Keyword Performance Dataset Overview

Gathered over 7 months of a worldwide Google Ads campaign targeting keywords related to AI software. The dataset provides insights into search terms that triggered ads, their performance, and associated costs. Each row in the dataset corresponds to a unique search term and its respective metrics. Below is a description of each column in the dataset:

1. Search term: The specific query entered by a user that triggered the ad. This reflects actual user intent and can reveal new opportunities for targeting or refining keywords.

2. Match type: Indicates how closely the search term matches the targeted keyword. Common match types include:

   • Exact Match: The search term matches the keyword exactly.
   • Phrase Match: The search term contains the keyword or a close variation in sequence.
   • Broad Match: The search term is loosely related to the keyword, providing a wider reach.

3. Impr. (Impressions): The number of times the ad was displayed to users. This metric helps gauge the visibility of the ad for each search term.

4. Clicks: The number of times users clicked on the ad after seeing it. This measures engagement and relevance of the ad to the search term.

5. Currency code: The currency in which the campaign costs are reported (e.g., USD, EUR). It ensures financial metrics like cost-per-click (CPC) are appropriately understood.

6. Avg. CPC (Average Cost-Per-Click): The average amount paid for each click on the ad triggered by the search term. It provides insights into the cost-efficiency of the campaign.

7. Keyword: The targeted keyword that matched the search term. Understanding the relationship between the keyword and the search term can inform optimizations, such as adding negative keywords or refining match types.

This dataset provides a foundation for analyzing campaign performance, identifying trends, and optimizing ad spend. By exploring metrics like impressions, clicks, and cost-per-click, advertisers can refine targeting and improve ROI.

Google Trends on selected keywords from 2017M1-2023M1 on monthly basis. The trends represent how popular a given Google Search is, in this case, it has been taken worldwide.

Each column represents the trends for a given 'Google Search'.

The data is gathered from https://trends.google.com/trends/ .

The HadAT2 data are global radiosonde gridded temperature anomalies at standard levels (850, 700, 500, 300, 200, 150, 100, 50, and 30hPa) in the troposphere and in the lower stratosphere from 1958 to December 2012. This monthly timeseries are available on a 10 degree longitude by 5 degree latitude basis. This dataset supersedes the HadRT dataset. All values are anomalies relative to the monthly 1966-95 climatology.

This dataset contains comprehensive information on global fitness trends from 2018 to 2023 based on Google Trends data. It includes monthly data on the popularity of various fitness-related keywords such as 'workout', 'home workout', 'gym workout', and 'home gym', both globally and by country. The data is structured to help analyze trends in fitness-related activities and products over five years.

The HadAT1 data are global radiosonde gridded temperature anomalies at standard levels (850, 700, 500, 300, 200, 150, 100, 50, and 30hPa) in the troposphere and in the lower stratosphere from 1958 to December 2002. This monthly timeseries are available on a 10 degree longitude by 5 degree latitude basis. This dataset supersedes the HadRT dataset. All values are anomalies relative to the monthly 1966-95 climatology.