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Anindya Ghose with P. Ipeirotis and B. Li Stern School of Business New York University

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Anindya Ghose with P. Ipeirotis and B. Li Stern School of Business New York University

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    2. Before We Start… “ How can I find a 5-star hotel in Miami, near the interstate highway with easy access to beach in an area with lots of nightlife, and also provides a great price for what it offers? “

    3. Customer Search in Travel Search Engines Rudimentary ranking facilities using a single criterion: i.e., name, price per night, class, customer reviews. This approach has obvious shortcomings, first, it ignores the multidimensional preferences of consumers. And what’s more important, it largely ignores characteristics related to the location of the hotels. So, the original purpose of our research is to find a better way to help customers find the hotel they want. This approach has obvious shortcomings, first, it ignores the multidimensional preferences of consumers. And what’s more important, it largely ignores characteristics related to the location of the hotels. So, the original purpose of our research is to find a better way to help customers find the hotel they want.

    4. Introduction

    5. Research Agenda

    6. New Ranking Approach for Hotels Consumers ideally like the ``best product” shown first on the screen Best product: Highest ``value for money” Consumers gain ``utility” from product characteristics (WTP) Consumers lose ``utility” by paying for product (Price) Value for money: Difference of the two Transaction data from travel search engines Compute ``consumer surplus” for each hotel using location and service characteristics minus price Rank according to ``value for money”

    7. Main Data: Travelocity hotel reservations

    8. Identification of Hotel Characteristics So now, the first thing is to find out what characteristics we want to use.So now, the first thing is to find out what characteristics we want to use.

    9. Identification of Hotel Characteristics

    10. Identification of Hotel Characteristics Now we have know what characteristics we need to put into our model, then how can we collect them? What kind of data are they? Now we have know what characteristics we need to put into our model, then how can we collect them? What kind of data are they?

    11. Acquiring Location-based Characteristics Compared to Service-based, Location-based characteristics are more complicated. Previous work used rather rudimentary and non-scalable data-collection approaches, i.e., interview with hotel managers, personal observations. But introduce too much bias, the data precision of empirical study is discounted. Compared to Service-based, Location-based characteristics are more complicated. Previous work used rather rudimentary and non-scalable data-collection approaches, i.e., interview with hotel managers, personal observations. But introduce too much bias, the data precision of empirical study is discounted.

    12. Acquiring Location-based Characteristics

    13. Acquiring Location-based Characteristics For Geographic characteristics, for example beach, and downtown, the local search query can not get us the results. However, their corresponding images contain very rich textual information.For Geographic characteristics, for example beach, and downtown, the local search query can not get us the results. However, their corresponding images contain very rich textual information.

    14. Acquiring Location-based Characteristics So far, we have identified the important hotel characteristics, and also collected the corresponding data for each hotel by using different methods. And now, let’s look at how we can scale them.So far, we have identified the important hotel characteristics, and also collected the corresponding data for each hotel by using different methods. And now, let’s look at how we can scale them.

    16. Summary Statistics 16

    17. Acquiring Hotel Characteristics So far, we have identified the important hotel characteristics, and also collected the corresponding data for each hotel by using different methods. And now, let’s look at how we can scale them.So far, we have identified the important hotel characteristics, and also collected the corresponding data for each hotel by using different methods. And now, let’s look at how we can scale them.

    18. Framework of Structural Model First, consumer finds a subset of hotels that matches her own. Each hotel belongs to one of the following types of ``travel category”: Family Trip, Business Trip, Romantic Trip, Tourists Trip, Trip with Kids, Trip with Seniors, Pets Friendly and Disabilities Friendly. In order to capture heterogeneity in consumers’ travel category, we introduce an idiosyncratic “taste shock” similar in flavor to BLP (1995) model. Second, once the consumer has picked a specific travel category, she will make a decision based on her evaluation of the quality of the hotels. Pure characteristic model (Berry and Pakes 2007) to capture the differentiation among hotels within the same category Summary: Combine the BLP (1995) and Berry &Pakes (2007)

    19. Structural Modeling

    20. Estimation 20 Step 1: Calculating market share.

    21. Estimation 21

    22. Estimation 22

    23. Estimation 23

    24. Identification (BLP (1995) and PCM (2007) models) (i) Monotonicity – sj is weakly increasing and continuous in ?j and weakly decreasing in ?j-1, where ?j -1is the unobserved characteristics for the rival-products. (ii) linearity of utility in ? - if ? for every good is increasing by an equal amount, then no market share changes, and (iii) substitutes with some other good - every product must be a strict substitute with some other good. 24

    25. Economic Value of Characteristics

    26. Hotel Characteristic Impact

    27. Marginal Effects

    28. Marginal Effects 28

    29. Sample consisting of those hotels that have at least one review from either Travelocity or TripAdvisor. Estimations after extracting individual service features from the text of reviews. Estimations with hotel brand, convention center, distance from airport, etc. Estimations with Google Trends data to control for endogeneity of WoM and sales. Estimations with BLP (1995) model and PCM (2007) models. Estimations across only those cities where all location features present.

    30. Robustness Test (I) - Using Alternative Sample Split

    31. Robustness Test (II) - Using an Alternative Model - BLP

    32. Text mining method to extract & score service features 32

    33. Robustness Test (III) - Using Additional Features

    34. Model Fit: With UGC vs. Without UGC

    35. Model Validation 35

    36. Counterfactual Experiments

    37. Counterfactual Experiments (1)

    38. Counterfactual Experiments (2)

    39. Counterfactual Experiments (3)

    40. # 3 - Effects of competition under different location environments. | # of Competitors increases by 1 Price Cut 20% (i) Beach & Highway | -0.46% 1.43% (ii) Downtown, Transportation Amenity | -0.70% 1.18% Baseline | -0.59% 2.31%

    41. Value for Money Based Ranking We propose a ranking approach for hotels based on the value for money of each hotel for consumers on an aggregate level. This ranking idea is based on how much ``extra value” consumers can obtain after paying for that hotel. If a hotel provides a comparably higher value for money for consumers on an aggregate level, then it should appear on the top part of our ranking list. Higher ranked hotels can provide consumers with higher surplus (WTP) value, thus should be more often recommended to consumers.

    42. Results Based on Consumer Surplus Estimation (``Best Value for Money”)

    43. Ranking Evaluation - User Study … We want to estimate the robustness of this user evaluation result. This indicates the strong preference for hotels with the “best value”, but meanwhile it highlighted the need for a truly blind test, in order to eliminate the influence of titles.… We want to estimate the robustness of this user evaluation result. This indicates the strong preference for hotels with the “best value”, but meanwhile it highlighted the need for a truly blind test, in order to eliminate the influence of titles.

    44. Ranking Evaluation - User Study … We want to estimate the robustness of this user evaluation result. This indicates the strong preference for hotels with the “best value”, but meanwhile it highlighted the need for a truly blind test, in order to eliminate the influence of titles.… We want to estimate the robustness of this user evaluation result. This indicates the strong preference for hotels with the “best value”, but meanwhile it highlighted the need for a truly blind test, in order to eliminate the influence of titles.

    45. Ranking Evaluation - User Study (So, …to obtain a more objective evaluation,) Provide a logical way to present information, and help people make decision better. Based on the qualitative opinions of the users, “Diversity” is an important factor that improves the satisfaction of consumers, and an economic approach for ranking introduces diversity naturally. (So, …to obtain a more objective evaluation,) Provide a logical way to present information, and help people make decision better. Based on the qualitative opinions of the users, “Diversity” is an important factor that improves the satisfaction of consumers, and an economic approach for ranking introduces diversity naturally.

    46. On-going Work

    47. Personalized Model

    48. Weights of Hotel Characteristics Based on Different Travel Purposes

    49. User Study

    50. Estimation Results Capture Consumers’ Real Motivation

    51. Conclusion Using Econometric modeling Then, by incorporating the value of these characteristics in the local result ranking function, we can improve the quality of local search for such hotels. Using Econometric modeling Then, by incorporating the value of these characteristics in the local result ranking function, we can improve the quality of local search for such hotels.

    52. AMT demographics survey Surveyed AMT workers about their place of origin and residence, gender, age, education, income, marital status, household size, and number of children. We also asked them about the time that they spend every week on AMT, the amount of work that they complete, the payment they receive, and their reasons for participating on AMT. To ensure consistency in results, we conducted the survey six times, once a month in 2009. The results of the surveys suggest that AMT participants are well representative of the overall Internet population. Also asked them about their experience with visits to online travel search engines: Tripadvisor and Travelocity 52

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