Istanbul Mobile Application  – Learning Launch  Challenge    As an automobile industry employee living in Istanbul where has many transportation  issues  to  be  improved or solved, I was a team member for providing mobile apps for city’s Dolmuş System. Dolmuş is  a microtransit solution created by the city itself as a self-improvisation decades ago. Those are public  minibuses  operates in pre-designated and planned routes, leaves the station when it is full. The full price  is equal to a taxi price yet it is divided into 8 as the vehicle has 8 passengers. It is faster and more  comfortable than other public transportation solutions.     As the vehicle starts travelling when it is full, there is no chance to get in the vehicle in case you are not  on the departing station, especially for long routes. We thought creating a mobile app would help people  to make reservations, embrace the utilization of Dolmuş, and dispose the obligation to go to the  departing station by making a seat reservation and pre-payment.    Selection    We have conducted very intensive design thinking activities which ended nowhere as we received  multiple and various information that contradicts each other. Thus, we have created a clickable  prototype and provide social media and personal advertisements on the stations. Learning Launch then  commenced as a step.    Application    Our learning launch was to identify the potential customers that do not use Dolmuş as the vehicles  departs with no empty seats . Those potential passengers are the dwellers living at mid-roads, thus unable  to utilize the Dolmuş vehicles unless they arrive at the first station.  Thus, team prioritized the longest routes that has no passenger circulation on the road. Those routes also  are expensive ones, and drivers do not want to risk any rooms on the vehicle. We organized the drivers to  reserve seats for the applications made via clickable prototype application. Apart from the application,  every step was manual and human driven, as we were looking for the customer demand and operability  before creating expensive complete mobile application.  Surprisingly there were not even one demand for the seat reservation via mobile application. To be  honest, we were close to be confirmation bias in case there would be little sign of customer demand.    Actually, before the learning launch, we asked the dwellers of target areas if they had problems with  Dolmuş usages or would they open for alternative ways of public transportation via mobile apps? We  somehow interpreted the key parts of the application to the persons we thought to be right customer.  Thus, we set a minimum number, a percentage of  the people we talked to, because we needed  a big  enough sample of data  and we needed the right quality of data. Personally, we still think we had right  quality of data but, we could not distinguish behavior and attitude deferral.      Insight  The outcomes were a bit different from what we had expected. Despite, Customers are tend to utilize  mobile apps and made online reservations for ride hailing applications such as Uber and Bitaksi, the  demand for public transportation reservation was not that obvious. Furthermore, we kept customer  interviews, and found out that the target customers call the Dolmuş station and made empty seats if they  really desire to get on the Dolmuş. There was somehow an improvisation and our solution did not add  enough value to change the business model.  We avoided big investments by failing fast and saved our resources and kept studying for the mobility  solutions.

Approach    Team conducted two trials as Learning Launch and no customer demand reflected as an outcome. Team  maintained customer interviews and observations during the launches. The approach intended to  measure the possibility of attitude to utilize mobile application for Dolmuş travels via clickable  prototypes only. We did not put the idea into trash can as the timing would not be sufficient for the  model.


Istanbul Mobile Application – Learning Launch Challenge As an automobile industry employee living in Istanbul where has many transportation issues to be improved or solved , I was a team member for providing mobile apps for city ’s Dolmuş System . Dolmuş is a microtransit solution created by the city itself as a self - improvisation decades ago . Those are public minibuses operates in pre - designated and planned routes , leaves the station when it is full . The full price is equal to a taxi price yet it is divided into 8 as the vehicle has 8 passengers . It is faster and more comfortable than other public transportation solutions . As the vehicle starts travelling when it is full , there is no chance to get in the vehicle in case you are not on the departing station , especially for long routes . We thought creating a mobile app would help people to make reservations , embrace the utilization of Dolmuş , and dispose the obligation to go to the departing station by making a seat reservation and pre - payment . Selection We have conducted very intensive design thinking activities which ended nowhere as we received multiple and various information that contradicts each other . Thus , we have created a clickable prototype and provide social media and personal advertisements on the stations . Learning Launch then commenced as a step . Application Our learning launch was to identify the potential customers that do not use Dolmuş as the vehicles departs with no empty seats . Those potential passengers are the dwellers living at mid - roads , thus unable to utilize the Dolmuş vehicles unless they arrive at the first station . Thus , team prioritized the longest routes that has no passenger circulation on the road . Those routes also are expensive ones , and drivers do not want to risk any rooms on the vehicle . We organized the drivers to reserve seats for the applications made via clickable prototype application . Apart from the application , every step was manual and human driven , as we were looking for the customer demand and operability before creating expensive complete mobile application . Surprisingly there were not even one demand for the seat reservation via mobile application . To be honest , we were close to be confirmation bias in case there would be little sign of customer demand . Actually , before the learning launch , we asked the dwellers of target areas if they had problems with Dolmuş usages or would they open for alternative ways of public transportation via mobile apps ? We somehow interpreted the key parts of the application to the persons we thought to be right customer . Thus , we set a minimum number , a percentage of the people we talked to , because we needed a big enough sample of data and we needed the right quality of data . Personally , we still think we had right quality of data but , we could not distinguish behavior and attitude deferral . Insight The outcomes were a bit different from what we had expected . Despite , Customers are tend to utilize mobile apps and made online reservations for ride hailing applications such as Uber and Bitaksi , the demand for public transportation reservation was not that obvious . Furthermore , we kept customer interviews , and found out that the target customers call the Dolmuş station and made empty seats if they really desire to get on the Dolmuş . There was somehow an improvisation and our solution did not add enough value to change the business model . We avoided big investments by failing fast and saved our resources and kept studying for the mobility solutions . Approach Team conducted two trials as Learning Launch and no customer demand reflected as an outcome . Team maintained customer interviews and observations during the launches . The approach intended to measure the possibility of attitude to utilize mobile application for Dolmuş travels via clickable prototypes only . We did not put the idea into trash can as the timing would not be sufficient for the model .