Taking the Red Pill
“You take the red pill, you stay in Wonderland, and I show you how deep the rabbit hole goes. Remember, all I’m offering is the truth. Nothing more.”
Morpheus, The Matrix (1999)
By Leonard Greski, Chief Scientist, LiminalArc
Apply Occam’s Razor (the simplest solution)
Occam’s Razor is the idea in philosophy that when presented with multiple explanations for the same event, we should choose the one that introduces the fewest new assumptions, variables or dependencies. Knowledge that the performance measurement literature is at best very nuanced or at least relatively inconclusive frees us from having to slavishly apply a specific framework to improve performance of a specific company. We can start with a “first principle” to define the simplest measurement approach possible: every company has a demand value stream (how prospects become buyers) and a supply value stream (how products & services are delivered to customers). Therefore, any company can derive value from a measurement approach that focuses on three simple measures: growth, productivity, and usage relative to capacity.
| Growth Measures | Allow a business to manage the flow of prospects to buyers to repeat buyers and ideally, advocates for a company’s products or services. When the demand value stream is completed, a customer obtains a product or service for which the company receives revenue. Growth measures, therefore, are closely tied to the demand value stream.
|
| Productivity Measures | Help leaders understand the cost and time to deliver products and services, and to quantify the cost of quality. Productivity measures are tied to the business processes associated with business capabilities supporting the demand and/or supply value stream. Measurements of the processes associated with a capability are a direct reflection of the effectiveness and efficiency with which a product or service is delivered.
|
| Usage vs. Capacity Measures | Enable an organization to predict when an organization may run out of capacity to meet customer needs, or to assess the impact of volatility in demand on the cost of doing business. These measures are also aligned with the business processes associated with the organization’s business capabilities because they establish limits in the organization’s capacity to serve customers.
|
Furthermore, focusing measures on growth, productivity and usage encourages us to measure the fewest things possible because we can evaluate any potential new measure by questioning how it helps us understand and take action to increase growth or productivity, or to manage the risk of variable demand.
Additional guidance for simplification of measurements includes minimizing cross-unit dependencies, and constraining aggregates and cascades of measures to be additive. Cross-unit dependencies in a performance measurement model introduce mediation effects and potential non-linearity to the model, making it more difficult to establish cause and effect (which we will address shortly). It is related to the guidance about aggregates and cascades in that more granular scorecards (e.g. business unit) should aggregate to a higher level (e.g. corporate level) through addition. For example, if a company has five business units and a corporate sales target of $1 billion, the sum of the sales targets by business unit should also be $1 billion.
Organize Measures Around Value Streams and Capabilities
As noted previously, every company has a demand value stream and a supply value stream. These two value streams, as well as the business capabilities that support them, are a good basis for structuring a company’s measurement framework. Why? First, if every company has at a minimum these two value streams, they are the right starting point because these value streams generate the information we need to understand growth, productivity and usage relative to capacity.
Second, if it makes sense to organize around business capabilities as Melissa Roberts and I asserted in Organizing Around Business Capabilities (Greski & Roberts, 2023), it also is appropriate to align the measurement framework with capabilities, especially for measures of productivity and usage. Furthermore, as an organization develops a system of differentiating capabilities (Leinwand & Mainardi, 2011) that distinguish it from other companies in an industry or market, the capabilities system can also be used to avoid unnecessary measurement by focusing it on differentiating capabilities.
Finally, since business capabilities generate costs and benefits, the costs to operate a capability inform what is reasonable to instrument the capability and measure its performance. That is, if a capability generates $1 million in value annually, we should not implement a measurement framework that costs $5 million annually to operate. Furthermore, investments in instrumenting a capability should be managed as part of the cost to build and operate it, effectively aligning the measurement cost with the operations of the capability, not some centralized, monolithic data warehousing operation that no business operator one wants to fund.
Establish Cause & Effect
This step is not only the most important one, but it is also the most difficult to put into practice. Once an organization has basic measurements in place for growth, productivity and usage, the measurement model can be used as an experimentation engine to test the economic value of improvements to its business capabilities.
For example, the demand value stream can also be managed as a conversion funnel, where leaders can make and test hypotheses about actions to either bring more potential customers into the top of the funnel, or to improve the customer experience step by step within the value stream to increase the percentage of shoppers who complete the funnel by purchasing a good or service.
This kind of thinking – seeing the conversion funnel as a value stream, analyzing the barriers / customer dissatisfiers that reduce the conversion rate and addressing them with low-cost experiments allows a company to not only learn rapidly through its own experience, it also serves as a risk mitigation mechanism by reducing the time between the origination of an idea and measurement of its value to the business and its customers.
Using the Measurement Model to Enable Rapid Learning and Experimentation
The implementation steps for this type of experimentation include:
- Define a business problem and quantify its annual economic value in terms of growth and/or productivity. The problem should be scoped in size so it can be solved within 90 days, including deployment to customers or end users.
- Design a solution to the problem that can be measured through a combination of existing measurements and a small amount of additional instrumentation, including leading (usage / activity) and lagging (results) indicators. The anticipated impact is a hypothesis that will be tested once the solution is in use by end users.
- Implement the solution and supporting instrumentation.
- Make the solution available to a subset of customers / end users (ideally supported by A/B or multivariate testing) and compare the results across user subsets to confirm or refute the benefits hypothesis.
- Stop initiatives that fail to deliver expected value.
Example: a large home improvement retailer installed monitors to track shoppers entering and leaving their stores and learned that for every 100 people who came to the store, 70 left empty-handed. Research into the “lost demand” problem uncovered a pattern where customers often wanted to buy infrequently purchased products that were within the home improvement category but not stocked in the retailer’s stores.
One tactic they employed to convert more shoppers into buyers was to enter into an agreement with an industrial distributor whose supply chain was specifically designed to manage a broad array of slow-moving products. Customers of the home improvement retailer could purchase the distributor’s products at the retailer’s special-order desk and receive them within 48 hours. The agreement allowed the retailer to greatly expand their product assortment with no incremental inventory cost, and since the orders were processed in the retailer’s point of sale system, they could easily calculate impact of the program on their conversion rate.
Conclusions
Thirty years of academic research into corporate performance measurements has generated more questions than answers about the impact of non-financial aspects of business on financial results. That said, the absence of a clear consensus supported by statistically valid and reliable experiments frees practitioners up to implement the simplest measurement systems necessary to tie investments to value streams and business capabilities. This approach, a lightweight measurement system combined with rapid learning through incremental change cycles, solves the toughest of measurement problems – establishment of cause and effect between investments / actions we take and their impact on growth, productivity, and usage relative to capacity.
References
Balanced Scorecard Institute, Cascading the BSC using the Nine Steps to SuccessTM, Balanced Scorecard Institute, Cary, NC 2019.
Doerr, J. Measure What Matters: How Google, Bono, and the Gates Foundation Rock the World with OKRs, Portfolio / Penguin, New York, NY 2018.
Greski, L. and Roberts, M. Organizing Around Business Capabilities, Architecture & Governance Magazine, August 2023, IASA Global, San Antonio TX, 2023.
Kaplan, R. S. and Norton, D. P. The Balanced Scorecard, Harvard Business School Press, Cambridge, MA 1996.
Kopcho, J., Sinha, M. and Cox, I. Common Challenges Executive Leaders Can Overcome to Improve Strategy Execution, Gartner Research Note G00779580, Gartner Inc., Stamford, CT 2023.
Leinwand, P. and Mainardi, C. The Essential Advantage: how to win with a capabilities-driven strategy, Harvard Business Review Press, Boston, MA 2011.
Mtau, T. T. and Rahul, N.A. Optimizing Business Performance through KPI Alignment: A Comprehensive Analysis of Key Performance Indicators and Strategic Objectives, American Journal of Industrial and Business Management, 2024, 14 pp. 66 – 82.
Neely, A. UPDATE – The Evolution of Performance Measurement Research: Developments in the last decade and a research agenda for the next, International Journal of Operations and Production Management, Vol. 25 No 12, 2005, pp. 1264 – 1277.
Rompho. N., Vinayavekhin, S., Sajjanit C., & Asatani, K. Evolving Landscape of Performance Measurement Research: a bibliometric analysis, Measuring Business Excellence, 2024, 28 (3 – 4): pp. 439 – 457.
Owais, L. and Kiss, J.T. The Effects of using Performance Measurement Systems on Organizations’ Performance, Cross-Cultural Management Review, Volume XXII Issue 2, 2020.
About the Author
Len has over 30 years of experience helping large organizations generate billions of dollars in economic value by leading high risk, high visibility business and digital transformations. In his role as Chief Scientist at LiminalArc, Len leads the development of new service offerings that enable customers to organize around business capabilities, build products and services that customers love to use, and improve operational efficiency. He also leads engagements with large clients to help them increase quality, productivity and business value. Len received Bachelor and Master of Arts degrees in Sociology from the University of Illinois at Chicago with concentrations in Organization Theory, Research Methods and Statistics.