Analyzing Digital vs. Traditional Metrics in Online Advertising: Key Findings

Introduction

As internet use continues to increase, digital channels are emerging as dominant sources of competition. However, the main problem that arises from the use of digital marketing is the lack of analogousness between digital metrics and traditional metrics. Thus, it is hard to determine their effectiveness using the same tools. The study by Zenetti, Bijmolt, Leeflang, and Klapper (2014) study relies on a probit model to propose the appropriate methodology and metrics of making traditional and digital media comparable.

Theoretical Background

Measurement of Online Advertising Effectiveness

Online advertising effectiveness is measured using “percent or number of click-throughs, “impressions generated,” and “conversion rates or induced sales.” Notably, the three consumer metrics measure the direct effect of online advertisement; thus, they ignore probable future influence on sales and profitability. Consumer metrics on cognition, affect, and conation supports a greater assessment of Search Engine Advertising (SEA) effectiveness (Zenetti et al., 2014). Mainly, the cognitive dimension relates to thinking, beliefs, and knowledge. The measurement of cognition is based on memory through awareness, attitude, and liking. Typically, conation accounts for actual or intended behavior, and it is measured using purchase intention, consumption, or recommendation.

Search Engine Advertising Effects on Consumer Metrics

Based on prior study findings, the SEA may have a positive or negative effect on consumer metrics. Notably, banner advertising can increase performance metrics such as aggregate sales or interrupt consumer goals. In this case, Zenetti et al. (2014) hypothesized that exposure to SEA (with or without a click-through) positively affects advertising awareness, (b) brand awareness (c) brand image, and (d) brand consumption

Effects of Advertising Media Interaction on Consumer Metrics

Banner and TV advertising have positive interaction effects on consumer cognitive metrics. The use of different media channels improves consumer responses. Thus, Zenetti et al. (2014) hypothesized that there is a positive interaction effect of SEA (without a click-through) and TV advertising, SEA (with click-through) and TV advert, SEA (without a click-through) and banner advertising, and/or SEA (with click-through) and banner advertising on advertising awareness, brand awareness, brand image, and brand consumption.

Empirical Study Design

Empirical Setting and Experimental Design

The data is attained from a large-scale advertising tracking study from a multimedia advertising campaign conducted by MetrixLab. Mainly, the campaign was on international beer brand “BB” (Beer Brand) and took place in the Netherlands in 2009. MetrixLab conducted an experimental study and collected data from 5,001 representative Dutch customers out which the first 306 consumers were surveyed before campaign while 4,695 were surveyed through a controlled experiment after the manipulation of SEA. The survey was done five days after the experiment.

Response Variables: Consumer Metrics

Advertising awareness and brand awareness measurements were done using “top-of-mind aware,” “aided aware,” “spontaneous aware,” and “not aware” ordinal categories. Brand image is computed as the sum of positive responses to the four statements – “the brand is great,” “the brand feels comfortable,” “the brand stands for positive energy,” and “the brand is inventive.” On the other hand, brand consumption using the ordinal categories “no consumption,” “ever tried, “drink regularly” and “drink occasionally.”

Explanatory Variables: Media Exposure

The “opportunity to see” (OTS) score determined the likelihood that an individual saw the TV advert. Cookies measured the online exposure of banner advertising. The formula, “Search × TV,” “Search & Click × TV,” “Search × Banner,” “Search × TV × Banner,” and “TV × Banner” were used to determine the effect of interaction between media exposure. The overall campaign effect was determined using a “postcampaign” dummy variable.

Consumer Characteristics
DaysInternet Days of using the internet for one week
BeerConsumption Days of beer consumption in the last four weeks
ProductExperince, yes/no) Product experience with the BB advertised in the campaign
Gender Male/female
Age
Employment(yes/no) Full-time working
Education(yes/no) Higher vocational or academic education
Partner(yes/no) Marital status
Kids(yes/no)
Data Descriptives
Women 60%
Average age 36.8 years
Full-time work 73.8%
Higher vocational or academic education 56%
Median no days per month 4 days
Average Internet use per day 6.7%
With Partner 71.1%
With kids 36.1%
Exposure to SEA Based on Percentage Cases
No cases 35.2%
One case 22.4%
Two cases 22.1%
Three cases 21.3%
All Cases (one, two and three) 64.8%

Methodology

The study relies on a probit model in a Bayesian framework using a Markov chain Monte Carlo (MCMC) method.

Results

TV advertising has a significant positive effect on advertising awareness, consumption, and brand image. Banner advertising does not have a significant impact on the metrics. There is a significant positive interaction between banner advertising and TV advertising on brand awareness. “Search & Click” shows a significant interaction effect with the TV on both brand awareness and advertising. The metrics show higher values for younger and male respondents. More educated and employed individuals are considered to consume more beer. The number of days of using the internet has positive effects on brand image, brand awareness, and brand consumption. The experience with BB customized products, having children and having a partner does not have an impact on consumer metrics.

Conclusion and Discussion

The study estimates the effects of TV, banner, and SEA on advertising awareness, brand awareness, brand image, and brand consumption. The findings show that SEA has significant effects on consumer metrics of advertising and brand awareness after clicking. Besides, SEA has substantial effects on stated brand consumption and advertising awareness even without clicking, especially in individuals not exposed to TV advertising. Banner advertising has a positive impact on brand awareness but only after additional exposure to TV advertising.

Limitations and Future Research

First, the study was based on one product (BB) and one country Netherlands; thus, the findings cannot be generalized to other products or countries. Second, there is no information relating to advertising costs or profits per consumer, which could aid in determining the cost efficiency of each advertising channel. Third, the survey was based on measures of the effect of SEA five days after the experiment; thus, the data cannot be used to evaluate the long-term impact of advertising SEA advertising. In retrospect, future research should bring more insight into how the degree of congruence between the user’s search query and SEA affects advertising effectiveness.

Brief Summary

SEA has emerged as a dominant form of adverting using the internet. The study by Zenetti et al. (2014) has determined the efficacy of SEA utilization in a multimedia campaign by taking into account its interaction with banner and TV advertising. The study relied on about 5000 respondents to determine the effects of advertising awareness, brand image, brand awareness, and brand consumption. Using a multivariate probit model, the findings show that SEA has a significant effect on numerous consumer metrics even without “click-throughs” on the sponsored advertisement. Notably, there is a negative interaction effect between SEA and TV advertising. Mainly, banner advertising has a constructive effect on consumer metrics, but after blending it with TV advertising.

Reference

Zenetti, G., Bijmolt, T. H. A., Leeflang, P. S. H., & Klapper, D. (2014). Search engine advertising effectiveness in a multimedia campaign. International Journal of Electronic Commerce, 18(3), 7-38. Web.

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StudyCorgi. "Analyzing Digital vs. Traditional Metrics in Online Advertising: Key Findings." October 28, 2020. https://studycorgi.com/digital-traditional-metrics-of-online-advertising/.

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StudyCorgi. 2020. "Analyzing Digital vs. Traditional Metrics in Online Advertising: Key Findings." October 28, 2020. https://studycorgi.com/digital-traditional-metrics-of-online-advertising/.

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