From  Data  to  Impact  Big  Data  Marke,ng  Use  Cases  for  the  Finance  Industry  

12  May  2015,  Wijs  

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What  is  going  on?  Forces  at  Work  

Personal  Data  Protec,on  &  

Privacy  

Lowering  Cost  of  Data  Infrastructure  

Teradata Cloudera

Hortonworks EMC

Business  Intelligence  is  becoming  

Data  Science  

Reporting

SPSS / SAS

Python Pandas

R

Spark

Online  self-­‐service  

profiling  &  targe,ng  

prolifera,on  

Facebook Atlas

Google Ads Campaigns

Agencies

The  Customer  at  the  Center  

Conversation

In-bound Out-bound

CRM Millenials  @  The  Customer  

Side  

Co-creation

Ecosystems

Programs

Waterfall

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What  is  going  on?  The  Ba/lefields  of  Data  

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Banks  vs  Digitals  

Personal   Same  for  everyone  

Fast   Slow  

Intui,ve   Sta,c  

Integrated   Siloed  

Everywhere   Have  to  search  for  what  I  need  

Relevant   Doesn’t  surprise  me  

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The  Big  Enterprise  Challenge  Data  Silos  

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The  Status  Quo  is  Limited  to  Insights  

Gave  up  on  Customer  360  a]er  large  investments  in  

Datawarehouses  

Use  hindsight  in  BI/Analy,cs  solu,ons  building  complex  

diagnos,c  models  for  customer  segmenta,on  

Hire  an  army  of  data  scien,st  to  use  big  data  and  visualiza,on  tools  to  

discover  insights  

Rely  on  Rule  Engines  to  apply  segmenta,on  for  recommenda,ons  and  

targe,ng  

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.”

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How  You  Measure  Success  

IMPACT RELEVANCY

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Relevancy  –  What  Customer  Experience  is  All  About    PuHng  the  Customer  Upfront  and  Central  

OFFER THE RIGHT

PERSON THE RIGHT

TIME THE RIGHT

CHANNEL THE RIGHT

IMPROVED FREQUENCY

IMPROVED SEPARATION

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processing flow

key functions

The  Google/Facebook/LinkedIn  Architecture  Customer  centric:  Profiling,  Analy>cs  and  Ac>ons  @  the  Speed  of  Light  

streaming ingest user identification behavior observation & tracking

profile establishment targeting support: preference learning & contextualization micro-segmentation network analysis

service delivery (newsfeeds, timelines, search, check-ins, ads …)

data layer

consumer

data capturing & ingestion profiling & service enablement customer experience

online transaction and analytical processing on shared data platform

real-time / in-session / user-level analytics, scoring & targeting (for ad, service, next best offer, recommendations)

model training collaborative learning deep learning reporting

operational processing

chan

nels

portal

mobile

ads

…

service applications

interactive service calls

behavioral feedback data

service interaction user behavior

observations (streaming) data flows

(streaming) data flows

(streaming) data flows

profile enquiries

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The  Big  Enterprise  Challenge  Enterprise  IT  Architecture    

Where  is  the  customer?  

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Lily  Enterprise  OperaMonal  Customer  AnalyMcs  

Data/Models

OperaMonal  Systems  

External  Data  

Contract/Product  Data  

Customer  Opera,onal  Data  

Reference  Data  

ReporMng  /  AnalyMcs  

Enterprise  BI  and  repor,ng  

Enterprise  Analy,cs  Applica,ons  

Marke,ng  and  Social    Data  

Customer  Interac,on  Data  

Campaign  Data  

ERP/CRM  Data  

Data  Warehouse  Data  

Service  Desk  

Customer  CRM  and  IVR  Systems  

Web  and  Mobile  

Mobile  Apps  

Customer  Website  

Channel  /  Campaigns  Mail  

SMS  

Print  

Broadcast  

Marke,n

g  Campaign  Mgt  

Sales  Office  

Agent  /  Advisor  

Structured

Unstructured

Online

Feed

back

External

Social Partner Apps

Partners  Apps  

Social  Media  

Structured

Unstructured

Inpu

t

“MANAGE  CHAOS“  –  Manage  core  metrics,  don’t  try  to  control  everything  

Ken  Rudin,  Director  of  Analy,cs,  Plugged  in  Enterprise  Architecture  –  Improving  exis>ng  BI  landscape  

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customer removes multiple products from portfolio

6  OCT  

customer churns

11  NOV  

manual attrition score (bi-monthly)

portfolio size (weekly)

The  Importance  of  Real-­‐Mme  Customer  DNA  &  Scoring  Figh>ng  A/ri>on  Before  it  is  Too  Late  

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win-back period

customer removes multiple products from portfolio

6  OCT  

customer churns

11  NOV  

win-back sensitivity

manual attrition score (bi-monthly)

portfolio size (weekly)

The  Importance  of  Real-­‐Mme  Customer  DNA  &  Scoring  Factor  In  Win-­‐back  Sensi>vity  

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win-back period

win-back sensitivity Lily attrition score (continuous)

portfolio size (weekly)

customer churns

11  NOV  

customer retention actions

Lily alerts for in- creased attrition risk

customer removes multiple products from portfolio

6  OCT  

The  Importance  of  Real-­‐Mme  Customer  DNA  &  Scoring  Timely  Alerts  and  Ac>ons  for  the  Greatest  Impact  

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One  Customer  DNA  that  Serves  Many  Use  Cases  Enterprise  side  of  the  equa>on:  “CLTV”  

CUSTOMER  LIFETIME   ATTRITION  ACTIVATION  

MARG

IN  

PERSONAL  ADVISE  

CUSTOMER  SUPPORT  

UP  SELLING   PERSONAL  ADS   PARTNER  

PROGRAMS  RISK  

PROGRAMS  

CHURN  REDUCTION  

ACQUISITION  

360  view  for  advisor  Content  

recommenda,on  

Micro  campaigns  Anonymous  

Call  predic,ons  Script  

recommenda,ons  

Online  offers  121  Campaign  

Personalized  ads  Personalized  

Social  

Support  partners  apps  

Merchant  offers  

Akri,on  programs  Iden,fy  Fraud  

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Case  1:  Increased  customer  value  and  reduced  helpdesk  calls  

Predict who is going to call and what their issue will be... And take action before they call

HAPPY CUSTOMERS

FEWER CALLS

HUGE SAVINGS

A  personally  relevant  video  is  delivered  based  on:  •  Customer  data  

•  Specific  solu,on  

•  Preferred  products  

RESULTS  

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Case  2:  Decreasing  A[riMon  for  Retail  Bank  

•  Created  thresholds  and  set  alerts  based  on  con,nuous  trending  scores  on  all  available  data  and  delivered  more  predic,ve  ac,ons.  

•  Alerts  sent  to  bank’s  outbound  systems  to  take  ac,ons  reducing  akri,on  by  10%  

 

Result  

•  Compe,,ve  pressure  on  the  retail  business  •  Need  to  substan,ally  lower  akri,on  rate  (22%)  •  Increase  customer  life,me  value    

Objec,ves  

•  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  con,nuous  akri,on  scoring  

Solu,on  

“      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  a/ri>on  in  a  >mely  and  effec>ve  manner.”

—  CIO,  Large  InternaMonal  Bank  

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Case  3:  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  

•  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  

“      Introducing  Big  Data  and  Machine  Learning  not  only  resulted  in  higher  performance,  but  it  allowed  us  to  introduce  disrup>ve  business  concepts  and  opportuni>es.”  

—  Senior  Vice  President  

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Case  4:  Customer  DNA    Large  US  Wealth  Management  Bank  

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

Result  

•  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  

“      Tradi>onal  advice  channels  must  reinforce  the  value  of  comprehensive  planning  through  automated,  real-­‐>me  and  personalized  advisor  rela>onships  if  they  wish  to  maintain  their  margins  and  marketshare.”  

—  Senior  Vice  President,  Customer  Intelligence  

Thank  you!  Ques,ons?    stevenn@ngdata.com