Users often use API endpoints separately, but their true potential is unlocked when combining multiple endpoints. This approach allows for deeper insights and data analysis on a larger scale.
Consider that some endpoints, like historical search volume, share of voice, and sales estimates, require specific inputs. To effectively use these, you need a starting point. An effective strategy is first to use endpoints that provide a range of results. These results can then serve as inputs for the more specialized endpoints.
For example, you're researching which "tables" related keyword had the highest search volume in the past 12 months. You already have the keyword "tables," but the historical search volume endpoint only processes one keyword at a time and provides data for 12 months. To enhance your research, you can use the "keyword_by_keyword" endpoint. Input "tables" here to receive related keywords. Then, feed these keywords individually into the historical search volume endpoint to determine which has the highest search volume over the last year. Here is a quick example below.Another scenario involves analyzing the last 12 months of sales data for the top 50 ASINs in Amazon's Pet Supplies category. While the sales estimates endpoint can provide 12 months of data, you must first identify the top 50 ASINs in this category. If you combine the product database endpoint, filtered for the Pet Supplies category, and sorted by revenue, with the sales estimates endpoint, this will give you the top 50 ASINs' sales data over the last year.
The key is creatively combining different endpoints to formulate comprehensive solutions for your data queries.