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 Search analytics what why how - By Otis Gospodnetic
 Search analytics what why how - By Otis Gospodnetic
 Search analytics what why how - By Otis Gospodnetic
 Search analytics what why how - By Otis Gospodnetic
 Search analytics what why how - By Otis Gospodnetic
 Search analytics what why how - By Otis Gospodnetic
 Search analytics what why how - By Otis Gospodnetic
 Search analytics what why how - By Otis Gospodnetic
 Search analytics what why how - By Otis Gospodnetic
 Search analytics what why how - By Otis Gospodnetic
 Search analytics what why how - By Otis Gospodnetic
 Search analytics what why how - By Otis Gospodnetic
 Search analytics what why how - By Otis Gospodnetic
 Search analytics what why how - By Otis Gospodnetic
 Search analytics what why how - By Otis Gospodnetic
 Search analytics what why how - By Otis Gospodnetic
 Search analytics what why how - By Otis Gospodnetic
 Search analytics what why how - By Otis Gospodnetic
 Search analytics what why how - By Otis Gospodnetic
 Search analytics what why how - By Otis Gospodnetic
 Search analytics what why how - By Otis Gospodnetic
 Search analytics what why how - By Otis Gospodnetic
 Search analytics what why how - By Otis Gospodnetic
 Search analytics what why how - By Otis Gospodnetic
 Search analytics what why how - By Otis Gospodnetic
 Search analytics what why how - By Otis Gospodnetic
 Search analytics what why how - By Otis Gospodnetic
 Search analytics what why how - By Otis Gospodnetic
 Search analytics what why how - By Otis Gospodnetic
 Search analytics what why how - By Otis Gospodnetic
 Search analytics what why how - By Otis Gospodnetic
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Search analytics what why how - By Otis Gospodnetic

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  • 10 days of data (5K/min)
  • Transcript

    • 1. Search Analytics What? Why? How? Otis Gospodneti ć – Sematext International
    • 2. About Otis Gospodneti ć
      • Member: Apache Lucene, Solr, Nutch, Mahout
      • Author: Lucene in Action 1 & 2
      • Entrepreneur: Sematext, Simpy
    • 3. About Sematext
      • Consulting, development, support:
      • Search (Lucene, Solr, Elastic Search...)
      • Big Data (Hadoop, HBase, Voldemort...)
      • Web Crawling (Nutch)
      • Machine Learning (Mahout)
    • 4. Agenda
      • Intro: Otis & Sematext - DONE
      • What
      • Why
      • Reports
    • 5. What is Search Analytics?
      • Input: queries and clicks
      • Output: reports – over time
      • Next: actions
      • The means, not the goal
      • Ongoing, not one-off
    • 6. Search Analytics and SEO
      • Not the same
      • SA can help with SEO
    • 7. Search vs. Web Analytics
      • User intent and information needs vs. inferring
      • Hand in hand
      • Ideally you can relate data from both or even unify it
    • 8. Why Search Analytics?
      • Measure and monitor everything
      • Supports (re)design, navigation choices
      • Helps with content acquisition & enhancement
      • Improve search experience
      • Mula
    • 9. Report Groups
      • Failures vs. non-failures
      • Actionable vs. non-actionable
    • 10. Failures
      • Be aware of failures, but don't be one.
      • Zero hits
      • Low query CTR
      • High search exit rate
      • Irrelevant results
      • Over N refinements
    • 11. Report: Zero Hit Queries
      • Popular queries; percentage, not raw count
      • Misspellings?
      • Synonyms?
      • No matching content?
      • Need (different) tagging?
      • Bad analysis?
      • Multilingual issue?
    • 12. Report: Zero Hit Queries (cont.)
      • Use Query Spellchecker (aka DYM)
      • Using AutoComplete
      • Using DYM ReSearcher
      • Designing No Results page
    • 13. Report: High Exit Rate Queries
      • Disappointed, frustrated users
      • Major revenue loss, no second chance
      • Marriage with Web Analytics
      • Relevance bad?
      • Default ordering bad?
      • Titles of hits need adjusting?
      • Search terms highlighting looking bad?
      • Bad thumbnails? Need thumbnails?
    • 14. Report: Irrelevant Result Queries
      • Manual: top N hits of top N queries
      • Judge relevance of each hit and assign score
      • Per-query score: sum scores of top N hits
      • Cumulative top N query score: sum per-query scores
      • Automated: Mean Reciprocal Rank (MRR)
    • 15. Report: Total Queries
      • Search vs. navigation/browsing
      • Search vs. overall site usage
      • Related report: % of visits with search
      • Segment: new users vs. return users, etc.
      • Questions: do you count paging? Facet selection? Re-sorting?
    • 16. Report: Total Distinct Queries
      • What's distinct? Car vs. Cars
      • # Total Queries / # Distinct Queries = Avg. #
      • Tied to performance and query cache utilization
      • Extension: Total distinct words in queries
    • 17. Report: Words Per Query
      • Informative, slowly changing, not terribly actionable
      • Can affect search box size
      • Use AutoComplete if queries are long
    • 18. Report: Top Queries
      • User intent and information needs
      • Ensure good results
      • Calculate MRR for top N queries
      • Calculate MRR for each top N query
      • Compare to global MRR
    • 19. Report: Top Queries (cont.)
      • New top queries – new trend? New demand?
      • Best Bets (aka Query Elevation in Solr)
      • Expose before search is needed
      • Seasonality – hour of day, day of the week, etc.
        • Adjust content presentation and availability (e.g. week vs. weekend, business vs. personal)
        • Anticipate demand in the next cycle
    • 20. Clickstream Analysis
      • Query analysis is not a complete story:
        • Queries
        • Clicks
        • (Trans)action
    • 21. Query and Hit Valuation
      • Query: by popularity (count)
      • Query: by CTR
      • Query: by subsequent (trans)action count/pct.
      • Hit: by click count
      • Hit: by subsequent (trans)action count/pct.
    • 22. Query and Hit Valuation (cont.)
      • Maximize: pop(query) + ctr(query) + action(query) clicks(hit) + action(hit)
      • Failures: high pop(q), yet low ctr(q)
      • high pop(q), high ctr(q), yet low action(q)
      • Integration with backend required
    • 23. Report: Low CTR Queries
      • Popular queries; percentage, not raw count
      • Relevance bad?
      • Default ordering bad?
      • Titles of hits need adjusting?
      • Search terms highlighting looking bad?
      • Bad thumbnails? Need thumbnails?
    • 24. Report: Queries with Most Clicks
      • i.e. Queries with Highest CTR
      • Informative? Yes
      • Actionable? Somewhat: expose relevant content outside of search
    • 25. Search Session
      • Search activity aimed at satisfying a specific information need in a some limited amount of time.
      • i.e. it's very fuzzy
    • 26. Interesting Search Sessions
      • More than N queries in M minutes
      • Sessions that end in a failure
      • Sessions for specific type of info (e.g. person name, product name, event)
    • 27. Segmentation
      • Searches that resulted in conversion vs. not
      • Search metrics for
      • New vs. returning visitors
      • English vs. French vs. Spanish vs. …
      • Chrome vs. IE
      • ...
    • 28. More SA Reports/Questions
      • % of queries from DYM vs. AC vs. typed
      • Most common queries per clicked hit
      • Which hits are generally popular?
      • Which hits are trending up?
      • Are there docs that are never ever clicked on?
      • Average number of queries per session
      • Breakdown of queries by number of hits
    • 29. More SA Reports/Questions
      • Breakdown of queries by latency
      • Frequently used facets or sort criteria
      • Avg number of clicks per query
      • Time spent on site before/after searching
      • Search initiation pages
      • How deep into SERPs are people drilling?
      • Are too many clicks on pages other than 1 st ?
      • ...
    • 30. Data Collection
    • 31.
      • sematext.com
      • blog.sematext.com
      • @ sematext
      • @ otisg
      • [email_address]
      Contact

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