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Lookalike Modeling Panel Discussion Sherene Hilal Product Lead Oracle, Oracle Data Cloud July 18, 2014 Presented with

Profiling of Engagers and Converters with Audience Analytics and Look-alike Modeling

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Join Datacratic for the Profiling of Engagers and Converters with Audience Analytics and Look-alike Modeling discussion at the conference. How much are you able to learn about your current email and site converters? Do you have a way to extract learned attributes of your best audiences to guide and optimize your audience profile and personas? In this session, we will do deep dive into audience analytics capabilities that will help you discover new audiences and drive additional scale for digital marketing programs.

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Page 1: Profiling of Engagers and Converters with Audience Analytics and Look-alike Modeling

Lookalike  Modeling    Panel  Discussion    

1  

Sherene  Hilal  Product  Lead  Oracle,  Oracle  Data  Cloud    July  18,  2014  

Presented  with  

Page 2: Profiling of Engagers and Converters with Audience Analytics and Look-alike Modeling

Copyright  ©  2014  Oracle  and/or  its  affiliates.  All  rights  reserved.    |  

Safe  Harbor  Statement  The  following  is  intended  to  outline  our  general  product  direcNon.  It  is  intended  for  informaNon  purposes  only,  and  may  not  be  incorporated  into  any  contract.  It  is  not  a  commitment  to  deliver  any  material,  code,  or  funcNonality,  and  should  not  be  relied  upon  in  making  purchasing  decisions.  The  development,  release,  and  Nming  of  any  features  or  funcNonality  described  for  Oracle’s  products  remains  at  the  sole  discreNon  of  Oracle.  

Oracle  ConfidenNal  –  Internal/Restricted/Highly  Restricted   2  

Page 3: Profiling of Engagers and Converters with Audience Analytics and Look-alike Modeling

Copyright  ©  2014  Oracle  and/or  its  affiliates.  All  rights  reserved.    |  

Program  Agenda  

Panelist  IntroducNons  

Lookalike  Modeling  Overview    

Panel  Discussion    

1  

2  

3  

Oracle  ConfidenNal  –  Internal/Restricted/Highly  Restricted   3  

Page 4: Profiling of Engagers and Converters with Audience Analytics and Look-alike Modeling

Copyright  ©  2014  Oracle  and/or  its  affiliates.  All  rights  reserved.    |  

Program  Agenda  

Panelist  IntroducNons  

Lookalike  Modeling  Overview  

Panel  Discussion  

 

1  

2  

3  

Oracle  ConfidenNal  –  Internal/Restricted/Highly  Restricted   4  

Page 5: Profiling of Engagers and Converters with Audience Analytics and Look-alike Modeling

Copyright  ©  2014  Oracle  and/or  its  affiliates.  All  rights  reserved.    |  

Panelist Introduction

Sherene  Hilal,  Product  Lead,  Oracle  Data  Cloud   James  Prudhomme  CEO,  Datacra=c  

5  

Nate  Becker,  Digital  Media  Supervisor,  Op=Media    

Page 6: Profiling of Engagers and Converters with Audience Analytics and Look-alike Modeling

Copyright  ©  2014  Oracle  and/or  its  affiliates.  All  rights  reserved.    |  

Program  Agenda  with  Highlight  

Panelist  IntroducNon  

Lookalike  Modeling  Overview  

Panel  Discussion    

1  

2  

3  

Oracle  ConfidenNal  –  Internal/Restricted/Highly  Restricted   6  

Page 7: Profiling of Engagers and Converters with Audience Analytics and Look-alike Modeling

Copyright  ©  2014  Oracle  and/or  its  affiliates.  All  rights  reserved.    |  

Custom Data: Look-alike Modeling

•  Lookalikes are an audience modeled from your customers and converters to gain reach & efficiency.

•  BlueKai has built a modeling system on Datacratic’s Machine Learning platform to provide your brand with custom look-alike models that are built and crafted solely for you. – Creates reach off your 1st party data – Qualifies prospects based on behavior –  Performance-driven

Challenge: 1st party data is effective, but limited. How do you scale? Solution: Customized models using exclusive 3rd party data

Page 8: Profiling of Engagers and Converters with Audience Analytics and Look-alike Modeling

Copyright  ©  2014  Oracle  and/or  its  affiliates.  All  rights  reserved.    |  

Uni

quen

ess

Scale

CompeNNve  Advantage:    Unique  PredicNons  @  Scale  

1st Party Data!“Hotel Chain”!

Models!“Look-a-like”!

2nd Party Data!“Credit Card”!

3rd Party Data!“Expedia sold! to Marriott”!

Page 9: Profiling of Engagers and Converters with Audience Analytics and Look-alike Modeling

Copyright  ©  2014  Oracle  and/or  its  affiliates.  All  rights  reserved.    |  

Modeling System Overview •  Fully integrated within the BlueKai UI

–  Already integrated within BlueKai. Easy to get started.

•  Multi-variant –  Users’ probability score calculated based on all available

attributes + recency & frequency

•  Full BlueKai Dataset (+ your first party datasets) –  Leverage over 400MM available users across 40k attributes to

identify users based on behaviors

•  Daily Model Refreshes –  Keep your models current!

•  Customizable Threshold - Reach vs Precision –  Model scores are stack-ranked. Customize threshold for

performance (Top .01%) or scale (Top 20%)

Page 10: Profiling of Engagers and Converters with Audience Analytics and Look-alike Modeling

Copyright  ©  2014  Oracle  and/or  its  affiliates.  All  rights  reserved.    |  

How an Audience Data Modeling System Works

Continually Learning & Adapting  

Continually Learning & Adapting  

Page 11: Profiling of Engagers and Converters with Audience Analytics and Look-alike Modeling

Copyright  ©  2014  Oracle  and/or  its  affiliates.  All  rights  reserved.    |  

Program  Agenda  

Oracle  Data  Cloud  IntroducNon  

Panelist  IntroducNons    

Panel  Discussion    

1  

2  

3  

Oracle  ConfidenNal  –  Internal/Restricted/Highly  Restricted   11  

Page 12: Profiling of Engagers and Converters with Audience Analytics and Look-alike Modeling

Copyright  ©  2014  Oracle  and/or  its  affiliates.  All  rights  reserved.    |  

Take-Aways

12  

1.  Available  on  demand  within  your  BlueKai  UI  2.  Uses  same  trusted  1st  &  3rd  party  sources    3.  Leverages  Nme  series  and  behavior  sequencing  4.  Provides  visibility  in  to  the  “Black  Box”  5.  Applies  AdapNve  Machine  Learning  -­‐  More  than  simple  decision  tree  

logic.    +1  BONUS  –  Domain  experNse  in  markeNng  technology  

Page 13: Profiling of Engagers and Converters with Audience Analytics and Look-alike Modeling