Optimizing ad spend with AI-powered marketing mix modeling

Artificial intelligence (AI) is rapidly changing the world, and marketing is one of the areas most affected. While generative AI (GenAI) has been getting a lot of attention, machine learning (ML) and predictive analytics are also having a major impact.

One area where AI is making a big difference is in marketing mix modeling (MMM). MMM is a technique for measuring the effectiveness of different marketing activities. AI-powered MMMs can generate results faster and at more granular levels than traditional MMMs. This allows marketers to optimize ad spending in real-time.

AI-powered MMMs are still in their early stages of development, but they have the potential to revolutionize how brands optimize ad spending.

Here are some of the key takeaways from the article:

• AI is rapidly changing the world, and marketing is one of the areas most affected.

• AI-powered MMMs can generate results faster and at more granular levels than traditional MMMs.

• AI-powered MMMs are still in their early stages of development, but they have the potential to revolutionize how brands optimize ad spending.

Read More: Fast Company

Top Ways That Generative AI ChatGPT Helps You Identify And Fulfill Your New Year’s Resolutions

Generative AI and large language models can help people identify and fulfill New Year's resolutions, and these actions have mental health benefits. Statistics show that while many people make resolutions, few succeed in keeping them. AI can assist by analyzing preferences, providing emotional support, and suggesting realistic goals. However, there are potential downsides to consider, such as privacy concerns and biases. Despite these challenges, incorporating AI into resolution-making strategies can be beneficial.

Read More: Forbes

AI does not mean the robots are coming

Humanoid robots like Pepper have not lived up to the hype, despite significant investment. The recent advancements in AI have reignited enthusiasm for robotics, but the challenges in creating economically viable robots are primarily related to hardware, not software. AI doesn't solve the physical limitations of robots, such as the bulk and cost of motors, the difficulty in replicating human senses, and power source constraints.

Instead of focusing on entirely new machines, AI is more likely to enhance existing ones. Self-driving cars are a prime example, as AI can tackle the visual sensing required for autonomous driving. AI can also improve industrial robots and lightweight devices like robot vacuum cleaners, gradually increasing their capabilities and usefulness.

While AI will slowly contribute to the development of more advanced robots, a robot capable of performing complex tasks like cleaning a toilet remains a distant prospect.

Read More: Financial Times