The analysis was meant to demonstrate the differences in fare amounts between the types of areas (Urban, Suburban, Rural) that utilize the ride share service. We then compare and contrast our findings and determine what decisions can be made from this data.
From our table we can see the break down of KPIs that shape our results. We understand that urban has the most rides, drivers, and fares accumulated than the other two areas combined even though urban does hold the least average fare per ride and per driver. With urban as the most dense rural is the least dense and it holds the top spot of average fares per ride and driver with suburban holding second place in those two regards.
From the line chart above we can see the trend-line over the course of the months showing the rise and fall of fares for each area type, giving us an idea of how each area performs during this time and the amount of fare each generates at a given time.
Understanding that short breakdown we just need a quick recap of how a ride share prices, the price is normally determined by the amount of drivers available and the distance they have to travel. More drivers means people can pay less to go somewhere and shorter distances also mean less paid. Understanding those concepts make the data much more clear.
Rural has the highest averages but the lowest fares monthly as seen by the yellow bar on the graph. This in part because they have the least amount of drivers being utilized and most likely the longest drives to go. Rural areas are not as densely packed as suburban or urban areas which causes drivers to take longer trips for their customers to reach their destinations. Although the graph does show something interesting, that the ride share fares of the urban areas drop lower then the initial amount of the year showing that demand, as the year goes on, drops to near 0.
Urban is the reverse, plenty of drivers as we can see, causing surplus of supply compared to demand. With urban ride shares usually they are utilized to traverse short distances that customers dont want to utilize public transport or walking to reach. Or even used to get to a public transport service area instead of taking a ride share all the way to a desination. We see from the blue line that the ride share revenue goes up with a better trend compared to the other two areas, weather most likely playing a factor in its necessity. Its fares also never returns to its initial amount unlike rural or suburban, it does drop halfway in the time between March and April but returns to its previous February to March numbers in March to April.
Lastly we have suburban being the middle ground of those extremes, not far enough to want to drive and not as convenient to use public transportation, giving the ride share a decent customer base. From what we see on the red line for suburban it shows a decent start in January before diving down to $1000 in the beginning of February before a sharp spike at the end of the month and diving up and down through March and April before a steady increase in April. That sharp increase is most likely due to good weather and suburbanites wanting to be out even more.
Some recommendations I would have to boost business in areas would be to:
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Lower fares in rural areas. For the sake of increasing the number of rides over the course of those couple months. A small decrease in the fares charged to the customer could incentivize more riders to use the service thereby increasing use and fares gained in the longrun.
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Implementing a cap on drivers in urban areas. The amount of drivers to riders creates a surplus that is underutilized, thus damaging the averages of our dataset. Reducing the amount of drivers by 10-20% on the road could help our data show a more accurate depiction of the fares gained.
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Giving drivers a reason to not drive certain days. If a driver is on call for a certain amount of days maybe they get compensated instead of working. Could help reduce driver numbers and fix the averages of our dataset.