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Utilizing Recommendations & Relevance Marketing Tools To Drive eCommerce Innovation
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Utilizing Recommendations & Relevance Marketing Tools To Drive eCommerce Innovation

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Given at eTail East 2011

Given at eTail East 2011

Published in: Business, Technology

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  • Buzz word bingo cardSubmitted this title and abstract a long time in a galaxy far, far awayContent has diverge a little from the title
  • How many of you are on the business side?Technologists?How many have access to a data warehouse?How many have access to an A/B or multi-variant testing tool?Please ask questions along the way – I’d like this to be interactive and informative
  • This is an approach and a philosophy to how to you are going to look at your dataFocus on the last couple of words – worth testingYou want to gain and understanding of the data that you haveI asked earlier about multivariate testing toolsWhat do you want to know more about in your company and what do you want to test
  • we wanted to test if we could alter their behavior – engage moreMore add clicksMore add to cartsUltimately more conversion
  • Took a step back to analyze some data we already hadAnalyzed what we already hadPlugged it into a visualizerWe all draw graphs in excelSometimes experiments like this don’t realize results sometimes they do
  • This shows the results of our imports and a little data cleanup2 distinct groupings of product categoriesMen on the leftWomen on the rightInterestingly we only found one link between what men and women buy - sweaters
  • Customer revealed their preferencesImportant for us to recognize their “tell” so we can market to them more effectivelyWhat did we do with these CBVs once we had them?
  • Transcript

    • 1. Utilizing Recommendations & Relevance Marketing Tools To Drive eCommerce Innovation
      Matt Rainesmatt.raines@bluefly.com@matthewraines
      1
    • 2. Quick backgrounders
      About Bluefly
      Online retailer of high-end designer and contemporary fashion and accessories
      Launched in 1998
      $89m net revenue in 2010
      About Me
      Bluefly for 9 years
      Running Tech for last 6 years
      Internet companies for 15 years
      MC5 (remember this for later)
      2
    • 3. And a little about you …
      3
    • 4. What we’re going to talk about
      Exploratory Data Analysis (EDA) as a process
      How Bluefly went about this process
      Suggestions on how you can do this in your company
      4
    • 5. What we’re not going to talk about
      Programming languages
      Coding
      Data mining
      Hadoop
      5
    • 6. What is EDA?
      Exploratory data analysis (EDA) is an approach to analyzing data for the purpose of formulating hypotheses worth testing… -Wikipedia
      6
    • 7. We set out to learn more about our customers behavior
      7
    • 8. Visualizing the Data
      Extracted our purchase data for last 4 years
      Imported into visualization tool - Gephi
      Similar to “social graph” app on Facebook
      8
    • 9. 9
    • 10. Whatdid learn
      Customers stayed within their brand category preference
      Customers who bought designer continued to buy designer brands
      Customers who bought contemporary continued to buy contemporary brands
      Customers stayed true to their gender
      Customers didn’t buy for others (spouse, significant other, etc.)
      Very low gifting business (gift wrap numbers reflect this)
      We created a “Customer Behavior Value”
      (Gender)(Category)(Intensity)
      WD5 = Womens Designer 5
      MC1 = Mens Contemporary 1
      10
    • 11. Targeting based on buying preference
      The test:
      1) Target homepage content based on prior buying behavior
      Women’s Designer  homepage #1
      Women’s Contemporary  homepage #2
      Men’s  homepage #3
      11
    • 12. Women’s Designer
      12
    • 13. Women’s Contemporary
      13
    • 14. Men’s
      14
    • 15. Targeting based on buying preference
      The test:
      1) Target homepage content based on prior buying behavior
      Women’s Designer  homepage #1
      Women’s Contemporary  homepage #2
      Men’s  homepage #3
      Targeted email campaigns based on Customer Behavior Value
      15
    • 16. Targeted email program
      16
    • 17. Realized Benefits
      Increased open rates
      Open rates increased 50% of targeted segment
      Increased user site engagement
      Browser – more pages browsed
      Shopper – more add to carts
      Purchaser – more orders
      Reduced opt-out rates
      Increased customer relevancy
      "finally, bluefly got my email gender preference right"
      17
    • 18. Where do you go from here
      What’s your business objective?
      Are you collecting the right data?
      Do you have the right team?
      Can a pattern be identified in the data?
      What is a potential treatment to test the pattern?
      Test the optimal treatment.
      18
    • 19. 19
    • 20. Matt Rainesmatt.raines@bluefly.comBluefly.com@matthewraines
      20