Abstract
- A variety of help. A variety of AI assistants and co-pilots are available to help marketers explore marketing analytics.
- Integration of science. Advanced marketing analytics also relies on statistical modeling of data, so many tools are designed for data science.
- Seamless interaction. AI provides a communication layer so analysts can explore data without being locked into syntax.
With the rise of AI, marketers are faced with a plethora of AI marketing tools to choose from, especially when it comes to data analysis.
AI has introduced a new user interface that employs an inference engine that connects data reporting and structure. This revised workflow speeds up analysts' tasks and reveals important metrics to decision makers who rely on regular analysis.
However, marketers still face a fragmented martech market where the capabilities of AI marketing tools appear to overlap. AI and analytics are being built into many solutions, and new iterations are arriving at bullet train speed.
So how can marketers wisely choose the right solution to up their analytics game?
Considering one of the following 10 solutions is a great starting point. Each offers AI-based enhancements for data exploration and can enhance the skills marketing teams already have. The team must compare and choose the solution that best iterates his AI skills and analytical capabilities within the data stack. Let’s take a look at some of the most beneficial AI marketing tools.
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10 Useful AI Marketing Tools
1. Metabase
Metabase is a self-service cloud analytics solution that provides users with quick setup of dashboards for over 20 different database sources. This includes a visual query builder and segmentation settings to ensure users see visualizations that are most relevant to their work.
Metabase focuses on SQL in the cloud, and its AI assistance is provided by a Chrome plugin called Avanty. Avanty enhances metabase analysis with features such as AI-generated editing for SQL queries, AI-generated explanations for complex SQL queries, and auto-generated titles for new metabase charts. As a result, SQL queries are automatically debugged and errors are reduced.
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2. Row
A newcomer to the AI standalone tools marketplace is Rows.
There are organizational benefits to displaying tables and visualizations together.
Another benefit is that you can add and remove information without using scripts, add-ons, or code. You can also import data from familiar sources like Facebook ads, ultimately reducing the time you spend exploring and analyzing your data.
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3. Trifactor Wrangler
Alternatives to Excel have been around for a long time, from ZoomSheet for Zoom customers to the powerful Google Sheets. One of his lesser-known spreadsheet competitors that exemplifies where all Excel clones are heading is Trifacta Wrangler. Trifacta may not be as famous as Microsoft, but its acquisition of Alteryx is. Trifacta was merged with Alteryx and the acquisition was completed in 2022.
Spreadsheet solution Wrangler provides an AI assistant to clean and prepare data for analysis. The assistant can also provide suggestions for individual elements in a dataset, making it ideal for exploratory data analysis when there is uncertainty about missing values.
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4. Power BI
Power BI is very popular among data analysts. It's easy to use even for non-technical users, provides great scalability and performance, and integrates well with other Microsoft products.
AI in Power BI is embodied by Azure Cognitive Services, a suite of services that allows users to apply a variety of pre-trained machine learning models to gain insights and enhance data preparation.
A unique feature is the decomposition tree. This is a visualization that helps users drill down to the next dimension of their data based on specific criteria, allowing them to better understand relationships.
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5.Tableau
Like Power BI, Tableau is popular among data analysts. Not to be outdone in the AI space, Tableau introduced Tableau AI, a suite of features designed to make data analysis accessible to a broader audience.
Built on the foundation of Salesforce's Einstein Trust Layer, Tableau AI leverages generative AI to automatically derive insights from your data. Identify trends, anomalies, and correlations and display them in an easy-to-understand format. This allows users to discover valuable information without being data experts.
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6. Google Big Query
BigQuery was originally designed as a data warehouse with advanced business intelligence capabilities built in. However, its purpose is evolving.
BigQuery is a good choice because its architecture is well-suited to bring together the best aspects of two layers of AI: a storage layer that ingests, stores, and optimizes data, and a compute layer that provides analytics. is. This is not an assistant or co-pilot, but there are various AI integrations in place for all the contingencies an analyst using an assistant or co-pilot might face. Typically, users run their SQL queries through BigQuery, so AI-level features can power workflows where SQL is central to data exploration.
One of BigQuery's AI capabilities is BigQuery ML. It allows users to create and run machine learning (ML) models. It also provides access to large-scale language models (LLMs) and cloud AI APIs for performing artificial intelligence (AI) tasks such as text generation and machine translation.
Another AI integration is SQL Query Preview with Gemini, which lets you perform tasks like sentiment analysis, text summarization, and entity extraction directly on your data. Gemini can also explain SQL queries to explore data series. Additionally, Gemini provides several SQL and Python features, such as query generation and completion.
Users must request access to the query preview feature. This can be done through Gemini in the BigQuery preview form.
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7. Google Looker Studio
Looker Studio has come a long way since its origins. It will continue to function as a cloud-based data visualization solution, but its transformation extends beyond its 2022 rebrand from Google Data Portal. Google has subtly integrated his Looker into the workflow of other Google tools that are enabled with AI capabilities, with the aim of helping marketers explore data-related visualizations.
For example, there is a Sensitive Data Protection feature that can analyze structured data stored in BigQuery tables and calculate risk analysis for sensitive data that includes properties that may destabilize the model. Metrics from this analysis can be visualized using Looker Studio.
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8.ChatGPT Plus and ADA
We discussed the ChatGPT ADA extension in a previous post. This extension enables exploratory data analysis without requiring extensive knowledge of programming syntax. It works with the paid version of ChatGPT, so it works on a subscription-based model.
However, the versatility of prompts can reduce the workflow pain of data reconciliation. As mentioned in a previous post, it's easier to highlight corrections using simple prompt descriptions than by editing the syntax.
Users must remember that it is still essential to become familiar with the programming language and test the AI output to ensure that a workable solution is produced.
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9.RStudio
RStudio is clearly a data science tool, designed for analysts performing advanced data modeling such as correlation with regression, cluster analysis, and integration with machine learning frameworks such as TensorFlow, Torch, and Caret. .
However, RStudio integrates several data visualization libraries to make data model creation more user-friendly and shareable with non-technical experts who need to review the statistical structure of the data they model. Did.
RStudio serves as the ideal gateway for marketers to experience GitHub Copilot, the AI assistant co-developed by GitHub and OpenAI. Copilot evaluates your code in RStudio and suggests matching syntax. This speeds project completion and minimizes code errors. Although aimed at developers, it also helps marketers develop data models that go beyond the capabilities of Excel. R has libraries for importing data from various sources and writing SQL queries and Python functions. Marketers can use these tools to complete their documentation with data models and visualizations.
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10. Knife
Another data science tool is KNIME. KNIME, based in Zurich, Switzerland, is an open source analytics vendor with a platform that provides data science capabilities similar to RStudio. However, KNIME Analytics, a close relative of RapidMiner, allows users to use a visual interface to build data pipelines for data exploration, manipulation, and machine learning.
The latest version of KNIME Analytics, platform 5.2, was released in December. This update includes new generative AI features, including improved responses from vendor chatbots.
While the most welcomed feature among analysts is the simplified UI design for easier navigation, the main AI feature to look out for is K-AI, the KNIME AI assistant. This assistant helps you build automated workflows for data analysis tasks using natural language prompts.
Users can use K-AI to build complex analyzes fully automatically or collaboratively. The benefit for analysts is that they can improve their skills quickly and minimize stuck moments.
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AI Marketing Tools: Summary
Marketers will find many more AI marketing tools than these. But all of these options should help your team get a better start on data management with AI, regardless of how your data is structured.
