Becoming a Customer Centric Bank
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Transcript of Becoming a Customer Centric Bank
Becoming a Customer Centric Bank
NGDATA Webinar – June 18th 2014
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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
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Trends in Retail Banking
The core evolves from transaction to intelligence
Transaction history will emerge as a way to identify new product / service requirements or push contextual offers
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Trends in Retail Banking
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
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Trends in Retail Banking
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
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Challenges
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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
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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
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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
Broadcast
Offers
direct mail
ATM
web
Agent, IVR
mobile
chat
Relevance? Awareness?
Value? Timing? Clarity?
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ConnecHng with the Customer
Preferences
Affini@es
Context
Behavior
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Overcome “Analysis Paralysis”
Proac@vely Engage with
Customers
Cope with plethora of data
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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?
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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
SMS
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
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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
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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
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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
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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
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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
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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
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?
?
?
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
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?
?
Website Real Time Offer PersonalizaHon
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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
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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
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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
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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
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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
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ME
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Zettabytes
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Unknown Unknowns
Known Unknowns
Known Knowns
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