Editorial
The Gist
- AI in marketing analytics is moving beyond hype. Marketers are identifying practical applications of AI, especially in data analysis.
- BYO-AI streamlines workflows. Custom AI assistants help marketing teams work with familiar tools, improving efficiency.
- RAG is reshaping analytics. Retrieval-augmented generation enhances AI's ability to provide context-rich, industry-specific insights.
The AI developments that dominated 2024's tech landscape are evolving into practical business tools this year. Marketers are discovering where AI serves them best, particularly in analytics. Marketing analytics professionals are increasing their understanding of AI use case takeaways, and determining how AI solutions can best support their work. The support can be influential to objective key results (OKRs) — metrics that influence significant business objectives.
Let’s look at some of the major AI trends impacting strategies that emerge from marketing analytics.
Table of Contents
- The Rise of BYO-AI Sets Up Faster Ways to Visualize and Analyze Data
- AI Has Made Customer Data Easier to Explore and Clean
- The Rising Importance of RAG For Specialized AI Assistance
- What Changes Will Come Next for Analytics?
- Core Questions to Ask About AI-Based Marketing Analytics
The Rise of BYO-AI Sets Up Faster Ways to Visualize and Analyze Data
Remember the Bring Your Own Device (BYOD) trend when smartphones and tablets first gained popularity? Professionals experiment with different devices that suit their needs.
A similar emerging tech trend among professionals is occurring — bring your own artificial intelligence (BYO-AI). This trend is a behavior in which people learn how to integrate their personal AI assistants, be it a self-crafted tool or a purchased service, into their workflow. The benefit for marketing teams is that they have AI interfaces that feel familiar to them, allowing them to focus on the results of an analysis.
A number of marketplace introductions will accelerate the spread of these agents. The introductions depend on what applications are being used. One example is the AI assistants introduced for Independent Developer Environments (IDE). IDEs are used for making applications and software development, with some increasingly being used for data modeling, an advanced analytics topic used for market demand forecasts and other predictive analysis in business.
AI Assistants in IDEs Are Streamlining Marketing Analysis
The AI assistants in IDEs consolidate information and make recommendations; in this instance, for syntax, there are many agents, such as Github Copilot and Amazon Q. The end result is easier workflow, particularly for marketing analysis that is iterative.
The ultimate value BYO-AI brings to analytics is that analysts can leverage the devices and software they are already familiar with. This allows them to conduct their analysis faster. It also allows them to adapt cutting-edge new features and augmentative apps more easily and reduce training hiccups that can raise overall technology costs.
Mini GPTs Are Enhancing Data Analysis Efficiency
One clear influence from BYO-AI is the mini GPTs I explained in a separate post — ChatGPT’s custom GPTs and the Gemini Gems. MiniGPTs are agents that users can create around specific documents or media for private or public use. Each miniGPT has a builder that allows users to create the agent documents and instructions that serve as a set of instructions and assumptions.
These mini GPTs benefit data analysts by simplifying the review of large documents and datasets. Imagine white papers and statistical documents that can be used for creating forecasts on a given data set. Also, imagine guidelines for report documents already set within these assistants. The result is AI-influenced agents that help analysts spend less time creating exploratory data analysis and crafting reports in a desired corporate framework, extending the analysis use cases where AI can simplify tasks.
Related Article: Top 10 AI Marketing Analytics Tools
AI Has Made Customer Data Easier to Explore and Clean
As AI agents become more integrated into analytic-related workflows, analysts are discovering how to use assistants to identify statistical patterns in data much more quickly.
Large language models like Claude, ChatGPT, Gemini and Propensity are transforming data preparation from a technical challenge into an intuitive dialogue. For example, marketing teams can now create custom GPTs specifically trained on their data cleaning protocols and business rules, ensuring consistency across all analysts while maintaining company-specific requirements.
RAG Is Elevating AI-Powered Data Standardization
These AI tools are powerful when combined with retrieval-augmented generation (RAG) capabilities. By connecting to a company's historical data cleaning decisions and domain-specific rules, the AI can provide context-aware suggestions for data standardization. An analyst might ask ChatGPT to clean a new dataset while maintaining consistency with how similar data was handled in previous campaigns, or use Claude to identify patterns that deviate from established customer behavior benchmarks.
The impact extends beyond just cleaning data. These tools are becoming collaborative partners in data exploration, suggesting unexpected correlations and helping translate complex patterns into actionable insights. For instance, a custom-trained agent might automatically flag seasonal patterns in customer behavior that differ from historical trends, or identify emerging customer segments that traditional analysis might miss.
This evolution in data preparation and exploration capabilities bridges directly into how teams are visualizing and analyzing their data, making the entire analytics workflow more efficient and insightful.
The Rising Importance of RAG For Specialized AI Assistance
Retrieval-augmented generation is emerging as a game-changer for marketing analytics in 2025. This technology allows AI models to combine their general knowledge with specific, up-to-date company data, creating more accurate and contextually relevant insights. Marketing teams are finding RAG particularly valuable for several key applications.
For example, when analyzing customer feedback, RAG-enabled AI can pull from both historical customer interaction data and current market trends to provide deeper insights. This means marketing analysts can quickly identify emerging patterns in customer behavior while maintaining the context of their brand's unique market position and history.
RAG Is Breaking Down Data Silos for Smarter Insights
The technology is also transforming how marketing teams handle their vast data repositories. Rather than struggling with disconnected data silos, RAG allows teams to create AI assistants that can access and analyze data across multiple sources, from social media metrics to sales figures to campaign performance data, while maintaining accuracy and relevance to their specific industry context.
Cultural Intelligence in AI: How RAG Enhances Market Relevance
An illustrative example of RAG’s value is in the AI model Latimer, a large language model (LLM) designed to address cultural bias in AI models. By using RAG, Latimer can incorporate real-time cultural insights and demographic data alongside its base training, helping marketing teams create more culturally nuanced and relevant campaigns. This application demonstrates how RAG is not just about improving technical accuracy, but also about enhancing the cultural intelligence of marketing analytics.
The adoption of RAG in marketing analytics platforms is also streamlining the reporting process. Analysts can now generate insights that are both data-driven and narrative-rich, combining historical performance metrics with current market dynamics to create more comprehensive and actionable reports.
Related Article: The Changing Landscape of Customer Feedback in the AI Era
What Changes Will Come Next for Analytics?
The introduction of AI models dominated business and tech news for 2024. This year, we will see how AI-based solution features bring many of these analytic trends together. Marketers should look for automated insights that surface changes in key metrics, custom report generation that seamlessly conducts reporting tasks, and accurate detection of unusual patterns and anomalies.
These features will reveal practical use cases, particularly valuable ones when nuanced customer experience instances occur. For example, marketers will evaluate how well solutions like Google Looker leverage Al agents to align with their workflow.
If marketers are implementing AI-enhanced analytics, they should pay attention to how objective key results (OKR) are improved. Business performance often boils down to monitoring activity that influences OKRs, so AI-analytics for such monitoring should be a priority.
Agentic AI Will Reshape Marketing Analytics Strategies
The buzz on AI-influenced analytics will also evolve alongside Agentic AI, the coordination of AI assistants serving in an environment, be it on processes that serve customers or for software development by a team. Measuring agentic performance will soon be essential for achieving operational goals. Because Agentic AI is still brand new, marketers have time to understand the analytic tactics and identify best practices.
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The past two years have revealed an abundance of AI tools, overwhelming many professionals still determining which AI tools are worthwhile. Data analysts and marketing managers will need to find the best solution choices, ones that complete analytical tasks that are aligned with customer data strategy.
Core Questions to Ask About AI-Based Marketing Analytics
Editor's note: Here's a summary of two core questions around the topic of how AI has changed analytics:
What OKRs can be present within an analytics solution?
Identifying what OKRs should appear in analytics dashboards answers this question. Monitoring business activities means knowing what metrics need to be monitored. The management of those activities only happens when the associated metrics are visualized in a dashboard or with a data product.
What actions can AI impact on data?
AI can be applied in a lot of use cases. To understand the business value of an AI instance with analytics, marketers should take inventory of what AI agents can do with the data being analyzed.
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