The ability to develop short-term and long-term plans that account for the wide variety of possible scenarios a startup might experience during its earliest stages is central to its overall success and future viability.
Of course, this is certainly no easy task for even the most forward-thinking of startup entrepreneurs.
In order to overcome all of the unknowns that create challenges for any startup endeavor, it is necessary to consider the full range of possibilities in an attempt to develop an adequate and thoughtful plan for a successful future.
Planning for the Unpredictable
The ability to account for the unpredictable is just one of the many reasons the “Build, Measure, Learn,” model of development is viewed as such a substantial improvement over previously accepted development models.
Obviously, that which is unpredictable is, by its very definition, difficult to foresee in advance. Although it is absolutely difficult, it is also not necessarily impossible.
Startup entrepreneurs must therefore take actions to account for the inherently unpredictable challenges they will likely face during the earliest stages of development.
Developing and Testing Hypotheses
The development of a wide range of hypotheses is a critical step toward preparing for the variety of unknowns a startup is sure to encounter along the way.
These hypotheses should be based on any relevant data that may be available and then thoroughly tested for validation. As each hypothesis is tested, new data is produced for use in the future development of potential hypotheses.
A thorough approach to the process of developing and testing hypotheses is therefore instrumental in planning for the full complement of unknowns facing an early stage startup endeavor.
Utilizing Data Effectively and Making Adjustments
A thorough approach to the development and testing of hypotheses yields critical data that can then be utilized for the purpose of making any necessary adjustments for improving or refining the startup’s product or service offerings during each subsequent iteration.
This same process can be applied to a startup’s marketing campaign, as the data made available through the development and testing of a hypothesis often reveals the inherent strengths and weaknesses of a particular marketing strategy.
As more data becomes available through these processes, the startup is able to further refine the quality and overall efficacy of its efforts, whether those efforts are related to the creation and implementation of a marketing campaign or development and release of a particular product.
With the adjustments or refinements made during each subsequent iteration, a startup effectively eliminates — or, at the very least, mitigates — many of the unknown and clearly detrimental quantities that might otherwise hinder its ability to begin the scaling process in earnest.