To do this, gather the core team—ideally you want input from a diverse set of focus and expertise areas—and spend some time brainstorming all the potential killer issues for the project. Next, as a team, prioritize the top three to five issues that are most critical for success. These should be stated in the form of assumptions to be proved or disproved. Examples include:
• Target customers have a compelling unmet need that is this product solves.
• There is a way to produce and distribute this product profitably.
• The size of the target market is sufficient to meet the sales goals.
• The technology actually works in a real world setting.
• Target consumers are willing to pay the proposed price point.
It’s fine to capture a longer list of questions to address in the learning plan, but be sure to identify that limited set of ‘A’ priority assumptions that are really make or break and tackle those first. For example, it does not really matter if your package design is appealing if no one wants to buy the product inside.
The next important step is to determine what information, or level of proof, is needed to move forward. This will depend greatly on factors like: level of investment required, level of risk, and the current stage of development. Be diligent in identifying the minimum to proceed and also specify how the assumption will be addressed. For example, if you are trying to determine whether a technology actually works in a real world setting—is it enough to have it perform in “friends & family” testing or will management/investors demand large scale controlled research results? Stated another way, do you need definitive confirmation at this stage or just a good reason to believe something is possible?
The outcome of this approach should be a handful of killer questions with clear methodology and success criteria defined for each. Examples of methods to address the assumptions could include: consumer research, team brainstorming, financial modeling, desk research, interviewing an expert, or commissioning a consultant. Success criteria do not necessarily need to be numerical—it may be wholly appropriate to have more abstract or qualitative definitions, like “based on team judgment” or “most consumers react positively”. As a final step, add timing, owner(s) and budget required for each question.
This is different from many standard learning plan processes where teams would list out every question that needs to be answered or assumption that needs to be confirmed and proceed more or less along a timeline that mirrored the stages of product development. In a process like that, teams might be months, or even years, into a project before a red flag about the technology or product costs is uncovered. By focusing exclusively on the killer issues specific to a proposition, teams can quickly identify whether a project is viable and only then, continue on to additional learning and optimization.
By identifying the most critical questions and tackling those first and with the appropriate level of rigor—teams can help manage risk and learn quickly. If an issue does truly prove to be “killer” and cannot reasonably be solved, the decision makers can elect to shelve the project or pivot to a new direction earlier, saving valuable time and resources.