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Pierre Debois - Using Analytics for Sustainable Customer Experiences

  1. 1 Using Analytics for Sustainable Customer Experiences Pierre DeBois Chief Digital Marketing Strategists Zimana Analytics Services
  2. Hey There! • In Business since in 2009 • Services – Marketing Analysis, Website Development, Meteor.js • Instructor – Blue1647 Chicago • Contributor – CMS Wire
  3. Overview • Basic Challenges w/ Trends • Google Analytics vs. R Programming Basics • Relating Trend Analysis to Customer Experience
  4. Analytics 2017 We Know Analytics Is Essential... > By 2016, 70% of businesses will incorporate real time analytics to establish competitive advantage – Gartner (2013) > Adoptability Through Analytics Is Vital to Customer Experience
  5. Analytics 2017 …But “Essential” Has Complex Sources > Contextualizing Analytics Important to IoT (eMarketers) > Increase in analytics spending over next 3 years (Venture Beat)
  6. Segmentation >Channels >Device >Landing Pages >Social Media >Age Groups >Male/Female Demographics >Traffic Sources Consider Your Traffic Sources Available
  7. Trend Challenge > 30-60-90 day periods great for initial drilldown > Trend Changes Can Sometimes Be Hard To Discern > Conclusions Overlook
  8. About R > Open Source object-oriented programming > Object-oriented code for granular data > Wide range of libraries to support development needs > Interactive for fast exploration of models The Tool For A Granular Trend
  9. Why R + GA? > GA awesome for general trends, not so much for conclusions on granular data with statistical confidence > R code is scripted – reliably repeated and shared with analysts > GA User Interface Limits Dimension/Metric Views > Blend diverse datasets for models and visualization
  10. R Studio
  11. R Libraries >RGoogleAnalytics >ganalytics (requires lubridate and httr packages) >RGA >RSiteCatalyst >ggplot2 (author: Hadley Wickham)
  12. RGA > Obtain OAuth access in the Google Developer Console > Obtain Client ID and Client Secret key
  13. RGA > Google Query Explorer  Get Client ID key > Review syntax for desired dimensions and metrics
  14. RGA > Load a library – library(RGA) > Authorize from G.Dev. Console > Save retrieved data into a data frame variable
  15. RGA > Summary(“dataframename”) – max, min, mean, median, quartile > library(psych) – additional stats > describe(“dataframename”) – skew
  16. RGA > ggiraph – make ggplots interactive > googleVis – Google Charts API > dygraphs – HTML/JavaScript time series
  17. GA - R Ideas >Predictive Product Revenue >Predictive Bounce Rate based on Lead Time >How Did Device Traffic Changes Hourly / Weekly (Jeromy Anglim)
  18. Where To Now? >Affinity Reports >Alerts (Response in % vs Scale) >Bookmark Reports >Google Customer Journey
  19. Traffic Audit > Referral Traffic Exclusion > .htaccess file > Annotate Changes >Page load speed diagnostics (Pingdom, Charles/Fiddler)
  20. R Resources >MRAN (Microsoft) – research libraries >One R Tip A Day – >R-bloggers – news / tutorials / expo >Data Science Central >Dartistics >StackOverflow – asking specific questions
  21. Summary >Consider Available Traffic Sources >Use R to tease a granular trend > Where To Now? - Consider additional reports to discover mediums that connect to customers >Let the data assist how you organize to connect to customers
  22. MORE INFO Thank You!