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In the age of AI, there are three key ground rules for marketing analytics.

In the era of AI, marketing analytics is experiencing a significant transformation, with Machine Learning (ML) and Artificial Intelligence being increasingly utilized to enhance customer experiences. Kevin Lyons, the general manager of analytics services at Acxiom, outlines three crucial guidelines that brands should consider to ensure the success of their marketing analytics projects.

At present, there is a justified enthusiasm surrounding generative AI. Recent experiences have shown that AI, like ChatGPT, can arrive at conclusions that previously required weeks of research and testing by human teams, which is both thrilling and intimidating. However, it’s important to note that the AI’s solutions need validation, as not all use cases are straightforward to verify. There remains a distinction between AI solutions that learn from input and generate better outcomes and those that prioritize data security but have limited access to data. Avoiding bad data is crucial for a positive AI revolution.

While generative AI has its challenges, the application of ML and AI for analytics and business decision-making is rapidly progressing. These technologies, once used for tactical purposes, are now gaining prominence in strategic decision-making.

The idea of machines making all business decisions based on perfect and trustworthy data is theoretically plausible. In such a scenario, AI could optimize an entire business toward a specific outcome, such as achieving a corporate revenue target. However, in reality, this level of AI-driven decision-making is not yet attainable. It requires further work on technical, business, and ethical fronts. Ethical considerations, in particular, are essential, as the power of AI systems needs to be harnessed responsibly for the greater good.

When it comes to marketing decisions, the primary focus is on delivering the best possible customer experience for each individual. AI plays a critical role in making countless small, interconnected decisions at a speed and scale beyond human capability. It determines factors like the ideal offer, the appropriate channel, the optimal engagement time, and the most effective creative, all contributing to an overarching solution that delivers the desired business outcome.

To ensure success in machine-driven marketing analytics projects, three ground rules are essential:

  1. Insights must lead to action. Merely generating insights is not sufficient; they should be used to inform decision-making and drive action. The term “decisioning” encompasses this combination of decision-making and taking action, which is often overlooked by analytics teams.
  2. Models must be operationalizable. It is essential to consider practical implementation when developing AI solutions. Overly ambitious projects might prove unfeasible due to lacking capabilities, infrastructure, or resources. It is better to start with realistic solutions that can be implemented immediately and scale up when necessary.
  3. Implement guardrails. AI can provide optimal decisions based on specific outcomes, but it might not always align with the organization’s long-term vision. Setting strict parameters ensures decisions and actions stay within defined boundaries, including legal, ethical, and social considerations. Human oversight is crucial to avoid morally questionable recommendations.

While AI’s role in marketing analytics continues to evolve rapidly, these ground rules remain critical. Regardless of the mathematical sophistication, ensuring that analytics drive action, solutions are feasible to implement, and there are clear ethical guidelines in place maximizes the potential for successful projects, delivering exceptional customer experiences.

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