Amazon’s cross-border e-commerce industry has been developing for many years. In the initial stage of the industry, there was no systematic content and tutorials in the industry, which led to many practitioners only being able to “cross the river by feeling the stones” and slowly accumulate experience. After a period of time, some of these practitioners who have accumulated a certain amount of experience will switch to become training instructors or service providers, and the skills they have accumulated will then become the core methodology of “experience-based operations”.
“Experience-based operations” is the earliest operating system in Amazon’s cross-border e-commerce, and it is also a method that many novice sellers can quickly learn when they first come into contact with the Amazon platform. For example, when setting the listing price, the operator can adjust the price value to end with 99 (such as S7.99 or S9.99), which can attract users to click and promote conversion; when selecting products for listing, the operator can refer to the product attributes of the top 100 best sellers, and then contact the factory to produce similar products to compete at a lower price; when optimizing product images, if it is a clothing category, the operator can remove the model head from the image to prevent infringement….. All of the above methods belong to the category of “experience-based operation”. Its advantages are quick results and low trial and error costs, and its disadvantages are inaccurate optimization effects and insufficient objectivity of the methods.
When the operator has accumulated a certain amount of experience, it gradually changes from “experience-based operation” to “data-based operation”. This is caused by the following two reasons.
1 When the store performance improves and the number of products gradually increases, the operator will accumulate a large amount of data (order report data, financial data, personnel management data, return and exchange data, etc.). If these data are analyzed and applied, they can become the fuel for “data-based operation”.
2 When the operator’s team expands, it will inevitably involve the problem of multi-store management and team management. At this time, the original “empirical operation” methodology cannot be standardized, while the “data-based operation” methodology can effectively solve such problems.
“Data-based operation” emphasizes that everything is data-oriented, and pays attention to results and processes. For example, for “empirical operation” practitioners, it may be excellent to increase the store’s daily average from $100 to $1,000 in a month, but for “data-based operation” practitioners, not only must they be able to increase the performance from $100 to $1,000 per day, but they also need to find the core elements of performance improvement through data analysis. Is it keyword optimization that leads to performance improvement, or is it the outstanding advertising effect that leads to performance improvement? Only when both results and processes are obtained can it be proved from the perspective of “data-based operation” that its operation ideas are excellent and replicable.
When an operation team has the idea of “data-based operation”, it slowly enters the next stage – “fine operation”. Although “data-driven operation” can solve most operational problems through data analysis, there are still some areas that cannot be solved by data (or it can be understood that the current technology is difficult to solve the problem), such as photo shooting, brand design, marketing activity planning, customer service system construction, etc.
These areas have relatively high requirements for the operator’s perceptual thinking and design ability, but not for rational thinking represented by data analysis. In addition, when the operator has accumulated enough operational experience and data analysis experience, he begins to consider the problem from a more macro perspective. For example, a novice seller may spend a lot of energy learning how to use Excel to analyze data, how to put on the shelves, how to optimize advertising, etc. But an experienced operator may first think about the life cycle of this product category, when is the off-season, when is the peak season, and which link in the life cycle of different products needs the most attention. Therefore, the construction of “fine operation” thinking requires an operator to have strong comprehensive capabilities.