Online advertising constitutes one of the main sources of revenue for the majority of businesses on the web. Online advertising inventory was traditionally traded via bilateral contracts between publishers and advertisers, vastly through a number of intermediaries. However, what caused an explosion in the volume and, consequently, the revenue of online ads was the incorporation of auctions as the major mechanism for trading sponsored search ads in all major search engines. This reduced transaction costs and allowed for the advertisement of small websites which constitute the majority of Internet traffic. Auction-based markets were harder to establish in the display advertising industry due to the higher volume of inventory and the pre-existence of traditional intermediaries, often leading to inefficiencies and lack of transparency. Nevertheless, this has recently changed with the introduction of the ad exchanges, centralized marketplaces for the allocation of display advertising inventory that support auctions and real-time bidding. The appearance of ad exchanges has also altered the market structure of both demand-side and supply side intermediaries which increasingly adopt auctions to perform their business operations. Hence, each time a user enters a publisher's website, the contracted ad exchange runs an auction among a number of demand-side intermediaries, each of which represents their interested advertisers and typically submits a bid by running a local auction among these advertisers. Against this background, within this thesis, we look both at the auction design problem of the ad exchange and the demand-side intermediaries as well as at the strategies to be adopted by advertisers. Specifically, we study the revenue and efficiency effects of the introduction and competition of the demand-side intermediaries in a single-item auction setting with independent private valuations. The introduction of these intermediaries constitutes a major issue for ad exchanges since they hide some of the demand from the ad exchange and hence can make a profit by pocketing the difference between what they receive from their advertisers and what they pay at the exchange. Ad exchanges were created to offer transparency to both sides of the market, so it is important to study the share of the revenue that intermediaries receive to justify their services offered given the competition they face by other such intermediaries. The existence of mediators is a well-known problem in other settings. For this reason, our formulation is general enough to encompass other areas where two levels of auctions arise, such as procurement auctions with subcontracting and auctions with colluding bidders. In more detail, we study the effects of the demand-side intermediaries' choice of auction for three widely used mechanisms, two variations of the second-price sealed-bid (known as Vickrey) auction, termed PRE and POST, and first-price sealed-bid (FPSB) auctions. We first look at a scenario with a finite number of intermediaries, each implementing the same mechanism, where we compare the profits attained for all stakeholders. We find that there cannot be a complete profit ranking of the three auctions: FPSB auctions yield higher expected profit for a small number of competing intermediaries, otherwise PRE auctions are better for the intermediaries. We also find that the ad exchange benefits from intermediaries implementing POST auctions. We then let demand-side intermediaries set reserve (or floor) prices, that are known to increase an auctioneer's expected revenue. For issues of analytical tractability, we only consider scenarios with two intermediaries but we also compare the two Vickrey variations in heterogeneous settings where one intermediary implements the first whereas the other implements the second variation. We find that intermediaries, in general, follow mixed reserve-price-setting strategies whose distributions are difficult to derive analytically. For this reason, we use the fictitious play algorithm to calculate approximate equilibria and numerically compare the revenue and efficiency of the three mechanisms for specific instances. We find that PRE seems to perform best in terms of attained profit but is less efficient than POST. Hence, the latter might be a better option for intermediaries in the long term. Finally, we extend the previous setting by letting advertisers strategically select one of the two intermediaries when the latter implement each of the two Vickrey variations. We analytically derive the advertisers' intermediary selection strategies in equilibrium. Given that, in some cases, these strategies are rather complex, we use again the fictitious play algorithm to numerically calculate the intermediaries' and the ad exchange's best responses for the same instances as before. We find that, when both intermediaries implement POST auctions, advertisers always select the low-reserve intermediary, otherwise they generally follow randomized strategies. Last, we find that the ad exchange benefits from intermediaries implementing the pre-award Vickrey variation compared to a setting with two heterogeneous Vickrey intermediary auctioneers, whereas the opposite is true for the intermediaries.

The Web 2.0 phenomenon has produced a number of technology companies that in various rounds of venture capital funding, have attracted very indicative valuations. Following these rising valuations, Investment Banks took an interest in the sector. However, while the companies concerned seem stable as private entities, their novel approach to business makes their financial characteristics difficult to predict. Parallels are drawn between the 2001 dot-com bubble and the current Web 2.0 sector. This thesis highlights a dependency between modern highlights a dependency between modern web companies, and the established technology sector. It aims to identify the extent to which the contemporary technology sector (encompassing Web 2.0) has exhibited characteristics similar to those of the dot-com bubble. to that end, this thesis identifies characteristics of modern and historic bubbles, and uses them to formulate a hypothetical set of indicators, in the form of a conceptual model. To determine whether these indicators exist in real data, a novel, repeatable statistical test is developed. It first identifies statistical heuristics representative of bubble circumstances, and then compares other periods to them. Thus, given sufficient data, any period may be tested. Periods are analysed prior, during and after the dot-com bubble. During the dot-com bubble, consistently strong venture capital activity is observed, and linked to the growth in people using the internet. This is indicative of the poor decision-making by investors, documented at the time. In recent periods, patterns in venture capital investment describe an industry that is much more cautious that before, reducing the probability of the formation of a similar bubble. Looking at the past, this thesis observes investor activity that 'caused' the dot-com bubble as early as 1995-96, which raises questions about when the bubble started, and the lead-times on market collapses.

The topic of this thesis is the use of ‘Big Data’ as a catalyst for true precision target marketing, where online advertisements across all communication channels are so timely and relevant that they are welcomed by the consumer because they improve the customer experience. In particular, the research has been directed to demonstrate the link between investment in digital branding and sales revenue at the company level. This thesis includes a review of the accumulation of ‘Big Data’ from a plethora of social networks, and an assessment of its current use and application by marketing and sales departments and emerging others. The hypothesis tested was that companies most advanced in processing ‘Big Data’ by rules-based, algorithmic, digital analysis are the companies realizing the greatest return on investment in the use of ‘Big Data’. The research was conducted using a questionnaire and interviews with the top people working in large consultancy and related firms who are actively engaged in the utilization of social media and large datasets. As there is a lack of understanding within companies in terms of using social media, and many obstacles have to be overcome, the research was meant to unearth some insights into the effective use of data. The research indicated that companies that had certain organizational and operational characteristics actively use social media, although the utilization is often limited in scope. However companies that do use them effectively gain measurable ROI and tend to track users across many venues. The companies using advanced ‘Big Data’ analytical tools to describe and predict user characteristics, applying the intelligence to target, time, tailor and trigger the release of cogent content to the ‘dynamic throng of individual audiences’ are experiencing the highest return on social media investment. This thesis makes a contribution to the wider understanding of social media use by the large business entities, and to the current and future problems that this explosion of data is creating and is likely to create.

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