Testing Strategy with Multiple Performance Measures: Evidence from a Balanced Scorecard at Store24

Dennis  Campbell; Srikant M. Datar; Susan L. Kulp; V.G.   Narayanan

Article | Journal of Management Accounting Research | 2015

Testing Strategy with Multiple Performance Measures: Evidence from a Balanced Scorecard at Store24

by Dennis Campbell, Srikant M. Datar, Susan L. Kulp and V.G. Narayanan

Abstract

We analyze balanced scorecard data from a convenience store chain, Store24, during the implementation of an innovative, but ultimately unsuccessful, strategy. Quarterly strategic reviews, based in part on the firm's balanced scorecard, led executives at Store24 to identify problems with, and eventually abandon, this strategy over a two-year period. We find that formal statistical tests of the hypotheses underlying the firm's balanced scorecard and strategy map reveal problems with the strategy on a timelier basis. We also test alternative hypotheses to those underlying the firm's formal strategy map and scorecard that are consistent with concerns expressed by some of Store24's top executives during the initial stages of implementing the new strategy. Our analysis demonstrates that this firm's balanced scorecard contained useful and timely information for distinguishing between these alternatives. These results provide some of the first field-based evidence on the potential for a firm's balanced scorecard to provide useful information for detecting problems in its strategy.

Keywords: Balanced Scorecard; Business Strategy; Retail Industry;

Format: Print

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Citation:

Campbell, Dennis, Srikant M. Datar, Susan L. Kulp, and V.G. Narayanan. "Testing Strategy with Multiple Performance Measures: Evidence from a Balanced Scorecard at Store24." Journal of Management Accounting Research 27, no. 2 (2015): 39–65.

Case | HBS Case Collection | March 2019 (Revised May 2019)

Fetchr: A New Way of Last Mile Delivery

V.G. Narayanan and Eren Kuzucu

By mid-2016, five years of aggressive growth had transformed Fetchr from a small logistics startup to a 1,000-employee, full-fledged last-mile delivery company operating across four countries in the Middle East and North Africa (MENA). Already beneficiaries of the largest Series A round to the Middle East from an American firm, CEO Idriss Al Rifai and the Fetchr team had ambitions of raising another $40 million in Series B and deliberated continuing international expansion. But first, Al Rifai and his team needed to address the operational efficiency and profitability issues that led their investors to threaten the discontinuation of funding and put the company’s future at stake. The case provides background information on the logistics sphere in the Middle East and the challenges companies face in the region. The case then takes the reader through Al Rifai’s journey of founding and growing Fetchr, backed with innovations like their patented GPS-based delivery technology. Finally, the case zooms in on Fetchr’s management team as they race against time to perform evaluations of their operational efficiency, weigh cost-cutting options, and explore strategies to improve profitability.

Keywords: Startup; decision; financial strategy; UAE; KSA; MENA; Cost Accounting; Business Model; Business Startups; Transformation; Cost Management; Strategy; Disruptive Innovation; Technological Innovation; Growth and Development Strategy; Growth Management; Logistics; Service Delivery; Supply Chain Management; Performance Evaluation; Mathematical Methods; Mobile Technology; Transportation Networks; Middle East; United Arab Emirates; Dubai; Bahrain; Egypt; Saudi Arabia; North Africa;

Citation:

Narayanan, V.G., and Eren Kuzucu. "Fetchr: A New Way of Last Mile Delivery." Harvard Business School Case 119-018, March 2019. (Revised May 2019.) View Details

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This case follows Bill Booth, marketing manager of a regional wine distributor, as he applies unsupervised learning on data about his customers’ purchases to better understand their preferences. Specifically, he uses the K-means clustering technique to identify groups of customers who have purchased any number of 32 specific “deals” Booth offered over the year, differentiated by the wine varietal as well as its country of origin and a minimum number of bottles to purchase. Insights from this analysis may help him understand themes across the deals that can inform construction of new deals in the future.

Topics include unsupervised learning; similarity and proximity; K-means clustering, with measures of Euclidean distance and cosine similarity; Gaussian mixture models; and interpreting clusters.

This case builds directly on “Chateau Winery (A).” In this case, Bill Booth, marketing manager of a regional wine distributor, shifts to supervised learning techniques to try to predict which deals he should offer to customers based on the purchasing behavior of those customers closest to them.

Topics include supervised learning; collaborative filtering; K-nearest neighbor as a modeling technique, collaborative filtering with cosine similarity of customers, collaborative filtering with cosine similarity of products, and comparison of different prediction models.

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Testing Strategy with Multiple Performance Measures: Evidence from a Balanced Scorecard at Store24

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