SEARCH ENGINE RANKINGS: Query Spectrum, Qualifiers & Structure
•Descargar como PPT, PDF•
10 recomendaciones•12,560 vistas
Find out how to improve internal search engines for e-commerce websites.
Recomendados
Más contenido relacionado
La actualidad más candente
La actualidad más candente (20)
Similar a SEARCH ENGINE RANKINGS: Query Spectrum, Qualifiers & Structure
Similar a SEARCH ENGINE RANKINGS: Query Spectrum, Qualifiers & Structure (20)
Más de Myriam Jessier
Último
Último (20)
SEARCH ENGINE RANKINGS: Query Spectrum, Qualifiers & Structure
- 1. SEARCH ENGINE RANKINGS Internal Search FOR E-COMMERCE 2016 GET STARTED
- 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. Search experience and performance are heavily influenced by non-visible factors, such as search logic and product data integration. Intro 3
- 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. 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. Half prefer to use on-page navigation while 47% prefer to filter down on the product page. Onsite search vs Onpage navigation 6
- 7. 65% of test subjects required 2 or more attempts to complete their search Reality check 7
- 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. • 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. 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. • Keep the search query • Give filterable options • Go beyond generic filters • Have clear titles and descriptions Best practices 101 – Search Result Pages 11
- 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. • 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. • 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
- 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. 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. 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. 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. • 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. 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. 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. Persistent search makes the iteration process less frustrating. Persistent search 23
- 24. Faceted Search 24 Users can’t always Specify their queries Think of design details And filtering logic Faceted Search offers filters
- 25. Faceted Search 25 Dynamic Labelling system Map filtering types to the users' purchasing parameters Provide product specific filters
- 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. 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. 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. SEARCH ENGINE RANKINGS Users combine 12 query types mapped in 3 groups: • Spectrum • Qualifiers • Structure The Search Query 29
- 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. 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. • 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. 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. 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. Users look to solve specific problems and want products to help them solve it. Symptom Searches 35
- 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. 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. 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. 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. Relational searches are searches where users enter the name of entities involved with or related to the product. Relational searches 40
- 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Other e-commerce internal search solutions 51
- 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
Notas del editor
- Apache SOLR – Open Source Search Engine from Apache. Works on top of Lucene and is known for its full text search capabilities and rich document handling (Word, PDF etc.) Elastic Search – Also Open Source with Apache License, known for it being a Schema free, REST and JSON based document store. Highly scalable with powerful Distribution and sharding feature. https://blog.kissmetrics.com/ecommerce-website-search/
- Improving on-site search functionality can benefit a site by contributing to a better user experience, and by reducing the barriers for users to reach their destination page. Why internal search? It’s not SEO because you control the right content that shows up where it needs to You control how the internal search engine works - you control the set of rules that determine what the most relevant response to a user’s query should be. Prioritizing some factors Deprioritizing others Sara Wachter-Boettcher (2012-10-06T22:00:00+00:00). Content Everywhere: Strategy and Structure for Future-Ready Content (Kindle Locations 2183-2186). Rosenfeld Media. Kindle Edition. Internal search is not like Google, you can change the way you site’s search engine works, prioritizing some factors and deprioritizing others that help the right content show up where it needs to. In sum, it uses another set of rules and combs your site’s content, deciding what the most relevant response to a user’s query should be. Improving on-site search functionality can benefit a site by contributing to a better user experience, and by reducing the barriers for users to reach their destination pages. The search bar is part of the conversion journey:
- Fast, convenient, efficient: that's what e-commerce internal search should be. In a recent large-scale usability study benchmarking e-commerce websites by the Baymard Institute. six major areas of e-commerce search usability: query types, search form and logic, autocompletion, results logic, results layout, and results filtering and sorting. website’s score across the 6 to 15 guidelines within that area.good search experience: website partly adhering to all 60 of the search guidelines.mediocre search experience: website that partly adhered to 48 of the 60 guidelines. search and engines and designs can be assumed to hinder users as they search. In a recent large-scale usability study benchmarking e-commerce websites by the Baymard Institute. six major areas of e-commerce search usability: query types, search form and logic, autocompletion, results logic, results layout, and results filtering and sorting. website’s score across the 6 to 15 guidelines within that area.good search experience: website partly adhering to all 60 of the search guidelines.mediocre search experience: website that partly adhered to 48 of the 60 guidelines. search and engines and designs can be assumed to hinder users as they search.
- careful: poorly performing search experience can look as good aesthetically as high-performing search experience. gauging one’s own or a competitor’s search experience requires extensive testing and evaluation. The fact that search experience and performance are heavily influenced by non-visible factors, such as search logic and product data integration, is actually good because the competitive advantage you would gain from investing in them cannot be easily copied by competitors. So, while creating a truly great search experience will probably require substantial resources, it’s also an opportunity to create an equally substantial and lasting competitive advantage, one that competitors cannot easily piggyback on.roughly half of the 60 guidelines relate to user interface. This is especially true of the results layout and the filtering and sorting experience, which are areas that are usually easy to change but whose performance on most websites is currently below expectations.
- Out of 100 people surveyed regarding their search behaviors when looking for specific product online, half prefer to use on-page navigation while 47% prefer to filter down on the product page itself. https://blog.kissmetrics.com/ecommerce-website-search/
- https://blog.kissmetrics.com/ecommerce-website-search/
- Navigational: because they have visited the page before or because they assume such a page exists informational: Beyond the consumption of the information, no further interaction is predicted. Transactional: this type of interaction is the most difficult to evaluation. "the interaction constitutes the transaction defining these queries"
- According to a study in Retail Integration Online, websites with semantic search engines have lower rates of cart abandonment compared to sites with plain text search. Look on Wayfair or the find for an example of semantic search. This will beat plain text competitors every time. Stony de Geiter https://blog.kissmetrics.com/ecommerce-website-search/ Location: top right hand corner – follows shopper expectations. Search options: start with simple search and offer a link to advanced options. Case sensitivity: DON’T MAKE IT CASE SENSITIVE (except if you have a very good reason and that reason should be taken into account in the advanced search not the simple Search labels: label the button search and not go as it’s not inherently obvious for search. Search should be used on or near the search box. Results query: present the original search query on the results page in the search box and as a headline above the search results. This ensures the visitor knows what exactly was searched and allows them to refine it without having to retype the entire query. Misspellings: recognize all possible common misspellings. Result matches: Results should display exact matches first, with close matches second. Result highlights: It's beneficial to highlight (or bold) the words on the results page that were used in the query. Titles and descriptions: display a clear title and description pulled from page titles and meta descriptions or on-page content. Number of results: Results page should display 10-20 search results at the most, however it's a nice benefit to add an option to increase/decrease the number of results per page. Number of result pages: Links to additional search result pages should be provided and located at the top and bottom of the page. Zero results found: If no results are found, suggest alternative searches, refinement options and links to important areas of the website. You should never leave them with "no results found." Optimize title, descriptions and categories Enable predictive searches (auto-complete) Offset the searchbar in a different color to draw attention to it. Put text in the search box like "enter keyword Use 2 types of breadcrumbs: historical and categories
- . Someone searching for "red sneakers size 9" is further along the conversion path than someone browsing "sneakers". According to a study in Retail Integration Online, websites with semantic search engines have lower rates of cart abandonment compared to sites with plain text search. Look on Wayfair or the find for an example of semantic search. This will beat plain text competitors every time. Stony de Geiter https://blog.kissmetrics.com/ecommerce-website-search/ Location: top right hand corner – follows shopper expectations. Search options: start with simple search and offer a link to advanced options. Case sensitivity: DON’T MAKE IT CASE SENSITIVE (except if you have a very good reason and that reason should be taken into account in the advanced search not the simple Search labels: label the button search and not go as it’s not inherently obvious for search. Search should be used on or near the search box. Results query: present the original search query on the results page in the search box and as a headline above the search results. This ensures the visitor knows what exactly was searched and allows them to refine it without having to retype the entire query. Misspellings: recognize all possible common misspellings. Result matches: Results should display exact matches first, with close matches second. Result highlights: It's beneficial to highlight (or bold) the words on the results page that were used in the query. Titles and descriptions: display a clear title and description pulled from page titles and meta descriptions or on-page content. Number of results: Results page should display 10-20 search results at the most, however it's a nice benefit to add an option to increase/decrease the number of results per page. Number of result pages: Links to additional search result pages should be provided and located at the top and bottom of the page. Zero results found: If no results are found, suggest alternative searches, refinement options and links to important areas of the website. You should never leave them with "no results found." Optimize title, descriptions and categories Enable predictive searches (auto-complete) Offset the searchbar in a different color to draw attention to it. Put text in the search box like "enter keyword Use 2 types of breadcrumbs: historical and categories
- Keep the search query in the searchbox and add it as a headline. Avoid clearing the search history and forcing the user to redo a search from the start. Give filterable options product search results pages Size/color/style filters Allow inclusions/exclusions of sale/clearance items, new items and other popular categories Clear titles and descriptions Stony de Geiter https://blog.kissmetrics.com/ecommerce-website-search/ Location: top right hand corner – follows shopper expectations. Search options: start with simple search and offer a link to advanced options. Case sensitivity: DON’T MAKE IT CASE SENSITIVE (except if you have a very good reason and that reason should be taken into account in the advanced search not the simple Search labels: label the button search and not go as it’s not inherently obvious for search. Search should be used on or near the search box. Results query: present the original search query on the results page in the search box and as a headline above the search results. This ensures the visitor knows what exactly was searched and allows them to refine it without having to retype the entire query. Misspellings: recognize all possible common misspellings. Result matches: Results should display exact matches first, with close matches second. Result highlights: It's beneficial to highlight (or bold) the words on the results page that were used in the query. Titles and descriptions: display a clear title and description pulled from page titles and meta descriptions or on-page content. Number of results: Results page should display 10-20 search results at the most, however it's a nice benefit to add an option to increase/decrease the number of results per page. Number of result pages: Links to additional search result pages should be provided and located at the top and bottom of the page. Zero results found: If no results are found, suggest alternative searches, refinement options and links to important areas of the website. You should never leave them with "no results found." Optimize title, descriptions and categories Enable predictive searches (auto-complete) Offset the searchbar in a different color to draw attention to it. Put text in the search box like "enter keyword Use 2 types of breadcrumbs: historical and categories Using intelligent autocompletePrinterland worked with site search provider SLI Systems to add a smarter search (with autocomplete feaetures) to their website. This lead to a visitor value that's four times greater than previous sales. customers who land on autocomplete page suggestion are six times more likely to convert than those who don’t. Use 2 types of breadcrumbs: historical and categories. Everybody hates useless breadcrumbs such as Home > Search > Your Search Result. using proper breadcrumbs lets users filter their results via breadcrumbs without clearing their search and forcing them to start over. Kohl's is an example. key takeaway: don't force users into a tunnel of limited search results. They should be able to check, uncheck, clear, refine for a better search (that often lead to high conversions
- Stony de Geiter https://blog.kissmetrics.com/ecommerce-website-search/ Display 10 – 20 results per page Allow increase/decrease # of results Zero results found must offer alternatives Display matches: exact matches first & close matches after Number of result pages on the top and bottom of the page Using intelligent autocompletePrinterland worked with site search provider SLI Systems to add a smarter search (with autocomplete feaetures) to their website. This lead to a visitor value that's four times greater than previous sales. customers who land on autocomplete page suggestion are six times more likely to convert than those who don’t. Use 2 types of breadcrumbs: historical and categories. Everybody hates useless breadcrumbs such as Home > Search > Your Search Result. using proper breadcrumbs lets users filter their results via breadcrumbs without clearing their search and forcing them to start over. Kohl's is an example. key takeaway: don't force users into a tunnel of limited search results. They should be able to check, uncheck, clear, refine for a better search (that often lead to high conversions
- Dig into Search AnalyticsSearch Analytics offers a great opportunity to collect data about the way users search on your site. If site-search is tracked in Google Analytics, it should be under Content --> site search. You can get metrics such as percentage of visitors making refinements to their initial search, average time spent on time after making a search and the percentage of searchers who left your website after seeing the search results. You can also see some stats broken down by keyword. Here is some documentation to get started with GA : site search instructions. Using analytics, FootSmart improved their site search and made relevant adjustments, leading to an 82% improvement in conversion rates.Let search behavior guide site structureYou can gain insights into actionable items from this information such as the way users prefer to find content and how site architecture and navigation can be tailored to suit this behavior. you can also improve UX by helping visitors get straight to the pages which receive the most navigational search queries. Let search behavior guide site contentFor larger sites, looking at search terms that garner high volumes of searches but result in high bounce rates can give great insights as to which type of content or products users would like you to provide. Using constrained searchsearch can be considered a navigational tool that can help users find the page they need in a more effective way than browsing. websites that have a strict site architecture can reflect this in search by having a number of fields that constrain users to search in a way that matches their structure instead of having a free search text box. use Yelp search screenshotThis ensures that users enter enough search information to give them a quality result on the first search to avoid search refinement or bouncing behaviors.
- Optional: highjack search queriesTaking users directly to specific pages instead of a search result page can benefit certain websites. apply if it seems smart. For example users looking for a specific piece of content that is rather unique in your website. This can have an SEO benefit as users will be encourage to link to your content instead of search results pages. Do a weekly/monthly review of the top 100 searched for terms, identifiy navigational searches and map them to the intended target page. Unique URLs for each search resultDo not use a POST form directly to the results page or you will miss out on SEO traffic. Although Google typical avoids returning search results pages in its own results, it does happen and it can be relevant as it allows your site to compete in the long tail of web searches - like the check domain name owners websites. PPC Landing PagesSocial research shows that users searching for singular terms are further along the buying process and should be sent to a product page whereas plural searches (toasters vs tir ter) often infers a comparison search intent so the best content to offer is a ranger of options so sending PPC users to search results does make sense. Using web search keywordsGoogle's Internal Search SERP There is a feature within SERPs called "sitelinks search box". The sitelinks search box could appear below the brand website result when users search for navigational queries. This keeps people on your website.
- Here are 7 points to get started: Avoid returning low relevance results by searching he entire product data set and broadening the scope. Map synonyms & common misspellings Map symbols, abbreviations and full spellings to each other Audit auto-suggestions to weed out low-quality or redundant suggestions. Allow users to iterate by prefilling it in the search field on the results page Implement faceted search to offer filters that match users’ queries closely. Provide hierarchical breadcrumbs & history-based breadcrumbs e-commerce search isn’t user-friendly on most websites. That's an opportunity to gain a truly competitive advantage by offering a vastly superior search experience to their competitors’.7 points to get started: If few results of low relevance are returned, the search logic should broaden the scope and look for closely related spellings. the logic needs to search through the entire product data set, to include matches for product names and copied-and-pasted model numbers Map common product-type synonyms to the spellings used on your website to ensure relevant results for similar queries Map all commonly used symbols, abbreviations and full spellings to each other, so that all results are shown regardless of how something is written in the product data. Be cautious about auto-suggesting based on other users’ past queries because that often leads to low-quality and redundant suggestions. regularly check that auto-suggestions don’t lead to a dead end Allow users to easily iterate on their query by prefilling it in the search field on the results page Implement faceted search to suggest filters that match the user’s query more closely. For example, suggest product attribute filters that apply to a subset of the search results (60% of websites don’t do this). On product pages, provide both traditional hierarchical breadcrumbs (to support non-linear patterns of search) and history-based breadcrumbs, such as “Back to results"
- Solr has highlighting utilities offering control over the fields fragments are taken from, the size of fragments, and how they are formatted.
- Fuzzy matching allows for query-time matching of misspelled words. Phonetic token filters at index time can be used for sounds-like matching. It works by taking the term and building a Levenshtein automaton (big graph with all the strings) of the original string.
- https://emmaespina.wordpress.com/2011/01/31/20/
- the value isn't that it speeds up the process but that they guide users to better search queries: without typos, with the right scope for the search. 82% of websites offer it. Out of those 36% have severe usability problems. 2 main issues: query suggestions that are repetitive or lead to a dead end. auto-suggestions based on prior searches of other users or old catalog contents should be queried on a regular basis to weed out bad results. Redundant suggestions are a problem, especially when based on users' past queries. many suggestions that are redundant, or low quality, or typos are likely the result of devs sourcing suggestions from the website's search logs. You need to take into account the success of those queries. Success can be defined by whether a decent % of users found and purchased products after performing those searches. don't use search logs to generate auto-suggestions because that would result in redundant and low-quality suggestions. filter out duplicate suggestions. Allow users to iterate on auto-suggestions. implementation tipAutocomplete design and logic will alter what most users search for. Weed out dead ends and be selective in the inclusions of suggestions. it's vital. avoid suggestions based on other users' past queries, any machine learning should be based on the success rate or conversion of each query. During testing, 65% of all test subjects' attempts at searching consisted of 2 or more queries int he same search. however, 34% of ecommerce websites allow users to easily iterate on their query by prefilling the query in the search field on the results page. Make the search query persist otherwise the iteration process becomes frustrating. the users have a negative perception of a website that forces them to retype the same data within a short timeframe.
- This slide talks about suggestions not recommendations that lead a user directly to a product. Autocomplete has become a convention for ecommerce Poorly designed autocomplete distracts users These patterns don`t limit the design of the autocomplete widget Autocomplete is all about guiding a user and helping him or her construct his or her query to ensure more relevant search results Aim for 6 out of these 8 things: Style Auxiliary Data Differently from the suggested search term 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 Including Labels & Instructions – give user hints as to how to interact with autocomplete suggestions http://baymard.com/blog/autocomplete-design 82% of ecommerce website offer autocomplete It’s one of the few instances where simply copying the giants may actually be a decent strategy because users largely form their autocomplete design and interaction expectations on those sites.
- Suppose that you want to be able to retrieve search suggestions based on the tags field. The Completion Suggest feature is built precisely for this purpose. It's also built for extreme speed (at query time), which is especially important since autocomplete is a function that involves a rapid succession of many distinct requests. To properly set this up, you need to define a field of type completion in your mapping. You can find out how to do all this here: https://qbox.io/blog/quick-and-dirty-autocomplete-with-elasticsearch-completion-suggest https://qbox.io/blog/quick-and-dirty-autocomplete-with-elasticsearch-completion-suggest
- During testing, 65% of all test subjects' attempts at searching consisted of 2 or more queries in the same search.
- In a perfect world, users would make precise queries, knowing exactly what they want, and the website’s search logic would return just the right results.
- 1. Prefer a dynamic labeling system keep filter labels concise to indicate the scope-related implications of a filter. 2. Provide product-specific filters that relate directly to the user’s query (beyond price, brand or category). 3. Filter labels need to be dynamically renamed to indicate a scope if there is one implied by the search filters. 4. Map filtering types to the users' purchasing parameters like style, season and usage. “Best Practices for Designing Faceted Search Filters”). In a perfect world, we would have little need to filter and sort search results because users would make precise queries, knowing exactly what they want, and the website’s search logic would return just the right results. The site-wide search for “Tom Hanks” returned not only movies, but other product types, such as books (for example, biographies). Faceted search filters on Amazon allow users to quickly select “Movie Release Date: 2010 & newer” to see just the newest movies starring Tom Hanks. Without it, the users would have to first select a scope filter (movie, poster, book) in order to see the movie-specific filter “Release Date.” When in the “Digital Cameras” scope on Amazon, the filter labels are optimized for scannability by removing redundant scope terms, resulting in concise titles such as “Viewfinder Type,” “Image Stabilization” and so on (View large version)On the other hand, when users make a site-wide search, the (faceted) filtering suggestions are dynamically renamed to include the scope’s context, so that they now read “Camera Viewfinder Type,” “Digital Camera Image Stabilization” and so on, making it much easier for the user to infer that a category scope will be applied if selected. (View large version) only 40% have faceted search.
- Common symptoms of a non-optimal category system: The main heading of a page does not precisely reflect the filters selected. If most pages are non-indexable (canonicalise, noindex, disallowed in robots.txt) The products displayed on the page are changed with Javascript when a filter is selected, and there is not a no-js fallback You don’t have filters for attributes which your customers find important https://allotment.digital/learn/technical-seo/advanced-concepts/faceted-navigation-for-ecommerce-seo/ All products have different features or attributes. Product attributes are anything that a potential customer cares about in a product.
- The facets navigation is adaptive, allows multiple selection and it’s all one query. https://qbox.io/blog/elasticsearch-aggregations http://distinctplace.com/2014/07/29/build-zappos-like-products-facets-with-elasticsearch/
- You need 2 types of breadcrumbs to provided a good user experience. it gives users an easy way to go back to search results or to switch to a new navigational mode by going directly to the related product category. during testing, it was revealed that e-commerce websites need two different types of breadcrumb links — namely, hierarchical and history-based breadcrumbs. it gives users an easy way to go back to search results or to switch to a new navigational mode by going directly to the related product category. Without breadcrumbs, users will find it difficult to efficiently browser products. traditional hierarchical breadcrumbs are paramount for non-linear navigation such as search, because it enable users to see other products in the same category as an item in a search result. The hierarchy essentially acts as a cross-navigation link for finding related products, regardless of whether the user has accessed the category from a completely different part of the website. Users often want to go one step back after exploring a product page. History based breadcrumbs help people go back to search results. When there are only hierarchical breadcrumbs, subjects confused them as a way back to search results. IMPLEMENTATION TIP LINK Implement two types of breadcrumbs on product pages: hierarchical breadcrumbs, which allow users to infer and jump to categories that contain the current product, and history-based breadcrumbs (such as “Back to Results”), which minimize misinterpretation of hierarchical breadcrumbs as a way back to the search results.
- Users combine 12 query types that are mapped in 3 groups: Spectrum Qualifiers Structure
- Understanding the role of each aspect in a user's query helps design search logic that aligns with user behavior and expectations. Query spectrum: specifying the domain of product – provides the base of the search query. Query qualifiers: specifying conditions that the product should meet to refine the boundaries of the query spectrum. Query structure: determining how it should be interpreted by the search engine and includes the syntax and context of the query.
- Simplest query type: users search by specific product name or model number. Why? Because the search logic is probably solely based on matching keywords against the product title or main product description instead of a full data set. Acquiring all these local and alternate spellings can be challenging when vendors don't input this data on their own. Partnering with relevant industry databases can therefore be a good way to acquire this product information – especially if they are public and users are copy-pasting from them in the first place.
- This type of search must always have results, even if you don’t sell the product. Implementation tip - manually map common product type synonyms to the actual product types and category names. A better long term solution: build a keyword synonym logic that can be easily updated and tweaked (or personalized on a regular basis). Used on their own, these queries are to quickly access a category on the site. This search angles for a whole category of products, such as “Sandals”. You should enable relevant filtering and sorting options so that users can narrow down the list and compare products. to fully support type queries, the search engine's logic must go beyond exact titles and descriptions and look into the categories and product synonyms. "writing table vs writing desks". stop trying to make users follow your exact website jargon for their product queries like 70% of the websites tested. it's hard for a user to guess what the "correct" term is.
- Modify the synonyms.txt file located under the folder \server\solr\jcg\conf. Synonym mapping can be added two ways. Two comma-separated lists of words with the symbol “=>” between them. If the token matches any word on the left, then the list on the right is substituted. The original token will not be included unless it is also in the list on the right. A comma-separated list of words. If the token matches any of the words, then all the words in the list are substituted, which will include the original token. http://examples.javacodegeeks.com/enterprise-java/apache-solr/apache-solr-synonyms-example/
- Sometimes, users look to solve specific problems and want products to help them solve it. They will type their problem in search to find products. Integrating or interlinking help guides on the site on the search result pages is essential. Not applicable to all e-commerce industries but vital to drugstores, health & beauty, tools & supplies, cleaning & housekeeping, etc.
- The user is searching for something that isn’t a product, such as the return policy or shipping information. Search engines should also handle auxiliary content search like that as often users will have a hard time finding these in the navigational links.
- Query Qualifiers almost never make sense as standalone queries and therefore tends to be combined with a Query Spectrum (with the possible exceptions being #6 Thematic and #7 Relational searches). Depending on the user’s approach and knowledge about the product domain, they may specify certain #5 Features that the product must have, or describe a #6 Thematic context in which it must be used. Users may similarly try to find products by their #7 Relation to other objects, or define technical #8 Compatibility requirements. Finally, some users will inject #9 Subjective criteria that must be met (or approximated). Users rely on query qualifiers to refine and delimit the boundaries of the Query Spectrum. These qualifiers are used when the user has a set of criteria that they want the product to meet. For example, the user may limit the type of TVs by submitting a #5 Feature query such as “Plasma TV” or perform a #8 Compatibility search like “Lens for Nikon D7000” to filter out any lenses incompatible with their camera. Query qualifiers are the user’s way of “delineating the search boundaries” and includes 5 query types: #5 Feature Searches, #6 Thematic Searches, #7 Relational Searches, #8 Compatibility Searches, and #9 Subjective Searches.
- Users often have a set of criteria that they want the product to meet "leather jacket" would be a feature search, which should be understood to refer to any type of product aspect or attribute such as color, material, performance specs or format, price, brand. all significant attributes should be searchable even if all products on the site don't have the feature. During testing, this was the most common query qualifier. In terms of implementation, the ideal solution is to dynamically apply the features searched for as filters on the results page, as this increases transparency and user control – with the user being able to see what is and isn’t included and being able to quickly toggle related filters on/off.
- thematic searches are fuzzy concepts to define but they are very real notions to users. i.e. living room rug. A great deal of interpretation is required to support Thematic searches, both in terms of the meaning of the actual query itself but also in the internal tagging of products, as it’s vital that a query for e.g. “spring jacket” presents all the relevant products, not just the handful of products which happen to have those keywords in their title or description. This will often require some sort of thematic tagging of the product catalog to determine, e.g. which jackets would be suitable for spring (and which wouldn’t). Meanwhile, a search for a “Mother’s Day bouquet” requires products to be tagged with an occasion (a social concept). Despite thematic product browsing being a common browsing pattern and product arrangement in physical retail, 60% of the top e-commerce sites do not support thematic search queries. thematic searches include seasons, intended usage (outdoors, office, etc.), occasions (birthday, wedding), events (NBA, Olympics). This is yet another case where good product categorization and labelling will get you half the way there.
- This is the most commonly submitted query as a standalone query. Often it is due to the query spectrum being implicit (user takes for granted that only one type of product will show up for this relational search). For example, searching for “James Franco” could meaningfully bring up the books he’s authored, the blockbuster movies he’s acted in, or the art films he’s directed – meanwhile, the user likely had only one of those product types in mind.
- Products must be compatible with product models users own (strict compliance) Compatibility searches are similar to relation searches except that it requires strict compliance – products must be compatible with a product users already own. Integration with product finders and help content is beneficial, especially for generic queries that don’t include sufficient information to fully determine compatibility. Here a test subject has searched for “charger lenovo ideapad yoga” in hope of finding a new power adapter for her Lenovo IdeaPad Yoga laptop. Unfortunately, Best Buydoesn’t support compatibility searches, and so instead of returning “chargers” (i.e. power adapters) the site returns a bunch of Lenovo IdeaPad laptops.
- They are the trickiest to program a response to. In some cases, there are some valid solutions and in others there won’t be. Tips Approximate user intent by using one or more attributes as a proxy Identify an attribute that could serve as a useful proxy. Deconstruct what the query is about. Delicious snacks could be deconstructed: salty, sweet, spicy, etc. Like 84% of the major e-commerce sites, Wayfair doesn’t support subjective qualifiers, as is evident from this search for “high-quality kettle” which returns items with either no or poor ratings. This is despite Wayfair having plenty of kettles with 4.5+ star averages and numerous reviews, and while the first two results aren’t cheap, they are far from Wayfair’s most expensive kettles. Ex: “high-quality kettle”, one could reasonably favor products with high scores across a wide range of product attributes, such as user reviews, number of sales, and price, and use those to calculate the most relevant matches. Similarly, if a user wants to find a “cheap wine”, one could compute the total price range for “wine” and then select the bottom 15% (i.e., the cheapest) of those bottles. Or the search could simply return the “wine” category sorted by price.
- For instance, users may take certain aspects of their search for granted and leave them out of their query, resulting in #11 Implicit Searches such as “Pants” when what the user is really interested in is “Women’s Pants”. The syntax of the user’s query can be highly important too, with a symbol such as ‘-’ taking on significantly different meanings and functions in #10 Slang, Abbreviation, and Symbol Searches like “$10-100 sleeping bags” and “-10 deg. sleeping bags”. The Query Structure deals with how the user has put together their query, and consequently how it should be interpreted by the search engine. It’s about the syntax of the query and the context in which it was written. For instance, the user may apply various linguistic shortcuts in the form of #10 Slang, Abbreviations, and Symbols when they devise their search query. Other times, users may leave certain parts of their query #11 Implicit, typically because they take it for granted given their current context. Finally, as online search matures and voice input proliferates, #12 Natural Language searches will grow increasingly common – requiring search engines to take on a new level of linguistic intelligence but potentially also helping e-commerce sites approximate the level of support offered in physical retail outlets by human salespeople.
- Slang and abbreviations are the easiest type of queries to support: it requires mapping between different terms. Kicks = shoes ML = millilitre HP = Hewlett Packard Symbols are trickier as they change meaning depending on the arrangement of the query. A dash can be a minus or a range. On REI, searches for “11 foot paddleboard” and “11 ft. paddleboard” yield 0 results while “11’ paddleboard” returns 24. Due to this lack of support for slang, abbreviation, and symbol searches, it’s only the few lucky users who by chance happen to use the exact same spelling as the site that will get any results when they search.
- Certain aspects of search queries are left out because of the user’s context. You can bias when a search is performed from one of the categories. You can also suggest relevant search scopes or query clarifications - “did you mean…” You can auto-refine the user’s search query, i.e. auto-correct to include implied components. The most obvious variable is the page the user searches from – for example, if a user is in a “Women’s Dresses” category and searches for “pants”, it is reasonable to assume that the user is looking for women’s pants and not men’s or children’s pants. Other such variables include past pages the user has visited on the site, products in the user’s shopping cart, demographic information (either approximated or from any available account data), purchase history, how the user entered the site, duration since last visit, duration of current visit, and so on. Of course, if the user has an account and is signed in, information from their profile may also be used to inform #11 Implicit Searches.
- Increased NLP application: Google Search, Siri, FB graph search, etc. The search engine is able to interpret the meaning of a query made with regular spoken language and return highly relevant results.
- Users are often greatly influenced by prior experience with a site. Since the same search engine tends to be accessed by all platforms, any investments will typically pay off universally across those platforms Support 8-10 of the query types to be great. This means investing in good search logic and detailed structured product data. If they were successful searching for something on the site, they were more likely to use search on that site even if they prefer category navigation. Same goes for negative search experiences pushing users to category navigation even though it's not their favorite way of searching for things. Not investing in good search usability can cost sales in the short, mid and long term. not to post-pone investing in improvements to their e-commerce search experience, lest they be haunted by faulty user perceptions for years to come. And since e-commerce search engines are nearly always reused across platforms (different interfaces accessing the same search engine API), query support will be similarly poor on tablet and mobile sites / apps too.
- Depending on their particular needs and available information, this can go from highly specific #1 Exact Searches, to broader #2 Product Type Searches, to the inquiry-like #3 Symptom Searches. Finally, # Non=Product Search is used when users may be looking for non-product pages. The query spectrum sets the range of what should be searched. It includes 4 queries:
- Depending on their particular needs and available information, this can go from highly specific #1 Exact Searches, to broader #2 Product Type Searches, to the inquiry-like #3 Symptom Searches. Finally, # Non=Product Search is used when users may be looking for non-product pages. The query spectrum sets the range of what should be searched. It includes 4 queries:
- Depending on their particular needs and available information, this can go from highly specific #1 Exact Searches, to broader #2 Product Type Searches, to the inquiry-like #3 Symptom Searches. Finally, # Non=Product Search is used when users may be looking for non-product pages. The query spectrum sets the range of what should be searched. It includes 4 queries:
- https://www.internetretailer.com/vendors/site-search-solutions/site-search/
- (For more, see “An E-Commerce Study: Guidelines for Better Navigation and Categories.”) Query types need to be prioritized because they have a key role in the search experience. Improvement to search engine logic would benefit all platforms (desktop, mobile, tablet, etc.), whereas layout changes are typically platform-specific. Results layout = designing a clear overview of search results and providing sufficient information for users to accurately evaluate and compare results. It needs to adapt to the user's query and content. Poor results layout are often caused by the website relying on the same static layout for both search results and category product lists. Optimizing the results layout is a low hanging fruit with a large impact on the overall search experience. it mainly entails switching from reusing the static (category) results layout to a dedicated and slightly more dynamic search results layout. Filtering and sorting search results is a an overlooked area. It should also adapt to the user's query and context. faceted search is the foundation of a contextual filtering experience, only 40% of websites have it. the multiple elements of sorting site-wide search results identified are overlooked entirely, more than 70% of websites lacking key sorting types, 90% having no scope options or suggestions when users try to sort site-wide results.Given that filtering and sorting are much less resource-intensive to get right than query support, they should be a part of almost every optimization project for e-commerce search. many of the improvements are manageable enough to be implemented and optimized on an ongoing basis, and much of it can be reused to improve the sorting and filtering experience in category navigation.