Seeing the success of notable players like Uber and Lyft, you've decided to join a fledgling ride sharing company of your own. In your latest capacity, you'll be acting as Chief Data Strategist for the company. In this role, you'll be expected to offer data-backed guidance on new opportunities for market differentiation.
You've since been given access to the company's complete recordset of rides. This contains information about every active driver and historic ride, including details like city, driver count, individual fares, and city type.
Your objective is to build a Bubble Plot that showcases the relationship between four key variables:
Average Fare ($) Per City
Total Number of Rides Per City
Total Number of Drivers Per City
City Type (Urban, Suburban, Rural)
In addition, you will be expected to produce the following three pie charts:
% of Total Fares by City Type
% of Total Rides by City Type
% of Total Drivers by City Type
They urrently has a presence in urban, suburban and rural areas, the market share in each region is unequal. Means that the average fare, number of rides and drivers available will change by each developed environment.
The increasing drivers in cities with high demand to increase total profits is supported by the data in the pie chart “% of Total Fares by City Type”. While the average fare is smaller in urban cities, the quantity of rides is large enough to account for 62.7% of the total fares.
Additionally, we can confirm the total number of rides is proportionately related to the total fares in urban, rural or suburban cities. With that in mind, the next step is increasing Bonanza’s presence in the suburban cities with high demand.
There is an evident correlation between the number of rides and average fare. Urban cities have the lowest average fare with the highest number of rides. the implication is that urban drivers may have more competition and their fares are lower, rural and suburban drivers may have less competition and can charge higher fares. The bubble plot shows that relationship as well as the drivers per city. The insight that we can gain from this information is that there are cities with a high demand for rides but a disproportionately small number of Bonanza drivers to meet demand. It’s suggested that Bonanza addresses this imbalance as the ridesharing demand is present, but Pyber’s presence in those cities is not large enough. This will result in more fares collected in revenue as riders will not need to turn to ridesharing competitors or taxi services.
An alternative implication we could have deduced if distance traveled had been provided, is that urban riders travel shorter distances with more frequency and rural riders travel long distances with less frequency with the highest average fares in average. For that information we would need the ride duration to confirm.
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