Top 10 Rules For Vertical Revolutionaries* Dave McClure [email_address] www.500hats.com *with apologies to Guy Kawasaki, author of Art of the Start

Q: What is “Vertical Search”? domain-specific search, for people with a particular interest “I’m looking for a job” structured data and “metadata” (=data about data) occupation: software engineering company/industry: Electronic Arts, software: online games location: redwood city, CA structured search based on metadata “ show me all the contract PHP programming jobs for biotech companies in CA”

domain-specific search, for people with a particular interest

“I’m looking for a job”

structured data and “metadata” (=data about data)

occupation: software engineering

company/industry: Electronic Arts, software: online games

location: redwood city, CA

structured search based on metadata

“ show me all the contract PHP programming jobs for biotech companies in CA”

Top 10 Rules for Verticals Scratch a Rich Niche Find a Long Tail Pick the Right Porridge Mind the Metadata Give Great Search Tags 'R' Us Don't be Evil Buy Low, Sell High Find Your Inner Killer App Let's Get Vertical

Scratch a Rich Niche

Find a Long Tail

Pick the Right Porridge

Mind the Metadata

Give Great Search

Tags 'R' Us

Don't be Evil

Buy Low, Sell High

Find Your Inner Killer App

Let's Get Vertical

Rule #1: Scratch a Rich Niche Choose a Vertical with enough value to matter… … but not so much value that it’s on the radar for Y!/GOOG/MSN Houses, Jobs, Dating >> Shower Curtains, Bird Feeders webpages shopping cars travel jobs housing dating search complexity

Choose a Vertical with enough value to matter…

… but not so much value that it’s on the radar for Y!/GOOG/MSN

Houses, Jobs, Dating >> Shower Curtains, Bird Feeders

“ A Few Gorillas, Millions of Monkeys” Long Tail Data Fragmentation & Distribution # of data sources, # of records per data source Bad Tails / Good Tails Bad : one big ape Rule #2: Find a Long Tail

“ A Few Gorillas, Millions of Monkeys”

Long Tail Data Fragmentation & Distribution

# of data sources, # of records per data source

Bad Tails / Good Tails

Bad : one big ape

“ A Few Gorillas, Millions of Monkeys” Long Tail Data Fragmentation & Distribution # of data sources, # of records per data source Bad Tails / Good Tails Bad : one big ape OR too many monkeys Rule #2: Find a Long Tail

“ A Few Gorillas, Millions of Monkeys”

Long Tail Data Fragmentation & Distribution

# of data sources, # of records per data source

Bad Tails / Good Tails

Bad : one big ape OR too many monkeys

Rule #2: Find a Long Tail “ A Few Gorillas, Millions of Monkeys” Long Tail Data Fragmentation & Distribution # of data sources, # of records per data source Bad Tails / Good Tails Bad: one big ape OR too many monkeys Good : 3-5 apes (<50%) + lots of monkeys

“ A Few Gorillas, Millions of Monkeys”

Long Tail Data Fragmentation & Distribution

# of data sources, # of records per data source

Bad Tails / Good Tails

Bad: one big ape OR too many monkeys

Good : 3-5 apes (<50%) + lots of monkeys

Rule #3: Pick the Right Porridge (Data) Too much structure = no barriers to entry; anyone can do it Too little structure = hard for anyone -- including you Semi-structured = just right takes time & effort, but can be done

Too much structure = no barriers to entry; anyone can do it

Too little structure = hard for anyone -- including you

Semi-structured = just right

takes time & effort, but can be done

Rule #4: Mind the Metadata Use metadata to enable structured search acquire, integrate metadata -> help improve, target results Structured search >> generic search Gorillas can't beat you if you have better metadata for your vertical

Use metadata to enable structured search

acquire, integrate metadata -> help improve, target results

Structured search >> generic search

Gorillas can't beat you if you have better metadata for your vertical

Rule #5: Give Great Search Comprehensive, Fresh, Relevant results: Comprehensive : broad coverage of data sources Relevant : accurate results w/ deep match on what matters to me again -- use metadata (ex: LinkedIn lookup, GoogleMaps location mapping) Fresh : recent & frequent data breadth of data depth of relevant metadata recency / frequency of data

Comprehensive, Fresh, Relevant results:

Comprehensive : broad coverage of data sources

Relevant : accurate results w/ deep match on what matters to me

again -- use metadata (ex: LinkedIn lookup, GoogleMaps location mapping)

Fresh : recent & frequent data

Rule #6: Tags 'R' Us Millions of results are great, but help me find my top 3 (or top 1) Let your audience help gather additional data about your vertical Enable ratings & tagging

Millions of results are great, but help me find my top 3 (or top 1)

Let your audience help gather additional data about your vertical

Enable ratings & tagging

Rule #7: Don't be Evil (to your Vertical) Don't screw with organic results (except where it's obvious) Google has already trained users on ‘organic’ vs ‘paid’ real estate Build trust early : focus on great user experience & user benefits ORGANIC SPONSORED PAID

Don't screw with organic results (except where it's obvious)

Google has already trained users on ‘organic’ vs ‘paid’ real estate

Build trust early : focus on great user experience & user benefits

Rule #8: Buy Low, Sell High NOT Google vs Shopping.com, NOT Arbitrage… Google, Yahoo = wholesalers Shopping.com, NexTag, PriceGrabber = retailers Buy traffic, “develop” users, discover higher value

NOT Google vs Shopping.com, NOT Arbitrage…

Google, Yahoo = wholesalers

Shopping.com, NexTag, PriceGrabber = retailers

Buy traffic, “develop” users, discover higher value

Rule #9: Find Your Inner Killer App Use structured data & metadata not just for better results.. … but also to build vertical apps & solve problems Applications are sticky, engage the user example: comparison engines for travel, shopping What are the unique & common problems for your vertical ? search vertical search vertical app

Use structured data & metadata not just for better results..

… but also to build vertical apps & solve problems

Applications are sticky, engage the user

example: comparison engines for travel, shopping

What are the unique & common problems for your vertical ?

Rule #10: Let's Get Vertical (Content+Community) Community is a natural for verticals People who share the same goals, problems, stories Community -> Content -> Repeat Visitors vertical search search vertical app content & community

Community is a natural for verticals

People who share the same goals, problems, stories

Community -> Content -> Repeat Visitors

* Bonus Rule #11: Fun is Good Relevant doesn't mean you have to be boring User Experience Matters Possible for “defensible intellectual property” to mean: Funny Taglines Pleasing Use of Color Quirky Error Messages

Relevant doesn't mean you have to be boring

User Experience Matters

Possible for “defensible intellectual property” to mean:

Funny Taglines

Pleasing Use of Color

Quirky Error Messages

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