Towards an Agent Based Model of Sponsored Search Markets

Transcription

1 Abstract Towards an Agent Based Model of Sponsored Search Markets Agam Gupta*, Biswatosh Saha*, Uttam k. Sarkar* *Indian Institute of Management Calcutta, India Sponsored search advertising markets (SSMs), in which the search engine provides advertising slots alongside general search results on the search engine results page creates a marketplace where advertisers and prospective consumers interact. Sponsored search advertising is conducted as a generalized second price (GSP) auction, wherein, each winner only needs to pay the minimum price to maintain his current position. Most existing studies have used game theoretic equilibrium based approach to analysis and for analytical tractability have single auction as their unit of analysis. We argue that such an approach is restrictive on the following three grounds. Firstly, questions regarding the structure of market, emergent valuation of keywords have remained unaddressed. Secondly, equilibrium analysis requires certain assumptions which do not represent the reality well. Thirdly, literature on bid optimization in SSMs suggests that it is an NP Hard problem. In such a case the practice approach to manage optimization in SSMs can yield interesting insights. For example, all search queries are not independently managed, but, there are managed as a portfolio. In this paper we build an agent based model of SSM built from actual real life practices of advertisers which incorporates advertiser heterogeneities along with differences in search queries. We argue that this could yield interesting insights and expand the scope of questions hitherto addressed in literature. In addition, we explore systemic effects of advertisers bidding practices on search engine revenue, valuation of different keywords and advertiser s per click costs. Keywords: Keyword advertising, search engines, heterogeneous advertisers, in-practice optimization Introduction Keyword-based advertising on a search engine also referred to as sponsored search advertising (SSA) is one of the important formats of online advertisement which accounts for nearly half of the total online advertising spends (IAB, 2014). SSA is a method of displaying text-based ads alongside the organic search results in response to the search query of a user on a search engine. A typical search engine page along with ads is shown in Figure 1. Sponsored search ads are conducted as real time auctions and are technically referred to as Generalized Second Price (GSP) auctions (Edelman, Ostrovsky, & Schwarz, 2007) where an advertiser pays an amount necessary to retain her position. In addition, to improve performance and user experience, most search engines use Quality Score (QS) to access the quality of advertisers and rank the advertisers based on the product of QS and advertisers bids. Search Engines often use the historical click data in designing and improving QS. The importance of past click through rate in predicting QS is evident from the fact that QS can be quite well explained by historical CTR (R 2 = 72%) 1. However, the exact formalization of QS is a trade secret. Practitioners often conjecture about 1

2 the various possibilities of computing QS. One of these commonly held beliefs among the practitioners and often expressed in practitioner communities is that in addition to the keyword specific QS, there is also an account level or advertiser specific QS 2. From a research perspective, the effect of these different computations of QS has remained unexplored. Sponsored Search Ads Neutral Listing referred to as Organic Listings Figure 1: A snapshot of sponsored search ads on Google A change in the way QS is computed not only affects an individual advertiser but could have systemic effects as well because of the nature of sponsored search markets (SSMs). An advertiser may change her bid on a search query not only as a result of her own performance, but also, in response to changes in bids by other advertisers. These interactions create an intricately connected dynamical system where advertisers continuously keep responding and reacting to changes in bids of other advertisers while being influenced by a continuously updated history through the computation of QS. The dynamic, interconnected, non-linear and continuously evolving nature of the SSM makes it extremely difficult for any mathematical closed form analysis. Further, such systems rarely have closed form solutions and equilibrium as default outcomes (Arthur, 2013). Agent-based-simulation (ABS) which focuses on the process of change that is generated by local interaction of rule bound agents (Antonelli, 1997) is, therefore, considered a promising approach to study SSMs and understand the systemic effects of different QS computations, namely, keyword specific QS, advertiser specific QS, and a combination of both advertiser specific and keyword specific QS. 2 and

3 The research findings contribute to the existing literature on the following two counts. Firstly, it extends the current understanding on the existence of skewed distribution of clicks among advertisers in SSMs. While budget heterogeneities among competing advertisers in an SSM (Mehta, Saberi, Vazirani, & Vazirani, 2007) are known to contribute to its concentration, this research shows that in addition to these heterogeneities, advertisers account management practices coupled with the design of the sponsored search markets can endogenously generate concentration. Secondly, this research also contributes to literature of product differentiation by highlighting the role of systemic effects inherent to the design of advertising mediums that lead to emergent perceived differentiation. The rest of the paper is organized as follows. Section 2 discusses the relevant literature on SSMs are to emphasize that methodological constraints have limited the scope of questions that could be addressed. Arguments favouring ABS as a marked departure from conventional game theoretic analysis are presented to investigate the structure of SSMs. A realistic agent based model of SSMs built using Ethno-methodological study of industry practices is described in Section 3. Section 4 presents the findings from detailed simulations. Interpretations of the findings and key implications of this research are presented in Section 5 and Section 6 concludes the paper. Related Literature A review of extant literature in SSA suggests that methodological constraints have limited the scope of enquiry, and consequently, macro characteristics of the market have received almost negligible attention. The literature on SSA has hitherto taken a static equilibrium-based approach to analyze these markets borrowing heavily from auctions literature in economics. Sponsored search auctions are technically referred to as Generalized Second Price (GSP) auctions and are not truthful (Edelman et al., 2007), that is, bids do not reveal the true value of click to the advertiser. Consequently, the characteristics of the auction, its equilibrium properties, and design of alternate mechanism that are truthful or have better revenue than GSP have received substantial academic attention (Abrams & Ghosh, 2007; Edelman et al., 2007; Even-dar & Feldman, 2008; Feldman & Muthukrishnan, 2008; Goel & Munagala, 2009; Gonen & Pavlov, 2007; Mehta et al., 2007). Another stream of literature has looked at optimal budget allocation among keywords for an advertiser (Borgs, Chayes, & Immorlica, 2007; Even-dar & Feldman, 2008; Feldman, Muthukrishnan, Pal, & Stein, 2007; Kitts & Leblanc, 2004; Yang et al., 2012; Zhang, Yang, Li, Qin, & Zeng, 2014). The unit of analysis for these studies has been a single keyword on which multiple advertisers compete or a single advertiser who allocates resources between multiple keywords in a campaign portfolio, respectively. The choice of method and unit of analysis has precluded analysis of reasons behind the observed macro characteristics of the markets. Study of single advertiser or single keyword is insufficient and unable to reveal much about the dynamics of the market as a system.

4 The past experience of the researcher in managing sponsored search advertising campaigns for a number of small and big firms suggests that some of the assumptions made often do not reflect the reality well. For example, experience suggests that not only does the bid not reflect the value of the advertiser but also undergoes frequent revisions as a part of the optimization process. In addition, the bids are purposely kept higher than value to get better positions on the search engine. While some research exists on the dynamic and cyclical nature of bidding (Zhang & Feng, 2011) in sponsored search auctions, a detailed understanding of the consequences and reasons for such behaviour remain unexplored. One of the probable reasons for lack of such research is the dominance of analytically focused math modelling research. For reasons of analytical tractability, often the researchers have had to resort to simplifying assumptions or limit the scale of question to a single auction. In addition, the lack of macro level data (privy to the search engine) has restricted empirical findings or patterns that would trigger further investigations 3. Literature on optimal budget allocation among keywords suggests that despite making certain simplifying assumptions (like known independent value for individual keyword), budget optimization in a sponsored search ad campaign is an NP-Hard problem (Feldman et al., 2007). However, advertisers continuously grapple with this problem and their coping manifests through different heuristics used for optimization. Unfortunately, there is no research on some of the practical solutions (heuristics) developed by practicing advertisers. Agent based simulation which draws its philosophical roots from Semantic Conception and has model-centred way of doing science at its core (Henrickson & McKelvey, 2002) is found a promising approach. An agent based simulation specifically allows us to handle questions that are macro but have strong dependencies on the micro actions of the participants. Such a model is not only close to practice but allows for relaxing certain assumptions and allows a systemic understanding of sponsored search markets. Further, it is also capable to handle heterogeneities in advertisers, address what-if questions, and is not limited by the availability of data. ABS also allows us to explore systemic effects of design of these SSMs. In this paper we focus on the systemic effects of design and implementation of QS in these markets. We explore the effects of three alternative implementations of QS which are frequently conjectured by practicing advertisers. Through an agent based model of SSM which draws inspirations from the routines and practices of advertisers we show that both conceptualizations have significant implications which help us better understand theoretical and empirical facts observed in these markets. 3 The study by Robuab et al. (2009) is an notable exception to the empirical literature based on macro level data of all advertisers on Microsoft s search engine.

5 Experimental Setup In this research, agents are advertisers that bid for clicks on different search terms. The codification of the activities of an advertiser required an in situ (Schultze, 2000) understanding of the practices and heuristics adopted by the advertisers. The prior experience of the researcher in an advertising firm during proved to be very helpful, providing an ethnographic immersion which is considered useful in the context of researching lived experiences (Spry, 2001). Advertisers seldom manage their campaign accounts on their own. They interact with the search engine platforms through intermediary advertising agencies who participate in the market on behalf of advertisers. Hence the intermediary advertising agency was considered an apt site to gain an immersion into the practices of advertisers in SSMs. During the course of this research, in May 2013, another fortnight was spent in an advertising agency for multiple interactions with the search engine marketing team involved in managing advertising campaigns for a number of Fortune 500 companies. Participant-observation, numerous semi-structured interviews (in-person, telephonic and through video conferencing) and access to internal process documents further enhanced understanding about the practices of the advertising team. A typical agent-based model comprises of three essential components, namely, the world, the agents, and the rules of agent interaction. The first component details out the space in which agents interact with each other, the characteristics of this space and its composition. The second component gives a complete description of the agent, its endowments, actions, and objectives. The third component specifies the rules that govern how the agents interact with the world and amongst themselves. In the context of SSMs, while the mapping of agents as advertisers and interaction rules as the ranking and pricing rules of the search engine is intuitive, the conceptualization of the world is problematic. We propose a novel way to model the world as a semantic space where different search queries are connected to one another based on the similarity in the meaning implied by them. Figure 2 represents the mapping of various components of SSM into the essential components of an agent-based system. Each of these three components is discussed briefly. Figure 2: Representing sponsored search market as an agent-based system

6 World as the Semantic Space The world in the case of sponsored search markets is modelled as a semantic space because it was necessary to understand the relationship between different search queries and this relationship forms the basis for some of the practices of the advertisers. The structure of semantic space has been an active topic of interest (Ferrer I Cancho & Solé, 2001; Gaume, Venant, & Victorri, 2006; Masucci, Kalampokis, Eguíluz, & Hernández-García, 2011). Research on the structure of semantic space reveals that it has the following three properties: (i) The semantic space can be represented as a small world network, where, (ii) the degree distribution of nodes in the network follows a power law, and (iii) the network has a high clustering coefficient compared to normal scale-free networks. Drawing from extant literature semantic space was modelled as a network of 15,000 nodes using the algorithm proposed by Holme and Kim (2002) such that it possesses the three characteristics mentioned above. An individual node in the network represents a search query and an edge between two words indicates similarity in meaning between those search queries. The greater the separation between the two nodes in the network; the greater is the difference in their meaning. Since an advertiser deals only a specific set of keywords that are of importance to the firm, we cluster the semantic space into smaller components where an individual cluster also symbolically represents a segment of a marketplace which would be of interest to advertisers in a particular industry. Agents as Advertiser Agents are the practicing managers of the sponsored search advertising space. Each agent is defined by some characteristics and some behavioral rules [or rules guiding action]. In a sponsored search market an advertiser (agent) is defined by its characteristics (place in semantic space (niche), budget and target CPC) and its rules (how to add keywords, how to bid, how to optimize bids, how to allocate budget etc.). An advertiser (as an agent) may be depicted as shown in Figure 3. Each of these characteristics and rules are described below. However, rather than describing the characteristics and the rules separately these are defined with reference to the process that an advertiser would go through. Advertiser (agent) Advertiser Characteristics i.e. Advertiser s. -Place in semantic space (niche) -Budget -Target CPC Advertiser Rules (How to?) -Add keywords -Bid -Optimize bids -Allocate budget Figure 3: Representing an Advertiser as an agent

7 An advertiser s niche reflects the area within the semantic space in which a set of advertisers is more relevant than other advertisers. For simplicity, we define niche as the center of this relevant space. In a given cluster of the semantic space a node that best represents the advertiser s business is referred to as its niche. The farther a node is from the niche node of the advertiser the lesser the relevance of the advertiser on that node and vice versa. The details of the rules followed by the advertisers were drawn from the actual practices of the advertisers with whom the researcher interacted during the research process 4. Rules of Agent Interaction as Rules of Auction Mechanism Rules of agent interaction determine which agents interact and how they interact with each other. In SSMs, advertisers that bid for overlapping set of keywords interact with each other. This interaction is mediated by the auction rules of the search engine, specifically those determining the position and the price charged for a click. For this research we specifically focus on the computation of QS. QS is a measure of how relevant the search engine feels an ad is to a particular user. The search engine measures the relevance of the ad to a user based upon a number of factors. While the exact details are a trade secret, two known factors include the historical clicks that an advertiser gets along with the similarity between the advertiser s offering and the user search query. We operationalize the latter by making advertiser relevance a function of the distance between the advertiser s niche and the search query being auctioned. Therefore, the QS of an advertiser on a search query at a distance distance from its niche node is modelled as: QS 5 = e distance To operationalize the former, we include a parameter which moderates the relevance such that an advertiser that gets more clicks (is more popular among the users) is more relevant. More specifically, this click based user feedback could be operationalized at an advertiser level, which is an aggregate of the performance on all keywords being bid by the advertisers, or at an individual keyword level, or a simultaneous presence of both forms of feedback. The three cases correspond to the presence of advertiser specific QS, keyword specific QS, or both, respectively. Account Level QS: measuring user feedback at the advertiser level The core idea behind this feedback is that the advertiser that gets more clicks (is more popular among the users) is more relevant. This feedback is referred to as a general feedback because it only looks at the absolute number of clicks that the advertiser gets across the portfolio and the implementation is indifferent to the clicks on specific keywords, hence not specific. Put differently, this implementation 4 The details of the models have been limited due to space constraints and can be made available on request. 5 QS is modelled as an exponential function because of the small world characteristic of the semantic space.

8 of the feedback acts at the second (aggregated) level of the SSM in contrast to implementations discussed later that acts at first (keyword) level. The formula for QS under such implementation can be represented as: QS = e adv_rel_para distance This feedback is implemented by updating the value of the relevance parameter adv_rel_para in the relevance function. A decrease in the value of the parameter increases the relevance because of the negative sign associated with the relevance function. The value of the relevance parameter is initially set to 1. After every x auctions, where, x is the sum of the daily search volume of the search queries in a particular cluster, the adv_rel_para is updated using the formula given below: Advertiser Clicks adv_rel_para = adv_rel_para (α Maximum Clicks among all advertisers ) The increase in relevance is proportional to the number of clicks that an advertiser gets relative to the maximum number of clicks received by any advertiser. The maximum value that the parameter can be updated to is fixed at The cap reflects the bounded nature of increasing returns and is contextually relevant as in practice the QS has a maximum value of 10. The strength of the feedback is controlled through α. Keyword Specific QS: measuring user feedback at an individual keyword level In contrast to the general feedback, this implementation treats each keyword distinctly and therefore is specific in nature i.e. relevance of the advertiser increases only for those keywords where the advertiser gets clicks and not across the available set of all keywords. This is implemented after each auction of the simulation by updating the value of the relevance parameter keyword_rel_para in the relevance function. This parameter is specific to a particular keyword of an advertiser i.e. every keyword of the advertiser has its own unique relevance parameter. The initial value of this parameter for all keywords of all advertisers is set to 1. The keyword_rel_para associated with the keyword being auctioned is updated for the advertiser that is allocated the click, using the formula below: keyword_rel_para = keyword_rel_para (β/search_volume_of_the_keyword) The maximum value to which the parameter can be updated is capped at 0.05 and the strength of the feedback is controlled through β. The formula for QS under such implementation can be represented as: QS = e keyword_rel_para distance Combination of Advertiser level QS and keyword level QS This implementation is based on a combination of two feedbacks acting at two different levels simultaneously, that is, QS function has a parameter for measuring user feedback at advertiser level

9 and a parameter for user feedback measured at an individual keyword level. The QS function takes the form: QS = e adv_rel_para keyword_rel_para distance To focus on the effect of QS computation, advertisers were assigned the same niche points in the semantic cluster being analyzed. Heterogeneity was introduced through the initial bids on the keywords which are picked from a normal distribution of a given mean and standard deviation (3 and 0.25 respectively in this case) and have the same target_cpc 6. Over time the bids for the keywords are changed by the advertisers following rules commonly followed in practice. Thus, in the beginning, the relevance for each keyword for each advertiser is the same, however, in subsequent iterations the relevance gets altered depending on the way in which QS is computed. Results To keep the size of simulations computationally manageable, all simulations for this study were carried out on one of the cluster which comprises 27 nodes and 62 interconnections and was derived from the network of 15,000 nodes. We explore the systemic effects of various implementations of QS along three dimensions. Firstly, we analyze the distribution of clicks between advertisers under the different implementations. Secondly, we analyze the effect of QS computation on the territorial prowess of various advertisers. Thirdly, we analyze the valuation of different search queries as average bids across them under the two types of QS computations. Effect of QS Computation on Click Distribution among Advertisers The difference in the distributions can be analyzed either through a Lorenz curve (Figure 4) or by observing concentration ratios. Concentration ratio measures the percentage of market share (clicks in this case) of the largest firms (advertisers in this case) in the industry (cluster here). Concentration ratio for largest 4 advertisers (in terms of number of clicks) and smallest 10 advertisers (in terms of number of clicks) were consequently analyzed for different kinds of feedback implementations (Table 1). It is observed that distribution is much more inequitable with advertiser level QS compared to the case when QS is keyword specific. In other words, the market exhibits far more concentration in the case of the former. 6 target_cpc refers to the maximum limit of the average cost per click that an advertiser can afford.

10 Cummulative % age of clicks Cummulative % age of clicks Distribution of Clicks among Advertisers under Different QS Computations % age of advertisers (ranked in ascending order of clikcs) No feedback Keword (Spefcific) feedback Advertiser (General) feedback Equal Figure 4: Lorenz curves for market concentration under different QS implementations The strength of the two feedbacks discussed above was controlled using parameters α and β respectively. An increase in the value of the parameter indicates higher strength of the feedback process. Changing the value of the feedback strength (within a range) under both feedbacks does not seem to effect the distribution of clicks and concentration of the SSM (Figure 5, 6). However, it must be noted that under the current implementation an increase in the feedback strength would effectively translate into an advertiser reaching the upper bound on the relevance faster (due to the increased step size). This means that there is a temporal shortening of the duration for which increasing returns are enjoyed by the advertiser. A large increase in the strength of the feedback in proportion to the upper bound would mean that very soon the system would start behaving like a no feedback system as all advertisers would have reached the upper bound Varying feedback strength 'α' in general (advertiser) feedback Cummulative % age of advertisers (ranked in ascending order of clikcs) Advertiser Only (α 0.01) Advertiser Only (α 0.02) Advertiser Only (α 0.03) Advertiser Only (α 0.04) Figure 5: Effect of strength of feedback on distribution of clicks (Advertiser level QS)

11 Cummulative % age of clicks Cummulative % age of clicks Varying feedback strength 'β' in specific (keyword) feedback Cummulative % age of advertisers (ranked in ascending order of clikcs) Keyword_absolute (β 0.01) Keyword_absolute (β 0.04) Keyword_absolute (β 0.08) Keyword_absolute (β 0.12) Figure 6: Effect of strength of feedback on distribution of clicks (keyword specific QS) The effect of multiple feedbacks on the distribution of clicks in an SSM was analyzed for different values of α and β. With α and β set to 0.01 the advertiser level QS dominates over keyword level QS and the distribution pattern of clicks is very similar to that obtained when only advertiser level QS is in operation. As the strength of the keyword specific user feedback β is increased the distribution starts to gradually shift towards the pattern observed with only the specific feedback operating (Figure 7). The strength of the keyword feedback which seemed to have no impact when it was the only feedback in operation (as shown in Figure 6) becomes an important parameter that controls the distribution of clicks when both the types of user feedbacks operate simultaneously Keyword_only (β 0.01) Key + Adv (β 0.01) Key + Adv (β 0.02) Key + Adv (β 0.04) Key + Adv (β 0.08) Key + Adv (β 0.16) Advertiser_only Cummulative % age of advertisers (ranked in ascending order of clicks) Figure 7: Lorenz Curves of clicks distribution with simultaneous presence of both QSs Effect of QS Computation on territorial prowess of various advertisers We define territorial prowess of an advertiser as a set of those keywords in the cluster where a particular advertiser garners a large proportion of clicks relative to other advertisers (market share). We focus our attention on the differences in the type of territorial prowess that happens under the two types of QS computation. Figure 8, 9 represent the pattern of click distribution across various search

12 queries among advertisers after 800 iterations of the simulation. The X-axis represents various search queries and the advertisers are represented on the Y-axis. Each advertiser is represented by a different color and the size of the marker represents the number of clicks that an advertiser gets. Observing the relative distribution of clicks among advertisers on different search queries in the cluster, we find that in the case of advertiser level QS one advertiser dominates all the search queries (for example, advertisers 11 and 20 in Figure 8) and few advertisers together dominate a large portion of the market (advertisers 4,6,12, 19, and 20 in Figure 8). However, this is not the case when we have a keyword specific QS. The keyword space in the cluster in the case of keyword specific QS is much more fragmented and no single advertiser dominates the entire set of keywords (Figure 9). Additionally, the distribution of clicks among advertisers on various keywords is not the same. Thus, there are differences with respect to who dominates a particular keyword and by how much which are not observed when QS is computed as an aggregate performance of an advertiser across the entire set of keywords. Each search query has its own distribution of clicks. In other words, when QS is search query specific the market from an advertiser s perspective is composed of heterogeneous resources. Some of these resources are dominated by an advertiser while others are not. This is a distinct difference between the two QS computations. Figure 8: Pattern of Click Distribution among Advertisers on Various Keywords Advertiser level QS

13 Figure 9: Pattern of Click Distribution among Advertisers on Various Keywords Keyword Specific QS Effect of QS Computation on average bids of various advertisers across different keywords Under the two different types of QS computation, it is observed that the average bids across the search queries in the cluster are quite different. Figures show the average bid of all advertiser across the 26 search queries in the cluster under observation after the 800 iterations of the simulations when the QS is computed at advertiser level and at an individual search query level, respectively. The colour of the marker indicates the value of the bid, deeper colours being associated with higher values. Two observations can be made from these figures. Firstly, the variation in the average bid across keywords is much more in the case of advertiser level QS with highest bids coming from the advertiser that gets maximum clicks. Secondly, the bid values are more heterogeneously scattered across search query space in the case of keyword specific QS and do not necessarily coincide with click share distribution in Figure 9.

14 Figure 10: Variation of Bids across Search Queries (Advertiser Level QS) Figure 11: Variation of Bids across Search Queries (Keyword Specific QS) Discussion The mechanism governing sponsored search markets gives rise to a form of product differentiation that is different from that prescribed in the extant literature. Product differentiation refers to the differentiation between the products of two or more sellers on the basis of what is important to a buyer and leads to a preference (Chamberlin, 1969). Heterogeneities in buyer preferences along with the intrinsic or perceived differences in the characteristics of the products are seen to give rise to this differentiation (Chamberline in Dickson & Ginter, 1987). While this matching may give rise to an

15 infinite number of products, only a few of these get produced because of the economies of scale arising from fixed costs of production (Caves & Williamson, 1985; Lancaster, 1979; Spence, 1976) or because of the information search cost associated with a buying the best product. As a result of the latter, a buyer invests in various types of information up to the point where the expected marginal benefits from a better choice among brands become equal to the marginal information costs (both in money and in time) and may, therefore, settle for a product that is not the ideal match. However, the mechanism of product differentiation in sponsored search markets is different on the following two fronts. Firstly, product differentiation in SSMs is an emergent process. We observe that despite the fact that advertisers had the same niche, started with exactly the same budget, and followed the same rules of bid optimization, each advertiser came to dominate different portions of the semantic cluster. Thus, product/advertiser differentiation is a path dependent process and the final outcome is emergent in nature. For example, under both the computations of QS, that is, advertiser level QS and keyword specific QS we see that which advertiser will dominate which keywords cannot be known a prior although there may be differences is the scale of dominance. The difference in click share on a particular keyword is referred to as perceived differentiation because more users respond to the ads from a given advertiser compared to others. The emergent nature of this differentiation is an outcome of increasing returns (Arthur, 1989) where historical success of an advertiser on a specific keyword affects future success. However, it is important to realize that this process is not solely governed by historical success and an advertiser can over bid on keywords to get better rank and more clicks. Secondly, the ranking algorithm that governs the way in which QS is computed plays an important role in creating these perceived differences. We observed that when the QS is computed at an advertiser level the outcome is more concentrated market and perceived differences among advertisers vary very little across the keywords. On the other hand, when QS is computed at an individual search query level then there is a lot of heterogeneity in the perceived differences across different search queries. The lack of heterogeneity in the case of the former is because of spill over benefits that are enjoyed by an advertiser that accidentally gets more clicks in the initial rounds. The computation of QS at an advertiser level allows for this QS advantage to spill over to other search queries where the advertisers had not done well initially. The high QS make it easier for this advertiser to get better positions in subsequent rounds. By proposing an alternative mechanism of perceived differentiation through this research we also add to the literature on the role advertising medium in creating perceived differences. The information processing view of the advertising suggests that it may be efficient for the seller to provide information rather than for the buyer to commission it from independent sources (Telser, 1964). Advertising as a medium is, therefore, crucial for dissemination of information and creating perceived

16 differences in products. While, literature has recognized the role of information in development of these perceived differences (Larson, 2011), little was known about the role of advertising medium in creating these perceived differences. This research aims to contribute to this void in literature by highlighting that mechanisms that are at play in different advertising media have important implications on the perceived differences that are but an emergent outcome of the interactions among users, advertisers and the design of these mediums. Additionally, perceived product differences can also be emergent outcome rather than actual differences in the products. Limitations & Conclusion To keep the focus of the research on the impact of different feedback mechanisms while maintaining the complexity of the phenomenon, certain aspects of the phenomenon like the difference between branded keywords and general keywords have not been considered and, the search volume of the keywords has been assumed to remain the same over time. This research has assumed a specific functional form of the feedback mechanisms, however, alternate functional forms could be tested to enhance and validate the findings. Further, to emphasize the effect of feedback, all advertisers were assumed to be homogenous is all respects viz. budget, niche, bid optimization process and target CPC, except the initial bids. Various heterogeneities could be introduced to increase the complexity and study various multifaceted and interesting aspects about sponsored search markets which have remained unexplored. Moving away from the neo classical view and taking a departure from the auction based literature which expectedly had emphasis on a single auction, this research tries to understand the structure of sponsored search markets as an outcome of various interactions among different advertisers guided by the rules of the search engine. Embedded in the complexity science paradigm of research this is the first study of sponsored search markets as a complex adaptive system, to the best of the knowledge of the researchers. This embedding allows relaxation of certain assumptions like, the known valuation of the click, which limited the scope of questions being asked and addressed in the literature on sponsored search markets and highlights the systemic effects of QS computation on market concentration, territorial prowess of advertisers, and the bidding profile across keywords. References Abrams, Z., & Ghosh, A Auctions with revenue guarantees for sponsored search. Internet and Network Economics: Springer Berlin Heidelberg. Antonelli, C The economics of path-dependence in industrial organization. International Journal of Industrial Organization, 15(6): Arthur, B. W Silicon Valley Locational Clusters : When Do Increasing Returns Imply Monopoly?

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