Comparing how these keywords trend seasonally as well as relative to each other provides the most insight on the variation in trend between these topics.
We don’t just want to know the trend of a specific keyword like “allergy”, but also its related keywords such as “allergy symptoms” and “seasonal allergies”. Within each category, the data presented represents an aggregate of 50 of the highest search volume keywords related to the category. Were people searching about other illnesses more during Covid or less because Covid was the only topic on everyone’s mind? To answer this question, we explore different trends related to illnesses and symptoms to see how current events (in this case, COVID-19) affects interest in other related topics. Not only did it affect everyone’s livelihoods, it also affected search. Trends can help you predict user behaviour and optimize landing pages, create new content ahead of these shifts, and prepare your content roadmap ahead of the new year.įor the healthcare industry, COVID-19 changed everything. For example in eCommerce, seasonality is very important and will help you decide how and when to market certain products.
There are many reasons why you may want to closely examine google trends data. We will be going through some of these visualizations below! Exploring A Relevant Trending Topic: COVID-19
GOOGLE TRENDS PYTHON SERIES
To visualize and make sense of this data, we use Matplotlib, a simple Python data visualization library, to output several time series charts. This results in a table with an interest level for each of the overall topics of interest, which can then be visualized to compare and explore patterns in and between each topic. Once the data has been pulled for every keyword, it then averages the overall interest level across all keywords in each grouping. Example populated output data table Processing and visualizing the data It then populates the table with the returned interest data. The process of pulling the interest levels via a Google Trends API is automated by iterating through and making requests to the API for each keyword in the input table. weekly) for which they would like the data. A user can also specify filters like the timeframe (e.g. The script takes in a CSV of hundreds of keywords and their groupings as an input (example below). In order to efficiently pull the required data to draw meaningful insights, we built a script that requests Google Trends insights data for a desired set of keywords and relevant topic of interest, and then visualize these search trends. Here at Ayima, we build custom Python scripts, allowing our clients to pull data from a variety of sources at any desired level of aggregation.
GOOGLE TRENDS PYTHON FULL
It allows for keywords to be grouped together forming higher-level topics of interest, and the analysis and comparison of trends across these groupings, rather than just looking at individual terms and not getting a full and representative picture of the overall trend. Here is where being able to use an API to automate and pull data for a customized set of keywords can be very powerful. This is a very tedious and inefficient task. To paint a bigger picture of a specific topic of interest, which requires comparing more than just the allowed 5 keywords, you would need to manually grab data for potentially hundreds or thousands of individual queries.
Google holds a lot of powerful data, but only a fragment of the data is shown in the Google Trends interface. It can be used to do keyword research when producing new content, determining seasonal trends to help decide when to launch new products, figuring out what topics were just a short-lived-fad and are no longer trending (whipped coffee and Tiger King perhaps) and so much more. The data from Google Trends can provide extremely valuable insights for many different use cases. For the year 2020, the top trending search queries globally were “Coronavirus” and “Election Results”. Google Trends is a trusted tool that analyzes the popularity of some of the top search queries.
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