As marketers, we thrive on storytelling. Our brands convey their essence, values, and the emotions they aim to evoke in consumers. But what about our data? Imagine if our data could narrate its own story. What messages does it convey? What actions does it suggest to us, the interpreters of this data?
With the advent of artificial intelligence, marketers can now create data stories in seconds, enabling them to derive insights and take action much more swiftly.
Not enough hours in a day
Marketing and business teams have increasingly become information processing organizations. With the explosion of data from e-commerce, social media, and automated data collection systems, we now have more data available than we have hours in a day to analyze it all. This has resulted in an increase in reporting but less time available for analysis, interpretation, and policy formation. In an era where generative A.I. can draft legal documents for attorneys, write code for programmers, and craft marketing copy for advertisers, it is no surprise that it can finally transform raw data into compelling narratives for marketers and analysts.
All LLMs (OpenAI, Google, Meta, Anthropic) make mistakes with basic calculations.
From Numbers to Narratives:
Large Language Models (LLMs) are largely responsible for the previously mentioned productivity improvements. However, when it comes to numbers, language models show their limitations. They handle numbers as words (actually as parts of words, called “tokens”) and this can lead to uncertain answers if not bland errors. Even simple additions that any calculator can perform, confuse a language model. This is why we at MetaMarketing created a methodology that strictly separates calculations from text generation. We developed the “Generative Data-to-Insights” method, patent pending, that uses language models for what they are best at – generating text – while keeping calculations and algorithms well outside of the LLM.
Mass Calculations and Data Algorithms
Consider a typical dataset that marketers work with, such as sales data or customer demographics. Even if it is cleaned and structured, various calculations need to be performed, such as growth between periods, differences between brands, and ratios. Automating these calculations saves analysts time and reduces the probability of errors. Today’s computing power allows for near-instant, “mass calculations”, a term we use to refer to running "every" possible calculation on every datapoint. Mass calculations go much further than what human analysts would tend to do and can disclose unexpected insights.
The next step in the analysis process is to determine which data tell a compelling story. We recommend using automated algorithms that mimic the operations of an expert analyst. Do the calculations show significant changes in the data? Are certain values outside usual boundaries? Are moving averages uncovering new trends? Algorithms can determine which calculations are important and which ones are not. Whether generic or custom-made, data algorithms enhance the productivity of analysts.
The combination of mass-calculation, data story algorithms and LLMs increases efficiency by drastically reducing the time required for data analysis. It enables marketers to generate more insights, faster and with greater precision. It ensures consistent quality and reduces the risk of human error by standardizing the analytical process. Additionally, these technologies uncover trends and patterns that might be overlooked by human analysts, providing deeper and more actionable insights while making advanced statistical tools accessible to all marketers, regardless of their technical expertise.
Conclusion
Mass-calculation and data story algorithms represent the future of marketing analysis. Large Language Models can work well with numbers provided no calculations need to be performed. By combining calculations, algorithms and LLMs in one platform, marketers can not only improve efficiency and accuracy but also gain a competitive edge through deeper, more actionable insights. As adoption of artificial intelligence continues to grow, a large part of data analysis and insights generation will become automated and marketers can focus on creativity and strategy formulation.
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