CH08 Hypothesis Validation and Pivoting Design Worksheet
Source: Chapter 8 (KM064–KM073). This worksheet takes the questions and items from KM066 (The Four Elements of Validation Design), KM067 (Small, Repeatable Experiments), KM068 (Data-Driven Pivoting), and KM073 (The Execute → Data → Revise → Re-execute Loop) and carries them over as-is for filling out. No new methodology has been added.
1. The Four Elements of Validation Design (KM066)
Take the questions that arose while reviewing the earlier chapters (customer, value, channel, price, effective market, etc.) and break them down into the four items below.
① What to Investigate — What do you want to check right now? (e.g., “Are there no overseas customers?” “Does the need for localization actually exist?”)
② Metrics — What specifically will you measure?
③ Method and Plan — What method will you use? (Interview / VOC / Key-man research / Desk research / Other)
④ Feedback Data — Which component (customer, value, channel, price, market boundary, etc.) will this validation result serve as grounds for revising?
Check: Has this research been connected in advance to “which hypothesis it’s for, who you’ll meet, what answer or behavior you’ll confirm, and which decision the result will change”?
2. Designing a Small, Repeatable Experiment (KM067)
What methods can you start with, without a finished product? (Interview / VOC / Key-man research / Partner outreach, etc.)
Is the cost and time going into this experiment at an affordable level? (Check that you are not betting the business’s fate on the MVP)
How can you shrink this experiment to be smaller than the business’s full scale? (e.g., testing one channel before nationwide sales, a sample store before full rollout, confirming customer behavior before building all the features)
Is this experiment one that will make the next decision more accurate, or one meant to show off its chances of success?
3. Data-Driven Pivoting Judgment (KM068)
As a result of validation, did the data support the existing hypothesis / prompt a revision / lead you to discard it?
(If you are considering pivoting) Is the basis for the judgment “the data shook the existing hypothesis,” rather than “a new idea came up”?
Which assumption turned out to be wrong? (Check the item that applies and note the evidence)
- Customer —
- Value —
- Channel —
- Price —
- Market boundary —
4. The Five Questions of the Execute → Data → Revise → Re-execute Loop (KM073)
Before Validation. What is the riskiest or most uncertain hypothesis right now, and what needs to be observed or measured?
Design the Execution. What is the smallest affordable action that can confirm this hypothesis?
After Validation. Did the data support the existing hypothesis, prompt a revision, or lead you to discard it?
Pivoting. Based on the result, which note (component) do you need to reopen, and what needs to change?
Knowledge Accumulation. Where will you store the back data and execution results used in this judgment so they can be reused in the next decision?