kea-dpd / Group-Car-Recommendation

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Revisist LOFA #29

Closed EmilBystrup closed 1 year ago

EmilBystrup commented 1 year ago

Were our assumptions correct? Does anything need to change, do we have the data to support our assumptions.

I had also hoped to find, just an oversight of what I might expect from next week? Do you have any core assumptions about the value the product brings (does it solve a need or a problem? and maybe even a plan to how to get in front of the audience, without having to rely on vanity numbers?

I hope to see e report like this next week, together with the next invoice, but I do hope that you'll get a chance to actually measure on how the intended customer segments react to your value offerings.

EmilBystrup commented 1 year ago

LOFA:

Value Assumptions:

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.

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.

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.

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.

Growth Assumptions:

We assume that users will act on recommendations.

This assumption is vital for the growth of our product. If users don't act on the recommendations, the product's value diminishes, and it becomes challenging to demonstrate its utility.

We assume that monetization through partnerships with car dealerships/brands is viable.

Without successful partnerships and lead generation, we may struggle to sustain our operations and provide a valuable service to users. Our ability to thrive depends on establishing and maintaining these partnerships, ensuring a steady flow of customers to the dealerships we work with, and sustaining the growth of our platform.

We assume that ongoing maintenance and updates are feasible.

The ability to adapt and improve the product over time is critical for long-term success, as it ensures that the recommendations remain accurate and relevant.

We assume that word-of-mouth referrals will play a significant role in driving growth.

While word-of-mouth referrals can be powerful, they often depend on the initial user base finding substantial value in the product. User trust and action on recommendations are prerequisites for word-of-mouth growth.

We assume that there is potential for international expansion.

International expansion is important for scaling, but it might be less crucial in the initial stages. We are focusing on serving our primary market effectively before expanding globally.

EmilBystrup commented 1 year ago

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 commented 1 year ago

We are in the initial stages of product development, and there are still several critical assumptions, that remain unverified. We will need to test these assumptions as we progress in our product development journey. Furthermore, we have not yet taken the growth Assumptions into consideration yet. So, this is merely the Value assumptions:

Firstly, lets explain the MVP:

Imagine a system where you share your specific car preferences with us. We take this information and apply our special algorithm to create a personalized list of brand-new cars that perfectly match your requirements. We consider everything, from the type of car you want to its size and the budget you have in mind. Our algorithm then compares your preferences with the extensive database of available cars to find the ideal match that suits you best.

With today's technology, the process of car shopping has evolved significantly. It no longer involves hours of endless driving and sifting through newspaper listings. With modern tools like online marketplaces and AI-powered comparison systems, finding the perfect vehicle has become faster and more convenient. However, these tools also place greater responsibility on buyers. Without careful consideration, it's easy to make uninformed decisions in pursuit of a good deal or desirable features.

We have yet to define clear-cut thresholds for making the decision to pivot or persevere. As a result, this might appear somewhat ambiguous. We will create new Issues for measurements and set more specific thresholds in the near future.

Regarding the assumption that people would like to make informed decisions when buying a car, we were right! This assumption perseveres.

The assumption that we could gain access to the data we wanted about new cars, was unfortunately not correct, so for the MVP. We are sticking with an Audi database. This assumption pivots.

The assumption that users trust our digital car recommendations. Is yet to be tested, so for now this assumption perseveres.

We assume that users are willing to input personal preferences. This assumption is yet to be tested, but from the data we’ve seen people are more that willing to fill in non-personal information into a recommendation algorithm. So, this assumption perseveres.

@lakruzz