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In this research, we evaluate the effectiveness of the buying funnel as a model for understanding consumer interaction with keyword advertising campaigns on web search engines. We analyze data of nearly 7 million records from a 33 month, $56 million (US) search engine marketing campaign of a major US retailer. We classify key phrases used in this c...
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... buying funnel is the consumer parallel to the organization"s sales funnel, which frames the customer buying process from the producer"s point of view with the aim of funneling the potential customers to a successful transaction [Dubberly and Evenson 2008]. Although there are various labels for each stage, one common labeling system is Awareness, Research, Decision, and Purchase (see Figure 1), which is the labeling scheme that we use in this research. ...
Citations
... The difference in the funnel order was not necessarily due to the B2B or B2C difference, but due to the type of business model. Additionally, a new level -Awarenesswas introduced as an initial step, allowing potential customers to discover the product solving their problem (Jansen and Schuster 2011). This phase was measured and analyzed to determine factors that generated high awareness instead of revenue, which is typical for the ecommerce business model. ...
Different challenges and uncertainty arise from digital transformation as managers are forced to find new channels or make alternative investments decisions. Companies can use experiments as knowledge-generating resources to mitigate the uncertainty surrounding the adapted business model. The B2B Startup Experimentation Framework (B-SEF) was developed for startups in the B2B environment to discover and validate a business model's desirability through business experiments. This study uses the criteria completeness, consistency, plausibility, accuracy, and feasibility to investigate to what extent the B-SEF can be adapted to conduct business experiments in a B2C environment. Based on the B-SEF approach, two experimentation rounds are conducted regarding multiple online advertising channels and their efficiency in generating new customers in a B2C cosmetic online shop. Findings show that the B-SEF's generic structure is also suitable for conducting business experiments in the B2C environment when two adjustments are made regarding the sales funnel in the macro-level of the framework. First, the funnel levels required reordering to represent the customer journey better. Second, the new funnel level "awareness" needs to be added, as tracking awareness is relevant in the success of an e-commerce store. This research contributes by providing a guideline for entrepreneurs who want to conduct similar business experiments and extracting the company's most and least efficient advertising channels to acquire new profitable customers. The study's originality lies in assessing the B-SEF's suitability in the B2C context and providing a tool for its application to conduct business experiments comprehensively and successfully, especially regarding documentation and data collection.
... oices and resource inefficiencies. Additionally, since paid search advertising operates at the keyword level, measuring keyword-level performance holds significance (Özlük and Cholette 2007;Rutz et al. 2011). Given the substantial financial investment in keyword advertising, marketers require broader insights into this process (Dhar and Ghose 2010;Jansen and Schuster. 2011;Lu and Zhao 2014). Ayanso et al. (2022) investigate whether advertisers can enhance their budgeting decisions by implementing keyword segmentation and performancebased budget allocation strategies. Effective keyword management involves identifying and categorizing different keyword groups to enhance budget utilization. ...
We utilize data envelopment analysis to evaluate and compare the pricing efficiency of keywords in the Google-sponsored search markets, specifically in relation to manual bidding strategies and automated bidding strategies. Two totally different sets of efficiency scores are obtained from Google Ads by using extensive data from a company in the online apparel retailing industry. Contrary to the big buzz in the industry, the automated bidding strategy does not improve the average efficiency of keywords. Manual bidding rewards efficiency for keywords more productive of transactions, revenue, and clicks. Automated bidding rewards efficiency for keywords more on cost per click, bounce rate, and E-commerce conversion rate. Automated bidding increases efficiency scores with apparel keywords consisting of words of “color” and “quality attributes.” Manual bidding has high-efficiency scores with keywords including words “promotion related,” “gender,” and “style attributes.” Manual bidding works better for modified match types.
... The second challenge concerns variability in interest shifts. Driven by psychological dynamics, consumers' interests in products may shift over time and result in highly diversified behaviors (Jansen and Schuster 2011). For instance, consumers may start with interests in certain products, but as the shopping journeys progress, consumers continuously inspect and compare the products and shift interests. ...
... The marketing literature provides an established perspective for understanding consumer shopping journeys with psychological dynamics, which has been recognized as stages through the metaphor of a funnel (Howard and Sheth 1969, Court et al. 2009, Mulpuru 2011. Generally speaking, a shopping journey consists of transitive psychological stages (e.g., awareness, consideration, preference, and action) where consumers gradually shift interests, which further drive the interactive behaviors with products until reaching the final purchases (Jansen and Schuster 2011, Zhang et al. 2020, Goldstein and Hajaj 2022. Concretely, consumers usually start with a number of potential candidates in mind for a product category (e.g., showing a diversified interest distribution on products at the wide end of the funnel) (Goldstein and Hajaj 2022). ...
... Inspired by well-established behavioral theories, such as theory of reasoned action (Shepherd et al. 1988) and theory of planned behavior (Ajzen 1991), consumer interest can be referred to as the attitude/ tendency to respond to a product with some degree of favorableness. As an important determinant of behavior, interest essentially motivates consumers' observed interactions with products in online shopping (Howard andSheth 1969, Jansen andSchuster 2011), often in a latent and hybrid manner because of subjectivity, uncertainty, and cognitive limitations (Edwards andFasolo 2001, Ajzen 2008). Thus, a common treatment in recommendation studies is to model consumer interest as a probabilistic distribution over the product set (He et al. 2018, Koren andBell 2021), whose shifting patterns can be further captured by the dynamic methods (Rabiu et al. 2020). ...
Recommender systems are widely used by platforms/merchants to find the products that are likely to interest consumers. However, existing dynamic methods still face challenges with regard to diverse behaviors, variability in interest shifts, and the identification of psychological dynamics. Premised on the marketing funnel perspective to analyze consumer shopping journeys, this study proposes a novel and effective machine learning approach for product recommendation, namely, multi-stage dynamic Bayesian network (MS-DBN), which models the generative processes of consumers’ interactive behaviors with products in light of their stage transitions and interest shifts. In this way, consumers’ stage-interest-behavior dynamics can be learnt, especially the variability in interest shifts. This provides managerial implications for practice. MS-DBN demonstrates significant performance advantage with general applicability by extracting the generalizable regularity during shopping journeys, which compensates the diversity and sparsity frequently observed in consumer behaviors. In addition, aided by the identification strategies integrated into the learning process, the latent variables in the model can be detected such that consumers’ invisible psychological stages and interests in products can be identified from their observed behaviors, shedding light on the targeted marketing of platforms/merchants and thus enriching the practical value of the approach.
... It is used to find a variety of information, such as things, events, individuals, and locations. Consumers frequently utilize Web search engines to get e-commerce information (Jansen and Schuster, 2011). Search engines have also grown in prominence as an important tool for businesses that utilize the internet to create their image and find their target clients. ...
... Paid advertising is a type of internet marketing in which businesses advertise their services and products on search engine result pages. Sponsored ads are sometimes referred to as keyword advertising, pay-per-click advertising, and search engine advertising (Ištvanić et al., 2017;Jansen and Schuster, 2011;Kritzinger and Weideman, 2013). Since its inception in 1998 (Fain and Pedersen, 2006) paid ads has evolved into the primary business model of the major search engines. ...
... (Jansen (Jansen and Mullen, 2008;Jansen et al., 2009). As such, key paid advertising has helped shape the nature of the web (Jansen and Schuster, 2011). The business models of the major Web search engines depend primarily on online advertising in the form of keyword advertising (Rosso and Janseny, 2010). ...
... Analyses of search volumes on Google have been used in a variety of fields, including nature conservation . In marketing theory, awareness (the realization of the existence of a subject) generally precedes information seeking, and both are considered crucial components of intention and behavior formation (Jansen & Schuster 2011). Thus, information seeking behaviors such as internet searches can provide valuable insights pertaining to Aichi target 1. ...
The first target of the Convention for Biological Diversity (Aichi target 1) was to increase public awareness of the values of biodiversity and actions needed to conserve it—a key prerequisite for other conservation targets. Monitoring success in achieving this target at a global scale has been difficult; however, increased digitization of human life in recent decades has made it easier to measure people's interests at an unprecedented scale and allows for a more comprehensive evaluation of Aichi target 1 than previously attempted. We used Google search volume data for over a thousand search terms related to different aspects of biodiversity and conservation to evaluate global interest in biodiversity and its conservation. We also investigated the correlation of interest in biodiversity and conservation across countries to variables related to biodiversity, economy, demography, research, education, internet use, and presence of environmental organizations. From 2013 to 2020, global searches for biodiversity components increased, driven mostly by searches for charismatic fauna (59% of searches were for mammal species). Searches for conservation actions, driven mostly by searches for national parks, decreased since 2019, likely due to the COVID‐19 pandemic. Economic inequality was negatively correlated with interest in biodiversity and conservation, whereas purchasing power was indirectly positively correlated with higher levels of education and research. Our results suggest partial success toward achieving Aichi target 1 in that interest in biodiversity increased widely, but not for conservation. We suggest that increased outreach and education efforts aimed at neglected aspects of biodiversity and conservation are still needed. Popular topics in biodiversity and conservation could be leveraged to increase awareness of other topics with attention to local socioeconomic contexts.
... This includes work by information science faculty like Bar-Ilan (2007a;2007b), Furner (2007, Saracevic (1999), Smith (1981), Spink et al. (2001), andZimmer (2008). Fairness in information retrieval is the central focus of Noble's book, a topic discussed by many other information science researchers beyond those previously cited, including Shah (Gao & Shah, 2020a;Gao & Shah, 2020b) and Jansen (Eastman & Jansen, 2003;Jansen & Schuster, 2011). ...
This review article examines the evolution of data science as a discipline, highlights contemporary challenges related to data bias and ethics, and underscores the pivotal role of information science in addressing these concerns within the broader framework of interdisciplinary research and humanistic approaches to data science ethics.
... A "feature" is defined as a functionality in the platform itself that is instrumental in the advertising process. For example, adjusting click price is a central feature in online ad platforms [30]. Features govern what is possible or not within the ad platform, for example, whether the advertisers can determine the price per ad click or whether the system does this on their behalf. ...
... In the case at hand, the power dynamics favor Google (and most online advertising companies that offer advertising platforms) over advertisers [58], as Google controls the majority of the critical aspects of online advertising exchange, including pricing [30], ad ranking [40,61], and matching search queries for a given ad [32]. Furthermore, the agent-Google-has detailed information about the events occurring in its ad marketplace and the algorithms that govern these events. ...
Businesses are increasingly delegating activities in the advertising process to dominant online advertising platforms. This delegation yields the ad platforms tremendous power, akin to the principal–agent dilemma discussed in economics. One of the major platforms is called Google Ads—this platform is the focal point of our study. Over the years, Google has made substantial changes to its platform’s features, which, in turn, govern what is possible and what is not for the advertisers. These changes impact the advertisers’ ability to act independently and make their own choices, referred to as human agency. To better understand this impact, we examined 362 industry news articles reporting changes in Google Ads from 2015 to 2020. The findings indicate that while most changes increase human agency, this effect is becoming weaker over time, driven by automation. To better understand advertisers’ attitudes towards automation, we surveyed 193 advertisers with Google Ads experience. Contrary to the popular belief that marketers are afraid of being replaced by algorithms, we found this to not be the case. Even though most advertisers indicated appreciation for maintaining their human agency, they did not perceive this agency being violated by the ad platform. However, we did observe interesting variability among respondents, reflected in three computational advertising attitude types: tinkerers, instrumentalists, and shepherds. We discuss the implications for advertisers in terms of strategizing in the face of reduced human agency and for ad platforms in terms of designing features that advertisers perceive as fair.
... In the world of marketing, the different models are labeled as "buying-", "purchase-", or "brand-funnel" too (e.g. Jansen & Schuster, 2011). It is obvious: in science and practice, there are many labels for the same topic. ...
This study analyzes the various stages of the customer journey (CJ) concept using the example of the lingerie product area. The fields of Customer Journey Management, Customer Relationship Management , and Customer Experience Management, which have so far been largely considered separately, are summarized into a comprehensive framework. In the second part, the study uses a representative survey of 1,050 women of generation X to establish the validity of the model empirically. It additionally analyzes in how far the data requires the expansion of the model by a secondary vertical meta-level to capture interlinkages not considered within a purely linear model of the CJ. The result, a two-dimensional network structure of the CJ, illustrates the links between different parts of the CJ and the requirement of a multidimensional approach towards the customer journey. Finally, the study presents an approach on how to model the willingness-to-pay as the central part of the CJ by implementing an artificial neural network (ANN) approach. The results show the ANN is ideally suited for such a complex background. The resulting model combines high explanatory power with the potential to increase it further by successively including newly available customer data, thus offering additional benefits for practitioners.
... purchase (Barry, 1987;Howard & Sheth, 1970;Vakratsas & Ambler, 1999). Several variants of the conversion funnel have been proposed, and most include four stages (Jansen & Schuster, 2011): (1) awareness, in which the consumer becomes aware of a need and wants to address this need with a product or a service; (2) research (or interest), in which the consumer becomes interested and engaged in informationseeking regarding products/services that can address the need; ...
... (3) decision (or desire), in which the consumer defines a set of options and enters a process of deciding among the options in the set; and finally; (4) purchase (or action), in which the consumer has decided to purchase a particular product/service and takes the next steps toward completing the purchase (e.g., price comparisons or convenience-of-purchase considerations) (Howard & Sheth, 1970;Jansen & Schuster, 2011;Lee & Seda, 2009). These stages may extend over days and even weeks (Bronnenberg et al., 2016;Wiesel et al., 2011). ...
... For example, display ads may be suitable for consumers in the awareness stage, whereas price discounts and retargeting may be suitable in the purchase stage (Nimetz, 2007). Moreover, a customer's stage in the funnel is likely to be predictive of their conversion likelihood (Jansen & Schuster, 2011). For example, a customer in the decision stage, who has already identified several desirable products, may be more likely to make a purchase than a customer who has just become aware of the need for a product. ...
The conversion funnel is a model describing the stages consumers go through in their journey toward a purchase. This journey often lasts several days to weeks and can include multiple visits to a seller’s website. A large body of literature has focused on using observable search patterns to identify consumers’ hidden purchasing stages and to estimate their likelihood of conversion. We propose a novel set of measures to better reveal the consumer’s hidden stage in the funnel. These measures are based on the diversity of the searches that a customer engages in while browsing an e-commerce website, and they include not only the number of different products that are searched for, but also measures that rely on unobserved similarities among products, captured in a product network (in which products are assumed to be “similar” if they are frequently co-searched). We operationalize and evaluate our proposed measures using a large-scale dataset from a medium-sized tourism website used for comparing and booking flights. We estimate a hidden Markov model to show that our proposed diversity measures are associated with progress in the funnel and consumers’ conversion likelihood. Specifically, we show that consumers go through different distinguishable stages (states) in their journey, characterized by different values of our proposed diversity measures. To demonstrate the managerial and business implications of our theory, we show that incorporating search-diversity measures into a baseline prediction model significantly improves the model’s performance in predicting purchase likelihood and churn.
... Traditional data typically have little information on awareness and decision phase as indicated by question marks. Alternative data typically occupy more than one spot in the funnel, as indicated by the arrow on the right stretching across all phases, in support of Jansen and Schuster (2011) who conclude that the process is more complex than the simple linear representation. ...
Worldwide macroeconomic data suffer from three fundamental problems - high dimensionality, a staggered release schedule, and poor data quality. Nowcasts are a popular set of tools that address the first two problems, and the advent of alternative or Big Data offers a chance to address the poor data quality. In this chapter, I provide an overview of nowcasting techniques, discuss the need for an ex-ante hypothesis to guide alternative data selection, and compare typical alternative datasets to traditional data on several quality dimensions such as timeliness and granularity. Finally, I present a case study that establishes that search data can statistically and economically significantly improve US government employment data along the timeliness and accuracy dimensions - a novel result. The case study nowcasts revisions to Non-Farm Payrolls (NFP) three months in advance of the government data, proves these revisions are news and not noise in the framework of Mankiw et al. (1984), controls for Wall Street
analyst predictions, and finds that machine learning techniques such as random forest and elastic net provide a substantial improvement over traditional linear regression methods.
