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Cluster Analysis User‘s Comparison Behavior OnSite

Analysis of user‘s comparison behavior on site, focusing on factors impacting revenue, search behavior, and clustering based on change in rental costs. Insights on station location, rental days, car categories, and visitor level clustering.

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Cluster Analysis User‘s Comparison Behavior OnSite

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  1. Cluster Analysis User‘sComparisonBehaviorOnSite München, 26. Februar 2017

  2. Analysis: User‘sComparisonBehavior • Users whomake a miniumoffouroffersearcheson DE * • Segmentedbased on thechange in rentalcostsfortheofferstheylooked at duringtheirsearches: Whatwearelooking at: *Size: Upto 110k users per month fall intothiscategory.

  3. Analysis: User‘sComparisonBehavior

  4. Analysis: User‘sComparisonBehavior • Change in Rental Costs (Higher Price | Lower Price | Aboutthe same) • First Search Input • Car Category • Rental Days • Days to Pick-Up • Distancetothe last locationlooked at sinceweassumethis was themostsuitable • NumberofSearches • Numberof Visits • Attributes Clusters • Interaction Segments Input Variables

  5. Analysis: User‘sComparisonBehavior Clustering based on searchattributeschanged: Cluster 1: Focus on Car Model Comparison (4.46 on average) Cluster 2: Focus on changingrental time & car model (daystopick-up, pickup - & return time) Cluster 3: Focus on car model, rentaldays & daystopick-up Cluster 4: Focus on Car Models and Station Location Info: These values show how many different unique attributes a user looks at in average Cluster 5: Focus on Car Models, Days to Pick-Up and Station Location Cluster 6: Strong Focus on Days to Pick-Up, Car Model & Rental Days

  6. Analysis: User‘sComparisonBehavior Interaction Segments User‘slooking at Large Categoryshowthestrongestcomparisonbehavior Whereasforexample User‘sinterested in Compact, Economy & Medium tendtocompareless.

  7. Analysis: User‘sComparisonBehavior WhichFactorshavethehighestimpact on revenue? High Impact on Revenue: Rental Days Car Categories Low Impact on Revenue: Station Location Days to Pickup Pick-Up Date Mixed Effect Model looking at the impact of Station Location (City) & Visitor ID on Revenue.

  8. Analysis: User‘sComparisonBehavior AboutUser‘s Search Behavior Thenegative correlationssuggestthatuserstendtochangerental variables onebyoneinsteadof multiple variables at a time. Mixed Effect Model looking at the impact of Station Location (City) & Visitor ID on Revenue.

  9. Analysis: User‘sComparisonBehavior AboutUser‘s Search Behavior Thenegative correlationssuggestthatusersinterested in I, M, X and E categoriesarethemostwillingto check forotherlocationsthantheir initial (bestfitting) one.

  10. Analysis: User‘sComparisonBehavior Cluster Overview: Grouped on Visitor Level In most cases user’s make changes one by one that’s why we can’t directly see the correlation between changing rental variables and evaluate repeated measures (visitor ids) effect either. The solution is to gain the main characteristics of each attribute for each visitor and group them on a visitor level. Based on this we receive the following clustering table:

  11. Analysis: User‘sComparisonBehavior Cluster Overview: Grouped on Visitor Level LoremIpsumdolor et

  12. Analysis: User‘sComparisonBehavior AboutUser‘s Search Behavior These arethe15 mostfrequenteventsubsequencesduring a user‘scomparisonbrowsing.

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