We make choices every day.
As consumers, we constantly make decisions on which book to read, which city to visit, which restaurant to book, etc. Our brain instantly measures the opportunity costs of picking an option over another.
Generally, we have two main motivations when making a decision:
- Reduce risks of failure or regret
- Increase chance of reward / pleasure
For each decision, we go through a process of thoughts and actions. Depending on the situation, this includes doing some research, reading reviews and studying alternatives.
Nevertheless, a study of McKinsey shows that over 80% of Americans seek for recommendations from friends and family when considering a purchase and that a recommendation from a trusted friend is up to 50 times more likely to trigger a purchase than is a low-impact recommendation. It is safe to say that recommendations play a major role in our decisions.
Ok so what is the problem?
The internet offers mostly low-impact recommendations and it is impossible to filter good from bad ones. With tons of options and contradictory reviews, the best option became the one that shows the least risks of regret.
Some examples of recommendations I can think of:
- Good theatres in Berlin?
- Good and affordable dentist in Zurich?
- What is the best book about B2B Sales?
- Which app to use for meditation?
It feels like there are many people we could trust to advise us on each of those questions. It is frustrating to know that we could make better choices if we had the right people around.
What is my idea?
The idea is a network built on trust that aggregates all the favorite recommendations of friends and experts into lists that are easy to oversee.
My idea solves the issue of not having the right people around to make the best choices.
I like to describe it as a new directory where entries are based solely on merit. An inspiration source that does not overwhelm but provides you only with the best.
Why is my project different than the current alternatives?
By limiting the number of votes users can give, I make sure that the quality of each list is as it high as it could get. Even for the least voted recommendation of a list, this was the best experience for someone you trust.
Each user decides on who he/she trusts and experts are personally evaluated and identified before approving their recommendations. By doing so, we avoid fake and/or irrelevant tips. We give users the full control over what he/she wants to see.
Every user sees different results based on his/her trusted network. Lists are tailored at the user level. The more people a user trust, the more personalized his/her own lists become.