When operators solve business difficulties through “experience-based operation”, they will encounter the first problem: should they maintain the current business scale and make stable profits, or expand the team to improve the business ceiling? If the answer is the latter, then operators or managers will inevitably encounter problems with personnel training and business process standardization. In the field of Amazon’s cross-border e-commerce, experience itself is difficult to replicate. For example, if the ACoS performance of an advertisement is poor, then the reason may be related to multiple factors such as exposure, clicks, conversions, and bidding. Perhaps “experience-based operation” practitioners can still handle the ACoS optimization of advertisements for a single store with ease, but when the team expands, if team members cannot solve similar problems through systematic data analysis ideas, but still rely on experience to make snap decisions, then the team will inevitably have management chaos and inconsistent execution problems in the later stage.
“Data-based operation” was born to solve this series of complex problems. When encountering the problem of poor advertising ACoS performance, “data-based operation” practitioners will use the funnel model (which will be introduced in detail in the advertising-related chapters of this book) to analyze data for each link, so as to accurately determine the factors that need to be optimized. At the same time, for complex problems of multi-ad group optimization, “data-based operations” practitioners can also flexibly use methods such as the four-quadrant analysis method to make judgments. Unlike “empirical operations”, “data-based operations” emphasize the replicability of methods. Therefore, if every member of an operations team has a data analysis mindset, when solving problems, they can use standardized methods to accurately find the core of the problem, and then derive a solution strategy through logical deduction.