Utilizing Recommendations & Relevance Marketing Tools To Drive eCommerce Innovation

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Given at eTail East 2011

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

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

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