There are 3 types of cities that PyBer operates in. The CEO, V.isualize, gave me and Omar the task to compare the fares and it´s average per ride and driver, among the 3 types of cities, which are Rural, Urban and Suburban. Going to know more about the differences will help in the future when me make decision on how to divide the budget when we grow the company.
Family´s finances are different in the types of cities. As we will see in the next charts and graph, People in rural cities tend to pay less than the ones in the Urban. Knowing the average fares in the cities will help us define the next fares in cities Pyber will enter.
In the following Summary DataFrame we can observe that actually the gap of difference among the city´s types is big. During the analysis Urban cities had malmost 3x times of rides than Suburban cities (1625 to 625), and 12x times more than Rural cities.
The total drivers of PyBer on each city are almost directly proportional to the amount of rides as expected. Due to the size of the Urban cities were we can find big corporatives, companies and real state developments, the demand of these services is way higer than the Suburban and Rural. There are 30x times more riders in Urban than in Rural, and almost 5x times than Suburban. The fares are proportinal as the first two data.
Here we can find our first interesting data or red flag. People in rural areas (where families earn less money) tend to pay more in average than people in Urban or Suburban cities. This is due to the distances people travel in Uber. Inside their own rural city, they are more likable to walk to the spot. So, if they use PyBer that means they will travel long distances, thus will pay more money.
This data is related to the last one. Still, people in rural areas tend to pay more in average than people in Urban or Suburban cities.
- A big area of opportunity to attack would be to plan lowering costs in Rural cities. If a mobility startup starts to focus on rural with low prices we will loose customers. So the first advice would be focus on lowering costs in Rural cities.
- The second recommendation comes from the fact that people in Urban cities tend to travel short distances. Rush hours can be really stressfull when it comes to moving. So with our data we can add an option to schedule a pick up to reduce time whenever people need to move.
- Last one would be that as we keep growing we can upsell other services. We have the trust of a lot of customers so adding an fintech service such as "PyBer Cards" or "Food Deliver" can help the company grow exponentially.
- Jupyter Notebook
- Pandas
- Python
After analyzing the data, we found out that more people use PyBer in Urban cities than in Suburban and Rural. And the least usage is in Rural. Now, people tend to pay more in Rural cities than in any other one. So basically, we found out that the thing we want to work the most is on the short and long distances our customers travel. We want to improve the experience and fares of our customers whenever they travel these distances. So, even when our competition tries to improve the prices and take our customers, we will be a step ahead.