The potential of large language models (LLMs) has sparked excitement across industries. These models are prefect for language-related tasks like content generation and general Q&A. However, LLMs struggle to handle numbers and generate reliable insights from them.
This problem is compounded by the fact that LLMs are trained on general information, making them too broad to offer actionable recommendations for a specific company. Deriving data-driven strategies that align with a business’s unique goals requires a much more tailored approach. In this post, we explore why general models fall short when it comes to data analysis and insights discovery, and how MetaMarketing has developed a solution to bridge this gap.
At Amazon some products were reportedly over- or underpriced because of mistakes made by LLMs
The Gap in General AI: Why Data-Driven, Industry-Specific Insights Matter
LLMs are trained on vast amounts of general information, mostly from public sources. While this broad knowledge base works for common questions, it lacks the precision necessary for interpreting industry- or company-specific data. Marketing, for example, requires not only an understanding of general market trends but also an ability to analyze company performance data and draw insights that are actionable, effective and adhere to a company’s guidelines. General LLMs are simply not equipped to do this level of data interpretation, nor are they designed to identify specific opportunities or challenges within a unique business context.
There are plenty of examples of this.
Amazon, the e-commerce giant, used LLMs to optimize pricing algorithms. In some cases, the model’s recommendations led to pricing errors, causing either significant overpricing or underpricing of products.
Airbnb used LLMs to predict property demand and set dynamic pricing. The model generated incorrect demand forecasts, resulting in suboptimal pricing strategies .
The music streaming service Spotify used LLMs to analyze user data and recommend songs. There were instances where the LLM misinterpreted numerical data on listening habits, leading to irrelevant song recommendations5.
With MetaMarketing, we address these limitations directly. Our technology uses industry-specific algorithms to identify which trends matter, interpret a company’s strengths and weaknesses, and provide targeted recommendations grounded in industry insights and company best practices. All of this we then send to an LLM to write sentences.
For example, when MetaMarketing generates a Sales Performance Report — an analysis of sales data to explain shifts in performance and uncover drivers behind these trends — the insights are far from generic. MetaMarketing’s algorithms dig into the numbers to prioritize the most relevant trends, highlight key strengths and weaknesses, and align recommendations with both the client’s established best practices and the latest industry standards. When broader industry context is beneficial, MetaMarketing layers in external benchmarks, ensuring that every recommendation is competitive and aligned with current best practices.
Harnessing Best Practices and Industry Knowledge
MetaMarketing’s approach goes beyond analyzing numbers; it also incorporates insights from trusted industry sources like MarketingWeek, Progressive Grocer, and Libre Service Actualités. (a subscription by our client remains required). These paywalled publications offer valuable, specialized information, which we use to contextualize recommendations. By integrating these insights, MetaMarketing ensures that our clients receive actionable advice that reflects industry standards and trends.
Moreover, many companies operate with internal “golden rules” or guidelines that set the boundaries for what tactics can and cannot be used. MetaMarketing embeds them into its analysis to provide recommendations that not only aim for improved performance but also align with a company’s specific operational guidelines and goals. This way, every recommendation MetaMarketing offers is both relevant and practically feasible.
The Power of Algorithms: Building a RAG-Ready Database for Marketing Insights
To further increase the relevance and precision of our insights, MetaMarketing uses a structured, RAG-ready database of industry knowledge. This approach leverages retrieval augmented generation (RAG) to tap into a curated collection of reputable, subscription-based sources, allowing MetaMarketing to identify essential trends, benchmark performance, and provide suggestions with context-specific backing.
Each insight generated by MetaMarketing links directly to its source material, providing transparency and enabling clients to understand the underlying data that informs our recommendations. With hyperlinks to best practices, case studies, and relevant guidelines embedded in each report, clients can quickly access the “why” behind every recommendation, adding an additional layer of clarity to strategic decision-making.
MetaMarketing’s goal is to go beyond general AI responses by delivering insights that are actionable and directly relevant to business growth. Our approach empowers companies by aggregating industry and company-specific knowledge and strategically interpreting it to align with the unique needs of marketing professionals. Through targeted algorithms, best practice integration, and data-driven insights, we help companies transform raw data into actionable strategies that drive meaningful improvements.
Conclusion
The real power of AI is in its capacity to deliver actionable, company-specific recommendations that drive performance improvement. LLMs are not specific enough for this and they cannot handle numbers reliably. At MetaMarketing, we have developed specialized algorithms to analyze data, extract insights, and provide tailored recommendations that reflect each company’s unique best practices and guidelines. By embedding benchmark data, integrating industry standards, and linking directly to source material, we equip businesses with the specific knowledge they need to perform their jobs better.
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