Big data mktg


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Big data mktg

  1. 1. “Big Data” For Marketing Follow me on Twitter @renee1williams © 2012Strategies That Pay OffDeep Consumer Data Analysis Is No Longer The Competitive Edge OfMarketers With Big
  2. 2. Using Big Data Tools For Effective Strategy Follow me on Twitter @renee1williams © 2012 • The technical definition of Big Data refers to pools of data that grow so large that makes it challenging to work with. Volumes of data that would almost cost too much time and money for traditional technology to manipulate. • As the new marketing buzz phrase Big Data refers to the volumes of deep consumer data lying behind Facebook likes, Foursquare check-ins, and collected by the Google browser and searches. • The phenomenal growth and popularity of social media not only means growing marketing dollars flowing to social platforms and app developers but also growing demand for proof of performance and return on investment (ROI)
  3. 3. Using Big Data Tools For Effective Strategy • Mega social media players like Facebook, Foursquare and Follow me on Twitter @renee1williams © 2012 Google are increasingly providing easy access to user friendly, do-it-yourself (DIY) data and analysis. This is fueled by • Recognition of the synergy between their success and that of their business customer • Need to demonstrate justification for increasing share of marketing expenditures The good news is access to robust data for actionable and more effective marketing decisions is no longer a competitive advantage for the largest companies with the largest marketing teams and budgets To make a value case to existing business customers- and help persuade potential new one - there’s a lot of robust data consumer data available even before committing to a
  4. 4. “Big Data” Facebook DIY Data Research• Step 1 • Go to set up Follow me on Twitter @renee1williams © 2012 a scenario for an ad campaign adjacent Old Navy’s company page • To the right note the page has a US audience of 158,025,620• Step 2 Target a geographic location relevant to your business• Step 3 We select target age range of 18- 30 and genderOf the US audience of 158,025,620 we want to reachwomen, age 18- 30 near zipcode 91104
  5. 5. “Big Data” Facebook DIY Data Research• Note the definition of “Precise Interests.”• By filling in the corresponding box our audience can be further refined• Here we choose to use “Broad Categories” to target Family Status of Expecting Parents• In the right column definition of our @renee1williams © www.successful-women- 2012 Follow me on Twitter target audience updates accordingly
  6. 6. “Big Data” Foursquare Merchant Dashboard• Among other data the Follow me on Twitter @renee1williams © 2012 dashboard offers • Total daily check-ins over time • Most recent visitors • Gender breakdown • Portion of check-ins that broadcast to Twitter
  7. 7. “Big Data” Facebook DIY Data Research Follow me on Twitter @renee1williams © 2012• Using Old Navy• As a company owner or business location manger we select our company to get our dashboard data
  8. 8. “Big Data” Foursquare Dashboard Follow me on Twitter @renee1williams © 2012• We see geographic map of our business location• We see our total check- ins to the right• And under “Tips” we see comments related to this location • On this screen only 1 from a consumer checking in • There could be valuable actionable marketing intelligence • A number of promotion and upsell tips from Old Navy intended to generate upsells
  9. 9. “Big Data” Google Analytics Follow me on Twitter @renee1williams © 2012• Here you can see of all the online marketing channels you used which influenced your buyers• Actual channel left column - channel share on the right• In this example the largest share comes from direct efforts followed by email• Note there have been cautionary observations that some Google search data may point marketers AWAY from the most accurate action for an individual - instead pushing or recommending ad or ad placement based on most popular in general instead of what would be most accurate for a particular individual
  10. 10. “Big Data” Google Analytics Follow me on Twitter @renee1williams © 2012• A company can could actually see visit volume to their webpage, or Facebook Page• Whether traffic was generated • From a campaign • Referral from another site • Email, etc.
  11. 11. “Big Data” For Marketing Strategies That Pay Off Follow me on Twitter @renee1williams © 2012• Wal-Mart was one of the early pioneers using Big Data to develop predictive consumer analysis to identify, and set strategy, based on customer preferences• They did so on a regional basis• They adjusted stock selections at locations accordingly• Others noticed the success and increased ROI Wal-Mart had and began to do the same• As marketers the accessibility of Big Data makes it evident that digging deeper into consumer analysis is the key to marketing successes
  12. 12. “Big Data” For Marketing Strategies That Pay Off Follow me on Twitter @renee1williams © 2012• Deep consumer data analysis is no longer the competitive edge of marketers with big budgets• It is no longer optional• With data so readily available you can be certain your competitors will take advantage• There are increasing pressures to use data analysis in order to successfully compete• Accessing and analyzing Big Data is the new essential marketing tool for all marketers big and small • To message in real-time • To talk to consumers like you know them • To generate upsell and real-time offers • To understand customer psychographics
  13. 13. “Big Data” For Marketing Strategies That Pay Off Follow me on Twitter @renee1williams © 2012• SOURCES • Google Analytics • • • Techcrunch; Rise of Big Data, May 4, 2012 • Techcrunch; Apptegic Uses Big Data, May 22, 2012 • Don Vergano, USA Today, Science Snapshot, May 16, 2012 •
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