Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Measuring the Efficiency of Marketing Efforts in the Indian Pharmaceutical Industry Using Data Envelopment Analysis


Affiliations
1 Management Science, School of Business, Alliance University, Bengaluru, Karnataka, India
2 Marketing, School of Business, Alliance University, Bengaluru, Karnataka, India
3 Economics, School of Business, Alliance University, Bengaluru, Karnataka, India
4 Business Analytics, School of Business, Alliance University, Bengaluru, Karnataka, India
     

   Subscribe/Renew Journal


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.
Subscription Login to verify subscription
User
Notifications
Font Size


  • 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.

Abstract Views: 380

PDF Views: 0




  • Measuring the Efficiency of Marketing Efforts in the Indian Pharmaceutical Industry Using Data Envelopment Analysis

Abstract Views: 380  |  PDF Views: 0

Authors

Mihir Dash
Management Science, School of Business, Alliance University, Bengaluru, Karnataka, India
Arunabhas Bose
Marketing, School of Business, Alliance University, Bengaluru, Karnataka, India
Samik Shome
Economics, School of Business, Alliance University, Bengaluru, Karnataka, India
Shamim Mondal
Economics, School of Business, Alliance University, Bengaluru, Karnataka, India
Dennis J. Rajakumar
Economics, School of Business, Alliance University, Bengaluru, Karnataka, India
Ramanna Shetty
Economics, School of Business, Alliance University, Bengaluru, Karnataka, India
Madhumita G. Majumdar
Business Analytics, School of Business, Alliance University, Bengaluru, Karnataka, India
Debashis Sengupta
Business Analytics, School of Business, Alliance University, Bengaluru, Karnataka, India

Abstract


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