Marketing to the Segment of One

© 2012 IBM Corporation

Marketing to the Segment of OneTrends and A Case Study

Kingshuk Banerjee, D.Sc.Leader, Center-of-Competence, Business Analytics and Optimization

IBM Global Consulting Services

Trends

1. Describe, Predict and Prescribe for A Specific Customer

2. Micro-segmentation, Personalization and Next Best Action

3. Big Data Leverage

4. Mining the Unstructured

5. Realtime decision-making: People, Process and Technology


Trend 1: Describe, Predict and Prescribe ...

Business Impact

Heroics

Foundational

Competitive

Differentiating

Break-away

•Spreadsheets•Extracts

•MDM •Data Warehouses•Data Governance

•Micro-Segmentation•Pattern recognition

•View Consolidation•Dashboards

•Mathematical Optimization•Reinforced Learning

DescriptiveCustomer 360 View

PrescriptivePrescribe the Optimized Action

Source: IBM; Davenport et al, “Analytics at Work”

PredictivePredict the Behavior

... for A Specific Customer


Trend 2: Micro-Segmentation, Personalization, NextBestAction

Driving a major Shift in Sales and Marketing Strategy.. from selling “what I have” to focusing on “what YOU need”

Allocate Optimized Offer

CUSTOMER Needs

ENTERPRISE Objectives

Who am I ?

What do I need?

When do I buy?

Where do I buy?

Whom should I offer?

What should I offer?

When should I offer?

How should I offer?

• Demographics• Purchases• Interactions• Preferences

• Purchase Cycle• Propensity to Buy• Purchase Drivers

• Purchase Triggers• Purchase Affinity• Activity Based• Life Event Based

• Shopping Trip Types• Channels / Devices• Locations• Occasions

Customer Profile Foundation

• Micro-

segmentation and

Personalization

Optimized Marketing Activities using Mathematics

and Simulation

• Offer Allocation

based on Goal and

Constraints

• Offer Timing

• Channel Selection

© 2012 IBM Corporation5

e.g. Banking Customer

Multiple Manifestation of the same Individual

Behavioral data- Orders- Transactions- Payment history- Usage history

Descriptive data- Attributes- Characteristics- Self-declared info- (Geo)demographics

Attitudinal data- Opinions- Preferences- Needs and Desires

Interaction data- Email / chat transcripts- Call center notes - Web Click-streams- In person dialogues

Who? What?

Why?How?

ConsolidateData across Lines- of-Business

AnalyzePredict and Prescribe

Describe customer holistically .. multiple dimensions .. 360 view

Care

Retain

Enhance

Bill

Collect

SellCross Sell / Up Sell

CentralizeData on Customer Interactions Across Channels

Retail Client --->

Small Business -��

Wealth Management

Trend 2 (continued): Understanding the Customer, Good Practices… must be in tandem with Societal Characteristics, Technology Adoption and Business Needs


This Asset focuses on Long Term Gain

This asset is based on Reinforcement Learning and Constrained Markov Decision Process

framework -π (s,a,r)� (s) - Customer is in some "state" (his/her attributes) at any point in time

� (a) - Enterprise's action will move customer into another state

� (r) - Enterprise's goal is to take sequence of actions to guide customer's path to maximize customer's lifetime value

Current marketing policy

Optimized marketing policy

Customer A’s path under…

BargainHunter

Repeater

LoyalCustomer

ValuableCustomer

One Timer

Repeater

Defector Defector

Repeater

LoyalCustomer

PotentiallyValuable

Action A

Action B

Action C

Action E

Action D

Trend 2 (continued): An IBM Research Lab Asset Next Best Action


Transactional & Application Data

Machine Data Social Data

• POS / e-commerce transactions

• Call detail records

• Utility meter readings

• RFID tag data

• Refinery sensors

• Web log data

• Tweets

• Blogs

• Social network members / actions

Enterprise Content

• Emails

• Document images

• Video archives

Are you tapping into data beyond the traditional, structured sources?

Trend 3: Big Data Leverage


Trend 4: Mining the Unstructured

How IBM Watson performs Natural Language Processing in Unstructured Data?


Trend 5: Real-time Personalization

Step 1: Customer walks into an Electronic store to window-shop for smart phone

Step 2: Customer ends up buying a Smart Phone gadget

Step 3: Pays by the Bank Mobile App

Next Best Action(NBA)

B2C Commerce Platform

Step 4: Transaction event and spending location are detected by the Bank Platform

Step 5: Based on past spending patterns, house-holding analytics, current location and transaction details – NBA suggests best offer for this customer from its eco-system partner, located nearby

Step 6: 15% discount on a Luis Vuitton bag in an outlet located in the same plaza; offer valid for this specific customer for next 2 hours

Payment Gateway

A Use Case

A Credit Card Company generating Next Best Offer at the time of Purchase

A Case StudyData-driven, Personalized Marketingfor an European Bank

Confidential MaterialNot for Public Share

© 2012 IBM Corporation12

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