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© 2014 IBM Corporation
Real-time analytics:
IBM Predictive Customer Intelligence
Theresa Morelli
Sr. Product Manager, IBM Business Analytics
tmorelli@us.ibm.com
© 2014 IBM Corporation2
Today’s topics
� The customer analytics imperative
� Big Data and the 360 degree view of the customer
� An integrated solution: IBM Predictive Customer Intelligence
� Analytics in action: use cases and customer examples
� The predictive journey
� Q&A
© 2014 IBM Corporation3
Let’s levelset: what is “customer analytics,” really?
Customer analytics is the practice
of collecting and examining many
types of structured and
unstructured data in order to
fully understand and predict an
individual’s needs, desires,
preferences, and likely behaviors,
so that you can take the best
action to foster a mutually
beneficial relationship.
© 2014 IBM Corporation4
Customer analytics: the key to the customer relationship
� Increasing digital usage
� Channel-preference shift
� Multichannel consumer decision journey
� Digital sales
© 2014 IBM Corporation5
BusinessPartners
Current & Prospective Customers
Improve customer satisfaction & loyalty
Manufacturing
Source: Gartner “Predicts 2013: CRM for Customer Service and Support in the Age of the Everywhere Consumer.” Nov 2012
The focus remains on the end-to-end customer experience
© 2014 IBM Corporation6
© 2014 IBM Corporation7
Analytics
Optimizing every touchpoint in the customer lifecycle with analytics
© 2014 IBM Corporation8
• Analytics are emotionally agnostic and unbiased
• Shift from anecdotal decision making to data-driven
• Know exactly what to do not just for that customer, but for that moment
Organizations have two seconds – the elevator ride – to connect with a customer. It is the single moment of truth.
Claiming the perishability of the moment: real-time analytics
© 2014 IBM Corporation9
Leading CMOs know they have to move fast…
To succeed in the digital era, you have to be totally in sync with the behaviour and preferences of your customers in a fast-changing landscape. You have to be quick and adaptable.
”CMORetail, United States
© 2014 IBM Corporation10
Leading marketers:1. Make more informed marketing investments with greater returns2. Engage with customers in personalized way3. Automate, deliver, guide & measure impact of marketing actions across all channels
Sources: IBM Center for Applied Insights: Why Leading Marketers Outperform (2012)
…and that technology provides the competitive advantage
© 2014 IBM Corporation11
CMOs are focused on using predictive analytics and deploying
insights through agile engagement channels
Source: Question CMO7–What is your plan around the usage of the following technologies over the next 3 to 5 years?
Intended use of digital technologies (3 to 5 years)
Mobile applications
Content management
Search engine optimization
46%47%
63%79%
62%80%
73%81%
68%87%
81%89%
80%94%
Customer relationship management
Collaboration tools
Reputation management
Email marketing
2013 2011
66%94%
Advanced (predictive) analytics
© 2014 IBM Corporation12
And organizations doing it are seeing results
� Improved customer retention rate at over
twice the rate of those that do not use
predictive analytics and reported a 5.8%
year-over-year growth in operating profit,
compared with 3.7% for non-users.”1
� Enjoyed a 75% higher click through rate and
a 73% higher sales lift than companies that
did not use predictive analytics”2
1Maximizing Customer Lifetime Value with Predictive Analytics for Marketing (pg 1), Aberdeen, February 2013. 2Divide & Conquer: Using Predictive Analytics to Segment, Target & Optimize Marketing (pg. 1), Aberdeen, February 2012. 3Source: IDC, The Business Value of Predictive Analytics, June 2011
3
© 2014 IBM Corporation13
All touchpointsAll Data
What could happen?
Predictive analyticsand modeling
What action should I take?
Decisionmanagement
What is happening?Discovery and
exploration
Why did it happen?
Reporting, analysis,content analytics
CognitiveFabric
Four key questions to answer
© 2014 IBM Corporation14
Let’s go back to our definition of customer analytics…
Customer analytics is the practice
of collecting and examining many
types of structured and
unstructured data in order to
fully understand and predict an
individual’s needs, desires,
preferences, and likely behaviors,
so that you can take the best
action to foster a mutually
beneficial relationship.
© 2014 IBM Corporation15
The three key ingredients to a customer analytics solution
ANALYZE datato gain critical insights
DEPLOYto real-time channels for point-of-impact action
ACCELERATEtime to value with focused solutions
© 2014 IBM Corporation16
•Reduce operational costs•Improve asset productivity•Increase process efficiency Accelerate time to value:
Acquire, Grow, and Retain customers
� Full analytics suite: Big data, predictive and advanced analytics, decision management, scoring and business intelligence
� Real-time capabilities
� Industry-specific samples: retail, telco, insurance and banking
� Operational connectors and an open architecture
IBM Predictive Customer IntelligenceA premier, integrated solution that analyzes a full spectrum of customer data to predict behaviors of individuals and deliver real-time personalized recommendations across all channels of engagement.
RETAILINSURANCETELCOBANKING
PERSONALIZATION
© 2014 IBM Corporation17
• An integrated solution focused on the deployment of predictive
• Ships with industry specific use cases and connectors to 3rd party applications• Value-based pricing
Bundled Products•SPSS Modeler Premium
•SPSS Statistics Standard Client
•SPSS Modeler Premium Server
•SPSS Statistics Standard Server
•SPSS C&DS
•SPSS Analytical Decision Management
•Cognos Business Intelligence
•WebSphere Application Server
•IBM Integration Bus
•DB2
Connectors •Unica Interact
•Lifetime Value Mazimizer (GBS Asset)
•InfoSphere Streams
PID
Use case examples•Banking : Product Affinity, Churn, Credit Card Default, Customer Segmentation
•Insurance : Churn, Propensity to Buy, Campaign Response, CLTV, Customer Segmentation
•Telco : Product Association, Churn, Campaign Response, Satisfaction, Sentiment
•Retail : Product Affinity, Customer Segmentation, Market Basket Analysis, Price Sensitivity, Campaign Response
Channels & Industries•Call Center, Website, Mobile Apps
•Telco, Insurance, Banking, Retail
Industry-specific
Predictive Customer Intelligence PID (eGA June 3, 2014)
© 2014 IBM Corporation18
Chat
Voice Email
Social Media
Interactive Voice Response
Mobile Apps
Short Message Service
Web
IBM Predictive Customer Intelligence delivers intelligence to marketing and operational systems
Data IBM Predictive Customer Intelligence
IBM EMM/Third-party Marketing
Multichannel Customer Interactions
HOW?Interaction Data•Email and chat transcriptions•Call center notes•Web click-streams•In-person dialogues
WHY?Attitudinal Data•Opinions•Preferences•Needs and desires
WHO?Descriptive Data•Attributes•Characteristics•Self-declared information•Geographic demographics
WHAT?Behavioral Data•Orders•Transactions•Payment history•Usage history
Acquisition Models
Campaign Response Models
Churn Models
Customer Lifetime Value
Lifetime Value Maximizer (GBS)
Market Basket Analysis
Price Sensitivity
Product Affinity Models
Segmentation Models
Sentiment Models
Up-sell/Cross-sell Models
IBM Predictive Customer Intelligence Available Both Inbound (Real Time) and Outbound (Batch)
Campaigns
Offers
Messaging
Lead Management
Cross-channel Campaign Management
Real-time Marketing
Marketing Event Detection
Digital Marketing
© 2014 IBM Corporation19
PureData for Analytics
Deep Customer Analytics,
Actionable Customer Data
Big InsightsExplore New
Customer Insights From All Data
MDMTrusted
Customer Data
Architecture overview
Predictive Modeling
Reporting
Real-time Scoring
Real-time Analytics Data
Repository
WA
S •
IBM
Integration Bus
Uns
tuct
ured
•S
truc
ture
d
Data Sources Points of Interaction
Direct Mail
Chat
Call Center
Mobile Apps
Web
Social
Chat
Call Center
Mobile Apps
Web
TransactionalData
Model Repository(Industry Specific)
SegmentationModel
Sentiment Analysis
Churn Model
Upsell/Cross-sell Model
AcquisitionModel
Campaign Response
Model
Lifetime Value MaximizerModel (IBM GBS)
IBM Predictive Customer Intelligence
InboundInteractions
Outbound Interactions
IBM GBS Lifetime Value Maximizer
Customer Lifetime Value and Segment Migration
Third-party
Marketing
Application
Cam
paign •Interact
Marketing E
xecution and R
ecomm
endation Engine
IBM InfoSphereStreams
Real-time Analytics At ScaleRapid Ingest to Process
Streaming Data
External Data —Social, Blog
CustomerDemographic Data
CustomerInteraction History
SMS
SMS
SMS
Industry Accelerators•Insurance•Banking•Retail•Communications
Third-party data sources
© 2014 IBM Corporation
Preparing for customer analytics:
Let’s talk about data!
© 2014 IBM Corporation21
Enhanced 360º View of the Customer
Need a deeper understanding of customer sentiment from both internal and external sources
Extend existing customer views (MDM, CRM, etc.) by incorporating additional internal and external information sources
Desire to increase customer loyalty and satisfaction by understanding what meaningful actions are needed
Challenged getting the right information to the right people to provide customers what they need to solve problems, cross-sell & up-sell
© 2014 IBM Corporation22
Data is at the heart of analytics
Behavioral data•Orders•Transactions•Payment history•Usage history
Descriptive data•Attributes•Characteristics•Self-declared info•(Geo)demographics
Attitudinal data•Market Research•Social Media
Interaction data•E-Mail / chat transcripts•Call center notes •Web Click-streams•In person dialogues
Traditional approach
High-value, dynamic approach- source of competitive differentiation WHY?
WHAT?
HOW?
WHO?
© 2014 IBM Corporation23
Professional LifeEmployers, professional groups, certifications …
Legal/Financial LifeProperty, credit rating,
vehicles, …
Contact InformationName, address, employer,
marital…
Business ContextAccount number, customer type,
purchase history, …
LeisureHobbies, interests …
Social MediaSocial network, affiliations,
network …
Creating a “market of one” from many personas
© 2014 IBM Corporation24
Where does big data come from?
© 2014 IBM Corporation25
The key is to leverage all the data
© 2014 IBM Corporation26
Individual silos can answer typical questions, one-by-one
Wiki
Who is best able to help this customer? Experts
What is her view of our company? Social
Media
Fulfillment
What issues has this customer had in the past? Support
Ticketing
Where else has she worked? External
Sources
Who is this customer?CRM
What is available inventory? Supply
Chain
How is her company using our products? Content
Mgt.
What products has she purchased? DBMS
… but an enhanced 360ºview provides answers in one application
© 2014 IBM Corporation27
Enhanced 360º View answers questions that require multiple systems
WikiExperts
What should I know before calling her for renewal? Social
Media
What marketing materials should I send? Support
Ticketing
What’s going on with this customer TODAY? External
Sources
What products can I upsell this customer? CRM
How can we increase engagement with her? Supply
Chain
How can we get more customers like her? Content
Mgt.
What impact will inventory have on her? DBMS
Fusion of data from multiple systems enables deeper insights—not just facts
Fulfillment
© 2014 IBM Corporation28
Big data and Entity Analytics: a single version of the truth
Customer ID
Last First Street Last Interaction
CRM123 Jones B. 35 West 15th
Called customer service with handset issues
ERP789 Jones William 36 West 15th
Customer was offered a marketing promotion in the past month but declined
Cookie info Checked website for international rates
billjones [at]gmail.com
Jones Bill 35 West 15th, Apt A
Email: “This is frustrating – competitor XYZ provides a 2 day turnaround”
Customer ID
Last First Street Insights
CRM123 Jones William 35 West 15th, Apt A
• Has issues with handset
• May travel internationally and be interested in international plans
• Declined a marketing promotion recently
• May defect to a competitor
CSR System
Campaign Management
Web logs
Unstructured text
IBM Big Data capabilities link sources of information to provide a single comprehensive view of the customer for optimized customer interaction
Fragmented customer
view
Singlecustomer
view
© 2014 IBM Corporation29
Informatio
n about contact
from external sources such
as LinkedIn
Real-time activ
ity fe
ed
shows new content from
many sources (DBMS,
Salesforce, SAP)
List of past purchases by
this contact from order
tracking system
Recent conversations from
multiple sources: e.g.,
CRM, e-mail, etc.
Product offers based on
past purchases and
conversations
Contact
information from
CRM
Accounts associated w
ith
contact (past a
nd present)
based on info in
CRM
Consolidated list of
products owned based on
account affiliation
© 2014 IBM Corporation30
Customer alerts
Social media
analytics
From
streaming
content
Customer campaign
analytics re
levant to th
e
user’s profile
Customer insights from
Activity Feed
Contextual
monitoring of
customer
views in
outside data
feeds
© 2014 IBM Corporation31
The data world: not a neat, structured place
© 2014 IBM Corporation32
•Natural Language Processing is a field of
computer science that involves a set of
linguistic, statistical, and machine-learning
techniques that analyzes text and extracts
key information
What do you think when you hear the word “boxer?”
Extracting meaning from unstructured data
© 2014 IBM Corporation33
•Social media analytics evaluates billions
of social media comments and provides
customized results in configurable charts
and dashboards
�Sentiment analysis analyzes how your
customers feel about your product or
service so you can predict individual
needs and also identify new segments.
These can include brand sentiment,
customer satisfaction, and influencer
impact.
Extracting meaning from unstructured data
© 2014 IBM Corporation34
Algorithms find the relevant data among the noise
Type Classification Segmentation Association
Enables�Identify attributes causing something to occur
�Find patterns and clusters of similar things, and outliers
�Discover associations, links, or sequences in your data
Examples
�What signals a customer leaving?�How many umbrellas will we sell in the next three months in Chicago?
�Who is likely to respond to a marketing campaign? �Which insurance claims should we investigate?
�What products are purchased together?�What is the series of clicks on my web page that leads to a sale?
Use to
�build alerts for call centers to take corrective action on customers identified as at risk for going to a competitor.
�Increase ROMI and reduce opt-out rate by reduce the number of people you market to by selecting only those most likely to respond.
�Increase average sales by building campaigns and promotions that combine items offered or provide recommendations for purchase
© 2014 IBM Corporation35
Many, many rich modeling techniques go into customer analytics
Real-time decision management
Customer Value Calculation
Demographic segmentation
Loyalty Segmentation
Campaign Management
Churn Modeling, Next Best Offer
Social Network Analysis
© 2014 IBM Corporation36
Taking action on predictive insights is the key
Analytics are only valuable if you do something with them!
D E P L O Yto operational systems
© 2014 IBM Corporation
A closer look at customer analytics in action:
Lily and VT Living
© 2014 IBM Corporation38
final
offer(s)
Records offer
presentation and
response or
non-response
Can be factored into self-
learning for future scoring…
…and factored into
future batch campaigns.
ARBITRATION
Self-learning algorithm
or external model
adjusts scores
revised
offer list
and scores
ADD/REMOVE
OFFERS
Adjust list of offers
using white/black lists
and suppression rules
candidate
offers &
scores
REAL-TIME
LOGIC
Uses combination of
segmentation, rules
and event pattern
recognitionReal-time
context
Customer
profile
white lists,
black lists
PRE-
CALCULATED
DECISIONS
Builds white lists
and black lists
Data from real-
time service calls
global
Offers can be:
segment-level
individual-level
offers
segment definitions
interaction history
Can use common:
Accept /
Reject
A closer look at the offer/decision process
© 2014 IBM Corporation39
Scenario 2: Real-time Offers and Cross-sell
• Anne’s product portfolio shows she recently bought a new home
• Recent spending patterns in her demand account and her bank card show she’s made a number of large household purchases recently
• Real-time transaction data shows Anne just purchased a kitchen appliance
Scenario 1: Optimizing Offers
• Pete called the bank contact center today to ask about loan processing times
• He checked mortgage rates on the bank website three times
• He tweeted for information on buying a second home
Delivering predictive customer intelligence to marketing & operations: Banking
Bank Action
• Proactively sends Anne an offer to her smartphone for an increase in her credit line and a reduction in interest rate—while she is still shopping. This heads off possible card offersfrom retailers.
• Alerts Anne to the bank’s secure digital vault service by simply taking pictures of important documents with a smartphone
Bank Action
• Sends an offer to Pete, via his channel of choice, for a mortgage with special terms
© 2014 IBM Corporation40
Scenario 1: Churn Reduction• Roger called the contact center today to talk about poor reception at his home• Roger has had 10 dropped calls in last week in his home area – that’s 10x the average of 1 dropped call per week!
• Roger will be eligible for a new Smartphone under his contract in 6 months
Delivering predictive customer intelligence to marketing & operations: Telco
Communications Service Provider (CSP) Action• Customer service representative acknowledges the issue, apologizes to Roger for poor network service at his home, informs him of the estimated time for resolution of the issue
• CSR knows that the client is at risk of churning and offers the client early upgrade to the latest Smartphone with a new signed contract .
• Roger accepts the offer, renews the contract for 2 years and is delighted at the exceptional service
Scenario 2: Improved Cross-sell / Up-sell • Sara changes jobs and starts consuming media content over 3G in the public transport• Sara tries 4G but decides not to subscribe to the Full High Speed Bandwidth for 4G Offer.
Communications Service Provider (CSP) Action• CSP’s PCI solution suggests that Sara may like temporary 4G access . Based on Sara’s mobility profile (locations visited during the day, time spent at each location) & demographics, Sara is similar to Jane who utilizes high speed access during her commute to wo rk on public transport.
• CSP offers Sara location based high speed access subscription to allow her to stream videos on the way and back from her using public transport (selected lines on partnered public transport networ k)
• The solution is custom-fit for Sara – Sara accepts the offer, CSP gets the additional revenue
© 2014 IBM Corporation41
Scenario 1: In-store real-time contextual offer
• Lily browsed VT Living’s smartphone app today to check out organic cotton sheets & cutting boards. Puts sheets in her checkout cart and abandons it.
• Social activity in the past 3 months shows her tweeting pics of first ever flat purchase in Brooklyn
• Lily as an opt-in customer walks into the VT Living store and is recognized via her smartphone in real-time
Delivering predictive customer intelligence to marketing & operations : Retail
Retailer Action• Sends a welcome message to Lily via SMS, directing her where to find the cotton sheets. A today only special offer of 10% off bamboo cutting boards when found spending more than 10 minutes in the area.
• Lily makes her purchases, delighted VT Living is helping her save time and money.
Scenario 2: Segment migration campaign• VT Living’s customer segmentation report shows Lily is in the medium value segment and scored as prime to
move to high value segment.
• Recent new behavior attributes about Lily from social data shows she is passionate for earth-friendly causes; web activity shows interest in eco-friendly products. Sales transaction data shows recent household purchases.
• Customers identified with the same passion for eco-friendly products have been scored &analyzed to show what eco-friendly products have been purchased
Retailer Action• Proactively send Lily an email offer thanking her for her recent store purchases and a special 2 day expedited free shipping offer for eco-friendly soda maker products.
• Lily purchases online and delighted that VT Living anticipates what offers she is interested in.
© 2014 IBM Corporation42
Scenario 1: Customer Lifetime Value & Customer Retention
• Karen scores high in the customer lifetime value scoring for Insurer. She pays premiums on time and has a good claims history as a 13 year policy holder.
• Karen and her husband have 3 cars, a home, a college-aged child’s renter’s policy, an umbrella policy, and 2 life policies all insured with Insurer. Karen and her husband have 3 children all who are insured on the auto policies.
• Karen posted a Facebook photo of her car with bumper damage that was hit in the parking lot while grocery shopping.
• She makes a call to her insurance company concerned about her insurance premium and repairs.
Delivering predictive customer intelligence to marketing & operations: Insurance
Agent Action
• The call center agent (CSR) has visibility to relevant information about Karen and her claim:
- Automated FNOL (first notice of loss) initiated 2 hours ago through the telematics data and notes that the claims adjuster has been trying to reach Karen
- Sees Karen’s Facebook post
- Online police report showing other driver is at fault and his insurance will cover damages
• The CSR informs Karen that they have been trying to reach her to make sure she was okay and that her premium should not be impacted. CSR transfers her to the adjuster so that Karen can get her car repaired.
• Karen is delighted with the exceptional and caring service received.
© 2014 IBM Corporation43
Scenario 2: Improved Cross-sell / Up-sell • Kathy is an existing life insurance policy holder of Insurer. This is her only life policy.
• Kathy purchased her life policy through Agent. Agent and Insurer only sell life insurance.
• Kathy recently was married and bought a new home. She has made social media posts about her wedding, honeymoon and house hunting.
• Kathy has recently made changes to her life insurance policy including a bank account and address change directly with Insurer.
• Kathy calls her agent’s office about changing her name and beneficiary.
Delivering predictive customer intelligence to marketing & operations: Insurance
Agent Action• Agent receives Kathy’s call and pulls up her complete (360 degree view) customer record
- Sees the recent policy changes
- Sees Kathy’s recent social media posts
• Agent congratulates Kathy on her wedding and asks if she recently bought a new home
• While fulfilling the requirements for Kathy’s name and beneficiary change, the agent asks if Kathy has considered if her current life insurance will cover her outstanding mortgage if something happened to her
• Agent presents Kathy with some options to consider. Agent, Kathy and Kathy’s new husband all agree to meet to develop the right coverage plan.
© 2014 IBM Corporation44
3
1. An activity occurs that calls for a decision.
2. The context from the activity is passed to the
decision process.
3. The decision process augments the context with
stored information and runs the decision
model.
4. One or more actions are recommended to the
activity.
5. The activity feeds back the results to help tune
the model over time.
ContextAction
Decision
Activity
Feedback
Information
Facts,recent events,
options
Decision input, actions and outcomes
3
5
41
2
Real-time decision loop allows predictive models to get
even smarter
© 2014 IBM Corporation45
Communications provider C Spire Wireless uses
predictive analytics and decision models to optimize
cross-selling and prevent churn
Business Challenge ⏐ Outcompete the resource-rich wireless giants, C Spire Wireless needed to beat them at the small things that matter most: getting closer to customers and keeping them satisfied. Its challenge was to convert what it knows about customers into actionable insights that help account reps craft the optimal offers that meet their needs and head off customer dissatisfaction.
Smarter Solution ⏐⏐⏐⏐ C Spire Wireless is using predictive models to examine the complexity of its customers’ behavior and determine which service mix is optimal for each customer’s need, as well as the indicators of imminent churn. By embedding these insights into its customer-facing processes, C Spire Wireless has empowered its reps to optimize their interactions with customers.
270% increasein cross-sales of
accessory products
Increased satisfaction by creating a more
personalized customer experience
50% increasein effectiveness of customer
retention campaigns
Excellent buy-infrom front-line crew
© 2014 IBM Corporation46
A multichannel Korean retailer personalizes communications and
optimizes offerings
� IBM® Cognos® Business Intelligence� IBM SPSS® Modeler� IBM Unica ® Enterprise Marketing
Management� IBM Netezza® Data Warehouse� IBM InfoSphere® Warehouse� IBM InfoSphere DataStage®
Reliable insightprovides decision support for senior management
Targeted campaignscan be developed for marketing
Precise measurementof cross-channel campaigns
Solution Components
Business Challenge: As sales increased for this retailer’s online shopping mall, management experienced increasing difficulty ensuring that an appropriate product mix was being presented to its customers.
The Solution: The company adopted sophisticated analytics and marketing automation to understand, predict and act on consumer buying behavior with confidence. Real-time marketing automation delivers personalized content to each shopper, triggered by their interaction history. Delivered at the right place and time, these offers can move the shopper toward a sale and even increase the size of the purchase.
“We have greatly improved our understanding of our customers, which is helping us to make smarter decisions that significantly improve business performance.”
—Spokesperson, multichannel Korean retailer
© 2014 IBM Corporation47
142 percent reductionin revenue erosion for customers at most risk of churning
$10 million+ savings/yearfrom increased retention and reduced customer service costs
5 months to achieve full return on investment
Solution components
The transformation: XO Communications had already taken the first steps in identifying customer retention risks through analytics; now it wanted to seize the opportunity to put these insights into action more effectively. By using IBM®
SPSS® solutions to hone its predictive models, the company built a richer, more up-to-date picture of its client base and began delivering this data to a greater range of employees.
“We are only just starting to realize the true potential that IBM analytics holds across the business.”
— Bill Helmrath, Director of Business Intelligence, XO Communications
• IBM® SPSS® Analytics Catalyst• IBM SPSS Modeler• IBM SPSS Modeler Server• IBM SPSS Statistics• IBM InfoSphere® BigInsights™ YTP03235-USEN-00
XO Communications takes control of customer satisfaction
© 2014 IBM Corporation48
© 2014 IBM Corporation49
maturity
valu
e
Deliver Smarter
Customer Experiences
Real-Time Decisioning
Deliver customized interactions at the point of impact & consistent experiences across all channels
Uncover hidden patterns and associations within consumer data to predict what they are likely to do next
Analyze historical consumer purchase behavior, preferences, motivations and interactions
Capture and consolidate disparate data about consumers across touch points for 1 version of the truth
Information Integration
Customer Insight
Personalized Communication
Understand the optimal offer, time and channel that is best for each individual consumer
Predictive Modeling
Analytics maturity is a journey
© 2014 IBM Corporation50
Key questions when beginning your customer analytics journey
� What key customer metrics am I trying to improve,
and how are they measured?
� Through what channels do I interact with my
customers, and how can I improve those
interactions? (“what if I could…?”)
� Where are the “moments of truth” in the customer
lifecycle?
� What operational processes will be affected by the
introduction of analytics?
� What data do I have, and what data do I need?
� Analytics skills – build or buy?
� How might I grow my analytics capability?
© 2014 IBM Corporation51
What if I already have Modeler Gold?
IBM Predictive Customer Intelligence includes all of the
components of Modeler Gold, PLUS:
�All the components required to deliver real time predictive modeling
− C&DS Real Time Scoring
− WebSphere Application Server
− IBM Integration Bus
�Integration with EMM to deliver a complete customer analytics solution
�Content accelerators for Telco, Insurance, Banking and Retail for key customer
analytics use cases
�Connector to integrate with InfoSphere Streams for Big Data problems
�Connector with GBS modeling assets to utilize unique IBM IP for customer analytics
�Automated install for all components takes less than 1 day on Linux
9/5/201451
© 2014 IBM Corporation52
SUMMARY: IBM Predictive Customer Intelligence
Differentiators
Cross-channel, Real-time Action• Including customer service, issue resolution, account management, response and billing, with
touchpoints managed in near real time via appropriate channel
Decision Management• Mature technology combining analytics and business rules creation, integration and execution• Near real-time recommendations beyond just marketing offers
Advanced Analytics• Marketplace-leading tools for predictive and advanced analytics• Integrated optimization techniques that combine analytic output for the best answer
Industry-specific Templates• Banking, retail, communications and insurance-specific reports, samples, algorithms
IBM Big Data Platform•Integration and management of the variety, velocity and volume of data•Phased approach for enhanced 360-degree customer view•Advanced analytics applied to information in its native form
© 2014 IBM Corporation53
Additional reading
All available at: https://ibm.biz/predictivecustomerintelligence
� Solution Brief: IBM Predictive Customer Intelligence
Create personalized, relevant customer experiences with a focus on driving
new revenue.
� Analyst Research: The Power of Customer Context Beyond Campaigns
Campaigns are far less effective at winning and retaining customers than
they once were.
� White Paper: The new frontier for personalized customer experience
BM Predictive Customer Intelligence gathers relevant information and uses
analytics to recommend the right offer or action during interactions with
individual customers.
� White Paper: Optimizing marketing results with business analytics
Build a foundation for successful, profitable marketing programs with
marketing analytics.
© 2014 IBM Corporation54
© 2014 IBM Corporation55
© IBM Corporation 2014. All Rights Reserved.
IBM, the IBM logo, ibm.com are trademarks or registered trademarks of
International Business Machines Corp., registered in many jurisdictions worldwide.
Other product and service names might be trademarks of IBM or other companies.
A current list of IBM trademarks is available on the Web at “Copyright and
trademark information” at www.ibm.com/legal/copytrade.shtml.
© 2014 IBM Corporation 55
Copyright and Trademarks
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