Grant Simmons, VP of Search Marketing at a large real estate portal, discusses using business and search data to build valuable content at scale. He outlines three types of data - core site data, user generated data, and site/search data. Simmons emphasizes starting with context and leveraging both business data like listings and user search data to tell unique stories aligned with user intent. He provides the example of a "Facets" project that generated long tail content by analyzing listings data. Simmons stresses presenting data visually and keeping content different, valuable, fun, and aligned with the Homes.com brand guidelines.

1. CONTENT MARKETING INTELLIGENCE
#C3NY
HOW TO USE SEARCH DATA TO BUILD
CONTENT THAT DRIVES VALUE AT SCALE
VP, Search Marketing
Grant Simmons

2. #C3NY @simmonet
Grant Simmons
VP of Search Marketing
Search marketer, father, sailor. Though not
necessarily in that order when there’s a fair wind.
Hello
@simmonet
/in/simmonet
www.grantisms.com

3. #C3NY @simmonet
• Real Estate Portal
(ahem… we’re not Zillow)
• 10M+ Visits / Month
• Private company
• Norfolk, VA
@simmonet
/in/simmonet
Hello
www.grantisms.com

4. #C3NY @simmonet
Let’s Go Back to the Future (2015)

5. #C3NY @simmonet
Let’s Go Back to the Future (2015)
cat

6. N E W Y O R K C I T Y | O C T O B E R 2 8 - 2 9 , 2 0 1 5
C3 2015 BREAKOUT SESSION
IT AIN'T OVER TILL
THE CAT LADY SINGS
CONTENT-BEYOND-KITTENS &
OTHER SEARCH STRATEGIES
VP of Search Marketing
& Consumer Engagement
Grant Simmons

7. #C3NY @simmonet
The Cat Lady
Content Conundrum
@simmonet

8. #C3NY @simmonet
Content to some
content marketers
is like...
@simmonet

9. #C3NY @simmonet
…cats to a cat lady
@simmonet

10. #C3NY @simmonet
…cats to a cat lady
i.e. you can never
have enough
@simmonet

11. #C3NY @simmonet
The cat lady content conundrum
More Better
@simmonet

12. #C3NY @simmonet
More is not better
(unless you want to fill a litter box)

14. http://unop.me/crap

15. #C3NY @simmonet
Let’s Go Back to the PRESENT (2017)
cat

16. CONTENT MARKETING INTELLIGENCE
#C3NY
HOW TO USE SEARCH DATA TO BUILD
CONTENT THAT DRIVES VALUE AT SCALE
VP, Search Marketing
Grant Simmons

17. Tweet this session!
#C3NY@simmonet

18. #C3NY @simmonet@simmonet
How Do We Scale Quality?

19. #C3NY @simmonetBigger Cats?

20. #C3NY @simmonet

21. #C3NY @simmonet

22. #C3NY @simmonet
Big & Small Data
Tells Stories
Content Marketing is
about telling a branded story

23. #C3NY @simmonet
Data Itself is Not Smart

24. #C3NY @simmonet
Data Itself is Not Smart
But it does help tell smart stories
Stories = content differentiation

26. “Every brand is unique. But if you cannot tell
that uniqueness through it’s stories, it will
spend it’s whole life being a fish.”
Grant Simmons

27. #C3NY @simmonet
• Leverage data
• Tell unique stories
• Smart
• Different
• Interesting
• Align with your brand
Don’t be a Fish

28. #C3NY @simmonet
Data Cat-egories
Different strokes for different folks

29. #C3NY @simmonet
3 Types
of Data

30. #C3NY @simmonet
Core Site
Data
• Listings
• Parts
• Products
• ‘Free’ datasets
Business | Interaction | Search

31. #C3NY @simmonet
User
Generated
Data
Business | Interaction | Search
• Internal clicks
• Purchases
• Time on page
• Surveys

32. #C3NY @simmonet
Site / Search
Data
Business | Interaction | Search
• Search engines
• Site search
• Filters

33. #C3NY @simmonet
3 Types
of Data
Business | Interaction | Search

34. #C3NY @simmonet
2 Types
of Data
Business | Interaction | Search
today

35. #C3NY @simmonet
Business Data
Stuff you own and / or have access to

36. #C3NY @simmonet
Yeah it can be kinda boring raw
• But key differentiator
• Cat-alog what you have
• Cat-alog relevant available data
• Align with search intent
• Align with search engine opportunities
Business Data

37. #C3NY @simmonet
Homes.com

38. #C3NY @simmonet
What Data? What Stories?

39. #C3NY @simmonet
• Listings data
• Census data
Data Sources

40. #C3NY @simmonet
• Lists
• Long tail queries
• Interesting facts
What are People Looking For?

41. #C3NY @simmonet
• Lists
• Long tail queries
• Interesting facts
What are People Looking For?
Did You Know?
A cat’s tail contains
approximately 10% of
the bones in its body

42. #C3NY @simmonet
• Lists
• Long tail queries
• Interesting facts
What are People Looking For?
Did You Know?
A cat’s tail contains
approximately 10% of
the bones in its body

43. #C3NY @simmonet
The “FACETs” Project
Leveraging Data for the Long Tail

45. #C3NY @simmonet
Fully Responsive (Presentation is Key)

46. #C3NY @simmonet
City / State Coverage – Census + Listings Data

47. #C3NY @simmonet
Lists / List Pages

48. #C3NY @simmonet
State-Level Pages / Facets

49. #C3NY @simmonet
State-Level Pages / Facets
Auto generated ranking
data, dynamic, random
and interesting

50. #C3NY @simmonet
State-Level Pages / Facets
Auto generated ‘facets’
based on frequency, listing
counts & user interests

51. #C3NY @simmonet
City-Level Pages / Facets
Auto generated ranking
data, dynamic, random,
relevant and interesting

52. #C3NY @simmonet
Descriptive / Phrase Facets
Parsing of descriptions
Mapped against phrase set

53. #C3NY @simmonet
Descriptive / Phrase Facets
Parsing of descriptions
Mapped against phrase set

54. #C3NY @simmonet
• Phase 1
• Basic internal linking
• Limited cities (thin content)
• Phase 2
• Expand value of pages (more data)
• Add user interaction cues
• Phase 3
• Combos
• Based on data
The Long Tail – in Action

55. #C3NY @simmonet
The Long Tail – In Action
• Lists
• Top 10s
• Attributes
• Price
• Size
• Features

56. #C3NY @simmonetScales Nicely

57. #C3NY @simmonetScales Nicely (& Intelligently)

58. #C3NY @simmonet
• What public data exists?
• What unique data exists?
• What would be worthy?
• How can you present it?
Business Data Recap

59. #C3NY @simmonet
• How many dog people?
• How many cat people?
Quick Survey

60. #C3NY @simmonet
• How many dog people?
• How many cat people?
Yes. I was a cat.
Quick Survey

61. #C3NY @simmonet
Search Data
Stuff you create and / or is created
for you through searcher’s behavior

62. #C3NY @simmonet
Search Data Is:

63. #C3NY @simmonet
Your users telling you stories
- You just need to listen
Search Data Is:

64. #C3NY @simmonet
Search Data Elements

65. #C3NY @simmonet
More Interesting (generally)
• Collection & storage
• Summarization
• Analysis & extraction
• Align with user interest
• Present with paw-pose 
Search Data Elements

66. #C3NY @simmonet
Search Data: Your Data
Can be a lot of it
• But key differentiator
• Cat-alog what you have
• Analyze the crap outta it
• Find interesting stories

67. #C3NY @simmonetIt Can Be Imperrfect…

68. #C3NY @simmonet
• Demand-side
• Intent
Site Search Data

69. #C3NY @simmonet
Always Start With Context
Content without Context is Like
a Cat Without a Tail

70. #C3NY @simmonet
Always Start With Context
What’s the breed of cat with no tail?
a question

71. #C3NY @simmonet
Always Start With Context
What’s interesting?
What’s are the goals?
questions

72. #C3NY @simmonet
• Start with largest dataset
• Easier to see patterns
• Validate results
Context – Reference Listing Type

73. #C3NY @simmonet
Simple Math

74. #C3NY @simmonet
Simple Math
Not Perfect Math.

75. #C3NY @simmonet
Popularity (Searches / Habitant)

76. #C3NY @simmonet
Map Data Over Time For Trends

77. #C3NY @simmonet
Dig Into Data Trends

78. #C3NY @simmonet
Overlay other
data sets for
more insights /
interesting info
What is your user
looking for??

79. #C3NY @simmonet
• Buyers understand offer potential
• Sellers understand time to sale
• Pricing considerations
• Inventory availability
A TON of cool info:
• For every city, state, county with data
• We call that…
Homes.com “Scarcity Index”

80. #C3NY @simmonet
• Buyers understand offer potential
• Sellers understand time to sale
• Pricing considerations
• Inventory availability
A TON of cool info:
• For every city, state, county with data
• We call that…
Homes.com “Scarcity Index”
SCALE

81. #C3NY @simmonet
• No need to stop there!
• More analysis can create more insights
• You never know what you can find
• Locations over time
• Attributes against attributes
Hold On Cowboy!

82. #C3NY @simmonet
Keep Digging Into Other Data Points

83. #C3NY @simmonet
There is no bad data,
there is only bad
analysis of good data*
I’m Innocent

84. #C3NY @simmonet
There is no bad data,
there is only bad
analysis of good data*
I’m Innocent
*and some bad data

85. #C3NY @simmonet@simmonet
If Content is King

86. #C3NY @simmonet@simmonet
If Content is King
Then data
is the crown

87. #C3NY @simmonet
Presenting Data

88. #C3NY @simmonet
It’s All About Presentation
• Know your audience
• Provide value
• Targeting
• Customization
• Keep It Simple
Or as simple as your audience

89. #C3NY @simmonet
• Search data
• Survey data
• Census data
• Jobs data
• Housing trends
Simple Visualization of Complex Datasets

90. #C3NY @simmonet
• Survey data
• Census data
Simple Visualization of Complex Datasets

91. #C3NY @simmonet

92. #C3NY @simmonet
More is Not Better

93. #C3NY @simmonet@simmonet
• Leverage data
• Tell unique stories
• Smart
• Different
• Interesting
• Align with your brand
Better is Better

94. Thank you!

95. #C3NY @simmonet

96. #C3NY @simmonet
Grant Simmons
VP of Search Marketing
Search marketer, father, sailor. Though not
necessarily in that order when there’s a fair wind.
Thank you
@simmonet
/in/simmonet
www.grantisms.com

97. #C3NY @simmonet
Grant Simmons
VP of Search Marketing
Search marketer, father, sailor. Though not
necessarily in that order when there’s a fair wind.
Thank you
@simmonet
/in/simmonet
www.grantisms.com

98. #C3NY @simmonet
• Real Estate Portal
(ahem… we’re not Zillow)
• 10M+ Visits / Month
• Private company
• Norfolk, VA
@simmonet
/in/simmonet
Hello
www.grantisms.com

100. #C3NY @simmonet@simmonet
Homes.com Content Strategy
Our Content / Messaging aspires to / should be:
• Different & unique
• Position Homes.com as home-buying resource
• Valuable
• Something fun or a little amusing
• Non-lethal
• Pass authority
• Mobile-friendly

101. Homes.com Content Guidelines
• Different
• Position
• Valuable
• Fun
• Non-lethal
• Pass authority
• Mobile-friendly
Keeping
it simple
Is it / Does it?

102. Content Strategy
• Guidelines
• Planning
• Ideation
• Scheduling
• Execution
Guidelines
Plan
IdeateSchedule
Execute