webinar: increase conversion with better search

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  • Sarath Jarugula, VP Lucidworks @sarath August 26, 2015

    IF THEY CANT FIND IT, THEY CANT BUY IT

  • IBM Commerce Technology Ecosystem + Lucidworks

    Iris Yuan - iyuan@us.ibm.com August 2015

  • 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 - sarath@lucidworks.com 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

    http://bitly.com/12_queries

  • If They Cant Find It, It Doesnt 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 Doesnt 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 arent part

    of the sites 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 Customers 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 Users Personalized Search Experience

    Use All Available Environmental Data Learn from Past History Refine Query Suggest Relevant Similar Searches

    Examples pants (from a Womens Apparel category page) charger cable(from an iOS Devices landing page)

  • Deliver Results based on Meaning of Users Spoken Language

    Go Beyond Keyword Matching Mainstream with Mobile Usage (Voice) Closest to In-Store Experience Integrate NLP into Your eCommerce

    Platform

    Examples mens 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 its 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 users 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

    Users 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 dataall in one place.

    Search backed analytics makes every user a data scientist.

    Lucidworks Fusion has unmatched scalability in search.

  • User inte