Benchmarking – Definition

Cite this article as:"Benchmarking – Definition," in The Business Professor, updated March 7, 2019, last accessed December 4, 2020,


Benchmarking Definition

Benchmarking is the comparison of one’s business and performance measurement to the best industry and other companies’ best practices.

More specifically, benchmarking is a method used to determine the best performance being realized either by a competitor, or another company in a different line of work. The information gained from this method can then be utilized in the identification of gaps in the workings of an organization to achieve a competitive advantage. The practitioners of benchmarking are required to:

  •         Understand the objectives of benchmarking and its use.
  •         Differentiate between competitor research and benchmarking.
  •         Ensure that benchmarking is aligned with the management objectives of the company.

In finance, a benchmark is a standard against which the performance of a security, mutual fund or investment manager can be measured. Generally, broad market and market-segment stock and bond indexes are used for this purpose.

A Little More on What is Benchmarking

There are numerous forms of benchmarking, but they can all be classified into three categories which are:

  1.      Internal benchmarking. This is used when a company needs to share the best practices it has already established and proved. A big company may present a wide range of performance. Internal benchmarking is also used by a company when there is a lack of comparable industries.
  2.      Competitive benchmarking. This benchmarking is usually used when a company is attempting to evaluate its position in the industry it belongs to. A company may also use it in identifying the performance targets of the industry’s leadership.
  3.      Strategic benchmarking. This is used to identify and analyze world-class performance when a company decides to go out of its industry. The company then sets goals which are relative to the benchmarks set by the world-class organizations.

The Benchmarking process is comprehensive and involves more than touring other companies’ facilities or just making inquiries. The benchmarking process should not be limited to the scope of one industry nor should it be limited to a one-time event.

Benchmarking Versus Competitor Research

Both competitor research and benchmarking serve the same purpose although there is a difference between the two. Determining which of the two adds most value depends on the availability of time and resources.

Differences between Benchmarking and Competitor Research

BenchmarkingCompetitor Research
Focuses on the best practicesFocuses on the performance measures
Continuously strives for improvementLooks for a quick fix
It involves partnering and sharing of informationSometimes it is considered as corporate spying
It is essential for a company to maintain a competitive edgeIt is a tool that is considered nice to have
After examining the best, it adapts based on the needs of customersIt tries to mirror the processes of another company

References for Benchmarking

Academic Research on Benchmarking

  • Performance benchmarks for institutional investors: Measuring, monitoring and modifying investment behavior, Blake, D., & Timmermann, A. (2002). In Performance Measurement in Finance (pp. 108-141). The paper undertakes assessment and examination of the benchmark currently used in the UK. The importance of performance benchmark includes; measure the institutional fund managers’ investment performance, provision of the reference point of performance monitoring and modifying the fund manager’s behavior. A good benchmark does not have built-in biases either favoring or against a particular class of asset. In particular, a dynamic financial system emphasizes the absence of biases against start-up capital; likewise, a good benchmark would have an accurate market weighting in venture capital securities. There is a probability of a good benchmark basing in multiple indices that cover all significant asset classes together with liabilities.
  • Long-term investment in infrastructure and the demand for benchmarks, Blanc-Brude, F. (2014). JASSA, (3), 57. The author argument is based on the long-term investments in assets like infrastructural projects which are thinly-traded raises the demand for investment performance monitoring of an investor which subsequently result in the need for performance metrics tools. Investments benchmarks for the long term are also significant to enable matching the supply and demand for long term capital, improve investor’s asset allocation results in addition to support economic development. The paper outlines essential methodological challenges to perform measurement development that are useful to both long term investors and economic regulators and similar to modern asset pricing theory.
  • Fund managers’ attitudes to risk and time horizons: the effect of performance benchmarking, Baker, M. (1998). The European Journal of Finance, 4(3), 257-278. An interview survey was conducted to 64 fund managers with objectives of performance appraisal identification and reward systems operated by fund managers and to Identify the impacts upon which investments are solved. The interview outcome showed that fund managers evaluation is based on a performance benchmark even though the valuation extent and benchmark choice vary depending on the fund type in question. The paper shows that there are effects of a fund manager’s attitude to risks, motivation, and period caused by a performance benchmark. Fund managers’ belief on quarterly relative performance monitoring to which fund and fund managers are exposed to, leads to short term attitude and approach to the fund in question.
  • How to measure mutual fund performance: economic versus statistical relevance, Otten, R., & Bams, D. (2004). Accounting & Finance, 44(2), 203-222. An exhaustive assessment of existing mutual fund performance model is presented using the survivor-bias free database for all US mutual funds. Exploring the value addition by introducing additional variables such as; size, a book to market value and bond index. Furthermore, evaluation of introducing time variations in betas and alpha was undertaken. The author summarizes that the added value of the current examination lies in the step-wise process of essential factors identification and use of comprehensive US database previously issued by the Center for Research in Security Prices.
  • Towards efficient benchmarks of infrastructure equity investments, Blanc-Brude, F. (2013). Investment Magazine, (91), 42. There is mixed research evidence and experience about the risk profile of the infrastructural investments even though the responsible economists’ suggestion is for the low-risk profile. Meridian and Campbell Lutyens’ supported research as a proportion of Equity Investment Management and Benchmarking research Chair at EDHEC, a risk institute. The reasons why and what research and benchmarking necessity in the creation of investments solutions that realign performance and expectations were described.
  • Benchmarks as limits to arbitrage: Understanding the low-volatility anomaly, Baker, M., Bradley, B., & Wurgler, J. (2011). Financial Analysts Journal, 67(1), 40-54. The author disagrees with the basic financial laws and states that high-beta and high volatile stocks have long underperformed low-beta and low volatile stocks. The deviation may be partly explained the issue of typical institutional investor power to outdo a certain benchmark demotivates neutral activity in both high-alpha and low-beta and low-alpha and high-beta stocks.
  • The investment performance of US equity pension fund managers: An empirical investigation, Coggin, T. D., Fabozzi, F. J., & Rahman, S. (1993). The Journal of Finance, 48(3), 1039-1055. The paper presents an inrush examination of the selectivity and market performance of a representative sample of US equity pension fund managers. The mean selectivity measure was found to be positive while a negative average timing measure regardless of the benchmark portfolio or estimation model. When managers are grouped by investment style, selectivity and timing appear to be sensitive to benchmarks choice. The finding of the study showed a negative correlation between selectivity and timing, but the author argued that the observed negative correlation in their data was largely an object of negatively correlated samples errors for the two approximations.
  • Performance measurement without benchmarks: An examination of mutual fund returns, Grinblatt, M., & Titman, S. (1993). Journal of business, 47-68. The paper launches a new measure of portfolio performance and uses it to undertake an examination of a large sample mutual fund performance. In comparison with the recent review of mutual fund performance, the measure applied in this examination uses portfolio holdings and does not need benchmark portfolio use. IT found that the portfolio options for mutual fund managers, especially those managing fast growth funds, received majorly positive risk-adjusted results in between 1976-1985.
  • The persistence of mutual fund performance, Grinblatt, M., & Titman, S. (1992). The Journal of Finance, 47(5), 1977-1984. The paper is about the analysis of the relationship of mutual fund performance and the past performance. Multiple portfolio benchmarks formed on securities characteristics basis are the bases of the study. Performance variations between funds’ remains overtime and remain consistence with fund managers’ capability in abnormal returns earnings.
  • Strategic behavior under regulatory benchmarking, Jamasb, T., Nillesen, P., & Pollitt, M. (2004). Energy Economics, 26(5), 825-843. The paper states that some have adopted incentive regulations based on performance benchmarking for the primary purpose of improvement of efficiency in the electric distribution networks. Less attention has been subjected to regulation benchmark even though it affects the regulation game. Data Envelopment analysis together with US utility data to examine implications of exemplary cases on strategic behavior reported by regulators. The result showed the possibility of gaming having a major impact on the firm’s established performance and profitability.
  • Institutional benchmarks for international real estate investment, Lim, L., McGreal, S., & Webb, J. (2008). Journal of Real Estate Portfolio Management, 14(2), 93-104. Institutional real estate investors require data regarding anticipated risks and return to invest in a foreign country. Investment Property Databank is now assembling returns data for over twenty years. The examination is based on the data from each country by property type and gives an overview of the returns, risks, and the coefficient of variation. The result shows that there are essential diversification advantages from the pooling of real estate investments from different countries; on the other hand, other combinations do not provide essential advantages.
  • Accountants’ usage of causal business models in the presence of benchmark data: A note, VeraMuñoz, S. C., Shackell, M., & Buehner, M. (2007). Contemporary Accounting Research, 24(3), 1015-1038. This paper sheds doubt on the ability of accountants and managers to understand how the models discussed link the performance measures with outcomes despite much attention given to casual business models by the accounting profession.
  • The buy-in benchmark: How staff understanding and commitment impact brand and business performance, Thomson, K., De Chernatony, L., Arganbright, L., & Khan, S. (1999). Journal of Marketing Management, 15(8), 819-835. This article examines the various ways through which grater staff understanding and commitment can increase the effectiveness of brand business and performance.
  • Business intelligence and analytics: from big data to big impact, Chen, H., Chiang, R. H., & Storey, V. C. (2012). MIS quarterly, 1165-1188. This study provides an introduction to the MIS Quarterly Special Issue on Business Intelligence Research which has a framework to identify the evolution, applications, and define as well as describe some emerging areas of research such as BI&A, BI&A 1.0, BI&A 2.0, and BI&A 3.0 in terms of their key characteristics and capabilities.
  • Benchmark data from more than 240,000 adults that reflect the current practice of critical care in the United States, Lilly, C. M., Zuckerman, I. H., Badawi, O., & Riker, R. R. (2011). Chest, 140(5), 1232-1242. This paper studies how the admission to an ICU in 2008 meant the use of active treatments which mainly included life support and counseling for the ones near the end of life and how this was associated with favorable outcomes for most of the patients.
  • Bigdatabench: A big data benchmark suite from internet services, Wang, L., Zhan, J., Luo, C., Zhu, Y., Yang, Q., He, Y., … & Zheng, C. (2014, February). In High Performance Computer Architecture (HPCA), 2014 IEEE 20th International Symposium on (pp. 488-499). IEEE. This article presents joint research efforts with several industrial partners on how the big data benchmarks are supposed to include the diversity of data and workloads, which form the prerequisite for the evaluation of big data systems and architecture.
  • Utilizing data envelopment analysis to benchmark safety performance of construction contractors, El-Mashaleh, M. S., Rababeh, S. M., & Hyari, K. H. (2010). International Journal of Project Management, 28(1), 61-67. This paper attempts to employ the use of data envelopment analysis to benchmark the safety performance of the contractors of construction.
  • A hybrid Delphi-Bayesian method to establish business data integrity policy: A benchmark data center case study, Chen, M. K., & Wang, S. C. (2010). Kybernetes, 39(5), 800-824. This article presents the results of a framework which points out that the enterprises should monitor the four operation elements for the improvement of their data integrity.
  • Information exploration shootout project and benchmark data sets (panel): evaluating how visualization does in analyzing real-world data analysis problems, Grinstein, G., Laskowski, S., Wills, G., & Rogowitz, B. (1997, October). In Proceedings of the 8th conference on Visualization’97 (pp. 511-513). IEEE Computer Society Press. This paper describes how the principal body of work related to network intrusion comes from the information exploration shoot-out that is organized by Georges G. Grinstein and supported by NIST
  • Beyond belief: A benchmark for human resources, Ulrich, D., Brockbank, W., & Yeung, A. (1989). Human Resource Management, 28(3), 311-335. This article researches over 10,000 individuals in 1200 businesses and 91 firms to establish a benchmark for HR professionals and HR Practices.
  • Rcv1: A new benchmark collection for text categorization research, Lewis, D. D., Yang, Y., Rose, T. G., & Li, F. (2004). Journal of machine learning research, 5(Apr), 361-397. This study describes the coding policy and quality control procedures that were utilized in the production of the Reuters Corpus Volume 1 (RCV1), the intended semantics of the hierarchical category semantics as well as the corrections necessary to remove data containing errors through the use of interviews with Reuter’s personnel and Reuter’s documentation
  • Virtue as a benchmark for spirituality in business, Cavanagh, G. F., & Bandsuch, M. R. (2002). Journal of business ethics38(1-2), 109-117. This article uses business leader spirituality as a performance benchmark.
  • The buy-in benchmark: How staff understanding and commitment impact brand and business performance, Thomson, K., De Chernatony, L., Arganbright, L., & Khan, S. (1999). Journal of Marketing Management15(8), 819-835.
  •  Accountants’ usage of causal business models in the presence of benchmark data: A note, VeraMuñoz, S. C., Shackell, M., & Buehner, M. (2007).  Contemporary Accounting Research24(3), 1015-1038.
  • The portrayal of African-Americans in business-to-business direct mail: A benchmark study, Stevenson, T. H., & Swayne, L. E. (1999). Journal of Advertising28(3), 25-35.
  • TPC-W: A benchmark for e-commerce, Menascé, D. A. (2002). TPC-W: A benchmark for e-commerce. IEEE Internet Computing, (3), 83-87.

  • The business benchmark on farm animal welfare 2016 report, Amos, N., & Sullivan, R. (2017). The business benchmark on farm animal welfare 2016 report.

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