using your data to reduce attrition in banking

Using Your Data to Reduce A3ri4on in Banking Webinar Wednesday, April 29th, 2015

2 Copyright 2015 NGDATA®, Inc. ConfidenCal – DistribuCon prohibited without permission

A3ri4on is a Top Issue for Banks, and for Good Reason

The opportunity cost of falling behind the compeCCon is extreme. Over half of customers have opened or closed at least one product in the past year and nearly as many, 40%, plan to do so in the coming year. Each of these customer represents a new business opportunity for a compeCng bank or financial service provider.

Global Consumer Banking Survey 2014, Ernst & Young


Advantage -‐ Banks

•  Financial Services have

many digital touch points with their customers where they can drive communicaCon

•  Financial Services don’t want to put communicaCon in the hands of third parCes, such as technology companies that could become compeCCon


Focus on the Most Effec4ve A3ri4on Program

Involuntary RotaConal Voluntary


TradiConal Advanced

Most Likely A3ri4on Predictors Use all your data to create smart Customer DNA Metrics

•  Socio-demographic •  Slow-changing metrics

-  product ownership -  subscriptions -  age

•  Behavioral •  Rapidly-changing

metrics -  usage -  service interaction &

consumption

ADVANTAGE: &

ADVANTAGE: &


Addressing the A3ri4on Challenge Making a difference throughout the process

1 Prepare the Data

Build a Better Attrition Model 2

Become Actionable & Learn From Feedback 3


Prepare Data 1

TradiConal Advanced

•  Prepare mulC-‐source, omni-‐channel Customer DNA which can serve mulCple use cases

•  Explore relevant predictors

•  Export dataframes to quickly build model in analyCcal workbench

•  IdenCfy problem & gather data from different sources specific to the problem

•  Understand the data completely

•  Sample the data


Build Be3er A3ri4on Models 2

TradiConal Advanced

•  Easily build models with perfectly prepared dataframes aligned to the individual churn dates

•  Combine socio-‐demo and historical predictors with behavioral metrics to define why and what as well as how and when

•  Combine and easily switch between short-‐ and long-‐term predictors

•  Focus on predictors indicaCng behavioral change, such as trend and accelera4on

•  Models built on subset of data at one point in Cme

•  Segmented data -‐ not related to individual customer acCons or intents

•  Model becomes outdated from day one: maintenance heavy


Become Ac4onable and Learn from Feedback 3

TradiConal Advanced

•  ConCnuous, real-‐Cme aariCon scoring of every individual customer

•  Detect & alert most appropriate moment of acCon for every individual customer

•  Capture feedback to learn about channel & offer type performance and preference

•  Batch scoring of customers

•  AariCon prevenCon acCon oben too late

•  Offer feedback informaCon oben unavailable

•  Slow & staCc process


The Status Quo of Insight Where is the Customer?

Gave up on Customer 360 aber large investments in

Datawarehouses

Use hindsight in BI/AnalyCcs soluCons building complex

diagnosCc models for customer segmentaCon

Hire an army of data scienCst to use big data and visualizaCon tools to

discover insights

Rely on Rule Engines to apply segmentaCon for recommendaCons and

targeCng

Most Many Several Few

Rowan Curran, March 2015, Forrester Research: “Digital experience delivery vendors have generally fallen short in their use of predic@ve analy@cs to contextualize digital customer experiences. Many of these vendors offer simple, rules-‐based recommenda@ons, segmenta@on,

and targe@ng that are usually limited to a single customer touchpoint.”


The Lily Revolu4on: Customer at the Center

DNA metrics can be sophisCcated models, coming from SAS, R, SPPS of other staCscal soluCons.

From Data to DNA – 1000s of metrics determine individual customer DNA

Alerts in real-‐Cme on all metrics to drive customer interacCon. Sets on DNA metrics to drive campaigns.

Trending – Keep track of historical values and trends of all DNA metrics in the system

Manage Big Data -‐ Breaking down data silos to gain insights on all customer interacCons in one place

> > > >

From Manual Work Step by Step to Continuous Automation


Customer Centricity Creates

IMPACT


customer removes multiple products from portfolio

6 OCT

customer churns

11 NOV

manual attrition score (bi-monthly)

portfolio size (weekly)

Figh4ng A3ri4on Before it is Too Late


win-back period


6 OCT

customer churns

11 NOV

win-back sensitivity

manual attrition score (bi-monthly)


Connect at the Sensi4ve Win-‐back Period for Op4mum Results


win-back period

win-back sensitivity Lily attrition score (continuous)


customer churns

11 NOV

customer retention actions

Lily alerts for in- creased attrition risk


6 OCT

Timely Alerts and Ac4ons for the Greatest Impact


Decreasing A3ri4on -‐ Banking

•  Created thresholds and set alerts based on conCnuous trending scores on all available data and delivered more predicCve acCons.

•  Alerts sent to bank’s outbound systems to take acCons reducing aariCon by 10%

Result

•  CompeCCve pressure on the retail business •  Need to substanCally lower aariCon rate (22%) •  Increase customer lifeCme value

ObjecCves

•  Aggregated all customer data (ATM, branch, call center, web, mobile, payment system, etc.)

•  Built individual Customer DNA based on hundreds of metrics

•  Focused on the high value customers (HVC) based on CLTV metric

•  Informed outbound systems of HVCs at risk based on conCnuous aariCon scoring

SoluCon

“ NGDATA is cri@cal in the way we capture, analyze and generate ac@onable intelligence from Big Data. With Lily in place, we were able to find and act on the customers most at risk of aMri@on in a @mely and effec@ve manner.”

— CIO, Large interna4onal bank


Lily Enterprise Connect with your customer by being relevant

Preferences

AffiniCes

Context

Behavior

Trends in Core Metrics and Preferences trigger relevant communications in all digital channels 3

Lily captures first and third party Customer Behavioral data, immediately translated into Core Customer Metrics and Preferences in Real Time 1

Core Metrics and Preferences actionable on individual customer level, continuously available for personalized customer communications 2


Deliver What Your Customers Want

OFFER THE RIGHT

PERSON THE RIGHT

TIME THE RIGHT

CHANNEL THE RIGHT

IMPROVED FREQUENCY

IMPROVED SEPARATION

Thank You And QuesCons


Ready to take the next step to reduce customer a3ri4on? Learn more about how Lily Enterprise can help your bank. Schedule an appointment with an NGDATA representaCve to get a personalized walkthrough.

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using your data to reduce attrition in banking

Data & Analytics

condencal distribucon

permission a3ri4on

permission tradiconal

subset of data

permission focus

likely a3ri4on predictors

cme segmented data

individual customer