Becoming a Customer Centric Bank

Becoming a Customer Centric Bank

NGDATA Webinar – June 18th 2014

2 Copyright 2014. Confiden@al – Distribu@on prohibited without permission

Trends in Retail Banking

Analytics gets real-time, and mobility is a priority

Banks will combine existing and real-time information of a customer, transaction, and product to integrate it with applications like location-based services

1



The core evolves from transaction to intelligence

Transaction history will emerge as a way to identify new product / service requirements or push contextual offers

2



Banker, retailer, telco, technologist: the new gang of four

Banks go from collaboration to co-creation, with new services and products that combine offerings from banking and non-banking entities

3



Life stage banking gets overlaid with lifestyle banking

All customers at the same life stage like education or marriage may not have the same needs — their lifestyle – influenced by geography, culture and interests – will define their banking solutions

4

Challenges


Big Gaps in What Can be Done Today

What banks say they can do today with many of their technology ini6a6ves is quite limited. (percent indica@ng a mature ini@a@ve)

29%

32%

46%

52%

55%

65% Demographic and segmenta@on data

Deposits, withdrawals, checks paid and other bank transac@ons

Digital transac@on data (credit and debit cards, etc.)

External data about customers

Social media ac@vity

Share of wallet data

Base: 100 executives at small, midsize and large banks worldwide Source: BBRS 2013 Banking Customer Centricity Study


Numerous Obstacles to Customer Centricity

16%

21%

24%

25%

26%

29%

31%

37%

40% Volumes of data from customer transac@ons and other sources are overwhelming our systems

Our analy@cal tools are not easy to use

Our company’s current systems inhibit our ability to quickly respond to customer and market insights

Data silos – customer informa@on dispersed in too many different systems and in different formats

Complexity and velocity of data is overwhelming our systems

Inability to process emails, tweets, phone calls and other “unstructured” data about or from customers

Inadequate funding to improve our systems or upgrade the skills of our customer-‐facing employees

Poor quality customer data – inconsistent records across our company or product lines, not current or otherwise inaccurate

Our systems are too slow – we cannot quickly analyze market and customer trends

Which of the following obstacles currently prevent or slow your organiza6on’s ability to more efficiently and effec6vely gather, analyze and profit from informa6on about your customers? (percent of respondents; limit of 5 answers)

Base: 100 executives at small, midsize and large banks worldwide Source: BBRS 2013 Banking Customer Centricity Study


Customer Experience Customer spamming is doing more damage than good

Company & Customer Ac6vity

Customer CRM

Systems

Customer Web & Mobile

Customer Channel Campaigns

Customer Service Desk

Social Data

Website & online apps

Mobile App Server

Mail SMS

Print

Broadcast

Offers

direct mail

ATM

web

Agent, IVR

email

mobile

chat

Relevance? Awareness?

Value? Timing? Clarity?


ConnecHng with the Customer

Preferences

Affini@es

Context

Behavior


Overcome “Analysis Paralysis”

Proac@vely Engage with

Customers

Cope with plethora of data



I have a customer -‐ what are the top 3 products he is likely to buy?

Answering the Tough QuesHons…

Which top hundred customers are likely to buy my product X today?

What is the best channel to connect with my customer, and when?

How can I turn around my most valuable poten6al churners?


Lily Enterprise Within the Current Enterprise Architecture – Improving exis@ng BI landscape

Lily Enterprise

Customer Back Office Systems

External Data

External Systems

Customer Transac@ons Data

Customer Opera@ons Data

Customer ERP/CRM Data

3rd Party Reference Data

3rd Party Master Data

ReporHng / AnalyHcs Enterprise BI and repor@ng

Applica@ons

Social Data

Customer Web and Mobile

Mobile App Server

Customer Website And Online Apps

Customer Channel Campaigns

Mail

SMS

Print

Broadcast

Marke@n

g Campaign Mgt

Customer Service Desk

Customer CRM systems

Company and Customer ac@vity

Customer DWH Data

3rd Party Opera@onal Data

Enterprise Analy@cs Applica@ons

Lily Enterprise

Con

nector – ETL Too

ls


Lily Enterprise

See everything together – comparisons with a Set defined by you, and evolving trend scores for each customer

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


Lily Enterprise

Dynamically created Sets defined by your own rules

More effec@ve Alerts based on real-‐@me customer metrics

Models available, or easily and dynamically add new models from all available metrics

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

With Lily’s Customer DNA and Machine Learning Engine, individual product Preferences are available each moment


Big Data Real Time Analy@cs

Tradi@onal Analy@c Systems

What will happen? What do I need to do?

What has happened? Why?

Large Volumes Unstructured & Structured Data

Real-Time Systems Batch Systems

Sampled Data Structured Data

What is Different?

“My tradi*onal BI environment will give the answers tomorrow of yesterday’s problem”

-‐ CIO Fortune 50 Bank


Lily Customer DNA -‐ Value

•  Bigger: Scale trumps Smarter and Beher •  Results Driven: Ac@onable DNA •  “AND” not “OR”: Co-‐exis@ng with DW/BI

•  Prescrip@ve: Trends beher than Values •  Big Data Governance •  Con@nuous learning •  Objec@ve: Facts on everyone’s desk •  Architecture: One Single View

Discover the Unknown Unknowns with a single view of your customers always available…

AnalyHcs TransacHons Strategic

Machine Learning

Bigger is Beher

Current BI Solu@ons

Prescrip@ve

Customer DNA Results-‐Driven

Genius of “AND”

Maximize Architecture

Objec@vity

Governance


Applied to SubscripHon-‐based Businesses Customer Life@me Value (Inbound, Outbound, Risk)

Banking

• Value-‐add Services such as Merchant-‐funded Coupons

• Customer Experience − Personalized service & products − Financial Advise

• Risk Assessment

• Marke@ng Targe@ng Efficiency


Retrofit

The Right Focus

Customer

Product & Channel Wrapper

Product Teams

Branch

Branch-Centered Customer-Focused

journeys

contact center

mobile

web

self-‐ Service

social media

branch web

social

ATM

telesales

Use Cases for Finance


?

?

?

Website Real Time Offer PersonalizaHon

Determine offer eligibility from: •  Joe’s product preferences •  Joe’s DNA (Interests, lifecycle, ac@vity,...)

Calls to Lilly with content: •  Customer info. •  Context info. •  Session data


?

?

?

Website Real Time Offer PersonalizaHon


Merchant-‐funded Mobile Offers Fortune 50 US Retail Bank

Individual Coupon delivery – Average targe@ng precision increased by 5-‐7x, results in increased redemp@ons and loyalty

Result

“ Introducing Big Data and Machine Learning not only resulted in higher performance, but it allows us to introduce disrup6ve business concepts and opportuni6es.”

— Senior Vice President

•  Improve coupon redemp@on rate through real-‐@me, loca@on-‐based personalized offers

Objec@ves

•  Real-‐@me ingest of payment transac@ons •  Behavior-‐based MCC preference learning •  Loca@on-‐ and preferences-‐based coupon selec@on & delivery in mobile wallet

•  Evaluate performance between collabora@ve filtering & KB-‐based preference learning

Solu@on


Mobile Customer Experience New Services, New Collabora@ve Models

Mobile Information Mobile Wallet Mobile Redemption

Joe can view and look up favorite shops, restaurants,...

Joe receives merchant offers in his Bank’s Mobile wallet

Joe can redeem coupons through his mobile wallet


Churn PrevenHon Retail banking

Real Time Churn Propensity – Being prescrip@ve on Churn and reduced ahri@on with more than 20%.

Result

“ To gain maximum profit from retaining customers, companies should consider not only the churn probability of customers, but also how to mi6gate that risk, the likelihood that they will respond to the right reten6on offer, and the cost of the offer itself.”

Director of CRM and Consumer Intelligence

•  Improve customer reten@on and loyalty through prescrip@ve churn scoring

Objec@ves

•  Real-‐@me ingest of Transac@ons •  Customer DNA with focus on usage, payment status, claims, helpdesk calls,...

•  Detect trends and trigger alerts to inform call center agents in real @me and to feed marke@ng ac@ons to s@mulate service usage and upselling

Solu@on


Customer DNA Large US Wealth Management Bank

Real Time AcHonable Customer DNA – Allows agents to provide beher and more efficient advice. Building increased customer loyalty

Result

“ Tradi6onal advice channels must reinforce the value of comprehensive planning through automated, real-‐6me and personalized advisor rela6onships if they wish to maintain their margins and marketshare.”

— Senior Vice President, Customer Intelligence

•  Improve financial advice sugges@ng the right investment at the right @me to the right customer

Objec@ves

•  Real @me ingest of the investment history of the customer

•  Monitor all customer interac@ons (payments, CC, calls, IVR, mobile and online, ...

•  Learning on new investment opportuni@es •  Develop customer DNA and preferences, with a focus on the poten@al new investments in line with the individual customer profile

Solu@on


Fraud Large Global Services Company

Real Time AcHonable Customer DNA – Fraud detec@on increased by x2

Result

“ By combining the power of new analy6c technologies and the accumulated knowledge gleaned from trillions of previous payment transac6ons, it is now possible to fight fraud with surgical precision at incredible speeds, so the consumer payment experience is not disrupted.”

—Senior Architect

•  Detect iden@ty fraud @mely based on customer behavior

Objec@ves

•  Real-‐@me ingest of payment transac@ons •  Real-‐@me ingest of customer interac@ons, including context like @me, place and device

•  Behavior-‐based preference learning •  Detailed DNA, focus on behavioral metrics •  Trending and aler@ng of customer behavior

Solu@on


Experience the Difference with Lily

Listen Bigger.

VOLU

ME

Learn Faster.

SPEE

D

Execute Smarter.

ANSW

ERS

Volumes of Data Availability Questions Answered

Start working with Lily to discover results from Day 1

Zettabytes

Exabytes

Petabytes

Terabytes

Gigabytes

Seconds

Minutes

Hours

Days

Weeks

Unknown Unknowns

Known Unknowns

Known Knowns

DW/BI DW/BI DW/BI


Date post:	13-May-2015
Category:	Data & Analytics
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Becoming a Customer Centric Bank

Data & Analytics