Marketing data analysis
市场营销数据分析代写 Think about assumptions or data cleanup steps related to data inconsistencies, many to many mappings, or broken database schemas
About the data: 市场营销数据分析代写
This data is meant to mimic a generic e-commerce dataset, not our business. This data was all generated by a script rather than actual users, but with some degree of thought to distributions and how it all fits together. We tried to keep everything that you'd look as a realistic approximation of real data, with all its challenges and inconsistencies. If you have questions about whether something that you're seeing is intentional or not, please ask us about it. In general, please assume that this data is from the company you work for, so you are free to make any assumptions you require as long as you explain your thought process.
For doing this analysis, please feel free to use whatever tools you are comfortable with. We have a preference for SQL, R or Python since we have those set up, but if something else works better, we'd be happy to see that. If you need a database set up, one easy approach is to install PostgreSQL on your local machine and then create a local database using PgAdmin: https://www.pgadmin.org/ ( https://www.pgadmin.org/ )
Questions
- Marketing Attribution
- What campaign was responsible for each user's finding our app?
Tips: 市场营销数据分析代写
- Think about assumptions or data cleanup steps related to data inconsistencies, many to many mappings, or broken database schemas in the data. These may cause SQL joins to return incorrect results. Thinking about timestamps is useful here
- Please describe how you decided what attribution logic to use. How much time would you allow between the attribution and the user creation, and why ? For example, you can compare simple vs complex methods and discuss the pros and cons
Low Sales
- It looks like sales have been a bit low the last couple of days of the sales data set. Is this something we should be worried about?
Tips: 市场营销数据分析代写
From a statistical perspective two things to lookout for are:
- Historical trends and seasonality
- Distribution of sales, statistical tests, time series techniques, conditional probabilities
From a business perspective:
- The link between sales, users, device and attribution data - can you find a potential root cause ?
- Comparison with the recent past and recent trends
Make sure you provide a clear conclusion and next steps (if any)
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