RED(), established in June 2013, is an online shopping and social networking platform in Mainland China.
The website claims to have 200 million users since January 2019. In its community, users and celebrities can share product reviews and tourist destination introductions.
RED uses machine learning to accurately and efficiently match massive amounts of information with people. It has accumulated massive amounts of overseas shopping data, analyzed the most popular products and global shopping trends. Based on this, RED provides good products to users with a shortest path and a most concise way.
Based on user data and consumer behavior data
Relationship between different lifecycles and purchasing amount:
Relationship between gender and purchasing amount:
Relationship between users' last month puchasing and purchasing amount:
Relationship between third party stores and purchasing amount: Conclusion:
Add one more variable lifecycle_C
prediction sales amount = 186.8375 + 0.0684 * comulative_purchase_amount + 62.6521 * last_month_engage_1.0 + 8.9856 * days_since_last_order - 31.0249 * lifecycle_C
Note: The linear regression effect of this data is not good and it is only used for practicing the linear regression.