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

Overwhelming targeting options : selecting audience segments for online advertising

Even as online advertising continues to grow, a central question remains: Who to target? Yet, advertisers know little about how to select from the hundreds of audience segments for targeting (and combinations thereof) for a profitable online advertising campaign. Utilizing insights from a field experiment on Facebook (Study 1), we develop a model that helps advertisers solve the cold-start problem of selecting audience segments for targeting. Our model enables advertisers to calculate the break-even performance of an audience segment to make a targeted ad campaign at least as profitable as an untargeted one. Advertisers can use this novel model to decide whether to test specific audience segments in their campaigns (e.g., in randomized controlled trials). We apply our model to data from the Spotify ad platform to study the profitability of different audience segments (Study 2). Approximately half of those audience segments require the click-through rate to double compared to an untargeted campaign, which is unrealistically high for most ad campaigns. Our model also shows that narrow segments require a lift that is likely not attainable, specifically when the data quality of these segments is poor. We confirm this theoretical finding in an empirical study (Study 3): A decrease in data quality due to Apple’s introduction of the App Tracking Transparency (ATT) framework more negatively affects the click-through rate of narrow (versus broad) audience segments

Publicidad digital en streaming de influencers chimbotanos y la decisión de compra de sus seguidores en redes sociales en el año 2022

La investigación tuvo como principal objetivo, determinar la relación entre publicidad digital en streaming de los influencers chimbotanos y la decisión de compra de sus seguidores durante el periodo 2022, Chimbote. Dada la metodología aplicada al estudio, fue de tipo básica, con un enfoque cuantitativo, de diseño no experimental. Las variables del estudio fueron, la publicidad digital y la decisión de compra. La técnica empleada fue la encuesta para cada variable de la investigación, la cual fue por conveniencia aplicada a 130 seguidores voluntarios de los generadores de contenido del estudio. Como conclusión, se demostró que no hay correlación significativa entre ambas variables del estudio, debido al resultado extraído de los cuestionarios, con una correlación de 0.138 lo cual rechaza la Ha, lo que indica que, bajo el contexto local la publicidad digital realizada por los influencers del medio chimbotano no es tan efectiva para incrementar la decisión de compra en sus seguidores

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

Harnessing search engine optimization experience to enhance the visibility of websites

Research has identified that websites can gain an added advantage by getting listed in Search Engine results Pages (SERPs) during search sessions by searchers as SERPS refer targeted traffic to the websites. Search Engine Optimization (SEO) enables websites to become visible in search engines during search sessions for featured products or services. SEO is a complex technique which is directly affected by the ranking algorithms of search engines such as Google. Bearing in mind that Google employs in excess of 200 dynamic ranking factors in its algorithm it can be seen that optimization is not straightforward. Given this complex environment, websites find it difficult to initiate and implement SEO. SEO knowledge and experience gained from optimizing websites in the past is highly valuable and applicable to optimize websites both now and in the future. Therefore the main aim of the research in this thesis is to investigate the problem of optimization of websites using the prior experience gained through the optimization of several case study websites. To facilitate this, novel process models have been designed in order to capture the experience of implementing essential techniques of SEO and to explain the procedure of implementation of fundamental on-page SEO techniques that have been shown previously to yield results (i.e. increases in ranking) for past case study websites. Quantitative experiments and qualitative evaluation were undertaken to verify the efficacy of the novel process models through their application to case study websites. Mixed methods were used in order to answer the research questions, inductive experimental methods to produce, finesse and test the process models and qualitative enquiry through means of a focus group to gather peer review from professionals within the field who had previously been trained and conducted a trial using the process models. Implementation procedures of acknowledged essential on-page SEO techniques were identified from past case study websites, which have been represented in the novel process models designed in the current research and empirically investigated by applying them in the experimental case study websites. These models were applied through quantitative experiments that identified essential on-page SEO techniques which were then implemented in two experimental case study websites as per the procedures represented in the process models. These experiments have yielded positive results, resulting in establishing and/or enhancing the visibility of case study websites in SERPs. Further the implementation procedures of essential on-page SEO techniques were represented in the designed process models and stored in an SEO experience base on the principle of INRECA-II methodology. Results of the focus group suggest that the process models do achieve credible results (i.e. establishing and/or enhancing visibility of websites in SERPs) through their application and are suitable for use by both novices and professionals alike. Overall the results achieved from both the quantitative experiments and qualitative evaluation provide promising support to validate the created knowledge