![Apply selected Linguistic Features
• Automatic language detection
• Approximate matching (spell checking) “cort”, “court”
• Lemmatization Noun: “car” “cars”
Verb: “break” “break”, “breaks”, “broke”
•
• Synonyms “color” = “colour”
“car” = “automobile”
• Proper Name and Phrasing /Spellcheck “Venus Williams”, “French Open”
• Anti-phrasing (Stopwords) “[I want a] Nikon camera”
• Character Normalization “Molière -> Moliere”
• Tokenization (CJK support) “market-shares” -> “market shares”
• Phonetic Search “Eyvind”, “Oyvind” -> “Eyvind”
• Automatic spelltuning Based on index contents
When implemented properly can drastically improve the
usefulness of a search](https://image.slidesharecdn.com/searchanalyticscomperio-130524021325-phpapp01/75/Search-Analytics-Comperio-21-2048.jpg)
Uploaded byComperio - Search Matters.
Search Analytics - Comperio
This document discusses search analytics and best practices for improving search experiences. It begins by distinguishing between web analytics, which shows what users do, and search analytics, which shows their intent. The document then outlines various challenges with search like inability to find information, outdated content, and poor user experience. It recommends monitoring search logs to understand frequent and unique queries as well as those with no results. The rest of the document provides tips for tuning relevancy, defining search goals and scope, applying filters and boosts, and establishing a search team for ongoing maintenance. It emphasizes the importance of search analytics for understanding users and improving search quality.
More Related Content
Similar to Search Analytics - Comperio
Search Analytics - Comperio
- 1. OSLO STOCKHOLM LONDONBOSTON SINGAPORE Search Analytics Comperio - Seminar on Searchdriven Websites and Analytics of Searchlogs Stockholm Digital Days 2013-05-22 Bo Engren
- 2. Agenda • What isSearch(log) Analytics? • Improving Search • Best Practices & Administration • QA
- 3. Web Analytics vs.Search Analytics The difference between Web Analytics and Search Analytics is that Web shows what the users actually have been doing, Search shows their intent. (and btw Search Analytics isn’t SEO either)
- 4. The challenges withSearch I can’t find what I’m looking for Content is old Duplicates and versions Not maintained Too many choices Language and domain vocabulary Poor user experience etc…
- 5. The relevancy threshold Byraising the relevance with 40%, we can move the search solution from low to high trust.
- 6. Tuning relevancy -toolboxes
- 7.
- 8. Best Practices &Administration
- 9. Operational steps forgood search DEFINE SCOPE IMPLEMENT RELEASE MAINTAIN Understand business needs • Understand what you are trying to achieve • Plan and define goals • Identify good trends, ROI Measure and refine • Monitor and use query information • Mine query logs • Measure effectiveness of search towards a target Output and benefits • Better search • Better results • Enhanced usability • Enhanced revenues Search customer
- 10. Analyzing search logs– fundamentals When you have defined your business needs Monitor your search logs... ...again and again and again Look for • Specific queries • General queries • Queries with zero results • Filter away junk!
- 11. Know your searchdistribution 350 10.0000 0 500 20% 80% Similar searches Unique searches Frequency Query term Can we find patterns in this type of searches? Take good care of your top queries
- 12. Frequent queries Visualized Searchhistory. Most frequent query terms
- 13. Unique queries example Alot of product code searches
- 14.
- 15. Zero Result Queries 9.95%of today’s queries return no results Create a synonym for the query Select time period
- 16. Empty result sets Howdo we fix empty result sets? • Investigate why! – Spelling errors? – Semantics? – UI difficulties? • Correct the underlying causes
- 17.
- 18. Top/Frequent queries How dowe serve frequent queries best? • Ensure good relevance • Apply best bets • If ambient, present options to narrow results • If specific, make sure user get to the goal
- 19. Content Search -Refiners • Filters are based on words in documents • Words are used to tag the document with predefined set of Filter names Result Refiners Enables filtering
- 20. Boosts and Blocks •Boosting is the process of changing the “natural” rank to alter the position of a document within the result set
- 21. Apply selected LinguisticFeatures • Automatic language detection • Approximate matching (spell checking) “cort”, “court” • Lemmatization Noun: “car” “cars” Verb: “break” “break”, “breaks”, “broke” • • Synonyms “color” = “colour” “car” = “automobile” • Proper Name and Phrasing /Spellcheck “Venus Williams”, “French Open” • Anti-phrasing (Stopwords) “[I want a] Nikon camera” • Character Normalization “Molière -> Moliere” • Tokenization (CJK support) “market-shares” -> “market shares” • Phonetic Search “Eyvind”, “Oyvind” -> “Eyvind” • Automatic spelltuning Based on index contents When implemented properly can drastically improve the usefulness of a search
- 22. Search statistics –several tools available • Start with the searchlogs: – Use the built in tools – Loggparsers (IIS loggparser etc.) – Webanalytics tools (Google Analytics, Webtrends etc.) – Log management (logstash, kibana) – Big data (Hadoop, pig)
- 23. Visual searchresults Comperio internalKnowledge Management DB February 2013
- 24. Statistic analysis –Best Practice • Zero hit results key to monitor and remove • Analyze the Top queries • Trends over time – group by day/week/month • Separate internal and external searches • Group the queries for better understanding (for example products, documents, persons)
- 25. Examples of Metricsfor Search Analytics – select a few initally Search perspective Measures Definition Metric type Total queries Total number of search queries # Clicks Total number of clicks that goes from search results to final file or page # Satisfied queries Percentage of search results with at least one click % Opportunity queries Percentage of search results with no click % Visits with keyword searches Percentage of web visitors that use search % Visits with guided product search Percentage of web visitors that use guided product search % Visits with browsing searches Percentage of web visitors that use browseing searches e.g. listings % Search result exits Percentage of web visitors that exit the website on the search result page % Searches with zero results Percentage of searches that end up with zero results % Search depth Depth after search result page # Refined searches Number of searches refined with new query text after result view # Result relevancy Relevancy of search results, based on recall/precision test model and test set # Query suggestion use Number of searches performed with suggested queries # Related queries Number of searches with related queries used # Filtered queries Number of searches with query refinement filters # Time to destination Time spent from search to final result Time Result sidebar use Percentage of clicks on sidebar results on result page views % Advanced queries Number of advanced queries performed with boolean or filter operators # Best bets use Percentage of clicks on manual top results when displayed %
- 26. Improve results ofsearches - Best Practice Improve similar searches (fat head) • Autocomplete • Best bets Improve uniqe searches (long tail) • Spellchecking • Synonyms • Adjust your content
- 27. Internal searches –do we understand the context of the user? • Start with the User – Study/test your User Stories. Example: You are going to start a new project. Do you find what you need to get started? – Use Online surverys for deeper insights
- 28. All search platformsneed maintenance • A team that specializes in search and related technologies – Front end search specialists – Search analysts • Examples of Tasks – Sounding board for proposed projects or reported problems – Cataloguing agreed search best practice – Control vocabularies and taxonomies – Monitoring and tuning – In-house training
- 29. Search Analytics –Summary 1 • Make someone responsible for search - Appoint a Search Manager • Set a search strategy which enables the business strategy and is in line with overall IT-strategy • Make the Business Case • Measure and Monitor Search Queries = Search Analytics • Enable User Feedback • Raise quality of information by adding metadata and doing content lifecycle management • Add metadata - manual, mandatory or automatic?
- 30. Search Analytics -Summary 2 • Establish processes to deliver feedback to your Stakeholders regarding the search logs – Separate External and Internal sites? • Educate information creators - simple handouts and sit-downs • Apply spelling suggestions, key-matches and auto- complete • What can we do as Editors and what do we need Techies to do? – You can do more than you think!
- 31. Thanks for listening andtime for QA!