Modeling customer dynamics and social CRM

Exploiting customer dynamicsCustomer Journey Identification

Social CRM Anita Prinzie

Exploiting customer dynamicsExploiting balance evolution for churn predictions

aCRM activities

Actively managing customer relationships:

1. Acquisition: identifying & attracting customers

2. Cross/up-selling (development): profitable usage stimulation

3. Retention: identifying customers who intend to churn (detection), and trying to keep profitable customers (churn prevention).

4. Recapturing: identifying lost-customers who might be valuable to re-acquire.

3

Customer RetentionChurn prediction

Benefit

A 1% improvement in retention can increase firm value by 5%

(Gupta et al., 2004)

Financial services Contractual churn for insurances

Non contractual churn for

4

Defection is not directly observed

Churn application

5

Churn definition

• Churn prediction using data warehouse from an International Financial-Services Provider (i.e. IFSP)

• Churner

customer who closed all his accounts in 2003

• Training sample: 8,127 customers (2.46%)

Hold-out sample: 8,127 customers (2.45%)

Including business knowledgeChurn prediction

Bank managers feel that a customer will churn if...

The account balance rapidly decreases

Regular deposits are no longer made

Etc.

6

Exploiting customer evolution

7

1

2

3

Represent the customer’s turnover evolution

Compare the customer’s turnover evolution with other customers

Identify segments with similar turnover evolution

Turnover

Assets

Balance short-term and long-term credit

accounts

Total debit on current accounts

Liabilities

Total amount on savings

Total amount on investment products

Credit on current accounts

Sum of monthly insurance fees

8

Customer’s turnover evolution

9

1

January

Turnover

2002

Febru

ary

Marc

h

Apri

l

May

June

July

August

Septe

mber

Octo

ber

Novem

ber

Decem

ber

March-January

January

June-April

April

October-July

July

December-October

October

Customer’s turnover evolution

10

1

March-January

January

June-April

April

October-July

July

December-October

October

0.03 -0.05 -1.25 -2.60Joe

5 1 2 3Joe

11

Element Relative Turnover

Values

Deletion/Insertion

Cost

0 0 0.2

1 ]-0.5, 0[ 0.4

2 ]-2.5,-0.5] 0.6

3 ]-10,-2.5] 0.8

4 ≤ -10 1

5 ]0,0.05[ 0.4

6 [0.05, 0.5[ 0.6

7 [0.5,2.5[ 0.8

8 ≥ 2.5 1

Compare customers’ turnover evolution

12

2

Similarity as SAM distance*

*SAM= Sequence Alignment Method

Joe

January

March-January June-April

April

October-July

July

December-October

October

5 1 2 3

Jane 0 1 5 0

Sequence Alignment Method

13

Origincomputer sciences (e.g. text editing)

molecular biology (e.g. homology detection)

Goal Investigating how similar or disimilar sequences are

SAM Distance

14

SAM Distance

Score expresses the number of operations needed to align the source sequence, a=a[a1,...,ai], to the target sequence, b=b[b1, ..., bj]

Higher score = less similar

SAM Distance

15

SAM Operations

Elementary Operations

Insertion of an element from the target in the source

Deletion of an element in the source

Substitution or replacement

Different SAM distance definitions: Levenshtein (1965), Hay (2003)

16

Element Relative Turnover

Values

Element

Deletion/Insertion

Cost

0 0 0.2

1 ]-0.5, 0[ 0.4

2 ]-2.5,-0.5] 0.6

3 ]-10,-2.5] 0.8

4 ≤ -10 1

5 ]0,0.05[ 0.4

6 [0.05, 0.5[ 0.6

7 [0.5,2.5[ 0.8

8 ≥ 2.5 1

η reorder=2.3, wd deletion= 1, wi insertion = 0.9


17

2

Joe 5 1 2 3

Jane 0 1 5 0

Step 1: identify longest common substring

1 2 3 4


18

2

Joe 5 1 2 3

Jane 0 1 5 0

Step 2: identify common elements not part of the longest

common substring

1 2 3 4

2.3 * 3


19

2

Joe 5 1 2 3

Jane 0 1 5 0

Step 3 a: unique elements in source are deleted

1 2 3 4

[1*(0.2+1)] + [1*(0.2+4)]


20

2

Joe 5 1 2 3

Jane 0 1 5 0

Step 3 b: unique elements in target are inserted in the source

1 2 3 4

[0.9 *(0.6*3)] + [0.9 *(0.8*4)]

Compare customers’ turnover evolution2

Total Sequence Alignment Distance to align Jane to Joe

[0.9 *(0.6*3)] + [0.9 *(0.8*4)]

21

[1*(0.2+1)] + [1*(0.2+4)]

2.3 * 3

SAM

distance

19.86

I

iinsieii

D

ddeldedd

R

rreorelr

poscw

poscwpos

SAMdist

1__

1__

1_

*

**

min

R number of reorderings

η reordering weight

posr_reorel absolute position of rth reordered element in the source

D number of deletions

wd deletion weight

cd_e element cost: cost for deleting particular element from source

posd_del position cost: position of element in source that is deleted

Segments with similar turnover evolution

23

3

D1,1 D...,... D1,N

D...,... D...,... D...,N

DN,1 D...,... DN,N

1 ... N

1

...

N

Exploiting Customer Evolution

24

Predictive Evaluation

. . . . . . .

. . . . . . .

. . . . . . .

V1 ...VN

Cust1

...

CustN

R1 R2 R3 R4

AUC = 0.91

...

Cust1

...

CustN

C1 C2 C3 C4

AUC = 0.96

C5V1 VN

CHURN

. . . . . . . .

. . . . . . . .

. . . . . . . .

Exploiting customer dynamicsExploiting evolution for cross-selling

Service Acquisition

Portfolio Maintenance

Young couple

Young

Parents 1

Checkings

accountSavings

accountCar

insurance?

Exploiting customer Evolution

26

Financial-services Cross-Sell Application

Services Group Description

1 Investment: low risk, fixed short term (<=10 years)

2 Investment: limited revenue risks and no capital risks, no duration

3 Investment: limited revenue risks and no capital risks, fixed long duration (>10 years)

4 Investment: some revenue risks and no capital risks

5 Investment: no revenue risks, some to high capital risks, no duration

6 Fire insurance

7 Car insurance

8 Other types of insurance (e.g. health, household, accident and life insurance policies)

9 Short-term credit

10 Mortgage

11 Checking account

Predict for each household the next financial service acquisition

Exploiting evolution for X-selling

27

Acquisition definition

Aget

Acqt Acqt+1

Investmentst

Loanst

Checking

accountt

Insurancest

Aget+1

Investmentst+1

Loanst+1

Checking accountt+1

Insurancest+1

Proxy for family-lifecycle

Ownership at moment t includes newly acquired service!

Exploiting evolution for X-selling

29

Measurement

Acquisition (Acq) 11 Acquisition of one or more services from

service category one to eleven

Ownership Investment (Own_inv) 4 1: no ownership

Ownership Credit (Own_Loan) 2: one or two services owned

Ownership Checking Account (Own_CA) 3: three or four services owned

Ownership Insurances (Own_Insur) 4: five or more services owned

Age (Age) 3 1: younger than 35

2: from 35 to 54 years old

3: 55 years old or older

Aget

Acqt Acqt+1

Investmentst

Loanst

Checking

accountt

Insurancest

Aget+1

Investmentst+

1

Loanst+1

Checking

accountt+1

Insurancest+1

Exploiting Evolution for X-selling

33

Managerial Insights: Initial ownership given FLC

1. Households in the retired couple stage tend to have more investments

55+ vs [35,54[

1 or 2 investments: 25% vs 13%

5+ investments: 4% vs 1%

2. Older households have less insurance policies

3. Ownership of credits decreases with age.

Aget

Acqt Acqt+1

Investmentst

Loanst

Checking

accountt

Insurancest

Aget+1

Investmentst+

1

Loanst+1

Checking

accountt+1

Insurancest+1

Acqt Own_Invt Own_Loant Own_CAt Own_Insurt 1 2 3 4 5 6 7 8 9 10 11

10 2 2 2 1 0.3 0.03 0.13 0.07 0.17 0.13 0.17

10 2 2 2 2 0.04 0.64 0.11 0.11 0.11 0

Acqt+1

tttttt InsurOwnCAOwnLoanOwnInvOwnAcqAcqP _,_,_,_,1

35

Services Group Description

1 Investment: low risk, fixed short term (<=10 years)

2 Investment: limited revenue risks and no capital risks, no duration

3 Investment: limited revenue risks and no capital risks, fixed long duration (>10 years)

4 Investment: some revenue risks and no capital risks

5 Investment: no revenue risks, some to high capital risks, no duration

6 Fire insurance

7 Car insurance

8 Other types of insurance (e.g. health, household, accident and life insurance policies)

9 Short-term credit

10 Mortgage

11 Checking account


36

Inflow into services categories

Service category 3 (investment services with limited revenue risks

but no capital risks for fixed long duration >10 years):

only from category 3


37

Inflow into fire insurance category

Aget

Acqt Acqt+1

Investmentst

Loanst

Checking

accountt

Insurancest

Aget+1

Investmentst+1

Loanst+1

Checking accountt+1

Insurancest+1


39

Managerial Insights: Portfolio growth/shrinkage

Portfolio shrinkage

Households with household head younger than 35 having 1 or 2

investments at previous acquisition have the highest chance to have no

investments at the next acquisition event.

Portfolio growth

Retired households having 3 or 4 investments at previous acquisition event

have over 63% probability to own five or more investments at the next

acquisition event.

wPCC

Model Estimation Validation Test

Coupled Markov Model 34.60 34.27 34.52

Coupled Hidden Markov Model 43.99 43.94 43.97

Ensemble: CMM_CHMM 46.28 46.09 46.18

Multinomial Logit with Continuous Ownership 33.89 33.97 34.28

Decision Tree 1: max. branches 2, max. depth 6, min. obs. 4919 33.56 33.64 33.96







Neural Network 1: 10 neurons 32.75 32.74 33.00






41

Predictive Evaluation

Customer Journey Identification

Decision Journey

44

Decision Hierarchy Models

Models which attempt to define the process through which

consumers decide to purchase and consume

Various Stages Models

45

Customer Decision Process

46

Purchase

Decision kAwareness Interest Desire

Post-purchase

Behaviour

TDk TDk TDk

TDk

Engel, Kollat & Blackwell

Customer Decision Journey

47

EKB Multi-Phase Decision Model

1. Problem recognition: Recognition of need by the

customer.

2. Information search: Search of information (internal or

external)

3. Evaluation of alternatives: selects one or more of interest.

4. Purchase Decision: whether, when and where to buy.

5. Post-purchase behaviour: After purchase, customer evaluates this decision – compares with expectations

Decision Journey

48

Benefits of Identifying customer’s journey

Understanding customers’ responses to marketing actions

Tailoring actions to identified stages in the purchase

decision sequence

cf. adaptive selling

Purchase

Decision kAwareness Interest Desire

Post-purchase

Behaviour

TDk TDk TDk

TDk

Engel, Kollat & Blackwell

Varying

information (ads,

demos,

testimonials)

Guide evaluation by

giving advice

(virtual sales

person)

Increase trust

in vendor

Time-

dependent

promotion

Dissonance reduction

Satisfaction with vendor

CustomerJourney Identification

50

Objectives

Increase the turnover rate and customer decision-making

satisfaction for the purchase of high involvement and/or complex

products

CustomerJourney Identification

51

Research Process

Develop a set of questions (scale) to measure the customer’s decision

journey

Predict a customer’s decision making progress using click-stream data

Customer-journey personalisation:

Tailor web information, marketing actions and customer-company

interaction to customer’s decision state (including decision progress

and prior product knowledge, etc)

1

2

3

Customer Journey Identification

52

Application

A B2C European retailer selling home appliances

• TV

• Laptops, netbooks and desktops

• Dishwasher (integrated)

• Fridge and/or Freezer (integrated)

• Tumble dryer

• Washing Machine Dryer (Integrated)

• Camcorders

• Digital Cameras

CustomerJourney Scale

53

1. Qualitative phase

Protocol interviews to assess clarity and understandability

of questions for measuring the customer’s journey when

buying a home appliance

1

CustomerJourney Scale

54

2. Quantitative phase using Mokken scale analysis

a) Pretest to purify scale and retain limited number of questions: 35 21

b) Survey to further reduce and purify scale

1

Decision Progress Scale

55

Decision Progress

Need Recognition Subscale (2)

Info / Evaluation Subscale (6)

Purchase Subscale(4)

Post-Purchase Subscale (1)

Decision Progress

Customer Journey Scale

56

Face Validity

1

Decision progress

Decision Time and Effort

Decision-making

confidence

Perceived Risk

Perceived Knowledge

Predicting customer journey

57

2

Using

developped

scales

For each

survey

respondent

Based on clickstream data one

month prior to survey date


58

Web statistics

Survey

WEB VISIT

ALL

Survey

WEB VISIT

LAST

Survey

WEB VISIT

NOT LAST

2


59

Web statistics

Variables on visiting particular product pages

Pages portraying a single product

Variables on visiting particular product category home

pages

A product category home page lists several products in a given category like televisions

2


60

Web statistics

2

Customer Journey Personalisation

61

Online Experiments with web-elements

optimized for decision progress

Assessing optimal web elements for each

decision state using POMDP model

Assessing added value of decision-state web

personalisation: increase in turnover rate

and decision-making satisfaction

3

Social CRMExploiting social conversations

Social CRM

63

Paul Greenberg (2009)

“…designed to engage the customer in a collaborative conversation in order to provide a mutually beneficial value in

a trusted and transparent business environment. It’s the

company response to the customer’s owning of the relationship.”

http://the56group.typepad.com/

http://the56group.typepad.com/

community ACTION

Phone calls

Emails

In person complaint

Feedback forms

CRMProfiles

History

Transactions

Preferences

Customer data

INTERACTIONS

ANALYSIS

Social CRM

Social CRM

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Include social data into aCRM models

Acquisition

Cross/up-selling (development)

RetentionRecapturing

Listen to customer conversations

Analyze those conversations

Relate this information to existing

information within your enterprise

Act on those customer conversations

WHYDID THEY CHURN

WHO CHURNED

Social CRM

67

Churn prediction

Churners Non-Churners

Social Conversations

Data cleaning & preparation

Selection of frequent words

and word combinations

Extraction of reasons of churn

by Discrete Factor Analysis

Profiling customers with specific churn

reason

Who churned (a service) & why?

Semi-automatic text mining process

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http://showroom.solutions2.be/Details.aspx?id=27&parentId=11

Questions?

Anita Prinzie [email protected] (+32) 9 2425045

Nicole Huyghe [email protected] (+32) 9 2425040

mailto:[email protected]

mailto:[email protected]

References

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Greenberg, P. (2009), CRM at the Speed of Light: Social CRM 2.0

Strategies, Tools, and Techniques for Engaging Your Customers,

Fourth Edition, McGraw-Hill Osborne Media, 698 pages.

Prinzie, A., Van den Poel, D. (2006), Incorporating sequential

information into traditional classification models by using an

element/position-sensitive SAM, Decision Support Systems, 42, 508-

526.

Prinzie, A., Van den Poel, D. (2011), Modeling longitudinal

consumer behavior with Dynamic Bayesian Networks: an

Acquisition Pattern Analysis application, Journal of Intelligent

Information Systems, 36, 283 – 304.

Modeling customer dynamics and social CRM

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