Big Data: Customer Intimacy & Develop New Business

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Information about Big Data: Customer Intimacy & Develop New Business
Business & Mgmt

Published on March 13, 2014

Author: MatthiasVallaey

Source: slideshare.net

Description

How can Big Data help Marketeers improve Customer Intimacy and Develop New Business.
What's the approach, which architecture to choose?
Who can help? The role of the Data Scientist?

Big  Data  Webinar  Series  

Big  Data  Webinar  Series   Customer  In4macy  &  Develop  New  Business  March 11, 2014 Opera4onal  Excellence  March 18, 2014 March 25, 2014 April 1, 2014 April 3, 2014 Big  Data  in  Public  Services   Big  Data  in  Healthcare  &  Life  Sciences   Big  Data  in  Human  Resources  

Big  Data  =  Hype?  

Transac4onal   Event-­‐Based   Response  Based   Geographic   Demographic   Basic  Info   ID   • Payment  History   • Call  Center  Interac4ons   • “Installed  Base”   • ‘Enriched’  data  Segments   • Name,  Adress   • 14C545J..K   Customer  Insight  

Customer  Insight:  Mass  Marke4ng  

Omni-­‐Channel  Customer  Experience   d   Web   Search   Web   Order  Online   Renew   Voice   Get  Help   In-­‐Store   Visit  Retail   Store   Print   Read     Ar4cle   See   Promo4ons     Read  Manual   Chat   Ask  for     Info   Email   Get     Confirma4on   Social   Ask  for  Advice   Tweet  about   Experience   Need   Research   Select   Buy   Recieve   Use   Maintain   Recommend   Ask for Recommendations Read Reviews Change Order Pickup Local Store

Step  1:  Acquire  &  Organize   Preference   Online  Behaviour   Context   Transac4onal   Event-­‐Based   Response  Based   Geographic   Demographic   Basic  Info   ID   • Sen4ment   • Like,  Unlike   • Follow  Web  Clicks   • Device  (PC,  SmartPhone,  Tablet,…)   • Loca4on   • Shopping  Basket   • Payment  History   • Call  Center  Interac4ons   • “Installed  Base”   • ‘Enriched’  data  Segments   • Name,  Adress   • 14C545J..K  

Step  2:  Enrich  &  Analyze   ID:  J23JHTTU   Jan  Steen   35  Year   Antwerp   ID:  LLHR765   Johan  Steenacker   35  Year   Antwerp  

Step  2:  Enrich  &  Analyze   ID:  J23JHTTU   Jan  Steen   35  Year   Antwerp   Married,  3  children   Teacher   Trainer  local  Kids  Soccer  Team   Football,  Ac4ve  Cycling,  Camping   ID:  LLHR765   Johan  Steenacker   35  Year   Antwerp   Complicated   Laywer   Chairman  Belgian-­‐Indian  Diamond   Trade  Associa4on   High  End  Exo4c  Travel,  Extreme   skiing,  Golf  

External Internal New Step  3:  Decide  &  Execute   email  Loca4on  Based   Offer  Coupon  Banner  …   Mul4  Channel  Campaign  Management   Insight  &  Recommenda4on   Selec4on   Repor4ng  &  Analy4cs  

Data  Flow  Concepts   Analyze  Acquire   Organize   Decide   OLTP Data RDBMS ETL DWH BI

Analyze  Acquire   Organize   Decide   RDBMS ETL DWH BI New Data OLTP Data Data  Flow  Concepts  

 Use  Case  drives  the  Data  Flows   Analyze  Acquire   Organize   Decide   RDBMS ETL DWH BI New Data OLTP Data

 Use  Case  drives  the  Data  Flows   Analyze  Acquire   Organize   Decide   RDBMS ETL New Data OLTP Data

Analyze   Streaming (CEP Engine) Organize  Acquire   Decide   New Data BI BAM BPEL  Use  Case  drives  the  Data  Flows  

Big  Data  Journey   Business   Defines  mandate  and   requirements   IT   Acquires  and   integrates  data   Data  Scien4sts   Build  and  refine   analy4c  models   IT   Publishes  new  Insights   Business   Consumes  insights   and  measures   effec4veness  

Cronos  Big  Data  Services  Offering   Use Case Discovery Workshop Big Data Analytics Implementation Services Proof of Concepts

The  role  of  the  Data  Scien4st   Business     Strategy   Analyst   Hadoop   System   Administrator   Hadoop   Developer   Data  Architect   Data  Analyst/   Sta4s4cian   Iden4fy  Business   Pains  &   demonstrate   through   Analy4cal  skills   how  the   available  data   can  be  exploited   on  a  Strategic   Level   Hadoop  Cluster   installa4on  &   administra4on.   Data  Loading   Build  the   relevant   dataset  by   cleansing,   filtering,   grouping  and   aggrega4ng   the  data  using   parallel   processing   languages  like   MapReduce   Hive,  Pig,   Impala,…   ETL  tooling,   MDM,  Data   Cleaning  &   Matching   Integra4on  with   Enterprise   Architecture   Data   Governance  &   Security   Through  sta4s4cal     Analysis,   conceptual  and   predic4ve  data   modeling,  machine   learning,…   Discover  pajerns,   trends,  insights.   Translate  these  to   Business   Opportuni4es  

Cronos  Big  Data  References   Use  Case   Discovery   Workshop   Proof  Of   Concept   Implementa4on   Services   Big  Data   Analy4cs   Call  Center   þ   þ   þ   þ   Healthcare   þ   þ   þ   U4lity   þ   þ   þ   Editor   þ   þ   þ   Telco   þ   þ   ISV   þ   Media   þ   Transport   þ   þ þ   Manufacturing   &  Distribu4on   þ   Online  Gaming   þ þ  

     Clever  usage  of  data   to  make  bejer  decisions   Conclusions  

Contact:    majhias.vallaey@cronos.be`      +32  496  57  66  27  

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