data science for crm in banks

Data Science for CRM in BanksA non-exhaustive overview

Peter Koglmann

Vienna Data Science GroupKnowledgefeed Vol 13, 2016-09-16

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)

The Aimof 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

Distinctivefeatures ofCRM in banks

• High expectation on privacyrequire careful handling andusage of sensitive data

• Banks were among the firstsectors heavily using IT

• Plethora of data

• Rather long terms contracts• Service oriented• Life cycle driven

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 mostrelevant and timely step

Predictive Modelling in CRM

Explainingvariables Log

Regr

RF

SVM

low

high

Classification

Socio-demographic

Behavioural (accounts, buying, contacts)

External (pricing, competitors, reputation)

propensity

Customer account record

Change in propensitycaused byintervention

Uplift model

Log Regr

RF

SVM

low

high

conversion

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 Lifetime Value: present value of

future revenues (e.g. Semi Markov models)

The right productat the right timein the right way

• Predict propensity to order a service/product– Classification models (Log. Regr., RF, SVM, …)

– Age, behaviour, etc. of customer as explainingvariables lead to information of „right time“

• Predict uplift & conversion rates for eachchannel

– Uplift models, A/B tests

• Next best offer– Recommender systems, Multinom. Log. Regr., …

Reduce costs andincrease sales

• Target only clients with high propensity andconversion rate. How many?

– Cost-benefit analysis (confusion matrix)

• Profile top clients and identify currentunderperformers within that group

– Cluster analysis

– Customer Lifetime Value

• Cross-sell and up-sell– Next best offer techniques

• Improve models on a continuous basis– Validation, benchmarking, trial and error