Challenge

Since 2011 I lead a product team at a Peruvian bank. I have the role of Product Manager, which involves  leading and coaching a team of cross-functional people to develop and evolve a product. The product we  were in charge was called Manyar and it´s a mobile application to look for restaurant information and  menu and also to get dishes recommendations (we used machine learning for this one).

However, as COVID-19 stroke in Peru (we are already in day 60 and restaurants have been closed all this  time) we needed to change our business model in order to survive. We have a limited budget and couldn’t  afford to undertake a big bet and be mistaken. This is where Design Thinking enters.

Selection

We decided to use the Learning Launch since this would allow us to test small bets and measure the results  in order to get fast and cheap feedback from our users -as explained in the video about Learning Launch.  We would then use this feedback to learn if our ideas would serve to have a business model pivot towards  a new one.

Since conducting this pivot in the traditional way would involve time and money (and both were limited  resources to us), Learning Launch became a natural alternative to our challenge, since as the course  stated, it provides a better solution that is based on feedback instead of the traditional way based on  assumptions and tweaks.

Application

We decided to create small experiments. We had a Business Model Canvas from the previous business  model and we copied it 4 times in order to create 4 pivots we would like to test on the real market with  real customers.

Our principal criteria was the find product-market fit, which would allow the new business model to grow  and ideally become profitable. So, we select the 4 most voted ideas from our visual brainstorming session-  conducted with stakeholders and  early adopters from our business model. It was challenging since we  had to conduct this sessions remotely due to social isolation but we managed to get everybody  communicated using the tool Mural which also allows us to use visual tools to facilitate communication  among people. Once in Mural, we managed to rank the 4 most voted pivots: gastronomy social network,  take-away and delivery solution, restaurant to consumer marketplace and recipes/cook-it-yourself app.

We got the usability results from our previous user research and used it to design 4 prototypes using the  Marvel tool in order to have a testable high-fidelity version of the 4 new pivots, presented as mobile  applications. Marvel also allows you to install those prototypes on the phones so we had to distributed  these new versions according to a split test.

We selected 100 users who liked or loved us (had an NPS higher than 8) and also 100 users who hated us  (NPS lower than 3). We then created 4 random samples of 50 people (25 from each segment) to test the  pivots. We called and emailed the people and got down to 40 people per sample since some of them  never answered. After that, we sent the prototypes via WhatsApp so they could download it directly on

their smartphones/iPhones. After the test, we conducted some interviews to get qualitative and  quantitative feedback from users.

Insights

The learning launch was successful. We managed to get information about the prototypes and learned  our customers didn’t care about the delivery and take-away pivot since they were still fearing the COVID  and believed restaurants could potentially lie about or make mistakes about security measures to prevent  COVID. Even though they idea sounded good, most of the people learned to cook and were improving at  their cooking skills as quarantine continued. This insight was radical to the point only 1 person (which lived  in upper-class part of Lima city) mentioned they would consider using this application.

This applied to restaurant-user marketplace since it also involved restaurants. It seemed like customers  were really doubting about restaurants implementing and executing security measures correctly. They  also wanted to have more control about the food they and their families ate.

On the other side, the prototypes about cook-it-yourself and the gastronomy social network were the one  with better appreciation. Insights about having recipes and cook-it-yourself content inside the social  network were flowing in more than 80% of users (82.5%). For cook-it-yourself prototype, people told they  wanted to have a social usage, to share and get recipes and information with their families and friends,  who where locked away due to COVID. 67.5% of the users provided this feedback.

Overall, this allowed us to unify both prototypes into a food social network we could them take to a next  iteration of the prototype and also allowed us to create surveys which we would pass to all our users in  order to understand which features they would prefer to have implemented first.

Approach

The usage of Learning Launch was successful since it has allowed us to launch a business model pivot  which we just launched and got 1000 active users in the first weekend.

However, what I would do differently is to couple Mind Mapping as an alternative to traditional  brainstorming since this would avoid getting bias and too much confirmation bias about own ideas and  instead focus the development of ideas around design criteria. It would also allow us to consider the  number of design criteria met as a way to rank ideas instead of conducting a simple voting, which can lead  to some powerful ideas because we resist them for their novelty.


Challenge Since 2011 I lead a product team at a Peruvian bank . I have the role of Product Manager , which involves leading and coaching a team of cross - functional people to develop and evolve a product . The product we were in charge was called Manyar and it´s a mobile application to look for restaurant information and menu and also to get dishes recommendations ( we used machine learning for this one ) . However , as COVID-19 stroke in Peru ( we are already in day 60 and restaurants have been closed all this time ) we needed to change our business model in order to survive . We have a limited budget and could n’t afford to undertake a big bet and be mistaken . This is where Design Thinking enters . Selection We decided to use the Learning Launch since this would allow us to test small bets and measure the results in order to get fast and cheap feedback from our users -as explained in the video about Learning Launch . We would then use this feedback to learn if our ideas would serve to have a business model pivot towards a new one . Since conducting this pivot in the traditional way would involve time and money ( and both were limited resources to us ) , Learning Launch became a natural alternative to our challenge , since as the course stated , it provides a better solution that is based on feedback instead of the traditional way based on assumptions and tweaks . Application We decided to create small experiments . We had a Business Model Canvas from the previous business model and we copied it 4 times in order to create 4 pivots we would like to test on the real market with real customers . Our principal criteria was the find product - market fit , which would allow the new business model to grow and ideally become profitable . So , we select the 4 most voted ideas from our visual brainstorming session- conducted with stakeholders and early adopters from our business model . It was challenging since we had to conduct this sessions remotely due to social isolation but we managed to get everybody communicated using the tool Mural which also allows us to use visual tools to facilitate communication among people . Once in Mural , we managed to rank the 4 most voted pivots : gastronomy social network , take - away and delivery solution , restaurant to consumer marketplace and recipes / cook - it - yourself app . We got the usability results from our previous user research and used it to design 4 prototypes using the Marvel tool in order to have a testable high - fidelity version of the 4 new pivots , presented as mobile applications . Marvel also allows you to install those prototypes on the phones so we had to distributed these new versions according to a split test . We selected 100 users who liked or loved us ( had an NPS higher than 8) and also 100 users who hated us ( NPS lower than 3 ) . We then created 4 random samples of 50 people ( 25 from each segment ) to test the pivots . We called and emailed the people and got down to 40 people per sample since some of them never answered . After that , we sent the prototypes via WhatsApp so they could download it directly on their smartphones / iPhones . After the test , we conducted some interviews to get qualitative and quantitative feedback from users . Insights The learning launch was successful . We managed to get information about the prototypes and learned our customers did n’t care about the delivery and take - away pivot since they were still fearing the COVID and believed restaurants could potentially lie about or make mistakes about security measures to prevent COVID . Even though they idea sounded good , most of the people learned to cook and were improving at their cooking skills as quarantine continued . This insight was radical to the point only 1 person ( which lived in upper - class part of Lima city ) mentioned they would consider using this application . This applied to restaurant - user marketplace since it also involved restaurants . It seemed like customers were really doubting about restaurants implementing and executing security measures correctly . They also wanted to have more control about the food they and their families ate . On the other side , the prototypes about cook - it - yourself and the gastronomy social network were the one with better appreciation . Insights about having recipes and cook - it - yourself content inside the social network were flowing in more than 80 % of users ( 82.5 % ) . For cook - it - yourself prototype , people told they wanted to have a social usage , to share and get recipes and information with their families and friends , who where locked away due to COVID . 67.5 % of the users provided this feedback . Overall , this allowed us to unify both prototypes into a food social network we could them take to a next iteration of the prototype and also allowed us to create surveys which we would pass to all our users in order to understand which features they would prefer to have implemented first . Approach The usage of Learning Launch was successful since it has allowed us to launch a business model pivot which we just launched and got 1000 active users in the first weekend . However , what I would do differently is to couple Mind Mapping as an alternative to traditional brainstorming since this would avoid getting bias and too much confirmation bias about own ideas and instead focus the development of ideas around design criteria . It would also allow us to consider the number of design criteria met as a way to rank ideas instead of conducting a simple voting , which can lead to some powerful ideas because we resist them for their novelty .