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Richmond Chamber of Commerce Marketing Data Science Masterclass

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Marketing data science masterclass for SMEs to understand how SEO and social media can be made more predictable and quantifiable.

Richmond Chamber of Commerce: http://richmondchamberofcommerce.co.uk/

Thank you to Artios: https://artios.io/

Published in: Marketing
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Richmond Chamber of Commerce Marketing Data Science Masterclass

  1. 1. April 2014! ANDREAS VONIATIS Marketing Data Science SEO and Social Media 21st April 2015
  2. 2. TODAY… •  Data science •  SEO •  Social Media ©2015 Copyright Richmond Chamber of Commerce and Artios
  3. 3. DATA SCIENCE…
  4. 4. WHY DATA SCIENCE •  More reliable for making decisions •  Quantifiable •  Online marketing is a data rich environment •  More complex ©2015 Copyright Richmond Chamber of Commerce and Artios
  5. 5. WHAT IS DATA SCIENCE Software Engineering Online Marketing Mathematics Statistics Probability Linear Algebra Multivariable Calculus Differential Equations Topology Python Mongo DB SQL MapReduce Hadoop R Machine Learning D3 Visualisation Apache Spark Apache Storm Scala Java Psychology HTML & CSS Javascript Social Media SEO Copywriting Online advertising Branding Research Customer Experience ©2015 Copyright Richmond Chamber of Commerce and Artios
  6. 6. HOW : DATA SCIENCE Data Design Data Wrangling Explore Data Statistical Tests Insights Visualise Data ©2015 Copyright Richmond Chamber of Commerce and Artios
  7. 7. SEO…
  8. 8. SEO : DEFINITION SEO stands for “search engine optimization.” It is the process of getting traffic from the “free,” “organic,” “editorial” or “natural” search results on search engines. •  Main components of SEO: •  Content •  Site design and architecture •  Links •  Social Media ©2015 Copyright Richmond Chamber of Commerce and Artios
  9. 9. WHO : COMPETITORS •  WHO has / doesn’t have the RANKINGS ? •  WHO is OFFERING to your customer groups? •  Disregard Wikipedia •  Choose Alexa as your outcome variable ©2015 Copyright Richmond Chamber of Commerce and Artios
  10. 10. WHO : EXAMPLE Volunteer please! •  Do a Google •  Check Alexa •  What we look for •  Comparability •  Full range ©2015 Copyright Richmond Chamber of Commerce and Artios
  11. 11. WHO : EXAMPLE ©2015 Copyright Richmond Chamber of Commerce and Artios
  12. 12. WHAT : SEO DRIVERS Google uses over 800 factors
  13. 13. WHERE : DATA SOURCES ©2015 Copyright Richmond Chamber of Commerce and Artios
  14. 14. IN : DATA •  Daily basis •  Random times of the day •  Your site •  Your competitors •  Select an element – Referring Domains ©2015 Copyright Richmond Chamber of Commerce and Artios
  15. 15. PREDICT : EDA Observations: •  Distributions – normal ? •  Outliers ? •  Variation ? •  Statistical tests (Mann Whitney, ANOVA, Welch’s t etc) ? ©2015 Copyright Richmond Chamber of Commerce and Artios
  16. 16. PREDICT : READING AGE ©2015 Copyright Richmond Chamber of Commerce and Artios
  17. 17. PREDICT : READING AGE
  18. 18. PREDICT : READING AGE ©2015 Copyright Richmond Chamber of Commerce and Artios
  19. 19. PREDICT : READING AGE 0 1 2 3 40 50 60 70 Average Page Title FKRE Density domain gelighting.com jcc.co.uk lighting.philips.co.uk osram.com qeglobal.com Feature Value Distribution: Average Page Title FKRE − QE Global ©2015 Copyright Richmond Chamber of Commerce and Artios
  20. 20. QUANTIFY : READING AGE Quantify the effect on traffic: •  Produce a scatter plot •  Fit a model •  Look at the coefficient of determination (R2) •  Anything over .14 indicates a small but significant relationship •  .75 or above suggests a significant relationship ©2015 Copyright Richmond Chamber of Commerce and Artios
  21. 21. QUANTIFY: READING AGE ●● ● ● ● ● ● ● ● ● ● ● ● ● ● r2 = 0.45 −1e+06 0e+00 1e+06 2e+06 40 50 60 70 80 Average Page Title FKRE AlexaRank Feature Regression − Average Page Title FKRE − QE Global ©2015 Copyright Richmond Chamber of Commerce and Artios
  22. 22. APPLY : READING AGE Now we have proven that reading age is a significant factor: •  Review all content not meeting reading age targets •  Rewrite the content •  Wait 30 days ©2015 Copyright Richmond Chamber of Commerce and Artios
  23. 23. TRACK : READING AGE 40 50 60 70 Mar 23 Mar 30 Apr 06 Date AveragePageTitleFKRE domain gelighting.com jcc.co.uk lighting.philips.co.uk osram.com qeglobal.com Feature Trend: Average Page Title FKRE − QE Global ©2015 Copyright Richmond Chamber of Commerce and Artios
  24. 24. SOCIAL MEDIA…
  25. 25. SOCIAL MEDIA : DEFINITION forms of electronic communication (as Web sites for social networking and microblogging) through which users create online communities to share information, ideas, personal messages, and other content (as videos) •  Main components of Social Media: •  Sharing •  Followers •  Memes ©2015 Copyright Richmond Chamber of Commerce and Artios
  26. 26. WHERE : DATA SOURCES ©2015 Copyright Richmond Chamber of Commerce and Artios
  27. 27. WHERE : DATA SOURCES ©2015 Copyright Richmond Chamber of Commerce and Artios
  28. 28. IN : DATA •  Daily basis •  Random Times of the day •  Your site •  Your competitors •  Select an element – Twitter volume ©2015 Copyright Richmond Chamber of Commerce and Artios
  29. 29. PREDICT : TWITTER VOLUME
  30. 30. PREDICT : TWITTER VOLUME
  31. 31. PREDICT : TWITTER VOLUME ©2015 Copyright Richmond Chamber of Commerce and Artios
  32. 32. PREDICT : EDA Observations: •  Distributions – normal ? •  Outliers ? •  Variation ? •  Statistical tests (Mann Whitney, ANOVA, Welch’s t etc) ? ©2015 Copyright Richmond Chamber of Commerce and Artios
  33. 33. PREDICT : TWITTER VOLUME 0.0 0.3 0.6 0.9 0 25 50 75 100 Twitter Volume Density domain gelighting.com jcc.co.uk lighting.philips.co.uk osram.com qeglobal.com Feature Value Distribution: Twitter Volume − QE Global ©2015 Copyright Richmond Chamber of Commerce and Artios
  34. 34. QUANTIFY: TWITTER VOLUME ●●●●●●●●● ●●●●●●● ●● ●●●●● ●●● ●● ● ●●● ●●● ● ● ● ●●●●●● ● ● ● ●●●●●●●●●●● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●●● ● ● ●●● ● ● ●● ●● ●●●●●●●●● ● ● ●●●●●●●●●●●●●● ●●●● ●● ●●●●●●●●●●●●●●●●●●● ●●●●●●●● ●●●●●● ● ●●●● ●● ● ●●● ●●●●●●●●●●●●●●●●●●●● ●● ●●● ●●●●●● ● ●●●●●●●●●●●●● ● ● ●● ●●● ●● ● ●●● ● ● ● ●●●● ● ● ● ●●●● ● ● ● ● ●●● ● ● ● ● ●●● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● r2 = 0.13 −1e+06 0e+00 1e+06 2e+06 0 200 400 Twitter Volume AlexaRank Feature Regression − Twitter Volume − QE Global ©2015 Copyright Richmond Chamber of Commerce and Artios
  35. 35. APPLY : TWITTER VOLUME Now we have proven that twitter volume is NOT a significant factor: •  Investigate other possible drivers of performance on social media •  Complete your social media data science studies ©2015 Copyright Richmond Chamber of Commerce and Artios •  Invest in an inbound marketing strategy
  36. 36. TRACK: TWITTER VOLUME 0 200 400 Feb 01 Feb 15 Mar 01 Mar 15 Apr 01 Date TwitterVolume domain gelighting.com jcc.co.uk lighting.philips.co.uk osram.com qeglobal.com Feature Trend: Twitter Volume − QE Global ©2015 Copyright Richmond Chamber of Commerce and Artios
  37. 37. THANK YOU… email: hello@artios.io web: artios.io twitter: @artios_maths phone: +44 (0)20 8213 5872

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