Your SlideShare is downloading. ×
Search sense interactive fuzzy search (venture lab 2012)
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

Search sense interactive fuzzy search (venture lab 2012)

934

Published on

SearchSense Interactive Fuzzy Search (Venture Lab 2012) - Fuzzy /Approximate Search

SearchSense Interactive Fuzzy Search (Venture Lab 2012) - Fuzzy /Approximate Search

Published in: Technology, Design
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
934
On Slideshare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
7
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide
  • Good e-commerce searches do more than just retrieve the right products. They must present the shopper with the right amount of descriptive information about the product to enable selection and decision making. The study showed that during the holidays (a peak time for mission-driven visits as opposed to casual browsing visits) “site searchers converted at a rate of 7.54% while all visitors converted at 2.79%.”
  • Transcript

    • 1. SearchSense Interactive Fuzzy Search Solution for Making Site Search Usable Venture Lab 2012
    • 2. Agenda  SearchSense Team  The Market  Problem  Impact  SearchSense Business Proposition  Business Proposition Hypothesis  Experiment  Hypothesis Results  Demo  User Behavior Adaptation
    • 3. SearchSense Team  Naveen Grover (Team Lead) naveengrover2000@gmail.com  Hery Zo Rakotondramanana heryzo@gmail.com  Lokesh KUMAR email.lokesh.kumar@gmail.com  Sonya Fox sonya.fox@gmail.com  Lem Griffin lemuel.griffin@gmail.com  Kamal Pasha Shaikh shaikhkamal@gmail.com  Samir Carecho digiwise@gmail.com  Jonathan Tanner ronin@ronindesign.net
    • 4. The Market | Non Search Engine Market Although Search Engines Are Still the Major Players, Search Isn’t Confined to the Engines Anymore Site search (non-search engine search) continues to show growth, but still represents a smaller percentage of overall searches.Source: comScore qSearch 2.0; “Search Engines” defined as properties falling under the Search/Navigation category in qSearch , January 2011
    • 5. The Market | Long Tail of SearchAddressing discoverability Seventy percent of queries in commercial searches are “long tail”queries, the sheer magnitude of which defies the labor-intensive efforts used for “head” terms.http://www.seomoz.org/blog/illustrating-the-long-tail
    • 6. The Market | Smart Phone Local Search and Buying BehaviorSource : 5th Annual 15miles/Localeze Local Search Usage Study Conducted by comScore February, 2012
    • 7. The Market | Primary Source for Local Business Information Consumers utilize multiple media sources when conducting a local search. 15miles/comScore Local Search Usage Study, 2010
    • 8. The Market | Reasons for Dissatisfaction Local Business Information Provided Source: 15miles/comScore Local Search Usage Study, 2010
    • 9. The Market | Site Search & Shopping BehaviorSource : The Value of Retail Search and Position Conducted by comScore Sponsored by Searchandise Commerce and iProspect July 2010
    • 10. The Market | User Behaviors - Number of ClicksThe Value of Retail Search and Position Conducted by comScore Sponsored by Searchandise Commerce and iProspect July 2010
    • 11. The Market | Site Search Page Perception of Top SectionSource : The Value of Retail Search and Position Conducted by comScore Sponsored by Searchandise Commerce and iProspect July 2010
    • 12. Problem | The Predicament From bad to worse – a small problem takes the user way off track Source : India famous IndiaTimes Shopping Site , http://shopping.indiatimes.com/ Accessed 18 May 2012
    • 13. Problem | The Predicament OR People Database Brad Pitt Queries against collection: Forest Whittacker Find all entries for “Forrest Whitaker” George Bush Find all entries for “Arnold Angelina Jolie Schwarzenegger” Arnold Schwarzeneger Find all entries for “Brittany Spears” … … …
    • 14. Problem | The Predicament Search Problem Actual queries gathered by Google* Correct Spelling 488941 Incorrect Spelling 146258 % of Problem 30%  Errors in queries  Errors in data  Search - Bring query and meaningful results* Source www.google.com/jobs/britney.html closer together
    • 15. Problem | The PredicamentSearch failure isn’t pretty… From bad to worse – a small problem takes the user way off track There is only one right way to search, but many wrong way … Which side you are !!!
    • 16. Problem | The Predicament Does this sound familiar?  It’s either all or nothing - zero results or thousands  Your site statistics show that many users try a few searches then exit the site without any other action.  In an online store, visitors frequently do not find what they are looking for, even when the item is available. The problem is usually caused by the eCommerce on-site search.  Users who found something useful by browsing on a previous visit can’t find it again when they return and use search. They are especially frustrated because they know the page exists.
    • 17. Problem | Impact eCommerce Site Search - the impact of negative search experiences  Poor search = lost revenue  Users who conduct site searches are almost three times more likely to purchase something while visiting a site – (WebSideStory study)  Users who had SUCCESSFUL site searches are twice as likely to convert (Enlighten study)  Half of all add-to-cart actions happened after a search. (Enlighten study)  Users who had NULL-RESULTS site searches were three times as likely to leave (Enlighten study)  Estimate - Up to 20% of the gains in user experience during a site redesign can be attributed to search improvements – “Laura Ramos of Forrester”  Alternative Search Properties - Apple moves into the top five with 120% intensity growth and 14% searcher growth over the past year. eBay leads with 855MM searches and 19.0 searches per searcher in January 2011; Facebook.com has the largest number of searchers (65MM). (comScore qSearch State of Search, January 2011)  Site features and overall site performance strongly affect online shopper loyalty. Online shoppers also look for rich product content, with 67% of online consumers finding this important, while 60% of consumers demand an effective site search. (August 17, 2009 eCommerce Web Site Performance Today An Updated Look At Consumer Reaction To A Poor Online Shopping Experience A commissioned study conducted by Forrester Consulting on behalf of Akamai Technologies, Inc.)
    • 18. SearchSense Business Proposition SearchSense fill the “linguistic gap”, which improve the “Findability”. It simply turns more of site visitors into buyers which means higher conversion rate, larger orders and higher revenue.
    • 19. Business Proposition HypothesisWe have tested our Value Proposition for the following HypothesisHA 1: Reduction of Failed Searches“Subjects using the SearchSense models will initiate fewer searches that fail to return anacceptable selection than subjects using any other search tool.”HA 2: Reduction of Search Refinements“Subjects using the SearchSense models will require fewer search refinements to locate theintended result items than subjects using any other search tool.”HA 3: Reduction of Search Time“Subjects using the SearchSense will take less time to complete the assigned searches thansubjects using any other search tool.”HA 4: No Significant Negative Impact on Precision“Subjects using the SearchSense will not receive significantly more search results thansubjects using any other search tool.”
    • 20. Experiment The primary research objective, to measure the effect of SearchSense, is accomplished by conducting an experiment in which a demo of SearchSense system (minimum viable product version) is provided to group of human subjects and observation being made and verbal feedback is collected to evaluate the performance of SearchSense system. Subject is asked to perform the same misspell search (linguistic problem) to other publicly available search system as well e.g. http://shopping.indiatimes.com to compare the results
    • 21. Hypothesis HA 1: Reduction of Failed Searches HA 1: Reduction of Failed Searches “Subjects using the SearchSense models will initiate fewer searches that fail to return an acceptable selection than subjects using any other search tool.” Result Analysis of the observation results demonstrates that for subjects using SearchSense system, the relevant results are shown even when subject misspell due to linguistic problem than subjects using any other publicly available search system e.g. http://shopping.indiatimes.com.
    • 22. Hypothesis HA 2: Reduction of Search Refinements HA 2: Reduction of Search Refinements “Subjects using the SearchSense models will require fewer search refinements to locate the intended result items than subjects using any other search tool.” Result Analysis of the observation results demonstrates that for subjects using SearchSense system, the relevant results are shown even when subject misspell due to linguistic problem hence subject would require fewer search refinements to locate the intended result items than subjects using any other publicly available search system e.g. http://shopping.indiatimes.com.
    • 23. Hypothesis HA 3: Reduction of Search Time HA 3: Reduction of Search Time “Subjects using the SearchSense will take less time to complete the assigned searches than subjects using any other search tool.” Result Analysis of the observation results demonstrates that for subjects using SearchSense system, the relevant results are shown even when subject misspell due to linguistic problem hence subject would require fewer search refinements to locate the intended result items than subjects using any other publicly available search system e.g. http://shopping.indiatimes.com , which causes reduction in search time per identified search term.
    • 24. Hypothesis HA 4: No Significant Negative Impact onPrecision HA 4: No Significant Negative Impact on Precision “Subjects using the SearchSense will not receive significantly more search results than subjects using any other search tool.” Result We were unable to test this hypothesis as no publicly data available against which we can test our hypothesis.
    • 25. Demo | SearchSense One Text Box for Category or Product Relevant Category and Product First Search Suggestion can be turned off Products Listing Product or Category Products Category Last Search Items Shown Search Suggest Can be turned off
    • 26. User Behavior AdaptationAdoption is ramping up quickly, but Instant is still only engaged on approximately 22% of queries Source : comScore qSearch State of Search, January 2011
    • 27. User Behavior Adaptation Instant appears to be driving users toward shorter queries Instant is clearly reducing users’ workload, but it’s also shortening the average querySource : comScore qSearch State of Search, January 2011
    • 28. Thank You!For more information please email naveengrover2000@gmail.com

    ×