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KDD Cup 2000 Question 5

KDD Cup 2000 Question 5. Overview. Aim Given a set of page views, which product brand (Hanes, Donna Karan, American Essentials, or none) will the visitor view in the remainder of the session? Data Raw Data - Clicks Aggregated Data - Sessions Number viewed later for each brand

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KDD Cup 2000 Question 5

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  1. KDD Cup 2000 Question 5

  2. Overview • Aim • Given a set of page views, which product brand (Hanes, Donna Karan, American Essentials, or none) will the visitor view in the remainder of the session? • Data • Raw Data - Clicks • Aggregated Data - Sessions • Number viewed later for each brand • Indicator: No Top Brand Viewed Later • Methods and Tools • Exploratory Data Analysis - S Plus • Amdocs Business Insight Tool • Classification Tree • Two-Class: View or not View • Multiple-Class: 3 Brands • RulesExtraction

  3. Decision Chart(Our process of identifying brand-hunters)

  4. General guidelines for discriminating between sessions • Crawler Agent? Many sessions terminated after the first click due to the fact that it was a crawler agent. If it’s a crawler agent, then STOP (No top brand is viewed). • Distinguish between viewing and not viewing a top brand Given that it isn’t crawler agent session, we differentiate between sessions in which none of the specific brands were viewed and those in which at least one was. If none will be viewed, then STOP. • Distinguish between each of the top brands Given a surfer will view one of the three product brands, we differentiate between sessions according to which of the three is viewed Donna Karren, Hanes or American Essentials.

  5. Percentage of sessions with any top-brand view per referring site- Visitors from FashionMall are more likely to be interested in brands.- Visitors referred from MyCoupons probably want to use the coupons for buying one of the brands’ products. - On the contrary, entering from ShopNow reduces the likelihood for brand-browsing.

  6. Percentage of sessions with any top-brand view per product category- DK and Hanes sell only Pantyhose, so entering this category increases probability of seeing at least one them.- Some brands have Basic Products collections, so entering this category might expose surfer to these brands.- Viewing Men or Women products increases probability of viewing one of the brands to 21%. Product Category

  7. Percentage of sessions with any top-brand view per segmentA few other interesting phenomena indicate the tendency to view a top-brand. Among those are sessions that last beyond the first click, returning customers and search engine usage.

  8. Segments likely to view Hanes Products- Viewing products that include the dressing room feature.- Not viewing any boutiques but viewing last a collection line.- Viewing Oroblu Products, which are very similar in type and in cost to Hanes products.

  9. Segment likely to view Donna Karan ProductsArriving from the shopping sites ShopNow or FashionMall result in viewing DK products.Surfers interested in DK might be heavy web shoppers.

  10. Segment likely to view American Essentials Products- Arriving from MyCoupons can indicate that there are coupons that can be used while purchasing AE products.- Free gift templates are associated with AE.- Viewing sports collections or men's products is related only to AE (Hanes and DK offer only pantyhose).

  11. Behavior Changes along Time

  12. The behaviour of viewing top brands changed over time Donna Karen (DK) products weren’t for sale on the site prior to February 26th. There is an extreme peak in views of American Essential (AE) products on March 1st and much milder peaks in the Hanes and DK views. On March 1st there were many discounted purchases. Most AE purchases are discounted On that day the amount of AE views was 10 times the daily average. The February 28th Ally Mcbeal ad probably caused the rise in product views. During March DK views remain relatively high, Hanes views drop (still higher than the February level) and AE views drop substantially.

  13. Amdocs Business Insight Tool Main Features • Data Preprocessor • Extracting, transforming and loading data • Data Mining Engine • Segmentation - Variable Selection, Variable Discretization • Sampling • Profiling - Classification Tree • Predictive Modeling • Scoring and Ranking • Rule Engine • Rule Extraction • Rule pruning • Similarity checks • Analyst User Interface • Create new or modify existing patterns • Visualization • Exploring high dimensional patterns

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