Predictive Customer Intelligence

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Predictive Customer Intelligence

Sogeti 2015

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Trade with me

Sharing data, location, and new ideas in return for better

products and value Educate me

Bringing expertise to every customer interaction

Let me choose

Options vs. prerequisites, roadmaps vs. checkboxes Grow with me

Data and insight connecting the lives of customers, households Find me

Using visualization and analytics to discover new customer

segments Ask me

Consulting customers on products, services, and social issues

Know me

Offer new products and services based on understanding my wants, needs

Excite me

Unexpected services at unexpected moments

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Organizational capabilities have been a hindrance to

customer centricity

Inability to gather and synthesize

insights

to customer behavior, needs and preferences from analysis of

multiple data sources

Difficult to deliver

omni-channel customer analytics solution able to analyze, score and determine most appropriate action with individual customer

Only historical view

of customer, resulting in inappropriate or incomplete offers or communications at the time of interaction

Challenged

in using analytics to add short-term value or enhance long-term strategy

Lack of channel integration

and siloed lines of business, causing

inconsistent or tactical customer interactions

Inconsistent service delivery

and weak customer relationships, resulting in low retention

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Chat

Voice

Email Social

media

Interactive voice response

Mobile

Short Message Service

Web

Acquisition models

Campaign response models Churn models

Customer lifetime value Price sensitivity

Product affinity models Segmentation models Sentiment models

Up-sell / Cross-sell models

Campaigns Offers Messaging Lead management Cross channel campaign management Real time marketing Marketing event detection

Digital marketing Customer services

The optimized customer insight and engagement process

Data

Predictive

customer insight

Real time or historical

Enterprise

_marketing

Multi-channel

_customer

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Predictive Customer Intelligence key capabilities

ANALYZE

data

to gain critical insights

DEPLOY

to real-time channels for

point-of-impact action

ACCELERATE

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Many, many rich modeling techniques

Social Network Analysis

Demographic Segmentation

Churn Modeling, Next Best Offer

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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.

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Built-in Connectors provide enhanced functionalities

InfoSphere Streams

Quickly ingest, analyze and correlate large

data sets from real-time sources and interact

with individual customers at scale.

IBM Interact

Allow the power of the deep algorithms to

be introduced at the moment of impact,

including the inclusion of contextual data

IBM Customer Intelligence

Optimizer, Lifetime Value

Maximizer

Optimize customer-specific actions/ offers to

maximize long term customer value by

moving customers to a “higher value state”

IBM BigInsights

(and other Hadoop Distribution)

Pull together large volumes of all different

types of data including social/unstructured

information and structured data like

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Online quote

(Adapted)

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Recommended Action:

Service Offer “Get ready for summer with

a free airco check”

Targeted Retention Offer “10% discount with 2 year fixed price

guarantee and lower deductible”

Targeted Retention Offer “10% discount and

lower deductible”

Targeted Retention Offer “10% discount with 2 year fixed price guarantee”

Likelihood of Cancellation

Loss Ratio Prediction

The retention offer decision

depends on the combination of

these three factors:

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Predictive Customer Intelligence Architecture

Overview

PureData for Analytics Deep customer analytics Actionable customer data Big Insights Explore new customer insights

from all data

MDM Trusted customer data Predictive Modeling and Optimization Reporting Real-time Scoring Data Repository for Real Time Analytics W A S / I B M In te gr a tio n B u s U ns tr u ct ur ed • S tr uc tu re d

Data Sources _InteractionPoints of

Direct Mail Email Chat Call Center Mobile Apps Web Social Chat Call Center Mobile Apps Web Transactional Data Model Repository (Industry-specific) Segmentation

Model Sentiment Analysis

Churn Model Up-sell / Cross-sell Model Acquisition Model Campaign Response Model

Lifetime Value Maximizer Model (GBS)

IBM Predictive Customer Intelligence

In bo u nd In te ra cti on s O ut bo u nd In te ra cti on s GBS Lifetime Value Maximizer Customer Lifetime Value & Segment

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Behavior-Based Customer Insight Solution for Insurance

Integration into Marketing & Distribution Dashboards

Behavior-based Segmentation Analysis

• Generates advanced segmentation and

individual insight based on behavior

• Identifies key target customers to retain

• Proactively identify "at-risk" customers early

• Enables channels to act

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