How to Use Search Data to Build Content that Drives Value at Scale
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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.
How to Use Search Data to Build Content that Drives Value at Scale
- 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)
- 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
- 18. #C3NY @simmonet@simmonet How Do We Scale Quality?
- 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
- 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
- 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
- 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
- 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