Super Bowl Ads Analysis | 偷笑惹的祸

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Tableau

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This report will analyze 249 ad data from Super Bowl ads aired between 2000 and 2020 using the data visualization tool Tableau, in order to delve into the commercial value of Super Bowl ads.

Introduction

The purpose of this report is to summarize the project's objectives, methods, experimental results, discussion, future work, and conclusions.

The dataset includes defining characteristics of Super Bowl ads from these 10 popular brands, two of which are Toyota and Bud Light. There are 249 different Super Bowl commercials with 25 different variables, different types of variables included in superbowl. Variables like views, number of likes, and number of dislikes are numeric values based off of YouTube statistics. Variables like brand, title, and description act as identifiers for individual ads. There were also 7 different ad categories, and a corresponding variable saying whether the ad does or does not belong to the category.

After cleaning and processing the data to ensure accuracy and consistency, we created various visualization charts using Tableau, including bar charts, line charts, heatmaps, and scatter plots, to show the trends and comparisons of various metrics, such as cost, duration, TV ratings, YouTube click-through rate, and likeability, for each brand's advertisement. Firstly, we cleaned and processed the data to ensure its accuracy and consistency. We used multiple charts to visualize the relationships between advertising cost, duration, television ratings, YouTube click-through rates, and likes.

Project Objective

Analyze the commercial value of Super Bowl ads and explore the relationship between ad effectiveness and advertising costs.

With the development of big data and machine learning technology, more and more companies are applying them to advertising analysis to understand the impact of advertising on consumers and the market performance of enterprises. As an important event in the advertising industry, Super Bowl ads have attracted much attention. Therefore, the aim of this project is to analyze Super Bowl ad data to explore the relationship between ad effectiveness and advertising costs, and to identify which ads have the best performance.

Related Analysis: By collecting and analyzing advertising data, we will identify the ads with the best performance and the impact of advertising costs on ad effectiveness.

Research: We will also explore the performance of ads in different years and industries, as well as the audience's response and popularity to different types of ads.

Recommendations: We will provide recommendations to businesses on how to develop effective advertising strategies and improve ad effectiveness.

Methodolgy

This study used Tableau for visual analysis, through data cleaning and processing and various types of visualization charts, to better display the changes and relationships of various brand advertising indicators, providing important reference and guidance for the analysis and research of the advertising market.

Collected advertising data aired during the Super Bowl from 2000 to 2020.

Cleaned and processed the data to ensure accuracy and consistency.

Used Tableau for data analysis and created various visualization charts, including bar charts, line charts, heatmaps, and scatterplots, to show the trend and comparison of cost, duration, TV ratings, YouTube click-through rates, and likeability scores of advertisements from different brands.

Analyzed and discussed the results to explore the relationship between advertising effectiveness and cost, as well as the response and popularity of different types of advertisements among viewers.

Provided recommendations to businesses on how to formulate effective advertising strategies and improve advertising effectiveness based on the analysis results and discussions.

Summarized the analysis results and recommendations and drew conclusions.

The initial plan was to create visualizations around the relationship between advertising types (i.e., the features of the ads) and the number of views. However, after data transformation and cleaning, there were issues with over-plotting in the visualizations, and no clear relationship emerged. Outliers also caused confusion in the visualizations (e.g., the 2012 Doritos ad had over six times more views than any other ad). To address these issues, the focus was shifted to looking at advertising from the perspective of the brand, and it was decided to quantify the usage of ads by frequency (i.e., the number of ads) rather than the number of views. To achieve the latter, a new variable, per_1000_likes, was added to the data frame, which represents the ratio of views to likes.

To show the relationship between brands and advertising types, the heatmap displays both the brand and advertising type. These subdivisions show how the ten brands in the dataset utilize each category. To address questions related to the performance of advertising types from 2000 to 2020, the frequency of advertising types over time was examined, and this relationship was displayed in the form of a line graph with the year on the X-axis and the indicator such as frequency, engagement rate, or cost on the Y-axis.

A trend analysis was conducted on advertising costs, advertising duration, television ratings, and YouTube click-through rates, and it was found that television ratings have been declining in recent years.

Analysis with a bar chart:This chart shows the advertising expenditure of different brands over the past 20 years. The x-axis represents the brands and the y-axis represents the advertising costs. The bars of different colors represent the advertising costs for different years. It can be observed that some brands have maintained a relatively high level of advertising expenditure over the 20-year period, while other brands have experienced significant fluctuations.

Analysis with a heatmap:This chart shows a heatmap based on the brands and years, where the colors change from green to red to represent the increasing advertising costs over time.

Analysis of the three categories of drink, automobile, and others:This analysis looks at the number of advertisements that appeared for each of these categories between 2000 and 2020.

Experimental Results

Through visual analysis, we found that there is a positive correlation between advertising costs and television ratings, but it is not always the case that the more expensive the ad, the higher the ratings. There is no clear correlation between ad length and TV ratings. There is a clear positive correlation between YouTube click-through rate and like rate. Additionally, we found that CocaCola, Pepsi, and Budweiser are the brands with the highest advertising costs and best ratings.

The analysis results show that Super Bowl ads play an important role in marketing and brand building. Through the analysis of 249 ads, we can discover the following points:

Firstly, In 21 years, Budweiser's TV Views and YouTube Views of advertising videos are the highest, and Budweiser has the largest number of advertising videos.

Secondly, 30s & 60s ads have high engagement rate. 30s ads cost around $2.95M to $5.6M while 60s cost around $5.9M to $11.2M. 30s ads is more cost efficient.

Thirdly, The ads have gotten longer and the brands increase their budget on Super Bowl YouTube ads.

Due to the viewership and influence of the superbowl game, advertisers tend to be willing to pay a premium for ad spots. but are the high costs justified by equally higher roi?

In this analysis, we will look at super bowl commercials' trendsover the years, and try to answer the question - what should the advertisers do to get the most out of their spend.

There is no clear trend in views through out the years. The vast majority of views come from TV (as opposed to YT).

Views per dollar on the x-axis measures the number of views per dollar spent (used to measure the roi). Size of the dot stands for the cost (spend) of a certain ad. ideally, advertisers want the ad to be away from the y-axis (high roi) and small (low cost). it seems that over time, brands are spending more but getting less in return (a good example is nfl - some of the largest dots but also closest to the y axis).

Dots sized by views per dollar (spent)

Views do not seem to be heavily associated with different elements in an ad. However some elements are clearly associated with less than ideal roi (views per dollar spent) - patriotic and celebrity. On the contrary, ads that are funny and shows product quickly seem to be associated with higher roi.

Advertisers should keep the ad placement cost below 15M (meaning shorter ads), avoid elements like patriotism, celebs and make their ads funny, sexy while showing the products early on. with no significant increase in viewership over the past 20 years, advertisers are still spending more cash than ever - what's the catch?

One possible reason i could think of is that with the rise of social media platforms over the past years, advertisers are not only getting views from the actual game being televised, but also publicity from possibly trending on social media. our data does not capture the entirety of the return.

Discussion

The analysis results show that Super Bowl ads play an important role in marketing and brand building.

There is a certain positive correlation between advertising costs and TV ratings, but it is not necessarily the case that more expensive ads will always get higher ratings. Through the display of scatter plots and heat maps, we can see that some relatively inexpensive ads can also achieve high TV ratings, while some high-cost ads may not perform as well.

There is no clear correlation between advertising duration and TV ratings. Through the display of scatter plots and line graphs, we found that even with shorter advertising duration, higher TV ratings can still be achieved. Therefore, we can conclude that advertising duration is not a decisive factor in affecting TV ratings.

There is a clear positive correlation between YouTube click-through rates and like rates. Through the display of scatter plots and line graphs, we can see that there is a certain correlation between the click-through and like rates of ads on the internet platform and their TV ratings, and this correlation is more significant. Therefore, by increasing the exposure and user feedback of ads on the internet platform, it can help to improve the overall effectiveness of advertising.

The performance of ads on the internet platform is different from that on TV. By comparing the click-through and like rates of ads on the internet platform, we can see that some ads have high TV ratings but their performance on the internet platform is not as good. Conversely, some ads may not have high TV ratings, but they are popular among users on the internet platform. Therefore, in formulating advertising strategies, it is necessary to develop different strategies for different platforms and target audiences in order to improve the overall effectiveness of advertising.

Future Works

Although this study has provided many insights into the main factors of Super Bowl advertising, there are still limitations and areas for improvement.

Although this study has provided many insights into the main factors of Super Bowl advertising, there are still limitations and areas for improvement. Therefore, the following are some future work that could be done:

Firstly, the data source of this study is limited, including only 249 ads from 10 brands. Thus, expanding the data scope to more brands and ads could provide a broader perspective and more in-depth analysis.

Secondly, this study only analyzed five main factors, including cost, ad length, TV viewership rating, YouTube click-through rate, and like rate. However, there are other factors that could affect ad performance, such as ad content, audience demographics, and ad timing. Therefore, future research could explore more influencing factors.

Moreover, this study only used Tableau for data analysis and visualization. Future research could use other tools or methods for data analysis, such as Python or R language. This could provide more analysis tools and methods to better uncover the value of the data.

Lastly, this study only analyzed Super Bowl advertising data from the past 20 years. Future research could extend the time range and analyze more Super Bowl ads to better understand the trends and changes in the advertising industry.

Conclusion

In this project, we analyzed the Super Bowl advertisement data from 2000 to 2020 to explore the relationships between brand companies, costs, advertisement duration, TV viewership rates, YouTube click-through rates, and like rates. To analyze the data, we used Tableau software to visualize the data, which effectively presented the data analysis results.