AI’s Three Primary Use Cases for CMO’s and Marketers- 1 of 3
AI isn't just for big companies with deep pockets and massive marketing budgets. AI is a tremendous force multiplier that can help small- and medium-sized companies punch above their weight and successfully compete, and win, against larger enterprises.
Over the next two weeks, I'll be posting a series of updates designed to be a quick primer for CMOs and business leaders who are new to AI-augmented marketing. Let's start right now, with a high-level introduction to the three most important use cases for AI in marketing:
1. Content creation: AI can help marketers and content creators produce better content faster than ever before, and the large language models (LLMs) behind the technology are maturing rapidly. Check out my previous post about the evolution of content creation here to learn why humans + AI deliver better outcomes.
2. Adavanced analytics: Data is the foundation of a modern marketing function, yet depending on which study you review, organizations are only analyzing anywhere from 1% to 30% of the data they generate. The reason for this is humans interacting with the nonstop proliferation of digital platforms and devices to create more and new types of unstructured data. AI excels at analyzing large amounts of this unstructured data (clickstream, written text, photos, videos, audio, IoT) that prior systems and tools simply could not handle. However, even leading AI organizations need time to catch up and identify the best use cases for their limited data science resources — they keep climbing up the mountain of data, but every morning they wake up and the mountain peak is higher than the day before. The next blog will go into more details about marketers using AI to achieve three primary analytic goals: understanding consumer behavior, predicting human behavior, and influencing human behavior.
3. Marketing spend optimization: The concept for spend optimization is the same as it's always been: CMOs and business leaders have a finite amount of funding and it's their job to determine where to place their bets to achieve optimal ROI and sustainable growth. The two most important changes in recent years are (1) the rapid expansion in the number of channels and media to reach consumers, and (2) the ability of AI to perform detailed analyses on exponential amounts of data generated by consumers throughout the customer journey. ID Graphs help connect all the dots for a consumer’s journey, but it’s just a baseline requirement to conduct the analyses. The journeys can be so convoluted that even the most advanced organizations are challenged to accurately attribute the right level of return for each dollar spent. In short, the new complexity competes with our new abilities to measure so much more than before. It brings us full circle to John Wanamaker’s quote over a century ago, “Half the money I spend on advertising is wasted; the trouble is I don't know which half.”
Check back next Wednesday for a deep dive into advanced analytics, and how it can be a game-changer for companies looking to stretch their marketing budgets. As always, please feel free to share your thoughts to the group or message me directly.