Once a collection of strategies has been identified, the intent is to quantifiably test each one. This will determine which strategy will be the winner. This is a risk management methodology to mitigate large strategic gambles. Strategies venture into new, undefined areas where sound data sets do not exist. It is difficult to determine if a strategy will be successful or not with or without data. By strategic testing, this will turn a gamble into a calculated decision.
When designing the test, the first goal is to determine the target customer. This is who will be the target population or user of the experiment. Ideally, the test will be able to segregate that population from other individuals to prevent false data outcomes. Ideally, an organization would want to run the experiments in three conditions of favorable, unfavorable, and most likely economic situations. This would provide insight into the timing as to when to execute the strategy.
This may also lead to identifying what would be the barriers of entry. Being able to identify internal and external factors to entry will provide further insight into the applicability of the strategy. Some barriers may delay or stop a successful entry. Testing prior to full implementation will highlight the barriers before they create major issues. This will also bring forward the unknown unknowns that have derailed numerous strategies. For example, executives had no idea that the release of the new Coca Cola in 1985 would be a disaster. Their new product was met with anger and vitriol by loyal customers that blindsided the leadership team (Braiker, 2019). These experiments may also provide insight into competitive response and how they will react to these organizational feigns.
Moving into the design of experiments, there are several methodologies that can be applied. The key is to design a process that will eliminate bias and provide insight as to whether the strategy would be successful or not. The other consideration to be taken into account is whether the test will be run without interference or if it will model reality and be action research in which mid-testing adjustments can be made. When running experiments the focus shifts from performance monitoring to gaining insights and learning outcomes. These insights include the speculation about how the competition will respond, what you would counter with, and what would be their response to the counter.
Lastly, the team will want to perform the CARVER analysis that will then outline the risks and vulnerabilities. Theorists created the CARVER analysis during World War II so that bomber pilots could more effectively drop munitions on enemy targets. Later, this became the threat analysis tool of choice for the Green Beret Special Forces and now more recently for the business environment (Bencie, 2018). The analysis scores six essential areas consisting of the threat or opportunities’ criticality, accessibility, recoverability, vulnerability, eventuality, and recognizability. The analyst weights the scores to provide a quantifiable insight into strategy selection.
The conclusion is the analysis portion to determine the results and outliers. Were there any anomalies in the testing conditions or was there any market volitivity that would have made a significant impact. Lastly, what changes would be made if these tests were run again? The team will shift to deciding what the key choice points will be once the data has been finalized. In the end, choice points determine which strategy will be selected. If the testing has gone against intuition, then the hope is that the quantifiable data will help sway public team opinion and will quickly enable a coalition of aid to support and apply resources.
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