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Data Mining + SEO

Data Mining + SEO. Timothy D’Auria BostonDecision.com March 29 th , 2012.

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Data Mining + SEO

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  1. Data Mining + SEO Timothy D’Auria BostonDecision.com March 29th, 2012 Disclaimer: Boston Decision believes the information contained herein to be accurate. However, Boston Decision, LLC makes no guarantees and no warranties, written, oral or implied, including without limitation any implied warranties of merchantability, fitness, or accuracy. Recipient assumes all responsibility for use of the information contained herein.

  2. Boston Decision • MINE – PREDICT – AUTOMATE • “Get into the minds of customers” • Provide the skills, resources, expertise

  3. Zero-Hour Insight™ Capability • No time to warehouse stale data

  4. Predictive Modeling in Boston • Rank #1 in Google • Rank #1 in Yahoo

  5. Last Time • Data Mining = pattern detection from data • Identify direct mail targets • Screen leads / automatic salesperson

  6. How we left off… • “For the next demonstration, we need a volunteer from our last meetup.  • We're looking for a small company, perhaps a start-up, in any industry.”

  7. This Time

  8. Subplot • Data Mining with No Data

  9. What is SEO? • Search engine optimization is the process of improving web site visibility through unpaid search.

  10. SEO Today • Domain Authority • On-page SEO • Link Context • Anchor Text • PageRank • User Signals***

  11. Domain Authority • Age • Popularity • Size • Registration duration

  12. On Page SEO • Meta tags • Title • Keywords • Description • Size (character count), relevance • Relationship between meta tags and content • Keyword density

  13. Link Context • Do links indicate that the linker has legitimate interest in linkee content? • One-way links • On-topic • Link adjoined by appropriate content

  14. Anchor Text • Relevance of first anchor text to content of target page

  15. Page Rank • Importance of page / site to the web

  16. User Signals

  17. Sharing • Importance of person sharing • Number of times “re-shared” • Relationships between sharer and sharee • Send link to friend • Send link to stranger

  18. CONTENT IS KING!!

  19. Who is Queen? DATA! - Steve Hopkins, nerejtech.com

  20. … finger on the pulse marketing

  21. NO DATA!!

  22. 2 Questions • Who is the target market? • What does the target market want?

  23. The Data • Bureau of Labor Statistics • Consumer Expenditure Survey (http://www.bls.gov/cex/) • Bureau of Economic Analysis • Personal Consumption Expenditures (http://www.bea.gov/) • American FactFinder (US Census) • http://factfinder2.census.gov

  24. The Technology • R Language – Open Source & Free • tm • gdata • plyr • twitteR • ggplot2 • maps • wordcloud

  25. Read the Data

  26. Univariate Model Limitations • 1 attribute at a time (Age, Income, etc..) • Not comprehensive (can’t look at age, income together) • Ignores relationships

  27. Multivariate Model = A Next Step • Control for Age while looking at income • Take many more factors into account

  28. A Naïve Geographic Scoring Algorithm • Age 25 – 34, $80K+, Associates or Bachelors • Created a scoring algorithm that ranks MA counties in these 3 areas.

  29. Jewelry Blogosphere Chatter in Middlesex? What does the target want?

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