Customer Intelligenc

8/6/2019 Customer Intelligenc

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Look Inside your Customers

D

N

A

Customers

Customer Intelligence&

Analytical CRMWWW.CUSTOMERS-DNA.COM

FREE Presentation - July 2010




D

N

A

Customers

Customer Insight&

Marketing



Overall CRM Framework

WWW.CUSTOMERS‐DNA.COM

1. Voice of the customer as top priority

7. Organization ‐ process, programs, KPIs, incentives

2.

Customer

Insight

9. Implementation approach ‐ “Do‐it, Try‐it, Fix‐it”

3.

Dynamic

segmentation

and customer

value

4.

Customer retention

& development

6.

Continuous

improvement

strategy5. Multi wave, multi‐

channel

campaigns

8. IT architecture and investment prioritization

10. New product development ‐ CRM input



TYPE OF INSIGHT Example of insight

Propensity to churn by customer

Drivers of customer valueLikely actions to drive cross-sell/up-sellDrivers of downward migration

Unfulfilled needsWillingness to pay for newproduct/service offering

Needs and behaviors by

segmentMedia consumption and channelusage by segment

Population distribution withincountry

Population distribution withincustomer base

Typical methodology usedto generate insight

Data mining

Targeted research

Focus groupsQuantitative research

Focus groupsExternal sourcesConjoint analysis

External sourcesData mining

Focus groups

CRM

New product

development

Branding

Network build‐out

Focus of this

presentation

C u s t o m e r i n s i g h

t

Customer Insight for CRM




Needs/Attitudinal Analysis to ascertain customers needs, wants or

attitudes pertaining to the company’s services

and products

• Brand image and product positioning

• Tailoring customer communications messages

• New

product

design

and

promotions• Supports service delivery strategy

• Targeting prospects/ acquisition

• New ‘offer’ development

• Branches Network Design

DemographicsDescribes the segment based on demographical

information many times retrieved from external

lists• Targeting for existing product management• Targeting churn prevention

• Life stage marketing

• Deferential Customer Relationship Marketing

• KPIs tracking and competition analysis

Behavioral Grouping people by how they act multi

dimensional techniques, e.g., detail usage,

buy, revenue

• Monitor and measure the number and valuegrowth within each segment (business planning)

• Execute service delivery strategy

• Investment resource allocation & prioritization

Value

Using a customer profitability measure, e.g.,

billed revenue, ARPU, MARPU, LTV

TYPE APPLICATIONS

Propensity

Grouping people using also derived

propensity values, e.g., present value VS

potential value, value VS risk

• Customer management strategy

• Efficient Campaign Targeting for better ROI• Support of Next Best Activity

Marketing Actions using different Segmentation Schemes




Predictive Scores Could Mean


• Likelihood to respond to an offer

• Which product to offer next

• Estimate of customer lifetime

• Likelihood of voluntary churn

• Likelihood of forced churn• Which segment a customer belongs to

• Similarity to some customer profile

• Which channel is the best way to reach the customer



Three Methods to Drive Retention & Development

A customer centric company needs to build expertise in three distinct

capabilities to drive retention and development initiatives

Campaigns




Type of Campaigns

Outbound

Inbound

Company initiates a contacteither based on a companyschedule or on an event –

e.g., customer moving house

Company-scheduled campaigns

The company decides to run a campaign towardsa target group at a time that fits the company’soutbound campaign execution schedule

USE OF CAMPAIGNS

A customer initiated contact isused to make a campaignoffer once the reason for thecontact has been dealt with

Event-triggered campaigns

An event in a customers life triggers a campaign tobe run towards that specific customer





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N

A

Customers

Data Miningin the Analytical CRM Framework



What is Data Mining

Data Information

This what Data Mining is all about




Data Mining Methodology – CRISP DM




Data Mining Applications in CRM

Segmentation

• Value Based Segmentation

• Present Value VS Potential Value

• Present Value VS Risk

• Behavioral Segmentation – Multi Attribute Segmentation

Prediction – Classification

• RFM• Churn Prediction

• Cross/Up Selling Modeling

• Credit Risk Propensity Models

• Acquisition Modeling

Campaigning

• Campaign Testing using Control groups

• Next Best Activity




Predictive Technologies in Data Mining

Logistic Regression Models• Predict likelihood of purchase

• Statistical knowledge needed – very effective for linear correlations

Neural Networks

• Predict likelihood of purchase• More complex (black box) but sometimes more accurate

Rule/tree induction Models

• Predict likelihood of purchase

• No theory assumptions needed – very descriptive regarding resultsClustering Models

• Find segments mostly based on product mix and transactions

• One of the most important tools for unsupervised segmentation

Association Detection• Groups of products typically held together

• Very popular as Market Basket Analysis – the simplest way to X selling

Sequence Detection

• Actions and events which indicate the next purchase

• Very popular for Web Mining – taking into account the time order




Behavioural Segmentation (mobile telephony)




Association Analysis (fixed telephony)




• Data mining significantly improves targeting and ROI of direct campaigns

• Data mining models can catch 5 to 6 times more churners than randomness

• Models accuracy is always dependent on data

Data Mining Effectiveness




- x-axis gives thepopulation percentile

- y-axis gives % of

captured responses

- customers with top10% of the scoresaccount for 30% of

responders

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Response Model

No Model

The Cumulative Gains Chart




Classification Trees





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N

A

Customers

Putting them All TogetherDeployment



Next Best Activity Components


The main NBA components

Credit ability and behavior (CreditScoring)

Current and expected customerValue (Customer Profitability andValue based segmentation)

Customer segments based onbehavioral, demographical, survey and financial attributes(Customer Segmentation)

Probability of customers’

accepting cross/up/deep sellingactivities (Supervised propensityModeling)

Probability of customers’

voluntary churn in the future(Churn Prediction)



Retention of high value

customers is always a

first priority task

Customer development

should be applied to low

risk customers

Next Best Activity in Action



The CRM House

Segment-Managementintegrated Multi-Channel Management

D i r e c t M ar k e t i n g

O u t b o un d E

-M ai l

Advance Analytical Techniques

Understanding of Customer Lifecycle and Migration Streams

Tactical Segmentation and Clustering

Product/Channel Affinity & Churn Risk Scores

Customer value and Customer profitability

I n b o un d Of f er i n g s

S er v i c e s t o S al e

I n b o un d Of f er i n g

B r an c h e s -M

i c r oMR K

P r of i l e b a s e d

Of f er i n gI n

t er n e t

S e gm en t b

a s e d

A d v er t i s i n g

S ur v e y s

&

F e e d b a c k


O u t b o un d

M ai l


O u t b o un d

C al l

C am p ai gnM an

a g em en t

A n al y t i c al C R M

O p er a t i on al C R M

D WH & D a t aM ar t s

1 t o

1I n s t r um en t s

A n al y t i c al

F o un d a t i on

C on t a c t

S t r a t e g y




Documents

Customer Intelligenc