Heterogeneous Rank Effects in Online Marketplace

This paper studies the rank effect heterogeneity in the online marketplace and suggests a practical implication for marketing managers to set the optimal digital marketing strategies. Because of the increasing economy of online marketplaces, the position or rank effect is a crucial issue in the marketing literature. The latest literature has focused on the effects of sponsored search results on search engine advertising, though it is known that organic results are more critical than search ads. This research is novel to focus on the effect of organic results in the online marketplace. For analysis on the unit of product level, this paper constructs the rank index through weighted average by keyword search volumes. In the model, the rank effect was specified by the interaction of product-level and category-level averaged variables with the rank index, with the covariates of product-level time-variant variables and two-way fixed effects. Some products were selected randomly to escape the curse of dimensionality. The estimation result suggests that product sales increased in rank and the number of Q&A and reviews. Meanwhile, categories with high price dispersion experienced a lower rank effect, and categories with information asymmetry experienced a lower rank effect. The overall characteristics of the category, such as average price, product attributes, and competition intensity, do not have a significant rank effect. In conclusion, I suggest that marketing managers implement search engine optimization in online marketplaces if their products are in the category with a higher rank effect. This paper finally took a snapshot of the online marketplace by exploiting a vast dataset and extending the marketing literature to the new area. Future research considering hierarchical modeling and endogeneity can investigate more robust and rigorous causality.Chapter 1. Introduction 2 Chapter 2. Literature Review 6 Chapter 3. Data 12 Chapter 4. Model 20 Chapter 5. Results 21 Chapter 6. Discussion 26 Bibliography 30 Abstract in Korean 34Maste

When Popularity Meets Position

Popularity information is usually thought to have a great impact on individual’s decision making and choice. However, most of the websites are displaying products by its popularity. This could potentially result in the popularity effect being overestimated because popularity is confounded with position when they are sorted in the descending order. In this paper, we try to fill this gap in the literature by bridging together popularity effect and position effect to understand whether popularity effect overcomes position effect. By conducting a series of lab experiments, our results suggest that popularity effect is overestimated in prior studies, and its effect becomes less salient when we consider the position effect. Our results have both theoretical implication and practical implication for the website designer

Keyword Targeting Optimization in Sponsored Search Advertising: Combining Selection and Matching

In sponsored search advertising (SSA), advertisers need to select keywords and determine matching types for selected keywords simultaneously, i.e., keyword targeting. An optimal keyword targeting strategy guarantees reaching the right population effectively. This paper aims to address the keyword targeting problem, which is a challenging task because of the incomplete information of historical advertising performance indices and the high uncertainty in SSA environments. First, we construct a data distribution estimation model and apply a Markov Chain Monte Carlo method to make inference about unobserved indices (i.e., impression and click-through rate) over three keyword matching types (i.e., broad, phrase and exact). Second, we formulate a stochastic keyword targeting model (BB-KSM) combining operations of keyword selection and keyword matching to maximize the expected profit under the chance constraint of the budget, and develop a branch-and-bound algorithm incorporating a stochastic simulation process for our keyword targeting model. Finally, based on a realworld dataset collected from field reports and logs of past SSA campaigns, computational experiments are conducted to evaluate the performance of our keyword targeting strategy. Experimental results show that, (a) BB-KSM outperforms seven baselines in terms of profit; (b) BB-KSM shows its superiority as the budget increases, especially in situations with more keywords and keyword combinations; (c) the proposed data distribution estimation approach can effectively address the problem of incomplete performance indices over the three matching types and in turn significantly promotes the performance of keyword targeting decisions. This research makes important contributions to the SSA literature and the results offer critical insights into keyword management for SSA advertisers.Comment: 38 pages, 4 figures, 5 table

Assessing stationarity in web analytics: A study of bounce rates

Evidence-based methods for evaluating marketing interventions such as A/B testing have become standard practice. However, the pitfalls associated with the misuse of this decision-making instrument are not well understood by managers and analytics professionals. In this study, we assess the impact of stationarity on the validity of samples from conditioned time series, which are abundant in web metrics. Such a prominent metric is the bounce rate, which is prevalent in assessing engagement with web content as well as the performance of marketing touchpoints. In this study, we show how to control for stationarity using an algorithmic transformation to calculate the optimum sampling period. This distance is based on a novel stationary ergodic process that considers that a stationary series presents reversible symmetric features and is calculated using a dynamic time warping algorithm in a self-correlation procedure. This study contributes to the expert and intelligent systems literature by demonstrating a robust method for sub-sampling time-series data, which are critical in decision making

Bid Coordination in Sponsored Search Auctions: Detection Methodology and Empirical Analysis

Bid delegation to specialized intermediaries is common in the auction systems used to sell internet advertising. When the same intermediary concentrates the demand for ad space from competing advertisers, its incentive to coordinate client bids might alter the functioning of the auctions. This study develops a methodology to detect bid coordination, and presents a strategy to estimate a bound on the search engine revenue losses imposed by coordination relative to a counterfactual benchmark of competitive bidding. Using proprietary data from auctions held on a major search engine, coordination is detected in 55 percent of the cases of delegated bidding that we observed, and the associated upper bound on the search engine’s revenue loss ranges between 5.3 and 10.4 percent

Network failure: digital technology in sponsored search advertising

The current study advances understanding of sponsored search advertising (SSA) by exploring failures in networks of SSA tools and human actors. SSA represents a novel form of information technology-bound marketing practice that has rapidly proliferated marketing over the last 25 years. The confluence of search technology and advertising has redefined how contemporary marketing is practiced, causing significant redistribution in marketing spent, advertising activity and the emergence of new actors. These shifts have attracted significant interest with rapidly growing number of studies addressing matters around SSA strategy, including various SSA features and functions. In radical departure from mainstream SSA literature, the current study adopts a practice-based view to provide a more nuanced understanding of how the networks of human and technological actors emerge, are stabilised and fail in SSA. By casting SSA as networked practice, the study highlights social construction and the dynamic, multiple and fluid nature of SSA. Actor network theory (ANT) theoretically frames failure in SSA and the networked nature of human and nonhuman actors that contribute to it. The study adopts a qualitative research design, where the data was collected through a 7-month ethnography and the data set includes semi-structured and insitu interviews, day-to day (participant) observations, images, field notes, secondary data and a detailed research diary. The data is anchored on events made up of relations – the principal units of analysis. The findings are presented as a set of ethnographic stories from problematised events. They show how SSA dynamism, fluidity and multiplicity can only be acknowledged accurately enough if human and nonhuman actors in networks are followed in their attempts to build heterogeneous relations. This enables enactment of several new actors, intentions and roles from the Google advertising practice in a specialised SSA agency. The findings provide novel insights that address several gaps in the marketing literature