Users often use Jungle Scout 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 require specific inputs, like historical search volume, share of voice, and sales estimates. To effectively use these, you need a starting point. An effective strategy is to first use endpoints that provide a range of results. These results can then serve as inputs for the more specialized endpoints.
For example, let's say 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 a 12-month period. 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 first need to identify the top 50 ASINs in this category. 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 to creatively combine different endpoints to formulate comprehensive solutions for your data queries.