Use order reports and third-party data to build Amazon user profiles
As the world’s leading e-commerce platform, Amazon has rich data sources for user portraits, including official data and survey results released by third-party research institutions. This article aims to explore how to use these data to build user portraits to guide daily operations.
Comparison of spending power between Amazon Prime members and non-members
According to data from Millward Brown Digital, the conversion rate for Amazon Prime members is as high as 74%, while the conversion rate for non-members is only 13%. In addition, Amazon Prime members spent an average of $1,400 on Amazon in 2018, while non-members spent an average of about $600. This means that the spending power of Prime members is approximately 5.4 times that of non-members. Based on this, operators can draw some important conclusions: Most of the buyers with high frequency shopping are Prime members, and they are more inclined to buy goods shipped by Amazon Fulfillment (FBA).
User portrait data source: order report analysis
For sellers, in addition to relying on third-party data, they can also use direct order report data to build user portraits to optimize links and ads. In the store order-order report, you can download the order report containing product SKU, selling price, order time, delivery address and other information.
Regional distribution analysis
By summarizing all order information in the past year and selecting the two columns of data, shipping state (ship-state) and product quantity (quantity-purchased), for analysis, you can understand the regional positioning of purchasing customers. Making visual diagrams through Excel can make analysis more intuitive. Once the operational impact is eliminated, the shopping preferences of users in different locations can be confirmed, thus providing assistance for later product selection and shelves.
Time distribution analysis
The order time (purchase-date) and product quantity (quantity-purchased) columns in the order report can help analyze the time period when users are actively placing orders. By setting the total number of orders in a day to 100% and calculating the proportion of orders in each period, the time period during which the user is active can be found. For example, the active ordering time of users of a certain store is mainly concentrated between 8:00 and 11:00 a.m. Pacific time in the United States, which is 0:00 and 3:00 a.m. Beijing time. Therefore, operational activities can arrange flash sales, promotions and other activities 1 to 3 hours before this period to facilitate warm-up.
It is worth noting that since the United States spans 4 time zones, the times in the order report are all Pacific Time (UTC-8). In order to analyze the user’s purchasing behavior more accurately, the order time needs to be converted to the local time zone. For example, after converting the order times in California (CA), Texas (TX), Florida (FL), and New York (NY), you can see that the peak time for users to place active orders is 10:00 local time ~12:00, and there are small peaks at 15:00, 18:00 and 23:00.
Conclusion
It can be seen from the above analysis that Amazon user portraits can be effectively constructed using order report data and third-party data. Whether it is regional distribution or time distribution, it can provide sellers with valuable insights and guide the formulation of operational strategies.