AI data analysis can provide detailed and calculated answers to users' data-related questions. Unlike regular models like ChatGPT, an AI analytics tool takes a mathematical approach. Its Python-based reasoning engine helps it analyse and process complex data and calculations. This helps business owners draw meaningful insights and suggest actionable steps.
One of the tools to analyse data is SYMSON’s AutoAnalyst. It combines the power of LLM with a Python-based reasoning engine to answer users' complex questions. This machine learning data analytics tool has many applications across business industries. You not only save time but get the most accurate answers and recommendations. This way, you can improve and move ahead in your business using data-driven decisions.
6 Practical Applications of AI Data Analysis
Using AI for data analysis helps businesses analyse store performance, and customer demographics, compare performance with other branches and find strategic recommendations. Users can simply ask a question and share a document containing metrics and allow the data analysis AI tool to break it down into simple insights. It also makes strategic recommendations based on the data processed. This way, businesses can always make data-driven decisions and minimize losses or decisions based on gut feeling.
Also Read: How LLM-powered Tools Help you Optimise Business Processes
1. Store Performance Analysis
Consider a store manager of a supermarket chain. Using Auto Analyst, the manager can upload performance data and ask various questions to understand and improve operations. For example, by asking, “What are my best-selling products?” the manager receives detailed insights, such as food and beverages being the top-selling category with sales amounting to $56,000.
2. Comparative Branch Performance
Auto Analyst also facilitates comparative analysis between different branches. By uploading data from multiple branches, the manager can see total sales, gross income, and average ratings for each branch. This helps identify areas for improvement and successful strategies from top-performing branches.
Read More: How to Use SYMSON’s AI Pricing Expert?
3. Customer Demographics
Understanding customer demographics is crucial for targeted marketing. Auto Analyst allows the manager to delve deeper into data, such as splitting branch performance by customer gender. This detailed analysis aids in creating targeted marketing strategies and improving customer engagement.
4. Advanced Analytics
Auto Analyst’s flexibility extends to more complex calculations, such as price elasticity. For instance, by asking for the price elasticity of the best-selling product, the model not only identifies the product but also calculates the elasticity, providing insights into how price changes might affect sales.
5. Strategic Recommendations
Beyond analysis, Auto Analyst can suggest strategies to enhance performance. For instance, it might recommend:
- Enhancing product selection in high-demand categories.
- Implementing targeted marketing campaigns.
- Improving customer experience and engagement.
These recommendations are based on the specific data of the user’s branch, ensuring they are relevant and actionable.
Read more: Optimising marketing Budget using AI-based Marketing Roas
6. Versatility Across Industries
While the example focuses on a supermarket chain, Auto Analyst’s capabilities are applicable across various industries, including finance, sales, marketing, operations, and supply chain management. Its ability to handle different data types and answer a wide range of analytical questions makes it a versatile tool for any organization.
How AI Data Analysis Works
Under the hood, LLMs generate Python code, which the reasoning engine executes. This process involves a seamless connection with underlying data, and results are fed back to the LLM to generate user-specific answers. This structure minimizes common issues like hallucinations and abuse, ensuring reliable and accurate outputs.
Implementation Process
Auto Analyst implementation typically follows a three-phased approach:
- Proof of Concept: Focuses on a specific dataset to demonstrate technical feasibility.
- Production: Integrates the proof of concept with desired dashboarding tools and ensures data integration with data lakes, warehouses, or other external tools.
- Maintenance and Continuous Integration: Ensures ongoing support and updates once the system is in production.
Our chatGPT technology in pricing is a powerful tool that democratizes access to advanced data analytics, enabling employees at all levels to make informed decisions. It reduces the need for costly dashboarding tools and enhances data security, offering open-source models for on-premise use if privacy is a concern. By preventing hallucinations and minimizing misuse, Auto Analyst ensures reliable and insightful data analysis.
Do you want a free demo to try how SYMSON can help your business with margin improvement or pricing management? Do you want to learn more? Schedule a call with a consultant and book a 20 minute brainstorm session!
