Data analysis practice: If I do data analysis for shared bicycle products

Data analysis practice: If I do data analysis for shared bicycle products
Many people are asking: How to improve data analysis capabilities? The author believes that on the one hand, we must master the basic analysis framework and analysis ideas, and on the other hand, we must continue to practice. A good way to practice is to analyze the design and operation ideas of typical products in the industry. Assuming that you are the company's data product manager , how would you analyze it? A while ago, I encountered a case on "Zaixing" where a student wanted to understand the data analysis ideas of shared bicycle products. This article will organize some points about this case for readers' reference. If there are readers who are using Mobike or ofo, please give me some advice on whether my idea is reliable. Step 1: Identify who the user is Taking Mobike as an example, there are two possible target users of its products: those who use the bikes and those who maintain them. The party that uses the vehicle is the vehicle user, and the party that maintains the vehicle is the vehicle provider. The user's demand is to have a bike to ride anytime and anywhere, and the riding experience should be good after paying. The maintenance party's demand is to serve the most vehicle users with the least number of vehicles and obtain profits from the use of the vehicles. Step 2: Identify user scenarios From the maintenance perspective, the simple scenario is as follows: 

 From the perspective of the car user, the scenario is as follows: 

 The reasons for clarifying the usage scenarios and processes are: first, our data comes from these scenarios; second, we need to analyze these data to ensure that each step of the user process goes smoothly and avoid loss; third, we must maximize corporate profits and further benefit users. Step 3: Define your analysis goals After defining the population and sorting out the process, we can simply define the analysis objectives for shared bicycles as:
  • Increase the number of successful rides - maximize user benefits
  • Increase gross income - maximize corporate profits
 Step 4: Break down the target The idea of ​​data analysis is to break down the target layer by layer and find problems from each sub-indicator. Based on the above goals, it can be broken down into: Number of successful rides = Number of app launches x Scan code unlock rate per launch x Success unlock rate x Success end rate Number of successful rides = number of trips per person per day x number of people Gross income = Recharge income – Input cost = ((each recharge amount – arrears amount) x number of recharges) – ((cost per vehicle + maintenance cost) x number of vehicles) Note: The above analysis varies from person to person and from experience to experience. Different formulas can be derived from different perspectives and should be adjusted based on actual operational objectives. Step 5: Clarify the role of data observers The sub-indicators need to be presented to people in different roles for analysis in different dimensions. Therefore, these roles must be identified before analysis. For example:
  • Decision-makers: focus on core indicators, transaction indicators, and time period trends
  • Maintenance team: focus on vehicle status, location, trajectory, failure rate, user feedback
  • Operations team: Pay attention to the number of rides, recharge status, deposit status, arrears status, and credit points
  • Product team: Focus on riding process, interaction path, and user feedback
  • Development team: Pay attention to request failure rate and App crash count
 Step 6: Identify data metrics Based on different roles, the disassembled sub-indicators can be further aggregated and integrated to form different statistical measurement values. One thing to note in this process is that every time a measurement value is produced, a purpose must be given. In other words, what conclusions can be drawn from this metric? Numerical values ​​without conclusions are meaningless. As shown below: Core data to evaluate promotion effect - number of registered users ; evaluate activity - number of starts, number of active users; evaluate business health - number of successful rides, rate of ride per start (vehicle usage density) ; evaluate cash flow health - total incoming, total outgoing, recharge amount, outstanding amount, total vehicle cost; evaluate vehicle health - total number of vehicles, number of faulty vehicles Operational data to evaluate promotional effects - number of registered users, number of download clicks; Operational effects of activities - number of top-up users, number of invited registered users, number of successful rides, points growth/consumption ; User quality - number of trips, distance, credit points, top-up, number of arrears, number of certified users Maintenance data Vehicle usage overview - total number of vehicles + vehicle location Real-time presentation - unused/in use/faulty/scheduled Evaluation of vehicle usage rate - number of used vehicles/total number of vehicles Evaluation of vehicle fault rate - number of faulty vehicles/total number of vehicles Evaluation of vehicle idle rate - number of unused vehicles/total number of vehicles for N consecutive days, and location of idle vehicles Product dataEvaluate the degree of satisfaction of needs/vehicle dispatch effect - riding rate per startEvaluate product usage - number of successful rides, number of abnormal rides, average riding mileage, average riding time, daily riding frequency, number of starts, average number of riding days, reservation operation success rateEvaluate product operation effect - recharge path, registration pathEvaluate product usage anomalies - average unlocking success rate each timeEvaluate user riding habits - riding trajectory aggregation, reference for dispatching routesEvaluate user satisfaction - number of positive user feedback/number of user feedback Financial data User amount: recharge flow, recharge times, recharge amount, deposit amount, insufficient balance, deposit refund amount Maintenance amount: vehicle production cost, vehicle maintenance cost Note: The above data is only for example and should be adjusted according to actual needs. Step 7: Clarify data dimensions Once you have the metric values, you need to think about the dimensions through which you can view these values, that is, you need to define the data dimensions. Common dimensions include: By time: hour, day, week, month, quarter, year... By region: province, city, district... By channel : invite to register, scan QR code to register, click on ad to register... By type: authenticated/unauthenticated, recharged/unrecharged... By location: GPS map positioning The above dimensions must also be continuously adjusted, expanded and optimized according to needs. Summarize After completing the above seven steps, a basic shared bicycle data analysis framework has been built. As a data product manager, on the one hand, you can design statistical system functions based on this; on the other hand, you can use it to regularly produce data analysis reports for different groups of people. But the above steps are just the tip of the iceberg. What really needs in-depth research is how to reasonably attribute changes in data after observing the data and propose improvement suggestions for the optimization of products and operating strategies!

Mobile application product promotion service: APP promotion service Qinggua Media advertising

The author of this article @申悦 is compiled and published by (Qinggua Media). Please indicate the author information and source when reprinting!

 

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