英文简历
Background of the project
1. Incorporate more consumers into Nike's own membership system: nike would like to record consumer purchases and other behaviors to lay a better foundation for future user analysis. This is our main goal of channeling traffic from offline to online: to allow more consumers to join our ecosystem. 2. Increase total revenue: According to the global market research at that time, member users will spend 30% higher than non-members, and will do transaction on more platforms. So for offline, we will also change the display in partner stores to achieve the same experience as direct stores. Finally, the revenue of the whole platform will increase.
Project Breakdown:
The part I am responsible for is mainly to design, maintain and monitor the functional usage data of online multi-platform channels, as well as some core indicators such as revenue, membership, product information and inventory.
1. For the online function part, we have our own app, WeChat miniprogram and official website. We will have some pushes based on geo-fencing or activities to stimulate consumers to buy something in a offline store, and there will also be some in-store functions such as scanning QR codes to query product information such as Inventory, after scanning, whether it will be tried by customer , and finally purchased offline or online. So for these online functions, we will examine the performance and conversion rate of the entire funnel, and do .
1. This project is the O2O digital transformation project of Nike as the core long-term development strategy of the world (Associated Partner Project: CP). Development expectations: formulate online and offline mutual drainage strategies, and on the basis of maintaining the original franchise structure, we will cooperate with cross functions and franchises to achieve a new digital operation model
Big goal:1. Incorporate more consumers into Nike's own membership system: nike would like to record consumer purchases and other behaviors to lay a better foundation for future user analysis. This is our main goal of channeling traffic from offline to online: to allow more consumers to join our ecosystem. 2. Increase total revenue: According to the global market research at that time, member users will spend 30% higher than non-members, and will do transaction on more platforms. So for offline, we will also change the display in partner stores to achieve the same experience as direct stores. Finally, the revenue of the whole platform will increase.
Project Breakdown:
The part I am responsible for is mainly to design, maintain and monitor the functional usage data of online multi-platform channels, as well as some core indicators such as revenue, membership, product information and inventory.
1. For the online function part, we have our own app, WeChat miniprogram and official website. We will have some pushes based on geo-fencing or activities to stimulate consumers to buy something in a offline store, and there will also be some in-store functions such as scanning QR codes to query product information such as Inventory, after scanning, whether it will be tried by customer , and finally purchased offline or online. So for these online functions, we will examine the performance and conversion rate of the entire funnel, and do .
2. For the offline part, we mainly focus on performance and revenue.
Background of the project:
Most of our departments are mainly responsible for supporting the compliance analysis of some cross-border e-commerce businesses in Europe and the United States. Other small partners around us mainly add labels to different types of goods and conduct compliance inspections. When I joined the team, it was at the beginning of COVID. Amazon began to work from home. The customer behavior and demand have changed a lot, and some changes have also taken place in the supply of distributors and the goods of merchants. In general, there are corresponding fluctuations in workload and work efficiency. Therefore, we hope to build an indicator system to measure workload and work efficiency, so as to track fluctuations in data and make corresponding predictions to optimize personnel structure and workload distribution.
Project Breakdown:
Part of what I do is begin with data processing side. Part of the data comes from the records of user feedback, comments and complaints in redshift and the time it took for employees to resolve these cases. Each Unit of records is a sku or a product. For the data stored in the data warehouse, sql will be used for cleaning and do calculation of such business indicators. Another part of the data comes from manual reports maintained by various teams on a regular basis. I mainly use python to standardize tables and other processing, and finally unify them into one-dimensional tables with consistent business logic. The business indicators we choose are mainly about work quality, workload and work efficieny such as the completion rate that represents the quality of work, dpmo (defects per million opportunities), how many cases (8h) are completed in one day, or how many hour it takes to resolve a case or even 90% of the case)
Most of our departments are mainly responsible for supporting the compliance analysis of some cross-border e-commerce businesses in Europe and the United States. Other small partners around us mainly add labels to different types of goods and conduct compliance inspections. When I joined the team, it was at the beginning of COVID. Amazon began to work from home. The customer behavior and demand have changed a lot, and some changes have also taken place in the supply of distributors and the goods of merchants. In general, there are corresponding fluctuations in workload and work efficiency. Therefore, we hope to build an indicator system to measure workload and work efficiency, so as to track fluctuations in data and make corresponding predictions to optimize personnel structure and workload distribution.
Project Breakdown:
Part of what I do is begin with data processing side. Part of the data comes from the records of user feedback, comments and complaints in redshift and the time it took for employees to resolve these cases. Each Unit of records is a sku or a product. For the data stored in the data warehouse, sql will be used for cleaning and do calculation of such business indicators. Another part of the data comes from manual reports maintained by various teams on a regular basis. I mainly use python to standardize tables and other processing, and finally unify them into one-dimensional tables with consistent business logic. The business indicators we choose are mainly about work quality, workload and work efficieny such as the completion rate that represents the quality of work, dpmo (defects per million opportunities), how many cases (8h) are completed in one day, or how many hour it takes to resolve a case or even 90% of the case)
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