Do You Know What Really Drives Your Business’s Performance?
Surprisingly few executives use data from their own organizations to test their assumptions about what factors drive financial performance. By gaining new insights into performance relationships within their own companies, managers can develop smarter strategies.
Managerial decisions are based on assumptions about the relationships between different aspects of performance. Investments in employees, for example, are often predicated on the assumption that well-rewarded and engaged employees deliver higher levels of service, resulting in customer loyalty that enhances financial performance. Some of the core assumptions about what drives financial performance have become so widely accepted that they are often viewed as facts. However, managers are frequently unable to justify the assumptions underlying their competitive strategies with data from their own organizations. The danger is that unless the core assumptions are sound and relevant to your own circumstances, you run the risk of developing wrongheaded strategies that will lead you astray.
Over the past 30 years, researchers have developed a considerable body of literature and tools to help managers understand the drivers of performance in their businesses. For example, academics and consultants such as Robert S. Kaplan and David P. Norton, the developers of “the balanced scorecard,” have encouraged managers to hypothesize causal links and to develop strategy maps to identify the key drivers of financial performance in their organizations.1 The problem is that developing strategy maps on the basis of managerial hypotheses means the maps are constrained by managers’ prior views of what drives performance. Managers often make assumptions about the relationship between, for example, customer loyalty and profitability, even when the presumed links haven’t been fully tested.2 Indeed, one study found that only 21% of managers who said they implemented strategy maps had actually tested the links within their own organizations, and many of those who had tested the links found their early assumptions were flawed.3 Failure to test such hypotheses means that critical assumptions go unchallenged, leading to misguided strategies.
Research related to the “service profit chain,” a well-known body of research focused on the drivers of performance in service industries, forms the foundation of our research. Developed by Harvard Business School professors James L. Heskett, W. Earl Sasser Jr., and Leonard A. Schlesinger, the service profit chain proposes a mirror effect between employee satisfaction and loyalty on the one hand, and customer satisfaction and loyalty on the other, which in turn drives financial performance.
1. R.S. Kaplan and D.P. Norton, “Having Trouble With Your Strategy? Then Map It,” Harvard Business Review 78, no. 5 (September-October 2000): 167-176; and R.S. Kaplan and D.P. Norton, “Strategy Maps: Converting Intangible Assets Into Tangible Outcomes” (Boston: Harvard Business Press, 2004). Approaches to strategy mapping also have been proposed by authors such as M.L. Epstein and R.A. Westbrook, “Linking Actions to Profits in Strategic Decision Making,” MIT Sloan Management Review 42, no. 3 (spring 2001): 39-49; A. Neely and M. Al Najjar, “Management Learning Not Management Control: The True Role of Performance Management,” California Management Review 48, no. 3 (spring 2006): 101-114; and K.R. Thompson and N.J. Mathys, “The Aligned Balanced Scorecard: An Improved Tool For Building High Performance Organizations,” Organizational Dynamics 37, no. 4 (October-December 2008): 378-393.
2. Neely and Al Najjar, “Management Learning Not Management Control”; F. Buytendijk, T. Hatch, and P. Micheli, “Scenario-Based Strategy Maps,” Business Horizons 53, no. 4 (July-August 2010): 335-347.
3. C.D. Ittner and D.F. Larcker, “Coming Up Short on Nonfinancial Performance Measurement,” Harvard Business Review 81, no. 11 (November 2003): 88-95.
4. J.L. Heskett, W.E. Sasser Jr., and L.A. Schlesinger, “The Service Profit Chain: How Leading Companies Link Profit and Growth to Loyalty, Satisfaction, and Value” (New York: Free Press, 1997).
5. “Internal service quality” is defined here as the quality of the working environment. The idea is that a good working environment, where employees are given the tools and training to deliver service capability, will generate high levels of employee satisfaction and loyalty.
6. Reviews of these empirical studies can be found in H. Evanschitzky, F.v. Wangenheim, and N.V. Wünderlich, “Perils of Managing the Service Profit Chain: The Role of Time Lags and Feedback Loops,” Journal of Retailing 88, no. 3 (September 2012): 356-366; and S.P. Brown and S.K. Lam, “A Meta-Analysis of Relationships Linking Employee Satisfaction to Customer Responses,” Journal of Retailing 84, no. 3 (September 2008): 243-255.
7. W.A. Kamakura, V. Mittal, F. de Rosa, and J.A. Mazzon, “Assessing the Service-Profit Chain,” Marketing Science 21, no. 3 (August 2002): 294-317.
8. This body of research has been reported in a number of articles: R. Silvestro, “Performance Topology Mapping: Understanding the Drivers of Performance,” International Journal of Production Economics 156 (October 2014): 269-282; M. Pritchard and R. Silvestro, “Applying the Service Profit Chain to Analyse Retail Performance: The Case of the Managerial Strait-Jacket?” International Journal of Service Industry Management 16, no. 4 (2005): 337-356; R. Silvestro, “Dispelling the Modern Myth: Employee Satisfaction and Loyalty Drive Service Profitability,” International Journal of Operations & Production Management 22, no. 1 (2002): 30-49; and R. Silvestro and S. Cross, “Applying the Service Profit Chain in a Retail Environment: Challenging the ‘Satisfaction Mirror,’” International Journal of Service Industry Management 11, no. 3 (2000): 244-268.
9. Silvestro and Cross, “Applying the Service Profit Chain.”
10. See, for example, M. Yu, I. Atmosukarto, W.K. Leow, Z. Huang, and R. Xu, “3D Model Retrieval With Morphing-Based Geometric and Topological Feature Maps,” Proceedings of 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2 (2003): 656-661.
11. For a full exposition of the tool, see Silvestro, “Performance Topology Mapping.”
12. The relationship between productivity and employee loyalty was complex at the home improvement retail chain. There was evidence of a positive correlation between employee loyalty (measured by labor stability) and labor productive.,mployee loyalty and productivity are linked both positively and negatively in the performance topology map.
13. See, for example, K. Legge, “Human Resource Management: Rhetorics and Realities” (Chippenham, U.K.: Macmillan Business, 1995).