YouTube Ads Creative Analysis – Google for Developers
Extract statistically significant features from the ML model and interpret their effect on VTR. For example, “there is an xx% observed uplift in VTR when there is a logo in the opening shot.”
Feature Engineering
Data Extraction
Consider 2 different YouTube Video Ads for a web browser, each highlighting a different product feature. Ad A has text that says “Built In Virus Protection”, while Ad B has text that says “Automatic Password Saving”.
The raw text can be extracted from each video ad and allow for the creation of tabular datasets, such as the below. For brevity and simplicity, the example carried forward will deal with text features only and forgo the timestamp dimension.
Ad
Detected Raw Text
Ad A
Built In Virus Protection
Ad B
Automatic Password Saving
Preprocessing
After extracting the raw components in each ad, preprocessing may need to be applied, such as removing case sensitivity and punctuation.
Ad
Detected Raw Text
Processed Text
Ad A
Built In Virus Protection
built in virus protection
Ad B
Automatic Password Saving
automatic password saving
Manual Feature Engineering
Consider a scenario where the goal is to answer the business question, “does having a textual reference to a product feature affect VTR?”
This feature could be built manually by exploring all the text in all the videos in the sample and creating a list of tokens or phrases that indicate a textual reference to a product feature. However, this approach can be time consuming and limits scaling.
Pseudo code for manual feature engineering
AI Based Feature Engineering
Instead of manual feature engineering as described above, the text detected in each video ad creative can be passed to an LLM along with a prompt that performs the feature engineering automatically.
For example, if the goal is to explore the value of highlighting a product feature in a video ad, ask an LLM if the text “‘built in virus protection’ is a feature callout”, followed by asking the LLM if the text “‘automatic password saving’ is a feature callout”.
The answers can be extracted and transformed to a 0 or 1, to later be passed to a machine learning model.
Ad
Raw Text
Processed Text
Has Textual Reference to Feature
Ad A
Built In Virus Protection
built in virus protection
Yes
Ad B
Automatic Password Saving
automatic password saving
Yes
Modeling
Training Data
The result of the feature engineering step is a dataframe with columns that align to the initial business questions, which can be joined to a dataframe that has the VTR for each video ad in the sample.
Ad
Has Textual Reference to Feature
VTR*
Ad A
Yes
10%
Ad B
Yes
50%
*Values are random and not to be interpreted in any way.
Modeling is done using fixed effects, bootstrapping and ElasticNet. More information can be found here in the post Introducing Discovery Ad Performance Analysis, written by Manisha Arora and Nithya Mahadevan.
Interpretation
The model output can be used to extract significant features, coefficient values, and standard deviation.
Coefficient Value (+/- X%)
Represents the absolute percentage uplift in VTR. Positive value indicates positive impact on VTR and a negative value indicates a negative impact on VTR.
Significant Value (True/False)
Represents whether the feature has a statistically significant impact on VTR.
Feature
Coefficient*
Standard Deviation*
Significant?*
Has Textual Reference to Feature
0.0222
0.000033
True
*Values are random and not to be interpreted in any way.
In the above hypothetical example, the feature “Has Feature Callout” has a statistically significant, positive impact of VTR. This can be interpreted as “there is an observed 2.22% absolute uplift in VTR when an ad has a textual reference to a product feature.”
Challenges
Challenges of the above approach are:
- Interactions among the individual features input into the model are not considered. For example, if “has logo” and “has logo in the lower left” are individual features in the model, their interaction will not be assessed. However, a third feature can be engineered combining the above as “has large logo + has logo in the lower left”.
- Inferences are based on historical data and not necessarily representative of future ad creative performance. There is no guarantee that insights will improve VTR.
- Dimensionality can be a concern as given the number of components in a video ad.
Activation Strategies
Ads Creative Studio
Ads Creative Studio is an effective tool for businesses to create multiple versions of a video by quickly combining text, images, video clips or audio. Use this tool to create new videos quickly by adding/removing features in accordance with model output.
Sample video creation features in Ads creative studio
Video Experiments
Design a new creative, varying a component based on the insights from the analysis, and run an AB test. For example, change the size of the logo and set up an experiment using Video Experiments.
Summary
Identifying which components of a YouTube Ad affect VTR is difficult, due to the number of components contained in the ad, but there is an incentive for advertisers to optimize their creatives to improve VTR. Google Cloud technologies, GenAI models and ML can be used to answer creative centric business questions in a scalable and actionable way. The resulting insights can be used to optimize YouTube ads and achieve business outcomes.
Acknowledgements
We would like to thank our collaborators at Google, specifically Luyang Yu, Vijai Kasthuri Rangan, Ahmad Emad, Chuyi Wang, Kun Chang, Mike Anderson, Yan Sun, Nithya Mahadevan, Tommy Mulc, David Letts, Tony Coconate, Akash Roy Choudhury, Alex Pronin, Toby Yang, Felix Abreu and Anthony Lui.
