Interpretable Deep Learning for Forecasting Online Advertising Costs: Insights from the Competitive Bidding Landscape

Abstract: As advertisers increasingly shift their budgets toward digital advertising, accurately forecasting advertising costs becomes essential for optimizing marketing campaign returns. This paper presents a comprehensive study that employs various time-series forecasting methods to predict daily average CPC in the online advertising market. We evaluate the performance of statistical models, machine learning techniques, and deep learning approaches, including the Temporal Fusion Transformer (TFT). Our findings reveal that incorporating multivariate models, enriched with covariates derived from competitors' CPC patterns through time-series clustering, significantly improves forecasting accuracy. We interpret the results by analyzing feature importance and temporal attention, demonstrating how the models leverage both the advertiser's data and insights from the competitive landscape. Additionally, our method proves robust during major market shifts, such as the COVID-19 pandemic, consistently outperforming models that rely solely on individual advertisers' data. This study introduces a scalable technique for selecting relevant covariates from a broad pool of advertisers, offering more accurate long-term forecasts and strategic insights into budget allocation and competitive dynamics in digital advertising.

• The paper’s main contribution is demonstrating that incorporating competitor-derived covariates significantly improves forecasting accuracy for cost-per-click in digital advertising.
• Using advanced time-series models such as ARIMA, Prophet, and LSTM, the study employs clustering techniques to extract competitive insights and distinguish market trends.
• Feature importance analysis reveals that competitor data enhances model performance, providing actionable insights for budget optimization in advertising campaigns.

Interpretable Deep Learning for Forecasting Online Advertising Costs

Introduction

The advertising ecosystem has witnessed a significant transition to digital platforms, particularly in the post-pandemic environment. This shift underlines the importance of accurately forecasting advertising costs to optimize marketing expenditures and improve campaign returns. The paper "Interpretable Deep Learning for Forecasting Online Advertising Costs: Insights from the Competitive Bidding Landscape" focuses on the challenge of predicting average cost-per-click (CPC) in the digital advertising domain. This research leverages time-series forecasting methods, enriched by competitor data, to enhance prediction accuracy.

Data and Methodology

The study employs a comprehensive data set sourced from the online advertising market. It explores the utility of incorporating external covariates, specifically competitors' advertising strategies, in predictive models. The methodological framework integrates time-series clustering techniques to identify patterns within these covariates. By clustering similar time-series data, the study aims to unravel competitive insights that can be incorporated into forecasting models.

The research deploys various advanced time-series forecasting models, including autoregressive integrated moving average (ARIMA), Prophet, and long short-term memory (LSTM) networks. Central to the methodology is the analysis of feature importance, which evaluates the contribution of different input variables, including competitors' budget plans, to the forecasting accuracy.

Results

The results section highlights the efficacy of integrating competitor-derived covariates into forecasting models. Quantitative analysis reveals a marked improvement in prediction accuracy when these covariates are included. The models demonstrate a superior ability to forecast CPC trends, thus potentially enabling advertisers to fine-tune their budget allocation strategies. Feature importance analysis indicates that competitor data significantly affect model performance, underscoring the value of competitive intelligence in digital advertising.

Policy Experiments and Discussions

Further policy experiments underscore the practical implications of the research findings. By simulating various budget allocation scenarios, the study explores how advertisers can strategically respond to competitors' moves. The experiments validate that incorporating competitor data not only refines forecasting precision but also provides actionable insights that can drive strategic decision-making.

The discussion section elaborates on the broader implications of these findings. The integration of competitor strategies into forecasting models presents a strategic advantage in closely contested digital advertising markets. The research advances interpretability in deep learning models by clearly elucidating the impact of diverse factors on forecasting outcomes.

Conclusion

The paper contributes to the field of advertising cost forecasting by demonstrating the value of incorporating competitive intelligence into predictive models. The results emphasize the potential for enhanced decision-making processes through improved accuracy in cost forecasts. Future research directions may explore expanding the scope of covariates or integrating real-time data streams to further refine model performance and applicability. These advancements hold significant promise for optimizing advertising budgets in a digitally-driven marketplace.