdar
4,
Debashis Sengupta
4
Affiliations
1 Management Science, School of Business, Alliance University, Bengaluru, Karnataka, IN
2 Marketing, School of Business, Alliance University, Bengaluru, Karnataka, IN
3 Economics, School of Business, Alliance University, Bengaluru, Karnataka, IN
4 Business Analytics, School of Business, Alliance University, Bengaluru, Karnataka, IN
1 Management Science, School of Business, Alliance University, Bengaluru, Karnataka, IN
2 Marketing, School of Business, Alliance University, Bengaluru, Karnataka, IN
3 Economics, School of Business, Alliance University, Bengaluru, Karnataka, IN
4 Business Analytics, School of Business, Alliance University, Bengaluru, Karnataka, IN
Source
International Journal of Business Analytics and Intelligence, Vol 3, No 1 (2015), Pagination: 1-6Abstract
Pharmaceutical companies have been spending huge amount of money on marketing and promotions, sales distribution, and travelling done by the sales representatives. However, they find it difficult to directly link the returns with these efforts. This study makes an attempt to examine whether the marketing efforts have significant influence on the sales performance in the industry. It uses the DEA model (Data Envelopment Analysis) to assess the efficiency of marketing efforts by pharmaceutical companies, and uses random effects maximum likelihood panel regression to assess the significance of the impact of marketing efforts.Keywords
Pharmaceutical Industry, Marketing Efforts, Sales Performance, DEA Model, Random Effects Maximum Likelihood Panel Regression.
References
- Agarwal, S., Ahlawat, H., & Hopfield, J. (2010). Optimizing spend: Changing the ROI game augmenting reach and cost with a quality assessment to make more informed investment decisions. Driving Marketing Excellence, Pharmaceutical and Medical Product Practice, McKinsey Report, 28-35.
- Anthony, R., & Govindarajan, V. (2003). Management Control Systems, (11th ed.). McGraw-Hill, New York, NY.
- Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring efficiency of decision making units. European Journal of Operational Research, 2(6), 429-44.
- de Boeck, P., Detlefs, S., & Villumsen, K. (2010). Ten ideas to improve the front line: The future of pharmaceutical sales force. The eYe of the Storm, Perspectives and Recommendations for European Commercial Pharmaceuticals, McKinsey Report, 72-78.
- Elling, M. E., Fogle, H. J., McKhann, C.S., and Simon, C. (2002), Making more of pharma’s sales force, McKinsey Quarterly Report.
- Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society, Series A, CXX, Part 3, 253-290.
- Gagnon, M., & Lexchin, J. (2008). The cost of pushing pills: A new estimate of pharmaceutical promotion expenditures in the United States. PLoS Med, 5(1).
- Gupta, M., & Nair, R. (2009). Making an Impact: Effective Sales and Marketing With Reduced Costs Leveraging offshore resources to do more with less, Indegene Report.
- Ittner, C., Larcker, D., & Meyer, M. (2003). Subjectivity and the weighting of performance measures: evidence from a balanced scorecard. The Accounting Review, 78(3), 725-58.
- Jakovcic, K. (2009). Pharmaceutical sales force effectiveness strategies: evaluating evolving sales models & advanced technology for a customer centric approach. Business Insights Report.
- Kaplan, R. S., & Norton, D. P. (1992). The balanced scorecard: measures that drive performance. Harvard Business Review, 70(1), 71-9.
- Kaplan, R. S., & Norton, D. P. (1993). Putting the balanced scorecard to work. Harvard Business Review, 71(5), 134-43.
- Kaplan, R. S., & Norton, D. P. (1996). The balanced scorecard. Harvard Business School Press, Boston, MA.
- Mariani, P. (2008), Sales Force Effectiveness in Pharmaceutical Industry: an Application of ShiftShare Technique, Simulated Annealing Theory with Applications, Sciyo, Croatia.
- Momaya, K., & Ambastha, A. (2004). Competitiveness of firms: Review of theory, frameworks & models. Singapore Management Review, 26(1), 45-61.
- Palo, J. D., & Murphy, J. (2010). Pharma 2020: Marketing the future. Which path will you take? Price Water house Coopers Report.
- Rust, R.T., Ambler, T., Carpenter, G. S., Kumar, V., & Srivastava, R. K. (2004). Measuring marketing productivity: Current knowledge and future directions. Journal of Marketing, 68, 76-89.
- Sinha, P., & Zoltners, A. A. (2001). Sales Force decision Models: Insights from 25 years of Implementation. Interfaces, 31(3), S8-S44.
- Zhu, J. (2000). Multi-factor performance measure model with an application to Fortune 500 companies. European Journal of Operational Research, 123(1), 105-24.
- Customer Attrition Analytics in Banking
Abstract Views :228 |
PDF Views:0
Authors
Mihir Dash
1,
Kajal Das
2
Affiliations
1 Management Science, School of Business, Alliance University, Karnataka, IN
2 School of Business, Alliance University, Karnataka, IN
1 Management Science, School of Business, Alliance University, Karnataka, IN
2 School of Business, Alliance University, Karnataka, IN
Source
International Journal of Business Analytics and Intelligence, Vol 5, No 2 (2017), Pagination: 7-14Abstract
In an era of mature markets and intensive competitive pressure, more and more companies realise that their most precious asset is their existing customer base. This realisation has resulted in a rise in emphasis on customer relationship management, in order to retain customers. This is a major area on which banks need to concentrate. Banks tend to be reactive to customer attrition, and many times it is too late to retain a customer. Customer attrition needs to be minimised, and loyal customers need to be rewarded.
The objective of this study to identify the factors affecting customer attrition of trust accounts for a leading American financial services company. The company realised that its trust accounts were getting closed after a period of seven to twelve years. Initially, the company tried to identify the ischolar_main cause using a small set of data, but they were unable to do so. This triggered the use of analytics to build a model to predict customer churn, and come up with strategies to retain customers. This was achieved by applying data mining techniques to the transactions history of the accounts that closed down as against those that remained active.
Keywords
Customer Attrition Analytics, Customer Relationship Management, Data Mining.References
- Au, W., Chan, C. C., & Yao, X. (2003). A novel evolutionary data mining algorithm with applications to churn prediction. IEEE Transactions on Evolutionary Computation, 7, 532-545.
- Datta, P., Masand, B., Mani, D. R., & Li, B. (2001). Automated cellular modelling and prediction on a large scale. Issues on the Application of Data Mining, 485-502.
- Gray, J. B., & Fan, G. (2008). Classification tree analysis using TARGET. Computational Statistics & Data Analysis, 52, 1362-1372.
- Hadden, J., Tiwari, A., Roy, R., & Ruta, D. (2006). Churn prediction using complaints data. Transactions on Engineering, Computing and Technology, Enformatika 13, 158-163.
- Hwang, H., Jung, T., & Suh, E. (2004). An LTV model and customer segmentation based on customer value: A case study on the wireless telecommunications industry. Expert Systems with Applications, 26, 181-188.
- Jacob, R. (1994). Why some customers are more equal than others. Fortune, 149-154.
- Ma, G., & Li, S. (1994). Applications of the survival analysis techniques in modelling customer retention.
- Workbook for the 5th Advanced Research Techniques Forum, American Marketing Association.
- Mozer, M. C., Wolniewicz, R., Grimes, D. B., Johnson, E., & Kaushansky, H. (2000). Predicting Subscriber Dissatisfaction and Improving Retention in the Wireless Telecommunications Industry. IEEE Transactions on Neural Networks 11, 690-696.
- Customer Attrition Analytics : The Case of a Recruitment Service Provider
Abstract Views :252 |
PDF Views:0
Authors
Affiliations
1 Alliance University, Bengaluru, Karnataka, IN
1 Alliance University, Bengaluru, Karnataka, IN
Source
International Journal of Business Analytics and Intelligence, Vol 10, No 1 (2022), Pagination: 4-15Abstract
Customer attrition is the phenomenon wherein a customer leaves a service provider. With the growing competition in the service sector, preventing customer attrition has become critical for sustainability, as it is well established that retaining existing customers is more profitable than acquiring new customers (Jacob, 1994). This gives customer attrition analytics the challenging task of predicting which customers are likely to leave, and of subsequently designing and implementing retention programmes for these customers. Customer analytics has made many strides in marketing, employer desirability, and branding, but has so far made limited strides in the recruitment industry space. The objective of the study is to identify the factors affecting a candidate’s decision to accept a job opportunity in an organisation, using predictors such as the industry verticals, the candidate’s skillsets, workplace location, gender, compensation offered, and the notice period of the candidate. The model developed is a logistic regression model, to determine whether a candidate selected will accept a job opportunity in an organisation or not. The analysis was performed based on a sample of 443 candidates who were provided job offers in the period 2013-2015 by a recruitment service provider.Keywords
Customer Attrition Analytics, Factors Affecting Customer Attrition, Logistic Regression Models.References
- Au, W., Chan, C. C., & Yao, X. (2003). A novel evolutionary data mining algorithm with applications to churn prediction. IEEE Transactions on Evolutionary Computation, 7, 532-545.
- Dash, M., Singh, A., Mishra, N., & Gupta, G. (2009). A study of human resource outsourcing in Indian IT companies. SSRN Working Papers.
- Datta, P., Masand, B., Mani, D. R., & Li, B. (2001). Automated cellular modelling and prediction on a large scale. Issues on the Application of Data Mining, 485-502.
- Gray, J. B., & Fan, G. (2008). Classification tree analysis using TARGET. Computational Statistics & Data Analysis, 52, 1362-1372.
- Hadden, J., Tiwari, A., Roy, R., & Ruta, D. (2006). Churn prediction using complaints data. Transactions on Engineering, Computing and Technology, Enformatika, 13, 158-163.
- Hwang, H., Jung, T., & Suh, E. (2004). An LTV model and customer segmentation based on customer value: A case study on the wireless telecommunications industry. Expert Systems with Applications, 26, 181-188.
- Jacob, R. (1994). Why some customers are more equal than others. Fortune, 149-154.
- Ma, G., & Li, S. (1994). Applications of the survival analysis techniques in modelling customer retention. Workbook for the 5th Advanced Research Techniques Forum, American Marketing Association.
- Mozer, M. C., Wolniewicz, R., Grimes, D. B., Johnson, E., & Kaushansky, H. (2000). Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industry. IEEE Transactions on Neural Networks, 11, 690-696.