IBM - Z Analitiko Do Vecje Prodaje

© 2014 IBM Corporation

Real-time analytics:

IBM Predictive Customer Intelligence

Theresa Morelli

Sr. Product Manager, IBM Business Analytics

[email protected]

© 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


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.


Customer analytics: the key to the customer relationship

� Increasing digital usage

� Channel-preference shift

� Multichannel consumer decision journey

� Digital sales


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



Analytics

Optimizing every touchpoint in the customer lifecycle with analytics


• 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


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


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


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


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


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


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.


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


•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


• 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)


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


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

Email

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


Preparing for customer analytics:

Let’s talk about data!


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


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?


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


Where does big data come from?


The key is to leverage all the data


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

Email

How is her company using our products? Content

Mgt.

What products has she purchased? DBMS

… but an enhanced 360ºview provides answers in one application


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

Email

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


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


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


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


The data world: not a neat, structured place


•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


•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


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


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


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


A closer look at customer analytics in action:

Lily and VT Living


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


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


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


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.


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.


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.


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


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


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


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



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


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?


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


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


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.



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Other product and service names might be trademarks of IBM or other companies.

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© 2014 IBM Corporation 55

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IBM - Z Analitiko Do Vecje Prodaje