Search analytics (Ofer Abarbanel online library)

Search analytics is the use of search data to investigate particular interactions among Web searchers, the search engine, or the content during searching episodes.[1] The resulting analysis and aggregation of search engine statistics can be used in search engine marketing (SEM) and search engine optimization (SEO). In other words, search analytics helps website owners understand and improve their performance on search engines, for example identifying highly valuable site visitors,[2] or understanding user intent.

[3] Search analytics includes search volume trends and analysis, reverse searching (entering websites to see their keywords), keyword monitoring, search result and advertisement history, advertisement spending statistics, website comparisons, affiliate marketing statistics, multivariate ad testing, et al.[4]


Service Date Started Cost/mo Data Collection Data Verification Reverse Search Search Vol. Search Vol. History Keyword Monitoring Result History Advertisement History Ad Spending Website Comparisons Affiliate Stats Multivariate Testing
Google Trends 2004 $0 Owns Data Not applicable Top 10 Relative Yes No No No No Yes No No
Google Insights 2008-8-5 to 2012-9-27 $0 Owns Data Not applicable No Relative Yes No No No No Yes No No 2008-7-4[5] $500 ISP No Yes Yes No No No No Yes Yes No No
SpyFu 2005-5-5[6] $60 Scraping Cached SERPs Yes Yes No No Yes Yes Yes Yes No No
Serpstat 2015 $49 Scraping Cached SERPs Yes Yes Yes No Yes No Yes Yes Yes No
Moz 2004 $79 Scraping Cached SERPs Yes Yes Yes Yes Yes No No Yes No No

Last updated: 2020-04-25

Data collection

Search analytics data can be collected in several ways. Search engines provide access to their own data with services such as Google Analytics,[7] Google Trends and Google Insights. Third party services must collect their data from ISP’s, phoning home software, or from scraping search engines. Getting traffic statistics from ISP’s and phone homes provides for broader reporting of web traffic in addition to search analytics. Services that perform keyword monitoring only scrape a limited set of search results depending on their clients’ needs. Services providing reverse search however, must scrape a large set of keywords from the search engines, usually in the millions, to find the keywords that everyone is using.

Since search results, especially advertisements, differ depending on where you are searching from, data collection methods have to account for geographic location. Keyword monitors do this more easily since they typically know what location their client is targeting. However, to get an exhaustive reverse search, several locations need to be scraped for the same keyword.


Search analytics accuracy depends on service being used, data collection method, and data freshness. Google releases its own data, but only in an aggregated way and often without assigning absolute values such as number of visitors to its graphs.[8] ISP logs and phone home methods are accurate for the population they sample, so sample size and demographics must be adequate to accurately represent the larger population. Scraping results can be highly accurate, especially when looking at the non-paid, organic search results. Paid results, from Google Adwords for example, are often different for the same search depending on the time, geographic location, and history of searches from a particular computer. This means that scraping advertisers can be hit or miss.

Market conditions

Taking a look at Google Insights to gauge the popularity of these services shows that compared to searches for the term Adwords (Google’s popular search ad system), use of search analytics services is still very low, around 1-25% as of Oct. 2009.[9] This could point to a large opportunity for the users and makers of search analytics given that services have existed since 2004 with several new services being started since.


  1. ^Jansen, B. J. 2006. Search log analysis: What is it; what’s been done; how to do it. Library and Information Science Research, 28(3), 407-432.
  2. ^Ortiz-Cordova, A. and Jansen, B. J. (2012) Classifying Web Search Queries in Order to Identify High Revenue Generating Customers. Journal of the American Society for Information Sciences and Technology 63(7), 1426 – 1441.
  3. ^Rose, D.E., & Levinson, D. (2004). Understanding user goals in web search. In S. Feldman, M. Uretsky, M. Najork, & C. Wills (Eds.). Proceedings of the World Wide Web Conference (WWW ’04) (pp. 13–19),. New York: ACM
  4. ^Felix, R., Rauschnabel, P.A.; Hinsch, C. (2016). “Elements of Strategic Social Media Marketing: A Holistic Framework”. Journal of Business Research. 70: 118–126. doi:10.1016/j.jbusres.2016.05.001.
  5. ^“Internet Archive Wayback Machine”. Archived from the original on 2016-06-02. Retrieved 2012-07-09.
  6. ^“GoogSpy really serious competitor research”. Archived from the original on 2011-08-07. Retrieved 2012-12-30.
  7. ^“Analytics Tools & Solutions for Your Business – Google Analytics”. Retrieved 2018-09-03.
  8. ^“About Google Trends – Google Trends”. Retrieved 2012-07-09.
  9. ^“Google Trends”. Retrieved 2012-12-30.
  10. ^Google Analytics: How Site Search metrics are calculated?

Ofer Abarbanel online library

Ofer Abarbanel online library

Ofer Abarbanel online library