Non‐response bias in internet‐based advertising conversion studies

Park, Sang Won; Fesenmaier, Daniel R.

doi:10.1108/17506181211265077

Cited by 10 publications

(7 citation statements)

References 64 publications

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“…However, a statistically significant ( t = 5.818, df = 134,984, p = .000) difference of $42.85 was found between the unweighted sample ($598.74) and the weighted sample ($641.59) for mean total destination expenditure. This finding indicates that use of the unweighted sample results in an underestimation of approximately 6.7 percent and is consistent with the findings of Park and Fesenmaier (2012). Therefore, the results discussed in this paper are based on a weighted sample that corrects for response bias.…”

Section: Methodssupporting

confidence: 87%

“…Respondent email addresses were obtained separately for each of the 40 destination advertising campaigns and, in total, 264,317 online surveys were successfully delivered to American travelers 18 years and older. This aspect of the methodology is important in that it avoids selection bias based on destination which leads to a more precise analysis of tourist demand as it includes not only those people who travel and purchase but also those who do not (Park and Fesenmaier 2012). In order to increase the response rate, the following three-step process was followed: (1) an initial invitation was sent out along with the URL of the survey; (2) four days later, a reminder was delivered to those who had not completed the survey; and (3) the final request for participation was sent out to those who had not completed the survey one week later.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, a response bias analysis was conducted because of the relatively low response rate that was achieved in this study. This study adopts a weighting adjustment technique using inverse propensity scores to identify nonresponse error following Rosenbaum and Rubin (1984) and Park and Fesenmaier (2012) whereby the sample data collected were reweighted based on known characteristics of the study population, including PRIZM segment, market area, state of residence, and advertising campaign.…”

Section: Methodsmentioning

confidence: 99%

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Evaluating Destination Advertising

Stienmetz

Maxcy

Fesenmaier

2013

Journal of Travel Research

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Park, Nicolau, and Fesenmaier proposed the Destination Advertising Response (DAR) model as a means to more effectively evaluate destination advertising campaigns by incorporating the key decisions or components (i.e., facets) that comprise a trip. While this model appears to be an attractive alternative to traditional destination advertising evaluation, little research has been conducted to examine its validity. The goal of this study is to evaluate the potential usefulness of the DAR framework based upon current understanding of the travel decision-making process and industry practice. Additionally, the framework is evaluated based on a series of empirical analyses that consider the impact of destination advertising on the destination decision as well as on several trip-related decisions. The implications of this model for destination advertising are substantial in that it provides a much richer foundation for the development of destination marketing strategies.

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Section: Methodssupporting

confidence: 87%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Evaluating Destination Advertising

Stienmetz

Maxcy

Fesenmaier

2013

Journal of Travel Research

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“…Specifically, this study used the inverse propensity scores as a weighting adjustment technique to quantify any possible nonresponse error (Rosenbaum and Rubin 1984). The collected data were then reweighted based on several known characteristics of the study population (i.e., PRIZM segment, market area, the state of residence, and advertising campaign), as described in Park and Fesenmaier (2012). The unweighted and weighted samples were compared to determine whether or not nonresponse statistically significant bias was present in a given survey.…”

Section: Methodsmentioning

confidence: 99%

Measuring Destination Marketing

Choe

Stienmetz

Fesenmaier

2016

Journal of Travel Research

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Conversion studies are a standard technique by which marketers evaluate advertisement effectiveness. A review of the tourism literature finds that advertising conversion studies use a number of metrics that have evolved to include most aspects of visitor decisions; however, this literature also indicates that there are few comparative studies focusing on the variability of response. The overall goal of this study is to identify and compare estimates of impact on visitor expenditures using four different approaches ranging from traditional advertising models (i.e., gross conversion rate and net conversion rate) to a more comprehensive facets-based destination advertisement response (DAR) model. The various models result in a huge range of impact estimates and it is concluded that the DAR model offers, both theoretically and practically, a powerful framework for evaluating advertising response in that it incorporates the possibility that destination advertising affects decisions related to a range of trip-related activities.

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“…Decision makers may make costly mistakes because of misleading insights about account performance. For instance, Park and Fesenmaier (2012) found that using unweighted data to estimate advertising effectiveness may lead to considerable over estimation of success. Because SEA campaigns use a multitude of independent variables, there is a need for robust metrics that account for the combined influence of factors operating within SEA campaigns.…”

Section: Introductionmentioning

confidence: 99%

Conversion potential: a metric for evaluating search engine advertising performance

Jansen¹,

Clarke

2017

JRIM

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Purpose This research is based on the premise that current metrics for search engine advertising (SEA) are misleading and do not sufficiently allow managers to evaluate traffic and conversions simultaneously. This study aimed to conceptually develop and assess conversion potential (CvP) as a unifying construct for both measuring and evaluating the performance of SEA campaigns. Design/methodology/approach A data set of nearly seven million records covering almost three years of a multi-million-dollar keyword marketing campaign from a major US retailer was used to validate the construct of CvP. Findings Results empirically validate how CvP measures both campaign traffic and sales in SEA, using the optimization factor of ad rank, which is one of many possible factors. Research limitations/implications Although the data set is large and covers a lengthy period of time, it is limited to one company in the retail sector. Practical implications The research instantiates CvP as a metric for overall SEA account performance while demonstrating that it is a practical tool for future campaign planning. The metric simultaneously incorporates a sales ratio and a traffic ratio. Originality/value This is the first study to formalize and provide a working definition of CvP in the academic literature. The contribution is a theoretical and practical managerial framework to mutually evaluate, measure and make decisions about SEA efforts.

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Non‐response bias in internet‐based advertising conversion studies

Abstract: ABSTRACT

Cited by 10 publications

References 64 publications

Evaluating Destination Advertising

Evaluating Destination Advertising

Measuring Destination Marketing

Conversion potential: a metric for evaluating search engine advertising performance

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