1. No category

TEC 114
How to use Sentiment Intelligence to nurture your
contact base
Alexander Hofer, Pradeep Kumar, Dr. Michael Rey; Applications on SAP HANA
SAP TechEd 2013
Disclaimer
This presentation outlines our general product direction and should not be relied on in making a
purchase decision. This presentation is not subject to your license agreement or any other agreement
with SAP. SAP has no obligation to pursue any course of business outlined in this presentation or to
develop or release any functionality mentioned in this presentation. This presentation and SAP's
strategy and possible future developments are subject to change and may be changed by SAP at any
time for any reason without notice. This document is provided without a warranty of any kind, either
express or implied, including but not limited to, the implied warranties of merchantability, fitness for a
particular purpose, or non-infringement. SAP assumes no responsibility for errors or omissions in this
document, except if such damages were caused by SAP intentionally or grossly negligent.
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
2
Session goals
You will …
… learn, how unstructured texts
can be interpreted using SAP’s
latest technology innovations
… see, how this is merged with
other consumer interactions and
made available to marketing by
SAPs Social Contact Intelligence
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
3
Agenda
Blog Intelligence - Research project of the Hasso-Plattner-Institute
 Analyze the Blogosphere and reveal its information
Interpret unstructured texts
 Motivation
 Using text analysis inside SAP HANA
 How to feed into marketing follow up processes
Merging additional marketing channels
 Merging the interactions together
 How to lay the basis to feed into marketing follow up processes
Q&A
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
4
Marketing Blogs
Research project of the Hasso-Plattner-Institute
on Social Media Analyses
Social Media Analyses
Divers Channels for Marketing
What happens within an internet minute?
 Journalistic Networks (Weblogs)
 100 000 tweets1
 300 000 Facebook updates2
 Professional Networks (Xing, LinkedIn)
 80 000 blog posts3
 Personal Networks (Facebook)
 Short-Message Services (Twitter)
 Picture and Video Sharing
(Instagram, YouTube)
 people spend nearly 9 hours a day online4
1http://www.digitaltrends.com/
2http://www.intel.com/
3http://digitalbuzzblog.com/
4http://www.dailymail.co.uk/
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
6
Social Media Analyses
Complexity
Challenges
 Personalized Blog Search
 Ever-changing corpus
– scans million posts for each user query
 Define own Ranking Formula
 Structured and Unstructured Data
 Queries unknown beforehand
– User-defined criteria
– scans all posts, 500 million links and all extracted
sentiments
 Predict Trends
– scans one million unique terms and their occurrences
 Understand Information Flow
 Link structure grows exponentially
– scans 500 million links joined with all blog posts
– E.g. rank calculation runs for each node until convergence
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
7
Social Media Analyses
Analyze
Visualize
 Personalized Blog Search
 Define own Ranking Formula
 Predict Trends
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
8
Demo
Interpret unstructured texts
What vs. Why
It’s often said that
•
•
Structured data tells us “what”
Unstructured data tells us “why”
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
11
Marketing
Marketing collects data, interprets the information and triggers engagement actions
Acquire & Collect
Analyze & Understand
Response & Engage
Structured Data:
 Forecasts, economic estimates, …
 Leads, Sales, …
 Clickstreams
Marketing Actions:
Analyze

Understand

Unstructured Data:
 Blogs, forum postings, social
media (e.g. Twitter, Facebook,
Google+, …), Wikis
 Emails, contact-center notes
 Surveys, service entries, warranty
claims
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
Merge
Analyze
Analyze
Analyze
Understand



Real-time discovery & predictive
consumer segmentation
Contact Data Enrichment and
Engagement
Omni channel Campaigns & Loyalty
Real-time Recommendations
Consumer Service Excellence
12
Marketing
Marketing collects data, interprets the information and triggers engagement actions
Acquire & Collect
Analyze & Understand
Response & Engage
Structured Data:
 Forecasts, economic estimates, …
 Leads, Sales, …
 Clickstreams
Analyze 
Understand
Unstructured Data:
 Blogs, forum postings, social
media (e.g. Twitter, Facebook,
Google+, …), Wikis
 Emails, contact-center notes
 Surveys, service entries, warranty
claims
Marketing Actions:
Analyze
Analyze
Analyze

Merge


Understand





Real-time discovery & predictive
consumer segmentation
Contact Data Enrichment and
Engagement
Omni channel Campaigns & Loyalty
Real-time Recommendations
Consumer Service Excellence
Unstructured data today are analyzed with high effort in isolated separate containers
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
13
Analyzing Texts
Extracting and Transforming Unstructured Data
Text Analysis….
Unstructured Text
<PERSON>John</PERSON> bought <QUANTITY>300</QUANTITY> shares of
<ORGANIZATION>HANA Corp.</ORGANIZATION> in <DATE>2006</DATE>
<SENTIMENT> I <StrongNegativeSentiment> hate </StrongNegativeSentiment>
<TOPIC> XYZ </TOPIC> because … </SENTIMENT>
1. Extract meaning
2. Transform into structured
data for analysis
Predefined Entity Types for Core Extraction
Fact Types for Voice of Customer
Who: people, job title, national id. numbers
Sentiments: expression of a customer’s
feelings about something
What: companies, organizations, financial
indexes, products
When: dates, days, holidays, months, years,
times, time periods
Problems: a statement about something which
impedes a customer’s work
Requests: expression of a customer’s desire
for an enhancement/change
Where: addresses, cities, states, countries,
facilities, internet addresses, phone numbers Profanity: defines a set of pejorative
vocabulary
How much: currencies and units of measure
Generic Concepts: “text data”, “global piracy”,
and so on
Emoticons: expression of someone's feelings
about the whole sentence or situation
Unlock Key Information from Text Sources to Drive Business Insight
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
14
SAP HANA Text Analysis - Domain Fact Extraction
Voice of Customer
Within each major Voice of Customer fact type, finer-grained sub entities are classified, e.g.:
SENTIMENTS
Strong Positive Sentiment – expression of a strongly positive opinion
 great, excellent, love, etc.: I love BusinessObjects.
Weak Positive Sentiment – expression of a weakly positive opinion
 good, nice feature, fine, like, etc.: I like BusinessObjects.
Neutral Sentiment – expression of an opinion which is neither positive nor negative
 ok, acceptable, can live with, etc.: I’m ok with respect to X’s latest product offerings.
Weak Negative Sentiment – expression of a weakly negative opinion
 bad, don’t like, etc.: I don’t enjoy working with company X.
Strong Negative Sentiment – expression of a strongly negative opinion
 hate, horrible, terrible, unusable, etc.: Their office suite is horrible.
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
15
SAP HANA Text Analysis - Domain Fact Extraction
Voice of Customer
The following major fact types are classified:
 Sentiments: expression of a customer’s feelings about something
 Problems: a statement about something which impedes a customer’s work
 Requests: expression of a customer’s desire for an enhancement/change
 Profanity: defines a set of pejorative vocabulary
 Emoticons: expression of someone's feelings about the whole sentence or situation
Within each of these rules certain sub entities are classified. Any rule may have an associated TOPIC
sub entity which, in addition to the sub entitles described on the following slides, describes the person,
service, product, etc. which the sentiment, problem, or request is about.
I hate this book.
I never received the book.
Please send me a new book.
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
16
SAP HANA Text Analysis - Domain Fact Extraction
Voice of Customer
PROBLEMS
Major Problem: expression describing an impediment the customer cannot work around
 crashes, fails, etc.: Your database installer crashed my computer.
Minor Problem: expression describing an impediment the customer can work around
 reboot, slows down, etc.: Running X in the background seems to slow down my computer.
REQUESTS
General Request: request for something new or for an enhancement to an existing product/service/etc.
 would like, please create, etc.: I would like a product that will handle my SQL data.
 please make x do y, would like, etc.: I would like to have an XI plugin for Excel.
Contact Request: request for direct and immediate contact
 Call me now at 555-1212.
 Send me information on Text Data Processing.
Contact Info: Phone numbers or e-mail addresses associated with a contact request
 Call me now at 555-1212.
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
17
SAP HANA Text Analysis - Domain Fact Extraction
Voice of Customer
PROFANITY
Ambiguous: words and phrases that are pejorative only in certain contexts
 Those hooligans threw toilet paper on my lawn.
Unambiguous: words and phrases that are always pejorative
 I cannot express how angry I am with this asshole.
EMOTICONS
Weak Positive: extracts emoticons conveying weak positive sentiment
 Loving my new BlackBerry!  No iPhone needed over here.
Strong Positive: extracts emoticons conveying strong positive sentiment
 The show was hilarious :-D
Weak Negative: extracts emoticons conveying weak negative sentiment
 I hate this phone I'm using :-(
Strong Negative: extracts emoticons conveying strong negative sentiment
 The Dow Jones fell 200 points :-(((
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
18
Demo
Social Contact Intelligence: Sentiment Engagement
Additional marketing channels
Marketing
Marketing collects data, interprets the information and triggers engagement actions
Acquire & Collect
Analyze & Understand
Response & Engage
Structured Data:
 Forecasts, economic estimates, …
 Leads, Sales, …
 Clickstreams
Marketing Actions:
Analyze

Understand

Unstructured Data:
 Blogs, forum postings, social
media (e.g. Twitter, Facebook,
Google+, …), Wikis
 Emails, contact-center notes
 Surveys, service entries, warranty
claims
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
Merge
Analyze
Analyze
Analyze
Understand



Real-time discovery & predictive
consumer segmentation
Contact Data Enrichment and
Engagement
Omni channel Campaigns & Loyalty
Real-time Recommendations
Consumer Service Excellence
21
Marketing
Marketing collects data, interprets the information and triggers engagement actions
Acquire & Collect
Analyze & Understand
Response & Engage
Structured Data:
 Forecasts, economic estimates, …
 Leads, Sales, …
 Clickstreams
Analyze

Understand
Unstructured Data:
 Blogs, forum postings, social
media (e.g. Twitter, Facebook,
Google+, …), Wikis
 Emails, contact-center notes
 Surveys, service entries, warranty
claims
Marketing Actions:
Analyze
Analyze
Analyze

Merge

Understand





Real-time discovery & predictive
consumer segmentation
Contact Data Enrichment and
Engagement
Omni Channel Campaigns & Loyalty
Real-time Recommendations
Consumer Service Excellence
Merging structured with unstructured data across channels currently is a challenge
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
22
Merging Interactions
Extracting the common denominator for marketing
All Interactions
Topics are the common denominator
Interactions record the way in which persons communicate or are involved directly with somebody
else. Topics are the items the interactions are about. Topics are part of every interaction.
1. Identify the topic
2. Cluster topics to
“interests”
Unstructured data
Structured Data
Text Analysis Core Extraction:
Business Documents:
Identification of “What”: products, companies,
organizations, financial indexes, etc.
Reuse of taxonomies like product
categories
Application logic:
Text Mining of description fields and free
texts
Text Mining, clustering of topics.
Topic clusters become “Interests”
Clickstreams:
Tags and page content relate to topic
clusters
Events:
Agenda and content relate to topic
clusters
Understand topic clusters across channels and trigger marketing actions
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
23
Leveraging SAP HANA for Sentiment Intelligence
Desktop or mobile user interface:
 HTML5 based. Supports analysis and engagement
HTML5 Desktop
AS ABAP 7.4
Mobile
Application Logic
Application Logic:
 Cluster topics, derive interests
 Trigger follow up actions, feed marketing channels
Text Analysis
 Interprets raw source data
 Linguistic Analysis, Voice of Customer, identifies
topics
SAP HANA
SAP Text Analysis
Core Extraction
Voice of Customer
Applicationtables
Calculation Engine
Interactionrecords
Predictive Models
Interests
Scoring
…
…
RFC
HTTP
SAP Data Service
SAP Replication Server
Data Harvesting
 Any unstructured text data
 Structured data like clickstreams, event participations
 Business documents from SAP or any other system
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
Posts
e.g. tweets, forum
entries,
E-mails, Contact forms
Other Interactions
e.g. clickstream,
feedback, surveys
events
SAP ERP/ CRM
Sales Orders,
Opportunities,
Leads, Activities,
Complaints, …
24
SAP Social Contact Intelligence powered by SAP HANA
Understand consumer and their interest. Target them strategically.
Have all information available
when you meet your account
DEMO
Analyze unstructured data from public
social media and other text based sources
SAP
HANA
Create focus list of mentions and
contacts for insight and action
Nurture your leads to
drive revenue
Target people based on their interests
with the right go-to-market resources
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
25
Demo
Social Contact Intelligence: Contact Engagement
Further Information
SAP Public Web
help.sap.com/hana
help.sap.com/cei
help.sap.com/fra
scn.sap.com/community/developer-center/hana
scn.sap.com/community/developer-center/front-end
Related TechEd Sessions
TEC112 SAP Liquidity Risk Management
TEC113 SAP Fraud Management
TEC208 SAP Accelerated Trade Promotion Planning
RDP261 Fulltext Search, Fuzzy Search, and Text Analysis in SAP HANA
CD202 Development of High Performance Applications using SAP HANA, ABAP, SAPUI5
CD263 ABAP on SAP HANA - Building an End-to-End App from HANA via ABAP to SAP UI5
www.sapteched.com/online
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
27
SAP TechEd Virtual Hands-on Workshops and SAP TechEd Online
Continue your SAP TechEd education after the event!
SAP TechEd Virtual Hands-on Workshops
SAP TechEd Online
 Access hands-on workshops post-event
 Available January – March 2014
 Complementary with your SAP TechEd registration
 Access replays of keynotes, Demo Jam, SAP TechEd
LIVE interviews, select lecture sessions, and more!
 View content only available online
http://saptechedhandson.sap.com/
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
http://sapteched.com/online
28
Feedback
Please complete your session evaluation for TEC 114.
Thanks for attending this SAP TechEd session.
SAP HANA Innovation Overview
HANA
Apps for Suite
BW on
HANA
New UX
Apps
BW
VDL
PLM
SRM
Apps
SAP
Business
Suite
HANA
Live
SAP
Business Suite
SCM
Client
CRM
New UX
ERP
SAP
Business
Suite
BusinessSuite
on HANA
HANA
Accelerators
SAP
Business
Suite
Virtual
Data
Model
SAP
Business
Suite
BW
HANA
Platform
(Datamart)
Cloud
on HANA
SAP BOBJ BI, VI
Client
SAP
Business
Suite
Data
Mart
Business One
on HANA
HANA
New Apps
SAP
Business One
Apps
HANA DB
HANA DB
OD/SF Solutions
& Any
App
Any
DB
HANA
DB
Any
DB
HANA
DB
HANA DB
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
Any
DB
HANA
DB
Any
DB
HANA
DB
Any
DB
HANA
DB
Any
DB
HANA
DB
30
Architecture Principles
Mobile & HTML5
ABAP Layer as Controller
Logic in HANA
1:1 Replication
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
31
HTML5
JavaScript
CSS
Mobile
R
Leverage native SAP HANA Capabilities
OData
AS ABAP 7.4
High Performance Application (HPA)
R
SAP HANA
HPA Content:
Tables, Views, Procedures, Search Models
Data-intensive Application Logic in SAP
HANA:
 Joins, Aggregations, Calculations and Conversions
Predictive Algorithms
 SAP HANA Predictive Analysis Library (PAL)
 SAP HANA Business Function Library (BFL)
SAP HANA intrinsic Search Capabilities
 Fuzzy, Full Text, Freestyle
 Documents and Attachments
Text Analysis
SAP HANA
HPA Content
Search Models
Text Analysis
<Fuzzy, Freestyle,
Documents>
<Voice of Customer,
Sentiments>
Predictive Models
Stored Procedures
< PAL, BFL, AFL>
<SQLScript, R, L>
Content Models
<Attribute Views,
Analytical Views,
Calculation Views>
Application Data
<Replicated Tables,
Local Tables>
 Linguistic Analysis
 Voice of Customer, Industries, Public Sector, etc.
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
32
Text Analysis in SAP HANA SP06
Predefined Domain Extraction of Sentiments
Voice of Customer
Sentiments: strong positive, weak positive, neutral, weak negative, strong negative and problems
Requests: general and contact info
Emoticons: strong positive, weak positive, weak negative, strong negative
Profanity: ambiguous and unambiguous
Added SP06
language
Language Support: English, French, German, Simplified Chinese and Spanish
These are starter packs that can be built upon for a specific deployment
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
33
SAP HANA SP06
Configuration options
LINGANALYSIS_FULL
EXTRACTION_CORE
EXTRACTION_CORE_VOICEOFCUSTOMER
Arabic
LINGANALYSIS_BASIC
LINGANALYSIS_STEMS



X
Catalan


X
X
Chinese (Simplified)




Chinese (Traditional)


X
X
Croatian


X
X
Czech


X
X
Danish


X
X
Dutch



X
English




Farsi



X
French




German




Greek

X
X
X
Hebrew

X
X
X
Hungarian

X
X
X
Italian



X
Japanese



X
Korean



X
Norwegian (Bokmal)


X
X
Norwegian (Nynorsk)


X
X
Polish

X
X
X
Portuguese



X
Romanian

X
X
X
Russian



X
Serbian


X
X
Slovak


X
X
Slovenian


X
X
Spanish




Swedish


X
X
Thai

X
X
X
Turkish

X
X
X
Language
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
34
Data Handling Fundamentals
Key Messages
 Data is handled according to its origin using different database schemas within SAP HANA
– Data from the SAP Customer Engagement Intelligence ABAP system is managed in one database schema
– Data from ERP and CRM is replicated into an own database schema each – all tables holding replicated data are read-only
 Replication from ERP and CRM is done via SAP LT Replication Server
 Data shall be replicated 1:1 with no mapping or transformation during replication
SAP HANA
used by SAP Customer Engagement Intelligence ABAP system
«database schema»
SAP_CUAN
↓
SAP<SID>
«database schema»
SAP_CUAN_ERP
↓
<REPL_ERP>
«database schema»
SAP_CUAN_CRM
↓
<REPL_CRM>
Holding all tables of the
Customer Engagement
Intelligence ABAP system
Holding all tables
(read-only)
for replicated data
from SAP ERP
Holding all tables
(read-only)
for replicated data
from SAP CRM
<SID>: System ID
of ABAP system
<REPL_ERP>:
can be defined
<REPL_CRM>:
can be defined
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
Schema Mapping during
technical configuration:
Schema name
as delivered by SAP
↓
Name of physical schema
in customer installation
35
System Landscape Fundamentals
Key Messages
Versioning
 SAP Customer Engagement Intelligence (CEI) is built on SAP
NetWeaver ABAP using HANA 1.0
 SAP ERP is supported from release ECC 6.00
 SAP CRM is supported from release 7.01 SP01 plus a set of notes,
or from release 7.01 SP08
 SAP LT Replication Server can run on any SAP system with SAP
NetWeaver Application Server ABAP 7.02 (Kernel 7.20EXT)
 SAP Solution Manager 7.1 SP5
SAP Customer Engagement Intelligence
(ABAP System)
Client 100
Client 200
Topology
 One ABAP system per SAP HANA DB currently
(SAP note 1661202)
 One CEI system can be connected to 0..1 ERP systems and 0..1
CRM systems
 One client in a CEI system can be connected to 0..1 ERP clients
and 0..1 CRM clients
 CEI can be operated in multiple clients in parallel with above
restrictions
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
Client 100
Client 900
Client 200
Client 910
SAP ERP
(ABAP System)
Non-SAP Systems
SAP CRM
(ABAP System)
36
HPA Deployment Options
Key Messages
• HPAs Customer Engagement Intelligence (CEI), Fraud Management (FRA), and Liquidity Risk Management (LRM) are built in a way that
they can co-exist and run integrated with Suite on HANA or BW on HANA
ERP
CRM
HPA
ERP
CRM
HPA
ERP
CRM
AS ABAP
AS ABAP
AS ABAP
AS ABAP
AS ABAP
AS ABAP
AS ABAP
AS ABAP
Replication
Replication
DB
DB
SAP HANA
(a) Side-by-side with replication
DB
HPA
BW
AS ABAP
AS ABAP
Replication
SAP HANA
DB
DB
SAP HANA
(b) Sharing HANA with Suite systems
without replication
(c) Side-by-side, sharing HANA with
BW
• HPA shares SAP HANA with 1 SAP ERP or 1
SAP CRM system reading ERP and CRM data
directly without replication
• Customers can extend HPA with BW data without
replication – or extend BW reports with HPA data
• Most wanted: CRM+CEI, ERP+CEI, ERP+FRA
• SAP Note 1826100: approvals for productive
co-deployments of CRM+CEI, ERP+CEI,
ERP+FRA
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
• Most wanted: BW+CEI, BW+FRA
• SAP Note 1661202 (“whitelist”): approval for
productive co-deployments with BW for LRM,
FRA, CEI
37
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of SAP AG.
The information contained herein may be changed without prior notice.
Some software products marketed by SAP AG and its distributors contain proprietary software components of other software vendors.
National product specifications may vary.
These materials are provided by SAP AG and its affiliated companies ("SAP Group") for informational purposes only, without representation or warranty of any kind, and
SAP Group shall not be liable for errors or omissions with respect to the materials. The only warranties for SAP Group products and services are those that are set forth
in the express warranty statements accompanying such products and services, if any. Nothing herein should be construed as constituting an additional warranty.
SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of SAP AG in Germany and
other countries.
Please see http://www.sap.com/corporate-en/legal/copyright/index.epx#trademark for additional trademark information and notices.
© 2013 SAP AG or an SAP affiliate company. All rights reserved.
38