#Google trends live case code#

EXACT_KEYWORDS=df_CODES.to_list() DATE_INTERVAL=' ' COUNTRY= #Use this link for iso country code CATEGORY=0 # Use this link to select categories SEARCH_TYPE='' #default is 'web searches',others include 'images','news','youtube','froogle' (google shopping) Step 3: Get Google trends data by exact keywords We need to make sure we search the right keywords. Simply search “Patagonia” will give us ambiguous results as it contains both search terms. For example, “Patagonia” could either be a clothing company or a region in South American. It’s not unusual that words often have multiple meanings. However, none of them builds automatic codes that could pull EXACT KEYWORDS.

There are many available methods to pull google trend data using python. Current available automation python methods don’t query exact keywords, meaning they’re not accurate. when to compare 50 apparel and footwear brands, you need to download 50 excels and combine them together.Ģ. You can still afford the time until you have too many keywords to pull. Thus, it is suggested to pull “Nike” trend and “Supreme” trend individually and separately. In this situation, you’ll get big errors when reporting “Supreme” search growth trend. For example, if you compare “Nike” brand with “Supreme” brand, you will basically get a flat line for “Supreme”. The less popular keyword will lose sensitivity quickly if you compare it with a popular one.

Individual keyword by keyword manually pulling is time-consuming.Īlthough Google Trends provides the “Compare” function to compare keywords, the downside is that it scales the results from 0 to 100 based on the most popular term entered. There are two main challenges to pull Google trends data in scale.ġ.

Google trends live case