How to Accurately Compare Google Trends Data in Spreadsheets Without Misinterpreting Search Interest

The article discusses a method for comparing Google Trends data in spreadsheets without confusing relative interest scores with actual search volumes. The author, who is building a tool called CleanScrape, explains that a Google Trends value of 100 does not represent 100 searches but rather a relative interest score within the comparison. The article provides a step-by-step guide using an Apify Actor to extract Google Trends data for terms like ‘ChatGPT’ and ‘Claude’ in the US over a three-month period. The process involves inputting specific JSON parameters to request interest-over-time data, which is then exported as a CSV or Excel file. The author emphasizes the importance of keeping both search terms in the same request to avoid separately normalized series that would make comparisons difficult. The article also warns against misinterpreting the data as representing actual search volumes, sales, or market share, noting that Google’s data intervals may be partial or incomplete. The author provides cost calculations for using the Apify service, explaining that each keyword at each timestamp is a billable row, with pricing at $0.003 per row. The article concludes with advice on how to create pivot tables and charts from the exported data, and encourages readers to try their own comparisons while being mindful of the data’s limitations. The author also mentions that the tool supports additional features like related queries and regional interest, but notes that some features may not always be populated by Google. The overall message is to use Google Trends data responsibly, understanding its relative nature and avoiding common misinterpretations.