webinar: increase conversion with better search
TRANSCRIPT
Sarath Jarugula, VP Lucidworks @sarath August 26, 2015
IF THEY CAN’T FIND IT, THEY CAN’T BUY IT
Partnership with Lucidworks Certified, open ISV ecosystem
Validated and integrated with Websphere Commerce
Websphere Commerce (WC): eCommerce platform to deliver complete omnichannel shopping experience - mobile, social, in-store
• Out-of-the-box storefronts for B2B and B2C commerce • Customer experience management (Commerce Composer)
Open, extensible ecosystem to plug into and build on top of WC
Extending the Commerce Platform with SearchLeverage a complete search solution on top of core WSC capabilities
Drive conversions and personalized shopping with scalable, responsive, relevant search experience to every customer
Powerful analytics and marketing to tie app and network performance to business results/ campaigns
Admin interface to maintain and scale search configuration, capture user activity
Apply advanced pipeline, signal processing, and recommendation features
Sarath Jarugula - [email protected] August 2015
Lucidworks and SolrCommercial steward of ApacheSolr project
Employs 1/3 of active Solr committers
Contributing 70% of committed code
Sponsors Lucene/Solr Revolution, the largest open source user conference dedicated to Apache Solr
ENVIRONMENT
FEATURES
SUPPORT LEVEL
ADDITIONAL SUPPORT
Availability Response Time Number of Incidents Pricing Model
DEVELOPER SUPPORT SOLR ENTERPRISE FUSION
DEVELOPMENT PRODUCTION
• How-To Support • Knowledge Base • Fusion Support
• Security • Log Analysis / SiLK Support • Dashboards & Reporting • Enhanced Admin UI
• Security • Crawlers & Connectors • Log Analysis / SiLK Support • Enhanced Admin UI • Data Enrichment • Machine Learning • Recommendation • Advanced Relevancy Tuning • WebSphere Integration
• 9x5 • SLA-Backed • Unlimited Incidents • Per Named Developer
• 24x7 • SLA-Backed • Unlimited Incidents • Per Node
• Dev Support (4 Contacts) • Operational Support • Regular Health Checks
• 24x7 • SLA-Backed • Unlimited Incidents • Per Node
• Dev Support (4 Contacts) • Operational Support • Regular Health Checks
Product Offering
Delightful Commerce Experience Delivered
1. Content and Query
2. Enrich Content, Query, and Results
3. Signals
4. Recommendations
Optimize Content & Query
The 12 Queries1. Exact Search 2. Product Type Search3. Feature Search 4. Thematic Search 5. Relational Search 6. Compatibility Search 7. Slang, Abbreviation, and Symbol Search 8. Subjective Search 9. Symptom Search 10. Implicit Search 11. Non-Product Search 12. Natural Language Search
Access white paper: http://bitly.com/12_queries
If They Can’t Find It, It Doesn’t Exist
Is this what your customers are experiencing?
A recent large-scale ecommerce survey observing users’ search functionality shopping experience
Users Perception
Assumed Relevancy
Expect Powerful, Helpful Search
Google Experience
Customer Assumes Store Doesn’t Carry the Item
• Include multiple title spellings • Variations with other query types • Intelligent handling of misspellings.
Examples• keurig k45 • stuhrling 879.03 mens watch • nikoncoolpixs2800
Customers Have Difficulty Finding Products on Your Site
• Product Type Synonyms as Categories • Include categories that are and aren’t part
of the site’s hierarchy • Suggest Categories as search scopes • Landing Pages
Examples• sandals • sofas • barstools
Customers Expect to Find Products by Their Features
• Store all Product Attributes • Add Tags from Description and External
Sources • Users Combine “Feature Search” with Other
Query Types
Examples• red knit sweaters • ceramic coffee grinders • manual espresso machine • 10gb ssd • waterproof bluetoothspeaker
Customers Expect to Find Products by Their Interests and Love
• Combine “Relational” Queries with Other Search Query Types
• Highlights and Contextual Snippets • Suggestions and Recommendations
Examples• new tom hanks movie • new anne rice novel • second matrix dvd
Find Products by Customer’s Interpretation, Attributes, and Opinion
• Enrich Data for Subjective Approximations and Proxies
• Look Beyond Catalog Data • Analyze Interpretive and Taste-based
Search
Examples• high quality tea kettle • cheap wine • light weight tent
Deliver User’s Personalized Search Experience
• Use All Available Environmental Data • Learn from Past History • Refine Query • Suggest Relevant Similar Searches
Examples• pants (from a Women’s Apparel category page) • charger cable(from an iOS Devices landing page)
Deliver Results based on Meaning of User’s Spoken Language
• Go Beyond Keyword Matching • Mainstream with Mobile Usage (Voice) • Closest to In-Store Experience • Integrate NLP into Your eCommerce
Platform
Examples• men’s sneakers that are red and available in size 7.5
Search Experience Delivered by most eCommerce Businesses
Impacts • Shopping
Experience
• Conversions
• Units per Transaction
Enrich: For Enhanced Experience
Enrich Across Content, Query, and Results
QUERY MODIFICATION
Increase the findability of documents and records with automatic creation of tags,
fields and meta-data
Curate the user experience in your application with artificial
result ranking, document injections and obfuscation
RESULT MANIPULATIONINDEX TIME ENRICHMENT
Perform real time decision making and routing in order to
map a users intention or enterprise policy
Lucidworks Fusion Pipelines
Leverage pre-defined OOB processes to add a stage to enhance the catalogue data
Instantly review how the data is processed at every stage before it’s updated in the index
Create custom stages to bring metadata from different repositories to enrich the product catalogue
Simple admin to add query stages and user profiles to enhance simple user’s query phrase
Instantly review how the query is processed at every stage and the final search results presented to the user
Create user specific personalized search experience
• Landing Pages • Security Trimming • Javascript (for custom scripting) • User Profiles
• Tika Parser • Exclusion Filter • Field Mapper • XML Transform Stage
• OpenNLP Entity Extraction • Gazetter Extractor • Regular Expression Extractor • Javascript (for custom scripting)
• Search Fields/Parameters • Facets • Boost Documents • Block Documents
Sample OOB Index Pipeline stage Sample OOB Query Pipeline stage
Stage-1 Stage-2 Stage-3 Stage-n
Solr Index (Collection) Stage-1 Stage-2 Stage-3 Stage-n
User ExperienceQuery PipelinesIndex Pipelines
Solr Index (Collection)
Solr Index (Collection)
Solr Index (Collection)
Solr Index (Collection)
Solr Index (Collection)
Index Cluster
Realtime Analytics - Respond to Interest Spikes and Events
Real time interactive analytics • Dashboards display real time users interaction • Integration will deliver pre-defined dashboards with most common
analytics • Drill down into the analytics data all the way to a single event or user
interaction • Create time-series to understand patterns and anomalies over time
Configure role based personalized dashboards • Administration interface to build new dashboards with minimal effort • Create personalized dashboard views based on business unit or job
role • Admin can setup dashboards per their business requirements to
enable realtime analysis of their products and user activity Proactive alerts • Configure alerts to notify new events • Realtime proactive alerts help businesses react in realtime
Search Driven Analytics
Signals - Differentiate from Competition
Signals power relevance.
Clicks, tweets, ratings, locations and much more can all be leveraged to provide high quality recommendations to users and deeper insight for data scientists. Connector Framework
Index Pipelines (ETL)
( )ScaleFault ToleranceReal-Time
Fusion APIs
Recommendations Personalization Contextual SearchRelevancy Tool
Machine Learning / Signal ProcessingAnalytics
Security
EcommerceSite
CustomerAnalytics
ProductCatalog
UserHistory
ConversionData
Signals power relevance
eCommerce Platform and IBM Analytics captures powerful signals
User’s activity of an eCommerce site including browsing and navigating through the landing and search result pages
Search and search activity • Select (click) on a product • Rate / recommend a product • Add products to a shopping cart or save to
shopping list
Algorithms to aggregate signals data to drive improved user experience and business performance
Signals framework is built to integrate events data from any application and data source.
Schemaless architecture makes it easy to load both structured and unstructured data
Play nice with elephants
Combine the power of Lucidworks Fusion + Hadoop.
Immediate access to customer, social, and promotional data—all in one place.
Search backed analytics makes every user a data scientist.
Lucidworks Fusion has unmatched scalability in search.
• User interaction signals • Clicks • Add to Wishlist / watch item • Rating • Reviews • Select navigation choices • Add to Shopping cart • Checkout, etc.
• Social Signals • Twitter mentions of products • Positive and negative mentions
• Learn user behavior via simple dashboards
• Configure or build new dashboards through admin screens.
• Search within your log, social, and clickstream data to discover insights and patterns
RealTime Analysis of Signals
Track users events
User’s device, os,
browser data
Specific user
Users Geo-Location
Recommendations - Increase Satisfaction & Transaction Size
Organic pre built machine learning algorithms offer OOB recommendations
Integrated Apache Spark helps to add custom machine learning components to leverage the data captured in IBM analytics and Commerce platforms
Predictive Recommendations
Up sell better products
Cross sell related products
Popular Recommendations
IBM Commerce + Lucidworks Fusion: Improve Shopping Experience & Conversion
Connector Framework
Index Pipelines (ETL)
( )ScaleFault ToleranceReal-Time
Fusion APIs
Recommendations Personalization Contextual SearchRelevancy Tool
Machine Learning / Signal ProcessingAnalytics
Security
Apps Mobile Silk
Database Web File Logs Hadoop
IBM Commerce Integration with Fusion
https://github.com/LucidWorks/fusion-solr-plugins
Simple Integration
Modify Solrconfig.xml to load the jars and enable search components
Import the WebSphere Solr collection in to Fusion.
Configure snowplow in eCommerce applicationDownload IBM Commerce
plugin jar
21 3 4
Demo
Co-Occurrence Graph Analysis
• Incoming users and sessions
• Co-occurring Products (products that were clicked on in the same search session).
• product id • query • user • session
Questions?
Download Fusion http://www.lucidworks.com/products/fusion
Contact Lucidworks http://lucidworks.com/company/contact/
Contact me [email protected] @sarath
