# data[].attributes.body
The business is not sustainable enough, in case we don't get enough donation to support the operation, we may face lack of found and go bankrupt.

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Couldnt come up with a great interface. Present day AI is not mature enough to recognise all kinds of handwritings.

Laxmikant

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Our competitors had to strong network effects in place. Which led to low adaption of our diversified business model.

# data[].attributes.body
The project that I am going to run requires data collection from all department, and the possible proposal and solution that i provide in the last stage may touch or hurt someone or some team's benefit and make them look bad. Thus I can foresee some difficulties or troubles during my project implementation which leads abrupt stop, aka, misarable failure.
Xia

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We overestimated the readiness and willingness to digitize shelters. They persitently stuck to their policies and to the old routines - this roadblock was too big and we couldn´t offer a critical amount of services that would standardize and digitize the adoption process.

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Even if we collected a considerable amount of donations, necessary to run our business, we never stopped burning money. Finally, we had to shut down business.

# data[].attributes.body
The competitor released an onw app that included many services we offered as well. This chrashed our selling point and we had not been able to convince a sufficient number of shelters to cooperate with us.

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Unfortunately, the growth perspectives were too optimistic and the food market proved to be even denser than anticipated. On top of it all, an established competitor from Germany entered the Swiss market driving us out of business eventually.

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To expand the business, partnerships with restaurants was key in our assumptions and business plan. Some restaurants may not be willing to pay a fee for a new app that matches travel schedules and customer´s willing to meet in their place. Specially, restaurants would need to acknowledge that the app still needs to be tested and restaurants are recovering from an economic slowdown after COVID pandemic.
Lola

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Our project failed for multiple reasons:
1) Revenue streams: the subscription model we envisioned for the renters prevented us from getting enough renters and enough revenue to keep going
2) Wrong marketing channels which resulted in less users
3) Higher CAC due to less users in the first year

Vladimir

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We failed on our capacity to procure donations. Apparently people are more willing to pay markup or transaction fee than constant, recurrent donations. Also, corporations dont like donating as we thought

# data[].attributes.body
Too optimistic unit economics along with lack of focus due to part time work assigned to the company.

# data[].attributes.body
Might end up as dating app. Another pandemic. CLV is low.

# data[].attributes.body
the partners were already struggling restaurants for a reason, our concept did not improve the situation, only delayed the 'inevitable' - closing of the business

# data[].attributes.body
unable to guarantee consistency across the partner/franchise locations

# data[].attributes.body
People are afraid to be controlled what they buy and what they cook at home and do not want to use our app

# data[].attributes.body
We thought we had a great idea, but the people simply dont care to have another app, that serves another small part of their life to save couple a bit of food trash

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To scale up and get the critical mass of users to have enough network effect will be cost intensive as we are focussing on a small group of people and we need to have enough transactions on the app for the marketplace participants to pay a high commission.

Anindya

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We grossly underestimated costs, and hence our profits. Unit economic were either negative or so low that the whole business did not make any sense of continuing.
Milan

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Some of our eaters got poisoned by the food that was sold over our platform. This opened us to a number of lawsuits that crushed the business in initial stages.
Milan

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We were unable to outgrow our direct competitor HomeMeal. Or alternatively, one of the Food Delivery giants (Delivery Hero, ...), decided to invade our niche.
Milan

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Local Kitchen: Growth projections too optimistic

# data[].attributes.subject
Local Kitchen: Growth projections too optimistic

# data[].attributes.body
Growth projections too optimistic. Network effects never kicked in. We were not able to outgrow fixed costs even with positive unit economics.
Milan

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Adhering to the legal obligations for non-professionals and keeping up the standard reduces the network strength.
-Monalisa

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Driving to alignment killed the sparks of great ideation and prevented pivoting of the initial idea. Hence, there was too little learning and the final products fell short vs competition.

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The Business model was based on exploiting large scale consumer enthusiasm for digital assets and also lack of understanding of them. The the major collapse of FTX several weeks ago (and the substantial loss in market cap for cryptocurrency), the fad passed.
Kevin

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Our estimation for orders was too optimistic. The number of orders for homemade food was not big enough to scale. It looks people doesn't like to think in advance what they will order for lunch or dinner, they prefer "on-demand" even if the food is not that tasty and healthy.

Josep

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We did not manage to come up with a technology that sorts the waste accurately enough that it would benefit the whole process of recycling.

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Having different people responsible for the pizza baking we were not able to ensure the high quality standard we wished for which ultimately led to business failure.
Marie

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Quality of prediction model and algorithm wasn't good enough and created wrong information leading in fact to the opposite of what we were promising leading to higher spend and more food waste.

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larger competitor implemented idea as well after learning about it and were able to attract more customers due to bigger size and higher brand awareness as well as more budget for marketing communication

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People looking for convience did not appreciate that they needed another app for more convenience

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People were not willing to pay for premium as the perceived benefits were too low.

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we were unable to develop the AI image recognition technology in time and licensing of other's technology was too expensive