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SEARCH ENGINE RANKINGS
Internal Search
FOR E-COMMERCE
2016
GET STARTED
Apache SOLR: full text search capabilities and rich document handling
Elastic Search: schema free, REST and JSON based doc...
Search experience and performance are heavily influenced by non-visible factors,
such as search logic and product data int...
Out of the 50 top grossing US ecommerce websites, few provide a great internal search experience.
The state of e-commerce
...
1.Gauging the competition’s search experience requires extensive testing
and evaluation.
2.That means that your internal s...
Half prefer to use on-page navigation while 47% prefer to filter down on
the product page.
Onsite search vs Onpage navigat...
65% of test subjects required 2 or more attempts to complete their search
Reality check
7
•Navigational: reach a specific page
•Informational: acquire information
•Transactional: perform a web-
mediated activity....
• Location – top right hand corner
• Simple search + link to advanced
options
• Case sensitivity
• Search labels – just ca...
Websites with semantic search engines
have lower rates of cart abandonment.
Why?
« red sneakers size 9 » shows a search
in...
• Keep the search query
• Give filterable options
• Go beyond generic filters
• Have clear titles and descriptions
Best pr...
Takeaway: don't force users into a
tunnel of limited search results.
Let them check, uncheck, clear, refine
their way to a...
• Search Analytics: get data on the way users
search on your site
• Let search behavior guide site structure for
better in...
• Unique URLs for each search result
for long tail SEO traffic.
• Use search result pages for PPC
• Google Search Console ...
Google Search Box
15
Here are 7 points to get started:
•Avoid returning low relevance results
•Map synonyms & misspellings
•Map symbols, abbrev...
There are 3 highlighters:
•Standard Highlighter: The swiss-army knife of the highlighters.
•FastVector Highlighter: it wor...
Tip: for better results and performance go for a fuzziness parameter of 1 (string of 3, 4
or 5 characters).
Typos and Miss...
Solr has a Phonetic Filter that has the double methaphone algorithm.
The SpellCheck component helps provides inline query ...
• Audit auto-suggestion from the
website’s search logs
• Machine learning should be based
on the success rate of a query
•...
Aim for 6 out of these 8 things:
•Style Auxiliary Data Differently
•Avoid Scrollbars – show 10 items max
•Highlight the di...
The Completion Suggest feature is built for extreme speed
(at query time).
You can find out how to do all this here:
https...
Persistent search makes the
iteration process less frustrating.
Persistent search
23
Faceted Search
24
Users can’t always
Specify their queries
Think of design details
And filtering logic
Faceted Search
offe...
Faceted Search
25
Dynamic
Labelling system
Map filtering types to
the users' purchasing
parameters
Provide product
specifi...
Damned if you do, damned if you don’t!
Putting everything in no-index or letting everything be
crawlable are not good SEO ...
Let’s go for aggregations to sculpt
precise multi-level calculations
that occur at query time within a
single request.
Mul...
Hierarchical breadcrumbs are great for non-linear navigation
History-based breadcrumbs give a way to go back to search res...
SEARCH ENGINE RANKINGS
Users combine 12 query types mapped in 3 groups:
• Spectrum
• Qualifiers
• Structure
The Search Que...
• Query spectrum: base of the search
query.
• Query qualifiers: refine the
boundaries of the query spectrum.
• Query struc...
1. Exact search
2. Product type search
3. Symptom search
4. Non product search
Query Spectrum – Setting the range
31
The q...
• Product title or number search
• Handle phonetic mistakes, products
having alternative titles.
The logic should search t...
Product type query: the user knows
the type of product he/she wants but
not the particular product.
This requires:
•Detail...
Synonym mapping can be added two ways:
•Two comma-separated lists of words with the symbol “=>” between them.
•A comma-sep...
Users look to solve specific problems and want products to help them solve it.
Symptom Searches
35
Search engines should also handle auxiliary content search like that as often users will
have a hard time finding these in...
Conditions for what should and/or shouldn’t be included.
Thematic, compatibility and subjective searches are a little more...
Feature is the most common qualifier.
Feature definition: any type of product
aspect or attribute.
• Color
• Material
• Pe...
This a common browsing pattern and product arrangement in physical retail.
Good product categorization and labelling will ...
Relational searches are searches where users enter the name of entities involved with or related to the
product.
Relationa...
Users often don’t know the name of
the accessory or spare part they need
– instead they know the details of the
product th...
Subjective qualifiers like “high-quality” or “cheap” are often vital to the user’s
purchase decision.
Tips
1.Approximate u...
1. Slang, abbreviations and symbol search
2. Implicit search
3. Natural language search
Query Structure – Constructing the...
Users rely on a wide range of linguistic
shortcuts when they search.
Slang and abbreviations are easier to
support.
Symbol...
Detected implied components can alter
the search experience.
•Bias when a search is done from a
category.
•Suggest relevan...
It’s about understanding semantics, context and relationships of the query instead of
parsing the query as a set of keywor...
Start with these 5 query types:
•#1 Exact
•#2 Product Type
•#5 Feature
•#6 Thematic
•#7 Relational Searches
Not investing ...
Query spectrums (what should be searched)
48
Query Type User behavior How you can support it
#1. Exact Search
“Keurig K45”...
Query Qualifiers (specify condition)
49
Query Type User behavior How you can support it
#5. Feature Search
“Waterproof cam...
Query structure (how the query is constructed)
50
Query Type User behavior How you can support it
#10. Slang, Abbreviation...
Other e-commerce internal search solutions
51
Always think of query types when setting up internal search.
Don’t rely on static layouts shared for category products and...
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UX: internal search for e-commerce

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Find out how to improve internal search engines for e-commerce websites.

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UX: internal search for e-commerce

  1. 1. SEARCH ENGINE RANKINGS Internal Search FOR E-COMMERCE 2016 GET STARTED
  2. 2. Apache SOLR: full text search capabilities and rich document handling Elastic Search: schema free, REST and JSON based document store. Internal Search Engines in this presentation 2
  3. 3. Search experience and performance are heavily influenced by non-visible factors, such as search logic and product data integration. Intro 3
  4. 4. Out of the 50 top grossing US ecommerce websites, few provide a great internal search experience. The state of e-commerce 4 82% have auto-complete… 36% of these are detrimental to UX 70% require jargon Only 40% offer Faceted search 60% fail with abbreviations Or symbols 16% fail with model or product number searches 18% fail with misspellings
  5. 5. 1.Gauging the competition’s search experience requires extensive testing and evaluation. 2.That means that your internal search efforts can’t be easily copied by competitors. 3.Poorly performing search experience can be pretty. A few things to keep in mind 5
  6. 6. Half prefer to use on-page navigation while 47% prefer to filter down on the product page. Onsite search vs Onpage navigation 6
  7. 7. 65% of test subjects required 2 or more attempts to complete their search Reality check 7
  8. 8. •Navigational: reach a specific page •Informational: acquire information •Transactional: perform a web- mediated activity. 3 Categories of « intent » 8 These are the same ”intents” as for generic web searches Navigational Transactional Informational
  9. 9. • Location – top right hand corner • Simple search + link to advanced options • Case sensitivity • Search labels – just call it search! • Put text in the search box • Search bar in a different color Best practices 101 – Search Engine 9 LOCATION SEARCH LABELS CASE SENSITIVITY DIFFERENT COLOR TEXT IN SEARCH BOX SIMPLE SEARCH ADVANCED OPTIONS
  10. 10. Websites with semantic search engines have lower rates of cart abandonment. Why? « red sneakers size 9 » shows a search intent that’s further along the conversion path than « sneakers » Best practices 101 – Search Engine 10 LONG TAIL SEMANTIC SEARCHES HIGHLIGHT RESULTS AUTOCOMPLETE COMMON MISPELLINGS
  11. 11. • Keep the search query • Give filterable options • Go beyond generic filters • Have clear titles and descriptions Best practices 101 – Search Result Pages 11
  12. 12. Takeaway: don't force users into a tunnel of limited search results. Let them check, uncheck, clear, refine their way to a better search. That often leads to higher conversions. Best practices 101 – Search Result Pages 12 • Fine tune the number and presentation of search results • Fine tune how and what gets returned
  13. 13. • Search Analytics: get data on the way users search on your site • Let search behavior guide site structure for better information architecture & UX • Let search behavior guide site content • Using constrained search to reflect a strict information architecture in search Search is good for the soul…and the site 13 DIG INTO SEARCH ANALYTICS USE CONSTRAINED SEARCH LET SEARCH BEHAVIOR GUIDE SITE STRUCTURE LET SEARCH BEHAVIOR GUIDE SITE CONTENT
  14. 14. • Unique URLs for each search result for long tail SEO traffic. • Use search result pages for PPC • Google Search Console for keywords • Google's “sitelinks search box” • Highjack search queries Search Engine Optimization and Marketing Tips 14 Unique URLs Use search keywords PPC Landing Pages Highjack Search Queries Sitelinks Searchbox
  15. 15. Google Search Box 15
  16. 16. Here are 7 points to get started: •Avoid returning low relevance results •Map synonyms & misspellings •Map symbols, abbreviations •Audit auto-suggestions •Allow users to iterate •Implement faceted search •Provide hierarchical breadcrumbs & history-based breadcrumbs Search: a competitive advantage 16 BROADEN THE SCOPE SMART AUTOSUGGESTIONS SYSNONYMS AND MISSPELLINGS FACETED SEARCH PREFILL SEARCH FIELD ABBREVIATIONS AND SYMBOLS BREADCRUMBS
  17. 17. There are 3 highlighters: •Standard Highlighter: The swiss-army knife of the highlighters. •FastVector Highlighter: it works better for more languages than the standard highlighter. •Postings Highlighter: This highlighter a good choice for classic full-text keyword search. https://cwiki.apache.org/confluence/display/solr/Highlighting Highlighting Results in Solr 17
  18. 18. Tip: for better results and performance go for a fuzziness parameter of 1 (string of 3, 4 or 5 characters). Typos and Misspellings in Elastic Search 18 Fuzzy matching allows for query-time matching of misspelled words. It functions by building a Levenshtein automaton (big graph with all the strings) of the original string. https://www.elastic.co/guide/en/elasticsearch/guide/current/fuzziness.html
  19. 19. Solr has a Phonetic Filter that has the double methaphone algorithm. The SpellCheck component helps provides inline query suggestions based on other, similar, terms. You can do this with terms in a field in Solr, externally created text files, or fields in other Lucene indexes. SOLR and misspellings 19 https://cwiki.apache.org/confluence/display/solr/Spell+Checking
  20. 20. • Audit auto-suggestion from the website’s search logs • Machine learning should be based on the success rate of a query • Filter out duplicate suggestions • Allow users to iterate on auto- suggestions Auto-suggestions 20 Autocomplete affects how and what a user searches for.
  21. 21. Aim for 6 out of these 8 things: •Style Auxiliary Data Differently •Avoid Scrollbars – show 10 items max •Highlight the differences •Support Keyboard Navigation •Treat Hover Expectations as a non- committal actions •Show Search History CSS :visited selector •Reduce Visual Noise •Consider Labels & Instructions Autocomplete design patterns 21
  22. 22. The Completion Suggest feature is built for extreme speed (at query time). You can find out how to do all this here: https://qbox.io/blog/quick-and-dirty-autocomplete-with- elasticsearch-completion-suggest Quick autocomplete with ElasticSearch 22
  23. 23. Persistent search makes the iteration process less frustrating. Persistent search 23
  24. 24. Faceted Search 24 Users can’t always Specify their queries Think of design details And filtering logic Faceted Search offers filters
  25. 25. Faceted Search 25 Dynamic Labelling system Map filtering types to the users' purchasing parameters Provide product specific filters
  26. 26. Damned if you do, damned if you don’t! Putting everything in no-index or letting everything be crawlable are not good SEO options. Faceted search and SEO 26
  27. 27. Let’s go for aggregations to sculpt precise multi-level calculations that occur at query time within a single request. Multi select within active bracket significantly improves and simplifies navigation experience for end customers. Elastic Search Facets and Aggregations 27
  28. 28. Hierarchical breadcrumbs are great for non-linear navigation History-based breadcrumbs give a way to go back to search results Breadcrumbs – Hierarchy & History 28
  29. 29. SEARCH ENGINE RANKINGS Users combine 12 query types mapped in 3 groups: • Spectrum • Qualifiers • Structure The Search Query 29
  30. 30. • Query spectrum: base of the search query. • Query qualifiers: refine the boundaries of the query spectrum. • Query structure: how it should be interpreted. Spectrum, qualifiers and structure help design search logic that aligns with user behavior and expectations.  Anatomy of a search query: spectrum, qualifiers & structure 30 QERY QUALIFIERS QUERY STRUCTURE QUERY SPECTRUM
  31. 31. 1. Exact search 2. Product type search 3. Symptom search 4. Non product search Query Spectrum – Setting the range 31 The query spectrum is used to indicate the range of what should be searched
  32. 32. • Product title or number search • Handle phonetic mistakes, products having alternative titles. The logic should search the entire data set to broaden the query’s scope. Exact Search 32 Exact search is the simplest query type
  33. 33. Product type query: the user knows the type of product he/she wants but not the particular product. This requires: •Detailed categorization & product labels •Proper handling of synonyms & alternate spellings of those groupings Product type searches 33
  34. 34. Synonym mapping can be added two ways: •Two comma-separated lists of words with the symbol “=>” between them. •A comma-separated list of words. Modify the synonyms.txt file located under the folder serversolrjcgconf: Mapping synonyms in Solr 34
  35. 35. Users look to solve specific problems and want products to help them solve it. Symptom Searches 35
  36. 36. Search engines should also handle auxiliary content search like that as often users will have a hard time finding these in the navigational links. Non-Product Searches 36
  37. 37. Conditions for what should and/or shouldn’t be included. Thematic, compatibility and subjective searches are a little more challenging from a technical perspective, but they are often used by users. Query Qualifiers – Delineating the search boundaries 37 • Feature search • Thematic search • Relational search • Compatibility search • Subjective search
  38. 38. Feature is the most common qualifier. Feature definition: any type of product aspect or attribute. • Color • Material • Performance specs • Format • Price • Brand Feature Searches 38 Tiny Cross Pattern Cushion Cross Medical Tiny
  39. 39. This a common browsing pattern and product arrangement in physical retail. Good product categorization and labelling will get you half the way there. Thematic searches 39 • Seasons • Intended usage (outdoors, office, etc.) • Occasions (birthday, wedding) • Events (NBA, Olympics)
  40. 40. Relational searches are searches where users enter the name of entities involved with or related to the product. Relational searches 40
  41. 41. Users often don’t know the name of the accessory or spare part they need – instead they know the details of the product they already own. Compatibility Searches 41 Common structure of a compatibility search: Name of brand + type of accessory or spare required
  42. 42. Subjective qualifiers like “high-quality” or “cheap” are often vital to the user’s purchase decision. Tips 1.Approximate user intent by using one or more attributes as a proxy 2.Identify an attribute that could serve as a useful proxy. Deconstruct what the query is about. Subjective Searches 42
  43. 43. 1. Slang, abbreviations and symbol search 2. Implicit search 3. Natural language search Query Structure – Constructing the query 43 It deals with how the query is constructed by the user. It cares about the context, the syntax and the search engine interpretation.
  44. 44. Users rely on a wide range of linguistic shortcuts when they search. Slang and abbreviations are easier to support. Symbols change meaning depending on the arrangement of the query. Slang, abbreviation and symbol searches 44 • Slang: “RayBan shades” • Abbreviations: “13in laptop sleeve” • Symbols: “sleeping bag -5 degrees”
  45. 45. Detected implied components can alter the search experience. •Bias when a search is done from a category. •Suggest relevant search scopes or query clarifications - “did you mean…” •Auto-refine: auto-correct to include implied components. Implicit Searches 45 Certain aspects of search queries are left out because of the user’s context.
  46. 46. It’s about understanding semantics, context and relationships of the query instead of parsing the query as a set of keywords. Natural Language Searches 46 Category: Women Product type: shoes Variation: red Variation: Size 7.5 RESULTS QUERY Red women’sRed women’s shoes 7.5shoes 7.5
  47. 47. Start with these 5 query types: •#1 Exact •#2 Product Type •#5 Feature •#6 Thematic •#7 Relational Searches Not investing in good search usability can cost sales in the short, mid and long term. Improving support for the 12 queries 47 FEATURE SEARCH THEMATIC SEARCH RELATIONAL SEARCHES EXACT SEARCH PRODUCT SEARCH
  48. 48. Query spectrums (what should be searched) 48 Query Type User behavior How you can support it #1. Exact Search “Keurig K45” Searching for specific products by title Basic keyword matching, along with support for multiple title variations and intelligent handling of misspellings #2. Product Type Search “Sandals” Searching for groups or whole categories of products Support for synonyms as well as categories that aren’t part of the site’s navigation / hierarchy #3. Symptom Search “Stained rug” Searching for products by querying for the problem they must solve Symptom database mapping “symptoms” to “cures” (i.e. problems to solutions) #4. Non- Product Search “Return policy” Searching for help pages, company information, and other non-product pages Search engine must index the entire website, not just products
  49. 49. Query Qualifiers (specify condition) 49 Query Type User behavior How you can support it #5. Feature Search “Waterproof cameras” Searching for products with specific attributes or features Intelligent parsing of product specifications (i.e. structured product data) #6. Thematic Search “Living room rug” Searching for categories or concepts that are vague in nature Interpretive labelling of products and categories #7. Relational Search “Movies starring Tom Hanks” Searching for products by their affiliation with another object Association data linking products and objects, ideally specifying the nature of the relationship too #8. Compatibility Search “Lenses for Nikon D7000” Searching for products by their compatibility with another item Compatibility database mapping compatible products to one another #9. Subjective Search “High-quality kettles” Searching for products using non- objective qualifiers Handling of quantifiable single-attribute degrees (e.g. “cheap”), quantifiable but multi-attribute mix (“value for money”), and taste-based (“delicious”) qualifiers
  50. 50. Query structure (how the query is constructed) 50 Query Type User behavior How you can support it #10. Slang, Abbreviation, and Symbol Search “Sleeping bag -10 deg.” Searching for products using various linguistic shortcuts Synonym mapping of slangs, abbreviations, and symbols, as well as interpretation of symbol intent (ranges, modifiers, etc) #11. Implicit Search “[Women’s] Pants” Forgetting to include certain qualifiers in the search query due to one’s current frame of mind All available environmental variables must be used to infer any implicit aspects of the user’s query #12. Natural Language Search “Women’s shoes that are red and available in size 7.5” Searching in full sentences rather than bundles of keywords Intelligent parsing and deconstruction of the user’s query
  51. 51. Other e-commerce internal search solutions 51
  52. 52. Always think of query types when setting up internal search. Don’t rely on static layouts shared for category products and search results. Adapt filtering and sorting to adapt to the user’s query and context. Faceted search is the foundation of a contextual filtering experience and less resource intensive than query support. Conclusion 52

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