Amazon product research tool drawbacks are becoming a hidden pain point for an increasing number of sellers. In this “data is king” era, countless Amazon sellers rely on various product research tools and keyword software to guide their operational decisions. From Helium 10, Jungle Scout to Keepa, these SaaS products have attracted a large user base with their convenience. But have you found that even with these “powerful tools,” product research remains challenging? Hot-selling products are hard to find, profits are meager, and you might even fall into the strange loop of “everyone does what the product research tool recommends,” ultimately making how to avoid homogenization in Amazon product research a difficult problem.

Why does the product research software, for which you’ve invested heavily, always seem to provide data that’s “just a bit off”? Why do those top sellers seem not to rely entirely on these public SaaS tools, instead possessing their own unique product research and operational logic? Behind this, are there inherent limitations of cross-border e-commerce product research software that current mainstream tools struggle to overcome?

This article will delve into the Amazon product research tool drawbacks of mainstream market offerings, exploring issues like data lag, incomplete fields, and convergent analysis models. It will also reveal why top sellers prefer to build their own data analysis frameworks and how methods like the Scrape API provided by Pangolinfo can be used to obtain real-time, comprehensive raw data, thereby establishing a true competitive barrier in the fierce market.