View
180
Download
2
Category
Tags:
Preview:
DESCRIPTION
Workshop for Indian bank managers at UGent August 2012
Citation preview
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
Compare customers’ turnover evolution
17
2
Joe 5 1 2 3
Jane 0 1 5 0
Step 1: identify longest common substring
1 2 3 4
Compare customers’ turnover evolution
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
Compare customers’ turnover evolution
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)]
Compare customers’ turnover evolution
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
. . . . . . . .
. . . . . . . .
. . . . . . . .
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
Exploiting Evolution for X-selling
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
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
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
Decision Tree 2: max. branches 3, max. depth 6, min. obs. 4919 33.56 33.64 33.96
Decision Tree 3: max. branches 2, max. depth 10, min. obs. 4919 34.14 34.14 34.42
Decision Tree 4: max. branches 3, max. depth 10, min. obs. 4919 34.14 34.14 34.42
Decision Tree 5: max. branches 2, max. depth 10, min. obs. 1000 34.31 34.26 34.56
Decision Tree 6: max. branches 3, max. depth 10, min. obs. 1000 34.60 34.60 34.84
Decision Tree 7: max. branches 10, max. depth 30, min. obs. 1000 34.60 34.62 34.91
Neural Network 1: 10 neurons 32.75 32.74 33.00
Neural Network 2: 15 neurons 33.17 33.28 33.45
Neural Network 3: 20 neurons 30.27 30.33 33.53
Neural Network 4: 25 neurons 30.66 30.66 31.01
Neural Network 4: 30 neurons 31.04 31.07 31.36
Exploiting Evolution for X-selling
41
Predictive Evaluation
Decision Journey
44
Decision Hierarchy Models
Models which attempt to define the process through which
consumers decide to purchase and consume
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
Predicting customer journey
58
Web statistics
Survey
WEB VISIT
ALL
Survey
WEB VISIT
LAST
Survey
WEB VISIT
NOT LAST
2
Predicting customer journey
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
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 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.”
community ACTION
Phone calls
Emails
In person complaint
Feedback forms
CRMProfiles
History
Transactions
Preferences
Customer data
INTERACTIONS
ANALYSIS
Social CRM
Social CRM
65
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
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
Questions?
Anita Prinzie anita@solutions2.be (+32) 9 2425045
Nicole Huyghe nicole@solutions2.be (+32) 9 2425040
References
71
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.
Recommended