Below you will find mostly excerpts of Tableau visualizations. You can find the corresponding workbook on my Tableau Public account.
We found out that our dataset describe rides in New York:
And via Googlemaps we quickly checked the how the distance is measured:
Through our data cleaning procedures we cut down the size of the data by 30% while improving the informative value of the dataset (left initial data/ right cleaned data):
- what about the distribution of Passenger Counts, as our taxi drones are only able to carry max. 2 people
- at what time do people take rides
Throughout the day the highest pickups are happening from 8am – 4pm being mostly steady with another increase to the peak from 5pm - 7pm. From there on the pickups are gradually decreasing until 4am. Higher Volumes of pickups are experienced on Friday and Saturday. Looking at the quartely development of the pickups you can see that the number of pickups from Januar to March are increasing gradually. Beginning of Q2 the number of pickups are decreasing gradually until June. Q1 and Q2 are developing in a reciprocal manner.
- where are the most traffic congestions
Looking into the Tableau Workbook you can see that the most important area is between Columbus Circle and Midtown Manhatten. The second most important area is between Ravenswood and Astoria. Thinking about pickup and dropoff nodes we might use flat rooftops of buildings or a tiny part of the Park further up north for the Columbus Circle/ Midtown Manhatten area. That would not distract the other traffic and you could make use of existing space. For the Ravenswood/ Astoria area you could use flat rooftops as well but also the harbor area which is not far away. It has the same advantages.
We have also user survey data that could help us to answer from a segmented point of view
- whether people would like to use a flying taxi or not (red=no, green=yes)
Looking at the Adoption by Income we can see mostly every Income Class is positive (appr. 80% in average) about the adoption in regard to a Flying Taxi. Least amount of interviewees have been the classes >200k (74% said Yes) and 0 – 20k class (80% said Yes). Looking at the Adoption by Age we can see that the majority in every class is positive about the adoption in regard to a Flying Taxi. Age Class between 35 – 40 and 52 – 55 shows the most doubts in relation to the whole age analysis. Looking at the Adoption by Gender we can see 80% of the Women want to use the Flying Taxi. Same Percentage is applicable for Men. You can see that more Women has been interviewed than Men. Having a look into the Adoption by Neighborhood of residence (here you see an sorted excerpt), especially the South and Center of Manhatten has a strong positive thought about Flyber with 100% positive feedback from the Financial District & Tudor City. More concerned Neighborhoods are Sutton Place (Center-West Manhatten and West Harlem (North Manhatten).
- which segments are willing to pay a certain price for our service (gender: blue=men, green=women)
Based on gender most are willing to pay prices up to USD 30. It seems that women are willing to pay more than men. Regarding age there are three groups who are willing to pay more: 40 - 50, 55 - 75 and 20 - 30. Based on the income level there are three levels who are willing to pay more: 120k – 200k, 80k – 120k and 40k – 80k. Regarding neighborhoods the following three areas are willing to pay the most: Battery Park City, Financial District, Midtown, Tribeca & Tudor City.
- what are the concerns of people who would not use a flying taxi
500 people has been asked of which 100 said they would not use flying taxis. We have categorized four main concerns. Key Drivers are Trust & Safety followed by Price. See below the drill down.
Age: There are three age groups who are willing to pay more:
- 40 – 50: Safety, Price, Added Value Concern
- 55 – 75: Safety, Price, Added Value Concern
- 20 – 30: more Price than Safety Concern
Interviewees between 30 – 40 are concerned about all topics:
- Safety
- Price
- Environmental
- No Added Value to the currently Situation
Income Level: Focussing on the three relevant Levels:
- 120k – 200k (Safety and far behind is Price)
- 80k – 120k (Safety and far behind is Price)
- 40k – 80k (Safety & Price equally)
These Income Levels do care most about Trust & Safety.
Neighborhood: People from Neighborhoods who are probably are willing to pay more have said No to Flyber because of:
- Battery Park City (South Manhatten): Price Concern
- Financial District (South Manhatten): no material sentiment
- Midtown (South-Center Manhatten): Safety Concern
- Tribeca (South-Center Manhatten): Safety & Price Concern
- Tudor City (South-Center Manhatten): nothing
Gender: It seems that women are willing to pay more than men. Women who said No to Flyber Taxi mentioned primarily Safety Concerns followed by a smaller portion who are concerned about the Price. Men tends to be equally concerned about Safety & Price Issues where Price Concerns seems to be a bit more present.
As we have limited resources for our business we have to decide how to invest it. Best days and time to operate is Friday and Saturday between 8am – 4pm as well as 5pm – 9pm; this makes up 24 operating hours per week Having a look on the distribution of pickup and drop-offs we can see that most of the action happens in Manhatten, mainly between Middle and South of Manhatten. These neigborhoods are also the same ones that are willing to pay higher prices than others. We would like to select without gender bias and as there is no specific takeaway from the age analysis there is no further tailoring necessary. The middle of Manhatten is the area with highest duration. A positive sideeffect of using places with high density and duration is that people driving by using usual taxis while waiting in the traffic can see the flying taxi in action and trigger the wish of using our service instead of waiting so much in the taxi because it takes so much time! Other frequently active areas are the Airports JFK and La Guardia. The goal with an MVP is to validate a potential product’s viability for solving user problems without the potentially disastrous results of spending large quantities of time and resources on a product the market doesn’t care about. Homegrown hardware would be more expensive than copters for the start of our journey. We would rather select copters for that reason. As most of the people are worried about the Safety and Price of our service we would suggest to work with a fixed price for now. We assume that there are no traffic jams in the air and we gain trust and reliability in our service. Having a look at the price distribution in average people are willing to pay USD 23. 95% of the interviewed population is willing to pay a price up to USD 41. Compared to the actual rides (see file 2.1) in average a taxi ride costs USD 16. 95% of the rides have cost up to USD 32. The median duration of a ride is 11min. Assuming a drone can fly 100 miles per hour (https://dronedj.com/2021/05/19/volocopter-4-seater-drone-taxi/); that would be 1,6 miles per minute.Example calculation:
- Airport La Guardia to Midtown; 10 miles takes approx. 6 min, adding 5min on & offboarding
- Midtown to Battery Park; 4 miles takes approx. 3min, adding 5min on & offboarding
According to our survery 80% (400 yes/ 100 no) would like to use the Flying Taxi. Assuming the survey is representative, our final target population sums up to: 1,3million people * 80% ~ 1 million possible user. With a price range between USD 23 – 41 we have an average TAM of USD 32 million only for the introduction phase in Manhatten.
The data gave us some good insights that enables us to shape our operations. Although we have gained some knowledge about the market, the customer profile and even ideas where to place our pickup/ dropoff nodes, there are still further political, economical, social-cultural, technical, ecological and legal aspects that needs to be checked. And we should have an eye on our assumptions. A very important topic is how we deal with the data of our customers. Legal implications are dealt within chapter 3. Lets say we figured everything out and started our business. The introduction went well and now we would like improve our product continously. In chapter 4 we will see how it is done.