Create Thresholds for Pivot or Perservere

[EmilBystrup]

We also discussed LOFA (https://github.com/kea-dpd/Group-Car-Recommendation/issues/5) and Value and Growth hypothesises. Can you share what are you building, what are you measuring, and exactly what it the threshold for pivot or persevere. Maybe this is exactly what you are doing in https://github.com/kea-dpd/Group-Car-Recommendation/issues/29 - by the way, love that you created a new issue rather than reopened https://github.com/kea-dpd/Group-Car-Recommendation/issues/5 - good thinking! 👏

[EmilBystrup]

One of the most challenging aspects of the lean startup approach is deciding when to pivot or persevere with your MVP. A pivot is a change in direction based on what you have learned from your experiments. A persevere is a commitment to continue with your current strategy based on positive evidence. The dilemma is that pivoting too soon can mean missing out on a potential opportunity, while persevering too long can mean wasting time and money on a failing product.

[EmilBystrup]

This our Final Leap Of Faith assumptions along with the threshold for pivot or perservere.

We established thresholds only for the value assumptions because we won't be able to test all of the growth assumptions. Would you like us to create thresholds for the growth assumptions as well? @lakruzz

1. We assume that users visit the car dealership based on the recommendation.

• This assumption plays a foundational role in making a profitable webpage, in order for our concept to work, we need the users to follow the leads.

• The threshold for this assumption is that 70% of users will browse the dealership's website.

2. We assume that users want to make more informed decisions when buying a new car.

• This assumption is of paramount importance. In the digital age, consumers value information and seek ways to make well-informed decisions, especially for significant purchases like cars.

• The threshold for this assumption is that 80% of users will utilize the recommendation tool.

3. We assume that we have access to adequate data about new cars.

• Data accessibility is essential for providing accurate recommendations. Without access to reliable data, the product's value proposition diminishes significantly.

• The threshold for this assumption is that fewer than 20% of users seek additional information beyond what we have collected.

4. We assume that users trust our digital car recommendations.

• Trust is a critical factor, and users are more likely to use and return to a service they trust. Building trust in digital recommendations is crucial for our success.

• The threshold for this assumption is that a minimum of 90% of users have confidence in our digital car recommendation tool.

5. We assume that users are willing to input personal preferences.

• User engagement and willingness to provide data directly influence the quality of recommendations. Without user input, it's challenging to offer personalized guidance.

• The threshold for this assumption is that every user desiring a car recommendation is willing to provide their personal preferences.

In summary, these assumptions collectively form the foundation of our business concept. They highlight the importance of user trust, data availability, and the user's active participation in the recommendation process. See Issue #63 on Github for elaboration.

[lakruzz]

@EmilBystrup I'm not sure what you are asking precisely - It seems that you have threshold numbers here?