New data shows AI bots pushing deeper into the web, prompting publishers to roll out more aggressive defenses. The viral virtual assistant OpenClaw--formerly known as Moltbot, and before that Clawdbot--is a symbol of a broader revolution underway that could fundamentally alter how the internet functions. Instead of a place primarily inhabited by humans, the web may very soon be dominated by autonomous AI bots. A new report measuring bot activity on the web, as well as related data shared with WIRED by the internet infrastructure company Akamai, shows that AI bots already account for a meaningful share of web traffic. The findings also shed light on an increasingly sophisticated arms race unfolding as bots deploy clever tactics to bypass website defenses meant to keep them out.

Attribution modelling lies at the heart of marketing effectiveness, yet most existing approaches depend on user-level path data, which are increasingly inaccessible due to privacy regulations and platform restrictions. This paper introduces a Causal-Driven Attribution (CDA) framework that infers channel influence using only aggregated impression-level data, avoiding any reliance on user identifiers or click-path tracking. CDA integrates temporal causal discovery (using PCMCI) with causal effect estimation via a Structural Causal Model to recover directional channel relationships and quantify their contributions to conversions. Using large-scale synthetic data designed to replicate real marketing dynamics, we show that CDA achieves an average relative RMSE of 9.50% when given the true causal graph, and 24.23% when using the predicted graph, demonstrating strong accuracy under correct structure and meaningful signal recovery even under structural uncertainty. CDA captures cross-channel interdependencies while providing interpretable, privacy-preserving attribution insights, offering a scalable and future-proof alternative to traditional path-based models.

Marketing Mix Modeling (MMM) is a statistical technique used to estimate the impact of marketing activities on business outcomes such as sales, revenue, or customer visits. Traditional MMM approaches often rely on linear regression or Bayesian hierarchical models that assume independence between marketing channels and struggle to capture complex temporal dynamics and non-linear saturation effects [@Chan2017; @Hanssens2005; @Ng2021Bayesian]. **DeepCausalMMM** is a Python package that addresses these limitations by combining deep learning, causal inference, and advanced marketing science. The package uses Gated Recurrent Units (GRUs) to automatically learn temporal patterns such as adstock (carryover effects) and lag, while simultaneously learning statistical dependencies and potential causal structures between marketing channels through Directed Acyclic Graph (DAG) learning [@Zheng2018NOTEARS; @Gong2024CausalMMM]. Additionally, it implements Hill equation-based saturation curves to model diminishing returns and optimize budget allocation. Key features include: (1) a data-driven design where hyperparameters and transformations (e.g., adstock decay, saturation curves) are learned or estimated from data with sensible defaults, rather than requiring fixed heuristics or manual specification, (2) multi-region modeling with both shared and region-specific parameters, (3) robust statistical methods including Huber loss and advanced regularization, (4) comprehensive response curve analysis for understanding channel saturation.

Marketing mix modeling (MMM) is a widely used method to assess the effectiveness of marketing campaigns and optimize marketing strategies. Bayesian MMM is an advanced approach that allows for the incorporation of prior information, uncertainty quantification, and probabilistic predictions (1). In this paper, we describe the process of building a Bayesian MMM model for the online insurance company Lemonade. We first collected data on Lemonade's marketing activities, such as online advertising, social media, and brand marketing, as well as performance data. We then used a Bayesian framework to estimate the contribution of each marketing channel on total performance, while accounting for various factors such as seasonality, market trends, and macroeconomic indicators. To validate the model, we compared its predictions with the actual performance data from A/B-testing and sliding window holdout data (2). The results showed that the predicted contribution of each marketing channel is aligned with A/B test performance and is actionable. Furthermore, we conducted several scenario analyses using convex optimization to test the sensitivity of the model to different assumptions and to evaluate the impact of changes in the marketing mix on sales. The insights gained from the model allowed Lemonade to adjust their marketing strategy and allocate their budget more effectively. Our case study demonstrates the benefits of using Bayesian MMM for marketing attribution and optimization in a data-driven company like Lemonade. The approach is flexible, interpretable, and can provide valuable insights for decision-making.

This study introduces an innovative Gaussian Process (GP) model utilizing an ensemble kernel that integrates Radial Basis Function (RBF), Rational Quadratic, and Mat\'ern kernels for product sales forecasting. By applying Bayesian optimization, we efficiently find the optimal weights for each kernel, enhancing the model's ability to handle complex sales data patterns. Our approach significantly outperforms traditional GP models, achieving a notable 98\% accuracy and superior performance across key metrics including Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Coefficient of Determination ($R^2$). This advancement underscores the effectiveness of ensemble kernels and Bayesian optimization in improving predictive accuracy, offering profound implications for machine learning applications in sales forecasting.

In this era of big data, AI tools should be every marketer's co-pilot. When you consider complex consumer habits, dwindling attention spans and ever-changing algorithms, you'll agree that you need AI to win. This means that there is no better time than now for you to stack up on intelligent tools that save you hours in manual labor and help you build highly-efficient, scalable processes. In this article, we'll go in-depth on the best AI tools for marketers based on your selected marketing channel and specific goals. Artificial Intelligence (AI) tools are software or digital tools built with machine learning algorithms to automate costly time-consuming tasks.

With the introduction of Artificial Intelligence (AI) in recent years, business owners may now be wondering how to best use it. AI is already making an impact on organizations through customer service, advertising, and marketing channels. As technology continues to advance at an exponential rate, AI will become even more integral to the success of businesses. Artificial intelligence is being used for more than just tech. With the rapid growth of AI, there are new ways for marketers to use it to grow their business.

According to a survey conducted by PwC, 72% of business leaders use AI for their business advantage. The Digital marketing world has been restructured immensely since the emergence of AI. It helps companies develop powerful digital strategies, optimizes campaigns, and improves return on investment. Teleflora, a floral company in the US, used AI marketing to build new customers' profiles and improve customer loyalty. Using these historical data, Teleflora used AI marketing to predict the future customer behavior of different audience segments.

According to a survey conducted by PwC, 72% of business leaders use AI for their business advantage. The Digital marketing world has been restructured immensely since the emergence of AI. It helps companies develop powerful digital strategies, optimizes campaigns, and improves return on investment. Teleflora, a floral company in the US, used AI marketing to build new customers' profiles and improve customer loyalty. Using these historical data, Teleflora used AI marketing to predict the future customer behavior of different audience segments.

According to a survey conducted by PwC, 72% of business leaders use AI for their business advantage. The Digital marketing world has been restructured immensely since the emergence of AI. It helps companies develop powerful digital strategies, optimizes campaigns, and improves return on investment. Teleflora, a floral company in the US, used AI marketing to build new customers' profiles and improve customer loyalty. Using these historical data, Teleflora used AI marketing to predict the future customer behavior of different audience segments.