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You probably realize that search volumes for some keywords are affected by seasonality.įor example, take a look at the Google Trends data for the keyword “umbrella” in the US.
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Identify seasonal trends, then create (and promote) content at the RIGHT time! Now let me show you how you can (and should) use Google Trends in your online marketing activities and during keyword research in particular. This is despite the fact that Keywords Explorer shows the search volume trend, whereas Google Trends shows the “popularity” trend (as outlined above). But in most cases, it does.įor example, if you take the keyword “Star Wars,” you’ll notice that the same spike (December 2015) appears in Google Trends and Ahrefs Keywords Explorer. Now you see that popularity used in Google Trends does not always correlate with query’s search volume. Search term popularity will also change if the total number of searches changes, even if the query’s search volume is constant (see June 2017 - July 2017 in my example above).Search term popularity changes when the query’s search volume changes (see May 2017 - June 2017).This example gives us two important takeaways: Scale these values proportionally so that the maximum value is 100.Calculate relative popularity as a ratio of the query’s search volume to the total number of searches.To build a graph the way Google Trends does, you need to take the following steps: Here’s the table I made for this simulation: They are just an assumption to demonstrate how things work.Īssumption 1: the total monthly number of all Google searches in the US is around 10 Billion ( Source)Īssumption 2: the search volume for the query “Facebook” in the US is 83 Million (according to Ahrefs Keywords Explorer) The numbers I will use below are by no means accurate. To demonstrate you how Google Trends builds its “Interest over time” graph, let’s pretend I have the same data Google has. Here’s the Google Trends graph for the query “Facebook” over the past 12 months (in the US): And it is important to note that Trends only shows data for popular terms (low volume appears as 0). Trends eliminates repeated searches from the same person over a short period to give you a better picture. The daily data simply interpolated from the weekly data.The resulting numbers then get scaled on a range of 0 to 100 based on a topics proportion to all searches.Query for multiple 9-month period with significant overlapping periods and use the overlapped period to have consistent scaling (similar to what is purposed here).(the dailydata function implemented in pytrends). The daily data concatenated from multiple 1-month queries and normalized by corresponding weekly trends data.Therefore, I used ‘iPhone’ as the keyword and reconstruct daily trends data over 35 months by the following three methods: I was curious about how those methods compared to each other and matched best with the original daily trends data. Comparing Methods for Reconstructing Daily TrendsĪlthough people have already purposed methods to circumvent it (for example: here, and here). However, it is not ideal for any predictive model which necessitate precision at daily scale and real-time applications (as weekly data will only be available until the current week ends). I found it not so obvious to obtain the daily search trends and people used the weekly trends as surrogate. My motivation into this subject was first inspired by the Rossmann competition in Kaggle where google search trends were used to predict sales number. For example, query for the last 7 days will have hourly search trends (the so-called real time data), daily data is only provided for query period shorter than 9 months and up to 36 hours before your search (as explained by Google Trends FAQ), weekly data is provided for query between 9 month and 5 years, and any query longer than 5 years will only return monthly data. However, google currently limit the time resolution based on the query’s time frame. To get most out of the search trends database, one can use the python module pytrends or R package gtrendsR. It has already been well explained by the Google News Lab and several articles have demonstrated data analytics based on the Google Trends, such as the cyclic pattern of ending a relationship with someone, predicting U.S. Scaling using overlapped period is better as long as there are enough search activities during the overlaping period.Īs google gained monopoly over the internet search, whatever we googled become another kind of measure of public interest over time.Scaling daily data by weekly trends could generated some artifacts.We can have search trends data at daily resolution for any duration.IPhone search trends, AAPL stock price and Apple key events