Neural networks applications in valuation of banner ad creative efficiency

Table of Contents
Abstract 3
1. Introduction 5
2. Literature review 10
3. Theoretical framework 19
3.1 Neural networks 19
3.2 Data augmentation 26
3.3 Visualizing convolutional neural networks 40
4. The application of neural networks in creative advertising strategies 52
5. Results of computational experiments 57
6. Conclusion 69
Bibliography 72
Additional materials 76

1. Introduction

Just a few years ago Artificial Intelligence (AI) was mostly a topic discussed by academics in computer science departments, futurologists, or just science fiction fans, and it seemed that we are still decades from actually interacting with it. Today this could not be any further from the truth, as we might not even notice it, but we interact a lot with machine-made decisions. Take for instance the recommendation systems, telling us what is frequently bought together with other products we have been browsing, chatbots, which help us with frequently asked questions, or even those masks people are using for their Instagram posts - all of these utilize AI techniques. While it is highly unlikely you will find yourself as the main character of the movie “Her” (https://www.imdb.com/title/tt1798709/) by tomorrow, we live in a time when developments in AI progress exponentially fast.
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2. Literature review

Neural networks as an instrument became very popular as soon as computing power, data, and even programming packages for the purpose became available. And quite fast, neural networks started showing great results in applications to finance, medicine, security, automation, and so on. In this work, an application to the advertisement banners is explored and therefore quite a lot of literature was explored on the topic. However, before diving deep into the researches on convolutional neural networks for the advertising industry, it was also worthwhile to take a step back and take a look at techniques, which are used to improve the output results of the neural networks for image classification, as well as try to understand how does a network classify the images. And thus the literature on convolutional neural networks, techniques on improving their results, explaining these results, and then their application in the industry will be explored.
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3. Theoretical framework

In this section, we will briefly discuss the technical aspects of building a solution to our problem of predicting advertisement banner efficiency. Unfortunately, building machine learning models can be quite a hassle due to many reasons which will also be discussed below. We will also explore methods which will help us battle these problems.

3.1 Neural networks

The model of choice for this work was a neural network, as was mentioned in the title. The choice was not arbitrary - currently, convolutional neural networks are a go-to model, when it comes to image processing. Therefore, as a part of the theory behind this work, neural networks are going to be discussed, so as to fully understand the model used.
Artificial neural networks, or simply neural networks, are models being used for machine learning problems, such as one discussed in this work - advertisement banner classification.
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3.2 Data augmentation

The upside of using convolutional neural networks is that they are very good at computer vision tasks. But this comes at a cost of heavy reliance on large amounts of data. And not only do we need more data than usual, but we are also highly likely to encounter a problem of overfitting on our data. Overfitting is a phenomenon, in which a neural network does not generalize well, because it has learned to perfectly model the training data. This becomes an additional problem in fields with not a lot of data - not only do we lack the data, but we are also overfitting on it. And one such area, as it happens to be, is advertising banners. Advertising agencies would love to have a lot of banners for testing, but their variety is usually quite limited. This is usually due to brand safety reasons, rather than time and money constraints.
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3.3 Visualizing convolutional neural networks

In this section of the work explanations of deep learning models will be discussed, as well as a particular method called LIME. LIME stands for Local Interpretable Model-agnostic Explanations. In this work model predictions will be attempted to be explained using LIME through visualization. Deep learning model explanations is a very important topic, which is unfortunately usually overlooked and it will be discussed why it should not be this way.
Deep convolutional neural networks have shown excellent performance in classifying image data. The first tasks to prove this were the famous hand-written digits classification problem, hence the classical image classification problem - the MNIST dataset, and facial recognition problems. Of course, that did not stop just there, as convolutional neural networks have shown outstanding results in many other image classification tasks. For one, take the CIFAR-10 dataset as an early example.
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4. The application of neural networks in creative advertising strategies

The goal of this work was to provide value for communication agencies in their pursuit to deliver value to the client as efficiently as they can. Today every business industry strives to utilize technological advances. One which was most discussed in the section devoted to technical aspects of this work was, in fact, medicine because it demonstrates many pitfalls in using convolutional neural networks in image analysis. However, it does not mean that convolutional neural networks for image classification are not successfully applied in medicine. In fact, they are used more frequently as time progresses and new advancements take place. Convolutional neural networks are successfully used in many industries, such as banking, agriculture, robotics, and so on. In fact, there are projects that are already utilizing artificial intelligence for the benefit of the advertising industry.
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5. Results of computational experiments

The objective of this work was to train a model that would help assess the effectiveness of advertising banners. In this part, the data, the assumptions, the process of creating a model will be discussed as well as intermediate results of the training.
For the data, we had a little over four thousand advertisement creatives for many different companies and their performance metrics. These banners creatives did not include textual advertisements with them, so the model training will be done without accounting for the offer in the text accompanying the image. Also, unfortunately, the initial purpose of the advertisement campaign is not available and thus creatives can not be grouped together in categories. These categories would potentially comprise the marketing funnel blocks, like awareness, consideration, and purchase, for example. With that said, the limitations of this work have been acknowledged and outlined above.
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6. Conclusion

The technological advances we have an opportunity to observe every day are astounding. In today’s world, companies undergo digital transformations in departments they never thought to be digitalized at all. As was discussed in the beginning, some things we perceive as common today, probably even ten years ago was considered a fiction. And in this work, we explored one particular process, which could be improved by machine learning.
The topic of this work was inspired by the advances in machine learning, but was motivated by a particular need of the advertising agencies. This work is written during the global COVID-19 pandemic, which has brought crisis to many industries. Unfortunately, advertising came to be one of them with clients cutting their marketing budgets and turning down many long-term projects. With that many business operations have been disrupted simply due to the fact that no one was prepared for such catastrophe.
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Bibliography

1. Liu, X., Wang, X., & Matwin, S. (2018). Interpretable Deep Convolutional Neural Networks via Meta-learning. International Joint Conference on Neural Networks (IJCNN), https://arxiv.org/pdf/1802.00560.pdf
2. Zeiler, M.D., and Fergus R. (2014). Visualizing and Understanding Convolutional Networks. European Conference on Computer Vision. https://arxiv.org/abs/1311.2901
3. Lundberg, S., & Lee, Su-In. (2017). A Unified Approach to Interpreting Model Predictions. Computing Research Repository, abs/1705.07874. https://arxiv.org/abs/1705.07874
4. Gosiewska, A., and Biecek., P. ( 2019). iBreakDown: Uncertainty of Model Explanations for Non-additive Predictive Models. arXiv preprint arXiv:1903.114. https://arxiv.org/abs/1903.11420
5. Zintgraf, L. M., Cohen, T. S., and Welling, M.(2016). A new method to visualize deep neural networks. CoRR, abs/1603.02518. http://arxiv.org/ abs/1603.02518
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