Drilling into the Data Well for
Deeper Customer Intelligence
©2013
9%
of marketers use customer data effectively

©2013
98%
of consumers are frequently mistargeted

©2013
What we’re talking about today

1 Collecting the right consumer data

2 Transforming data into insight
3

©2013

How to le...
Brands that win prioritize
engagement over exposure

©2013
Understanding customers
is the key
to Engagement

©2013
But what do marketers really know about us?

©2013
What does the future hold?

©2013
©2013
Internet Explorer and Firefox have 45% share

Internet
Explorer

Firefox

©2013
Say goodbye to tracking nearly ½ of consumers?

©2013
Marketers are incapacitated by data silos

Email

©2013

eCommerce

CMS

CRM

Subscription
Management
What’s the formula for
personalization?

©2013
Personalization
=
P = RD + C + TA
Right Data + Content + Technology
Automation

©2013
Collecting the right
consumer data

©2013
Is behavioral data all that matters?

It’s not who you are underneath…

…It’s what you do that defines you.

©2013
What defines us

©2013
Demographics
Who we are

©2013
Demographic data is still the most collected and
utilized by marketers

SOURCE: 2012 BRITE - NY AMA Marketing in Transitio...
Registration data is more important than ever

Registration
First name:*

Last name:*
Company name:
Email:*
Address:*
City...
And brands are taking notice

©2013
Fast

©2013

Easy

Permission

More Data
Social login leads to more registrations and data

Your Site

©2013
Social profile data is accessed with permission

NPR would like to access some of
your LinkedIn info:

©2013
Registration using social login is
faster and leads to better data quality

©2013
Psychographics
What we like

©2013
©2013
Social profile data

Demographics
Name
•
Gender •
Birthdate
Photo
•

•
•
•
•

Location
Educatio
n
Marital
Career

•

Psych...
But wait, isn’t all data from social
networks unstructured?

©2013
But wait, isn’t all data from social
networks unstructured?

No.
©2013
Facebook profile circa 2006

Favorite Books:
“mostly biographies
and textbooks”

©2013

Interests:
“making things”
In 2010, Facebook began enforcing validation
rules on profile fields

©2013
In 2010, Facebook began enforcing validation
rules on profile fields

©2013
Facebook profile circa 2010

©2013
Social profile data comes from multiple sources

©2013
Social network stream data

©2013
Social network streams offer inferred insights

Brand advocate

Customer service
candidate

In market

©2013
Surveys drive insight about how customers think

©2013
Behaviors
What we do

©2013
Web browsing behavior can predict intent

©2013
Determine preferences using transaction data
Image

Quantity

Price

Date

Canon Digital
Rebel Camera

1

$550.00

10/4/20...
Campaign interactions enable relevancy

©2013
Transforming data into
insight

©2013
4 steps to transform data into insight

1 Normalize

2 Cluster
3

4

©2013

Visualize

Segment
Data needs to be normalized
City
Portland

State
Oregon

Country
United States

City
Portland

State
OR

Country
USA

Loca...
Data needs to be normalized

First

Last

Last, First

Full Name

Michael

Olson

Olson,
Michael

Michael Olson

©2013
Use machine learning to create clustered segments

Likes
Animal House

Likes
Ace Ventura

Likes
Bradley
Cooper

Likes
Brid...
Understand the composition of your customers

©2013
Match content and offers to customer segments
Male

Age 45-64
Lives in
Rural Location

Female

Age 35-44

Environmental
Ad...
Match content and offers to customer segments

©2013
Implementing effective
segmentation strategies

©2013
Email segmentation

©2013
Universal Music segments emails based on
location and interests

Emails personalized
based on music
preferences and
locati...
Samsung creates targeted email campaigns using
social profile data
Results
34% more likely to
open emails
63% more likely ...
Paper Style employs behavioral segmentation
for emails

Results
•

•

©2013

161% increase in
email CTR
330% increase in
r...
Content and eCommerce
personalization

©2013
Comparing purchase conversion rates
In-Store

30%

©2013

Online

3%
The in-store shopping experience is personalized

©2013
The in-store shopping experience is personalized

©2013
Amazon pioneered personalization

©2013
Sun & Ski sports recommends products
based on behaviors

Results
•
•

©2013

79% increase in
purchase conversion
rates
25%...
Walmart’s Shopycat app recommends gift ideas
based on friends’ interests

©2013
…and lets consumers treat themselves
Interests:
Tetris, Bradley Cooper

©2013
Segmenting travel content based on
demographics

©2013
Segmenting travel content based on
demographics
Younger audience

©2013

Older audience
Ad Targeting

©2013
Deconstructing social data
Cookie-based data

•
•
•

More scale
Implicit data
Lower CTR and response
rate per impression

...
Ad targeting using social data

©2013
A few final tips

1 Know your buyer personas
2 Start simple – go GAL!
3

Align that content!

©2013
A few final tips

1 Know your buyer personas
2 Start simple – go GAL!
3

Align that content!

©2013
A few final tips

1 Know your buyer personas
2 Start simple – go GAL!
3

Keep that content in alignment!

©2013
A few final tips

1 Know your buyer personas
2 Start simple – go GAL!
3

Keep that content in alignment!

©2013
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Más relevancia: Campañas más efectivas potenciadas por datos profundos de tus usuarios

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"Más relevancia: Campañas más efectivas potenciadas por datos profundos de tus usuarios." Conoce la presentación de Jeff Mills en Digital Marketing University 2013 en Ciudad de México.

Dicen que los datos son “el nuevo petróleo.” Pero lograr un entendimiento profundo de quién es cada cliente ha sido históricamente difícil. Hemos evolucionado hasta el punto en que los marketeros ya no necesitan adivinar quiénes son sus consumidores a partir de datos de comportamiento. Mediante la fuente de datos adecuada, un marketero puede construir detallados perfiles de clientes y desarrollar un conocimiento en profundidad del perfil demográfico, intereses e intenciones de su audiencia. En esta sesión describiremos qué datos están disponibles para las empresas, cómo capturar esta información y utilizarla para potenciar la segmentación y targeting en tus campañas de marketing digital.

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Más relevancia: Campañas más efectivas potenciadas por datos profundos de tus usuarios

  1. 1. Drilling into the Data Well for Deeper Customer Intelligence ©2013
  2. 2. 9% of marketers use customer data effectively ©2013
  3. 3. 98% of consumers are frequently mistargeted ©2013
  4. 4. What we’re talking about today 1 Collecting the right consumer data 2 Transforming data into insight 3 ©2013 How to leverage your data
  5. 5. Brands that win prioritize engagement over exposure ©2013
  6. 6. Understanding customers is the key to Engagement ©2013
  7. 7. But what do marketers really know about us? ©2013
  8. 8. What does the future hold? ©2013
  9. 9. ©2013
  10. 10. Internet Explorer and Firefox have 45% share Internet Explorer Firefox ©2013
  11. 11. Say goodbye to tracking nearly ½ of consumers? ©2013
  12. 12. Marketers are incapacitated by data silos Email ©2013 eCommerce CMS CRM Subscription Management
  13. 13. What’s the formula for personalization? ©2013
  14. 14. Personalization = P = RD + C + TA Right Data + Content + Technology Automation ©2013
  15. 15. Collecting the right consumer data ©2013
  16. 16. Is behavioral data all that matters? It’s not who you are underneath… …It’s what you do that defines you. ©2013
  17. 17. What defines us ©2013
  18. 18. Demographics Who we are ©2013
  19. 19. Demographic data is still the most collected and utilized by marketers SOURCE: 2012 BRITE - NY AMA Marketing in Transition Study ©2013
  20. 20. Registration data is more important than ever Registration First name:* Last name:* Company name: Email:* Address:* City:* State/Province:* Zip/Postal Code:* Phone Number:* ©2013
  21. 21. And brands are taking notice ©2013
  22. 22. Fast ©2013 Easy Permission More Data
  23. 23. Social login leads to more registrations and data Your Site ©2013
  24. 24. Social profile data is accessed with permission NPR would like to access some of your LinkedIn info: ©2013
  25. 25. Registration using social login is faster and leads to better data quality ©2013
  26. 26. Psychographics What we like ©2013
  27. 27. ©2013
  28. 28. Social profile data Demographics Name • Gender • Birthdate Photo • • • • • Location Educatio n Marital Career • Psychographics Friends • • • • ©2013 Interests Music TV Political • • • • Movies Books Hobbies Religious
  29. 29. But wait, isn’t all data from social networks unstructured? ©2013
  30. 30. But wait, isn’t all data from social networks unstructured? No. ©2013
  31. 31. Facebook profile circa 2006 Favorite Books: “mostly biographies and textbooks” ©2013 Interests: “making things”
  32. 32. In 2010, Facebook began enforcing validation rules on profile fields ©2013
  33. 33. In 2010, Facebook began enforcing validation rules on profile fields ©2013
  34. 34. Facebook profile circa 2010 ©2013
  35. 35. Social profile data comes from multiple sources ©2013
  36. 36. Social network stream data ©2013
  37. 37. Social network streams offer inferred insights Brand advocate Customer service candidate In market ©2013
  38. 38. Surveys drive insight about how customers think ©2013
  39. 39. Behaviors What we do ©2013
  40. 40. Web browsing behavior can predict intent ©2013
  41. 41. Determine preferences using transaction data Image Quantity Price Date Canon Digital Rebel Camera 1 $550.00 10/4/2013 Universal Camera Case 1 $34.00 10/4/2013 Apple MacBook Pro Notebook PC 1 $2,299.00 7/22/2013 Kenneth Cole New York Men’s Shoes ©2013 Product 2 $321.98 4/16/2013
  42. 42. Campaign interactions enable relevancy ©2013
  43. 43. Transforming data into insight ©2013
  44. 44. 4 steps to transform data into insight 1 Normalize 2 Cluster 3 4 ©2013 Visualize Segment
  45. 45. Data needs to be normalized City Portland State Oregon Country United States City Portland State OR Country USA Location Portland, OR ©2013 Country US
  46. 46. Data needs to be normalized First Last Last, First Full Name Michael Olson Olson, Michael Michael Olson ©2013
  47. 47. Use machine learning to create clustered segments Likes Animal House Likes Ace Ventura Likes Bradley Cooper Likes Bridesmaids ©2013 Likes Office Space SEGMENT: Comedy Enthusiasts
  48. 48. Understand the composition of your customers ©2013
  49. 49. Match content and offers to customer segments Male Age 45-64 Lives in Rural Location Female Age 35-44 Environmental Advocate Male Age 18-29 HH Income $30-$50K ©2013
  50. 50. Match content and offers to customer segments ©2013
  51. 51. Implementing effective segmentation strategies ©2013
  52. 52. Email segmentation ©2013
  53. 53. Universal Music segments emails based on location and interests Emails personalized based on music preferences and location. Significant increase in email open rates ©2013
  54. 54. Samsung creates targeted email campaigns using social profile data Results 34% more likely to open emails 63% more likely to click-through emails ©2013
  55. 55. Paper Style employs behavioral segmentation for emails Results • • ©2013 161% increase in email CTR 330% increase in revenue per email
  56. 56. Content and eCommerce personalization ©2013
  57. 57. Comparing purchase conversion rates In-Store 30% ©2013 Online 3%
  58. 58. The in-store shopping experience is personalized ©2013
  59. 59. The in-store shopping experience is personalized ©2013
  60. 60. Amazon pioneered personalization ©2013
  61. 61. Sun & Ski sports recommends products based on behaviors Results • • ©2013 79% increase in purchase conversion rates 25% increase in net revenue
  62. 62. Walmart’s Shopycat app recommends gift ideas based on friends’ interests ©2013
  63. 63. …and lets consumers treat themselves Interests: Tetris, Bradley Cooper ©2013
  64. 64. Segmenting travel content based on demographics ©2013
  65. 65. Segmenting travel content based on demographics Younger audience ©2013 Older audience
  66. 66. Ad Targeting ©2013
  67. 67. Deconstructing social data Cookie-based data • • • More scale Implicit data Lower CTR and response rate per impression ©2013 First-party consumer data • • • Less scale Explicit data Higher CTR and response rate per impression
  68. 68. Ad targeting using social data ©2013
  69. 69. A few final tips 1 Know your buyer personas 2 Start simple – go GAL! 3 Align that content! ©2013
  70. 70. A few final tips 1 Know your buyer personas 2 Start simple – go GAL! 3 Align that content! ©2013
  71. 71. A few final tips 1 Know your buyer personas 2 Start simple – go GAL! 3 Keep that content in alignment! ©2013
  72. 72. A few final tips 1 Know your buyer personas 2 Start simple – go GAL! 3 Keep that content in alignment! ©2013

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