Feature
The Gist
- Diverse assistance. A range of AI assistants and copilots are available to help marketers explore marketing analytics.
- Science integration. Many tools are designed for data science since advanced marketing analytics also relies on statistical modeling of data.
- Seamless interaction. AI provides a communication layer so that analysts can explore data without being bogged down by syntax.
Editor's note: This piece was updated with new information and data May 23, 2025. It was originally published April 12, 2024.
With the rise of AI, marketers are facing a plethora of choices for AI tools. The most intriguing comes with AI combined with data analysis.
AI has introduced a new user interface that applies a reasoning engine that connects data reports and data structures. The result is a revised workflow that accelerates certain tasks for analysts and reveals indicators to decision-makers who regularly rely on the analysis.
But marketers are facing budget limits just as the martech marketplace has become fragmented. This means marketers need to carefully consider how analytics tools fit within their workflow and communicate the discovered insights.
This post will look at the latest tools aiming to bring marketers’ workflow with data analysis closer to a dream come true.
Table of Contents
- What Marketing Analytics Brings to the Marketer’s Workflow
- Top 10 AI Marketing Analytics Tools for 2025
- Metabase
- Rows
- Mode
- Power BI
- Tableau
- Google Big Query
- Google Looker Studio
- ChatGPT Plus With ADA
- Positron
- KNIME
- AI Marketing Tools: Choosing Tools That Deliver Real Value and Efficiency With AI
What Marketing Analytics Brings to the Marketer’s Workflow
Most marketing managers and analysts recognize what analytics is: a far cry from my early days working with clients on Google Analytics back in 2009. Marketers face a ton of data that represents information on how well their brand is serving its customers. That information must be analyzed so that marketers can gauge the customer experience. Analytics tools can provide that analysis.
Today, AI has unleashed new ways to ingest data, streamlining the workflow a marketer must manage to analyze and derive insights. That streamlined workflow is crucial for marketers managing within constrained budget limits. Gartner released findings from its 2025 CMO Spend Survey of 402 CMOs, which revealed marketing budgets have flatlined at 7.7% of overall company revenue.
And, according to the CMSWire State of the CMO Report for 2025, the most common roadblock for marketers is the challenge of measuring ROI, reflecting the growing importance of analytics for both the success and reputation of marketing teams. When asked about leadership’s expectations regarding the outcomes of their projects, significantly more marketing leaders report that leadership now expects quantifiable, measurable results for everything their department does, 69%, up from 59% two years ago.
Furthermore, the proportion of budgets allocated for martech tools like analytics has been falling year by year. The trend means marketers should be keenly aware of the differences each analytic tool brings so that teams can accurately access.
Related Article: The Predictive Analytics Models Marketing Leaders Should Know
Top 10 AI Marketing Analytics Tools for 2025
So, how do marketers make a wise choice for the right solutions that will elevate their analysis game?
Examining one of the following 10 solutions is a great starting point. Each offers AI-based enhancements that iterate data exploration. These can enhance skills that marketing teams are already employing. Marketing teams should compare and select the solution that leads to the best iteration of AI skills and analytic capability on their data stack.
Let’s take a look at some of the most beneficial AI marketing tools.
Metabase
AI-Powered SQL Debugging and Visualization Tools
Metabase is a self-service cloud analytics solution that offers users a quick dashboard setup against over 20 different database sources. It includes a visual query builder and segmentation settings so that the right user views the visualizations most relevant to their work.
Metabase is focused on SQL in the cloud, so its AI assistance comes from a Chrome plugin called Avanty. Avanty extends analysis work in Metabase with features such as AI-generated edits on SQL queries, AI-generated step-by-step explanations of complex SQL queries, and auto-generated titles for new Metabase charts. The result is an automatically debugged SQL queries that result in errors.
Who would find this useful?
Analytics teams that are constantly working with SQL when crafting a data-based analysis. SQL queries are meant to combine data from various tables, so organizations with data in various tables accessed by different teams will benefit.
Rows
Spreadsheet Interface With Built-In AI for Analysis and Visualization
A newcomer to the AI stand-alone tool marketplace is Rows. Rows is an AI twist on the familiar worksheet premise. It introduces AI assistance from ChatGPT within a tabular interface that displays visualizations alongside the given data. This provides an organizational benefit for users to view tables and visualizations together.
Another benefit is being able to add and delete information without scripts, add-ons, or code. Even data from familiar sources can be imported, such as Facebook Ad data, which ultimately shortens data exploration and analysis time.
Who would find this useful?
Analytics teams that use spreadsheets and datatables to appeal to SQL-fearing managers, but still require advanced analysis that AI can manage.
Mode
Multilanguage BI Tool for Advanced, Interactive Dashboards
Traditional BI tools with AI assistance are emerging on the market almost magically. One that brings its own magic to the business intelligence space is Mode.
Mode is a data platform interface that lets users conduct a number of analysis and dashboarding tasks using their data sources. Users can create interactive dashboards, build internal tools, or conduct ad-hoc data visualizations for advanced analysis.
Because Mode is accessed as an analysis hub, users have opportunities to curate data for specific analysis. Mode features querying using SQL, Python, and R – a convenient blend of the most popular programming options for exploring data.
There are three plans available for different scaling and team needs.
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Who would find this useful?
Marketing teams that need a BI tool alternative with the flexibility to access a variety of programming languages will consider this tool for advanced analysis.
Related Article: AI Marketing Tools 2024: When Hype Meets Reality
Power BI
Microsoft-Integrated Analytics With Pretrained AI Models
Power BI has become wildly popular among data analysts. It is user-friendly for non-technical users, offers good scalability and performance, and integrates well with other Microsoft products.
The AI in Power BI is embodied by Azure Cognitive Services, a suite of services that allows users to apply different pre-trained machine learning models to enhance data preparation with insights.
A unique feature is the decomposition tree, a visualization that helps users drill down to the next dimension of the data based on specific criteria, allowing for a better understanding of the relationships.
Who would find this useful?
Marketers who desire a Microsoft product for advanced analysis are familiar with PowerBI. The AI-based features are well integrated into the reporting interfaces, so analysts can upskill their data modeling capabilities without an extensive learn curve outside of their Power BI familiarity.
Tableau
Generative AI Surface Insights Through Familiar Interfaces
Like Power BI, Tableau has become a darling among the data analyst community. Tableau could not be left behind in the AI table stakes. So it introduced Tableau AI, a feature suite designed to make data analysis more accessible to a wider range of users.
Built on the foundation of Salesforce's Einstein Trust Layer, Tableau AI utilizes generative AI to automatically surface insights from the data. It can identify trends, anomalies, and correlations, presenting them in an easy-to-understand format. This helps users discover valuable information without having to be the ultimate data expert.
Who would find this useful?
Marketing teams well accustomed to this platform, yet seeking to implement AI features, will consider Tableau useful. The AI usage is well integrated into Tableau’s interfaces, so making the leap to integrating AI is not an intimidating step for analysts.
Related Article: AI in Marketing: Balancing Creativity and Algorithms for Marketers
Google Big Query
Cloud-Scale Data Warehouse With Built-In ML and Gemini AI
BigQuery was originally designed as a data warehouse with built-in advanced business intelligence features. But that purpose is evolving.
BigQuery is a good choice because its architecture is well-suited for AI to integrate the best aspects of its two layers: a storage layer that ingests, stores, and optimizes data, and a compute layer that provides analytics capabilities. While it is not an assistant or copilot, there are various AI integrations for every contingency that analysts using an assistant or copilot might face. Users typically run SQL queries through BigQuery, so the AI-level features can enhance workflows where SQL is central to data exploration.
One AI feature in BigQuery is BigQuery ML, which allows users to create and run machine learning (ML) models. It also provides access to large language models (LLMs) and Cloud AI APIs for performing artificial intelligence (AI) tasks such as text generation or machine translation.
Another AI integration is a SQL query preview with Gemini, which can perform tasks like sentiment analysis, text summarization, and entity extraction directly on your data. Gemini can also explain a SQL query for exploring a data series. Additionally, Gemini provides some SQL and Python functionalities, such as generating and completing queries.
Users must request access for the query preview feature, which they can do through the Gemini in BigQuery Preview Form.
Who would find this useful?
Marketing teams who need a seamless connection between Google’s suite of cloud services and the standard data languages often used with Big Query. Those who are already using Gemini extensively will benefit.
Google Looker Studio
Visualization Platform With Sensitive Data and AI Risk Tools
Looker Studio has evolved significantly from its origins. While it continues to serve as a cloud-based data visualization solution, its transformation extends beyond its 2022 rebranding from Google Data Studio. Google has subtly integrated Looker into the workflows of other Google tools where AI features are activated, aiming to assist marketers in exploring data-related visualizations.
For example, there is a Sensitive Data Protection feature that can analyze structured data stored in BigQuery tables and compute risk analysis for sensitive data containing properties that could destabilize a model. The metrics from this analysis can be visualized using Looker Studio.
Who would find this useful?
Like BigQuery, marketing teams who are acquainted with Google’s cloud services and Gemini, yet primarily produce data visualization for various sources. Also like BigQuery users, those who are already using Gemini extensively will benefit.
ChatGPT Plus With ADA
Prompt-Based Exploratory Data Analysis Without Heavy Coding
I covered this ChatGPT extension in an earlier post. This extension enables exploratory data analysis without requiring extensive knowledge of programming syntax. It works with the paid version of ChatGPT, so it operates on a subscription-based model.
However, the versatility of prompting can alleviate workflow headaches in adjusting data. As I noted in my earlier post, it's easier to highlight corrections through simple prompt descriptions rather than editing syntax.
Users should remember that it is still essential to be familiar with programming languages and test the AI output to ensure that a viable solution can be produced.
Who would find this useful?
Marketer teams already following genAI will find this option suitable for crafting quick analysis. Teams using proprietary data may prefer to use a locally hosted genAI solution through a desktop service like LM Studio.
Positron
Data Science IDE With AI Plugins for Advanced Modeling
RStudio has more history as a data science tool, meant for analysts conducting advanced data models for forecasting. It is a legend among data scientists.
But RStudio has been around for years, with excellent refinements for exploring and visualizing data. Despite this, the need to build an IDE to take advantage of plugins became clear. So Posit, the creator of the data IDE RStudio, launched a new IDE called Positron.
Positron is a fork of Visual Studio Code, Microsoft’s popular IDE. This means the features in VSC appear in Positron along with key features that made RStudio a popular choice among data analysts. Some RStudio features were even improved for the Positron release. For example, data explorer displays the data available as a tibble, a type of data table designed with printing and subsetting features for better management of large datasets.
This makes the adoption of Positron easy for developer teams already familiar with VSC while making the data models more sharable to nontechnical professionals who need to review the statistical structure of data to be modeled.
The one downside is that GitHub Copilot, an AI assistant jointly developed by GitHub and OpenAI, is not available. Copilot evaluates the code of a program and then creates a matching syntax suggestion. An intergration exist for RStudio.
Despite the unavailability, other AI Assistants such as Amazon Q is available. Positron can serve marketers who want to develop a data model that they can not do within Excel. R has libraries to import data from varying sources as well as being able to craft SQL queries or Python functions. Marketers can use those, and then finalize a document with the data model and visualization.
Who would find this useful?
Positron is a new IDE, but its Visual Studio Code-based user interface is already familiar to developers. Thus, marketing teams who are acquainted with crafting data models and visualizations for advanced marketing data analysis.
KNIME
Visual Data Pipelines and AI Workflow Automation With K-AI
Another data science tool is KNIME. Based in Zurich, Switzerland, KNIME is an open-source analytics vendor whose platform, like RStudio, provides data science capabilities. However, more akin to RapidMiner, KNIME Analytics allows users to build data pipelines for data exploration, manipulation, and machine learning using a visual interface.
The latest version of KNIME Analytics, Platform 5.2, was launched in December. The update features new generative AI capabilities, including improved responses from the vendor's chatbot.
Although its most welcome feature among analysts is a simplified UI design for easier navigation, the main AI feature to look out for is the KNIME AI assistant, K-AI. This assistant helps build an automated workflow for data analytics tasks using natural language prompts. Users can build complex analyses either fully automatically with K-AI or collaboratively using it.
Who would find this useful?
KNIME is very popular with analysts who rely on open source solutions to craft analyses and visualizations. Like Tableau, analysts benefit from a familiar environment that lets them quickly upskill and minimize workflow complexity.
AI Marketing Tools Comparison
This table summarizes the core features, AI capabilities and ideal use cases of top AI marketing analytics tools for 2025.
| Tool | Core Functionality | AI Features | Ideal Use Case |
|---|---|---|---|
| Metabase | Cloud-based SQL analytics | SQL query debugging, visual builder | SQL-heavy analytics teams needing fast setup |
| Rows | AI-enhanced spreadsheet | Data visualization, ChatGPT assistance | Teams favoring spreadsheet UI without coding |
| Mode | Multilanguage BI platform | SQL, Python, R support; interactive dashboards | Teams requiring programming language flexibility |
| Power BI | Microsoft-integrated BI tool | Azure ML models, decomposition tree | Microsoft-centric teams wanting scalable insights |
| Tableau | Salesforce-integrated analytics | Tableau AI, auto-insights, trend detection | Existing Tableau users upgrading to AI analysis |
| Google BigQuery | Cloud data warehouse with AI | BigQuery ML, Gemini preview features | Teams using Google Cloud and advanced ML |
| Looker Studio | Google data viz platform | Sensitive data protection, Gemini integration | Google Cloud users focused on data viz |
| ChatGPT Plus + ADA | Prompt-based EDA assistant | EDA via prompts, no coding | Teams exploring quick analysis without syntax |
| Positron | RStudio-based data science IDE | Tibble viewer, R/Python/SQL support | Developer teams needing advanced modeling |
| KNIME | Open-source data pipeline platform | K-AI assistant, visual workflows | Open-source focused analysts needing automation |
AI Marketing Tools: Choosing Tools That Deliver Real Value and Efficiency With AI
Marketers will certainly find more AI marketing tools than these. The martech world has seen a flood of solutions launched over the years. But as marketers face more constraints on their budgets, solutions must demonstrate how their integrations save marketing teams time and money when developing advanced data models that solve customer experience challenges.
If your team is facing any of these choices, you are in good hands. Any of these analytic solutions will help your team gain a terrific first start with modeling marketing data with AI.
