Lightning talk given at Vienna Data Science Group Knowledgefeed Vol 13 (2016-09-16) about the usage of data science methods in CRM of banks.

1. Data Science
for CRM in
Banks
A non-exhaustive overview
Peter Koglmann
Vienna Data Science Group
KnowledgefeedVol 13, 2016-09-16

2. Agenda
What‘s the aim of CRM* ?
What‘s special to CRM in banks ?
How can data science help in solving CRM
problems in banks ?
* Customer relationship management (CRM) is an approach to managing a company's interaction with current and potential future customers. (wikipedia.org)

3. The Aim
of CRM
• Create and keep valuable customers
• By offering to the customer
– the right product
– at the right time
– in the right way
• Reduce costs and increase sales

4. Distinctive
features of
CRM in banks
• High expectation on privacy
require careful handling and
usage of sensitive data
• Banks were among the first
sectors heavily using IT
• Plethora of data
• Rather long terms contracts
• Service oriented
• Life cycle driven

5. How can
data science
help in solving
CRM problems
in banks ?
• Create and keep valuable customers
• By offering to the customer
– the right product
– at the right time
– in the right way
• Reduce costs and increase sales
• Use data science to
– better understand the customers and predict
their future behaviour
– push prescriptive actions to take the most
relevant and timely step

6. Predictive Modelling in CRM
Explaining
variables Log
Regr
RF
SVM
low
high
Classification
Socio-demographic
Behavioural (accounts, buying,
contacts)
External (pricing, competitors,
reputation)
propensity
Customer
account record
Change in
propensity
caused by
intervention
Uplift model
Log
Regr
RF
SVM
low
high
conversion

7. Keep valuable
customers
• Predict churn
– Classification models (Log. Regr., RF, SVM, …)
– Uplift models to assess the effect of intervention
– Survival models (how long will customer stay?)
– Recommender systems (which actions?)
• How many to target?
– Cost-benefit & ROI
analysis based on
confusion matrix
predicted
yes no
actuals
yes TP => gain FN
no FP => -costs TN
• Who is how valuable?
– Customer LifetimeValue: present value of
future revenues (e.g. Semi Markov models)

8. The right product
at the right time
in the right way
• Predict propensity to order a service/product
– Classification models (Log. Regr., RF, SVM, …)
– Age, behaviour, etc. of customer as explaining
variables lead to information of „right time“
• Predict uplift & conversion rates for each
channel
– Uplift models, A/B tests
• Next best offer
– Recommender systems, Multinom. Log. Regr., …

9. Reduce costs and
increase sales
• Target only clients with high propensity and
conversion rate. How many?
– Cost-benefit analysis (confusion matrix)
• Profile top clients and identify current
underperformers within that group
– Cluster analysis
– Customer LifetimeValue
• Cross-sell and up-sell
– Next best offer techniques
• Improve models on a continuous basis
– Validation, benchmarking, trial and error