Cisco meetup-25 april2017

TM
Daniel Cadoch
Partners & Alliances
Coveo Product
Overview
Gauthier Robe
VP Products
Jean-Francois Cloutier
Director, R&D

Copyright©2016CoveoSolutionsInc.Allrightsreserved.ProprietaryandCONFIDENTIAL
Who we are
A brief overview of the company
Demo
Let’s start with the fun, and walk back
Our technology
Indexing, Connectivity, Relevance, Usage Analytics, Machine Learning
The future
What we are working on (cool stuff), more demo
2
Topics for today

3
Who we are

Coveo brings relevance to every interaction
customers, partners and employees have
with your organization
AI-powered search technology:
contextual + predictive = relevant
4
cognitive
computing
indexing
& search
predictive
analytics
300
employees
SaaS▸PaaS
intelligent search
& contextual insights
1,000+
installations
100+
SI Partners
82%
revenue growth
CY16 vs. CY15
75M$
Capital invested

2017 Gartner Magic
Quadrant for Insight Engines
“Coveo has particularly close partnerships to Sitecore
and Salesforce. Reference customers confirmed that they
use the platform to search Salesforce, and that such
capability is a strong selling point.”

Internal facing:
Intelligent Workplaces
“…for me” “…for me”
Customer
communities
Personalized
websites
Customer
self-service
Partner
& Dealer
portals
eCommerceIn-product
intelligence
External facing:
Intelligent Engagement
Contact
centers
CRM
& Sales
Employee
portals
Intranets
& KM
Where relevance affects business economics
Search &
recommend
[PaaS]

Reactive
Responsive
Proactive
Predictive
7 © 2017 Coveo Solutions Inc - Proprietary and Confidential
Coveo Relevance Maturity Model

Negative
economic
Impact
Positive
business
impact
& ROI
CRMM™
stages
0
1
2
3
4
5
6
LAGGARDS RESPONSIVE
relevance is personal
PROACTIVE
relevance is contextual
PREDICTIVE
relevance is predictable
LEADERS
Siloed
search
Federated
searches
Secured
unified
ranking
Content
navigation
Tunable
relevance
Contextual
relevance
Contextual
suggestions
Self learning predictive
recommendations
Coveo Relevance Maturity Model™
content intelligence
manual/hard automated/easy
efficiency proficiency

Negative
economic
Impact
Positive
business
impact
& ROI
0
LAGGARDS
Siloed search
Federated
searches Basic search captures user’s intent, and saves time
retrieving information. Entry-level search is resource
intensive, relies heavily on taxonomies for relevance.
Federated search fails at ranking combined result sets
relevantly. Poor relevance affects conversions,
clickthroughs, satisfaction, deflection, escalations,
productivity, innovation, work redundancy, ...
Net net, entry level search [cheaper] is the most expensive
strategy.
Siloed
search

1
RESPONSIVE
Secured
unified
ranking
Negative
economic
Impact
Positive
business
impact
& ROI
Secured unified ranking
Unifies and ranks information from multiple sources.

Negative
economic
Impact
Positive
business
impact
& ROI
Content navigation
Configurable rich facets, search tabs, folding and security
trimming provide the very first step of personalization for
users.
2
Content
navigation
RESPONSIVE

Negative
economic
Impact
Positive
business
impact
& ROI
Tunable relevance
…adapt relevance through weighting of ranking factors,
query ranking expressions, based on known content
characteristics.
3
Tunable
relevance
RESPONSIVE

Negative
economic
Impact
Positive
business
impact
& ROI
Contextual relevance
…in-product, in-task, and other contextual signals factor
the uniqueness of the user and query context and ranks
results with higher relevance.
4
Contextual
relevance
PROACTIVE

Negative
economic
Impact
Positive
business
impact
& ROI
Contextual suggestions
…related content, experts, products or services pushed to
expand user’s knowledge and abilities to do more, buy
more, learn more, engage more, etc.
5
Contextual
suggestions
PROACTIVE

Negative
economic
Impact
Positive
business
impact
& ROI
recommendations
Discovers user’s likely intent,
by analyzing behavioral data.
Machine learning auto-tunes recommendations and
ranking to maximize business outcome, enabling true one-
to-one user engagement and upskilling.
6
recommendationsCRMM™
stages
PREDICTIVE
relevance is predictable
LEADERS

16
Demo

17
Our technology

Timeline: Looking back, now and beyond
2015
Analytics &
Optimize
2014
Platform &
Federated Search
2016
Intelligence &
Insights
2017
Reach &
Scale

19
Today’s Developer Requirements
Easy Scaling Flexible
Ingestion Model
Extensible
Indexing Pipeline
Sophisticated
Query Language
Ready to Use
Search UI
Analytics and ML
Features

20
Indexing

21
Easy Scaling
Single-tenant
Multi-tenant
Content Permissions
Indexing Pipeline
On-premises Index
Indexer
Pipeline
Manager
Indexer
Manager
Indexer
Aggregation
Queue
Queue
QueueQueue …
Queue
Aggregation …
…
States
Connector
Security
Provider
Push API
Elastic Load Balancer
Blob Store
Scheduler
………
Converter
Queue
Security
Provider
Security
Provider
…
………
Security Cache
Scheduler
Permissions API Queue
Queue
Amazon S3
Custom
Application
………
Mapping
………
Connector
Public Front-end

Flexible Ingestion Model

23
Crawling Modules
withoutCrawlingModules
withCrawlingModules

24
Extensible Indexing Pipeline

25
Linking to 3rd Party Services

26

27

28
Sophisticated Query Language
Query Language
Relevance Management

29
Coveo Query Language
https://developers.coveo.com/display/public/SearchREST/Anatomy+of+a+Complex+Query+Using+Extensions
{{accounts=$type(name:'Account') @systitle="{!>Name}"}}
{{contacts=$join(fromResultSet: {{accounts}}, toResultSet: $type(name: 'Contact'), field: '@sfaccountid')}}
{{emailAddresses=$valuesOfField(field: '@sfemail', resultSet: {{contacts}})}}
$emailsExchangedWithThoseAddresses(addresses: {{emailAddresses}})
NOT @sysisattachment
$sort(criteria: 'datedescending')
(NOT @syssfid=={!>id})
(NOT @syssfcaseid=={!>id})
/*--- Extract related cases ---*/
{{relatedCases = @objecttype=="Case" $some(keywords: '{!>Subject}', match:1)
@sfparentcasenumber
$correlateResultSet(resultSet: @syssfid=={!Id}, field: '@sysconcepts', maximumValues: '25', modifier: '1000')}}
/*--- Extract parent cases of related cases ---*/
{{parentCases = $valuesToResultSet(values: $valuesOfField(field: '@sfparentcasenumber', resultSet:
{{relatedCases}}), field: '@sfcasenumber' )}}
/*--- Find some relevant knowledge ---*/
$some(keywords:$removeStopWords(keywords:$anyKeywords(keywords:$valuesOfField(field: '@systitle', resultSet:
{{parentCases}}))))
https://developers.coveo.com/display/public/SearchREST/Query+Extension+Samples
General Extensions
$q
$qf
$qre
$qrf
$weight
$sort
$fold
$loadParent
Extensions for Lists of Values
$valuesOfField
$keepMatchingValues
$removeMatchingValues
$removeEmptyValues
$removeDuplicateValues
$replaceInValues
$mergeValues
$joinValues
$splitValues
Join Extensions
$valuesToResultSet
$joinOnValues
$join
$filterJoin
Correlation Extensions
$correlateListOfValues
$correlateResultSet
$correlateUsingIdf
$some
$removeStopWords
$noStemming
Query ExtensionsExample 1:
filter query on all email messages with contacts from selected accounts
Example 2:
Extract unique keyword list from all related cases subject

30

31
Query Pipeline: Featured Results

32
Relevance Management - Query Pipeline
Ranking Expressions

33
Ranking Weights

34
Triggers

35
A/B Testing: Compare two Pipelines
A/B Testing

36
Usage Analytics & Machine Learning

38

Coveo Machine Learning
Coveo ML Salesforce Einstein
Relevance, Recommendations, Deflection Routing, Escalation, Scoring
1. Intelligent Query Suggest
2. Automatic Relevance Tuning
3. Recommendations
4. Intelligent Term Detection
5. Personalization
Coveo

40
1. Intelligent Query Suggest
Salesforce.com Help & Training

Standard Relevance
Most popular items
automatically promoted
MXtm 5500 match
cuay59 is part number of
the usb receiver
With Machine Learning
2. Automated Relevance Tuning
Logitech Self-Service

42
Intelligent
Recommendations
Embedded
Video Player
Advanced
Facets
3. Recommendations
Salesforce Help & Training Search

43
3. Recommendations
Salesforce Help & Training Search

44
How to setup a server as master –slave?
Like 1 master – 2 slave 2 slave shaold have same index
content so that we can make load balace url for 2 slave.
Please tell how to install.
How to setup a server as master –slave?
Like 1 master – 2 slave 2 slave shaold have same index
content so that we can make load balace url for 2 slave.
Please tell how to install.
4. Intelligent Term Detection
Coveo Self-Service Community

45
5. Personalization
Adobe Generic Website

46
5. Personalization
Adobe Website for Photoshop users

47
Photoshop
In-Product Search

49
Machine Learning configuration

For all features
All features use “Hyperparameter optimization” consisting of a mix of local search, simulated annealing and genetic
algorithms.
“Hyperparameter optimization” is used to optimize/personalize the model parameters for each client use case
Automated Relevance Tuning (ART)
Bayesian Classification of queries to documents, with time decay
Entire visit evaluated when learning the model. Models queried before the index.
Recommendations
Cooccurrence-based algorithm using a log likelihood ratio.
Query Suggest
Query Performance score combined with a customized distance metric based on Smith-Waterman and Monge-
Elkan approaches
https://www.google.com/patents/WO2016187705A1?cl=en
50
Some additional background…

51
The future

Behavioral Data
Clickstream, Profiles, …
Intelligent
Search
Intelligent
Recommendations
Coveo
Platform
Machine
Learning
Unified
Index
Documents, Records
KBs, Collaboration, Files, Database, WCM, Cloud…
Systems &
Silos
Machine Learning: Adding more behavioral data sources
Personalization

53
Current: Integrated Relevance, ML & UA
Relevance
Tuning
Usage
Analytics
Machine
Learning
Ranking
Suggestions
UI &
Query Engine
Ranking from
Models
Behavioral
Data
Events
Ranking
Rules

54
Future: Streaming Data Platform to connect to other sources
Relevance
Tuning
Usage
Analytics
Machine
Learning
Ranking
Suggestions
UI &
Query Engine
Ranking from
Models
Behavioral
Data
Events Other data
sources
Streaming
Data Platform
Events
Filtered
Events
xDB, Omniture…
Ranking
Rules

55
Future: Streaming Data Platform as a provider to other apps
Relevance
Tuning
Usage
Analytics
Machine
Learning
Ranking
Suggestions
UI &
Query Engine
Ranking from
Models
Behavioral
Data
Other data
sources
Streaming
Data Platform
Filtered
Events
Other
Apps / EnginesFiltered
Events
Events Events
Ranking
Rules

Applied to Salesforce
56
Index-Agnostic Platform
Coveo Platform – Index-AgnosticCoveo Platform – Enterprise
Relevance
UA ML UI Query
Connectors
Cloud Connectors Enter. Connectors
Coveo Index Salesforce Index (SOSL)
Relevance
UA ML UI Query

57
Standard Salesforce Search Coveo for Salesforce Express

58Coveo Lightning Components included in Salesforce Community Builder

Highlights
Leverages existing Elasticsearch
installed base
Full Coveo connectivity,
relevance (ML, UA), and UI
Works with any Elasticsearch 5.x.x
cluster
59
Index-Agnostic Platform on top of Elasticsearch
Coveo Platform – Enterprise
Relevance
UA ML UI Query
Connectors
Cloud Connectors Enter. Connectors
Coveo Index
Coveo Platform – Index-Agnostic
Relevance
UA ML UI Query
Connectors
Cloud Connectors
Elasticsearch

60
Focus on Developers

Public APIs
Micro-Services
Support for Dev. Community
Open-source when appropriate
61
Priorities

JS UI – Interface Editor
JS UI framework now publicly available on Github
https://github.com/coveo/search-ui

Cisco meetup-25 april2017

Recommended

Recommended

More Related Content

Similar to Cisco meetup-25 april2017

Similar to Cisco meetup-25 april2017 (20)

More from enterprisesearchmeetup

More from enterprisesearchmeetup (6)

Recently uploaded

Recently uploaded (20)

Cisco meetup-25 april2017