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E-Commerce Business Overview
Combine your Shopify, GA4, Google Ads, and Facebook data for insights across your business.
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Analyzing data can be a time consuming process that often requires at least one full-time data analyst on the team. But what if you’re a small business or individual looking out to carry out analysis?
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Luckily we have some amazing tools out there that allows everyday people to automate their data analysis with the click of a button. Here are 7 data analyzer tools I recommend in 2024:
Polymer Search is a web tool that lets you use AI to generate insights from your data.
It’s dead simple to use:
Using it’s simple point and click interface, you can get answers to questions you have within a few clicks.
Polymer Search is ideal for analyzing: marketing data, business data, sales data, survey data and more. Here’s an example of how to analyze marketing data using Polymer Search.
On top of being a data analysis tool, Polymer Search is also a presentation and business intelligence tool. Within seconds, you can generate reports and build interactive dashboards to present findings to executives, clients and people on the web. All this is shareable through a URL.
Akkio is a drag and drop tool that allows you to upload data and automatically train models which you can use to create predictions.
Akkio is ideal for predicting outcomes, customer behaviour and any sort of fluctuations.
Using Akkio, you can also deploy web applications where a user can input some variables like age, gender, location and the application will predict outcomes based on data you provided.
Try Akkio here.
Pandas Profiling is an open source Python module that allows you to perform exploratory data analysis in a few lines of code.
It might seem like an advanced tool that’s meant for data scientists only, but trust me, anyone is capable of learning this tool - even if you have no coding experience.
Pandas Profiling is ideal for exploring your data and seeing the distribution of each variable in your dataset. In a few lines of code, the tool will generate an entire report about your data - full of graphs & visualizations that makes it 10x easier to understand your data.
Try Python Pandas Profiling here.
Autopilot App is a business & marketing driven tool that lets you easily create reports and visualizations on the marketing funnel.
There are 9 types of visualizations including: funnels, cohorts, pie, line, column charts, tables, ledgers and geographical visualizations.
On top of that, you can use the Autopilot AI to create high performing email/SMS subject lines and email content from just an outline. The AI will understand customer sentiment and let you know what is working and what is not.
Autopilot app is ideal for: funnel data, geographic data, SMS & email marketing data and more!
Try Autopilot App here.
ATLAS.ti is a drag and drop tool that’s very popular among the social sciences.
Text mining and qualitative data analysis are very complex tasks, but ATLAS.ti allows everyday users to do these complex tasks.
It’s capable of analyzing unstructured data (e.g. a series of documents), video interviews, and long-form survey questions.
Some of its features include sentiment analysis, entity recognition and word clouds.
Try ATLAS.ti here.
Google Sheets is a free alternative to Excel that does pretty much the same thing. For over 10 years of working as a data scientist, I haven’t come across one thing that Excel can do, but Google Sheets can’t.
Many people might be shaking their head at this recommendation, but it’s often a good idea to use Google Sheets for cleaning and manipulating your data for easier analysis.
Oftentimes you want to split columns, remove certain parts from each cell, or generate more columns using formulas. A basic example is if you have a column for “cost” and “revenue,” you would want to create a column for “profits” as well.
Using the vast spreadsheet formulas available, you have plenty of reign over how you want to manipulate the data. And best of all - there’s plenty of information online on what formulas you should use.
So don’t overlook Google Sheets or Excel when it comes to data analysis!
Google Sheets is ideal for: data manipulation, creating pivot tables and quick graphs.
VBA stands for visual basic for applications. It is the programming language for Excel.
Now if you use Excel frequently, then learning VBA is a must if you want to automate data analysis.
Sure it requires programming, but it’s one of the easiest programming languages to learn and it’s often used by multi-billion dollar companies ranging from finance to marketing, making it a valuable skill to have.
Should you learn Excel VBA?
It depends on how much data you’re analyzing and how long you spend working with spreadsheets each day.
Often, people in big companies spend several hours a day working on spreadsheets. Learning VBA could save these people multiple hours each day.
But if you use spreadsheets 10 minutes a day, then there isn’t much to be gained from learning VBA.
In today’s fast-paced business environment, data is more valuable than ever. But what's even more crucial is the speed and efficiency with which you can analyze this data. This is where automatic data analysis comes into play. Automatic data analysis can streamline the decision-making process by quickly interpreting large sets of data and generating actionable insights.
Data analysis automation not only saves time but also reduces the chances of human error. With automated tools, you can run multiple analyses simultaneously, allowing you to gain insights from various angles. Whether you're monitoring customer behavior, tracking key performance indicators (KPIs), or conducting market research, automated analysis can provide you with the information you need, when you need it.
Investing in an automatic data analysis tool can thus give you a competitive edge, enabling you to make data-driven decisions swiftly and accurately. So, the next time you find yourself drowning in spreadsheets, consider automating your data analysis workflow.
Selecting the right tool to analyze data online can be a daunting task given the plethora of options available. Here are some tips to help you make an informed decision:
By keeping these factors in mind, you can select the best online data analyzer tool for your specific needs, ensuring that your data analysis is both automated and efficient.
With all the tools available out there, you should be able to easily get insights from your data even if you have little to no knowledge of statistics or coding!
If you want to carry out more complex analysis like multivariate analysis, then that's a different story. For those types of analysis, I recommend learning tools like SPSS (intermediate), Python (advanced) or R (advanced).
If you're a business looking to analyze data, I highly recommend you check out these posts:
See for yourself how fast and easy it is to uncover profitable insights hidden in your data. Get started today, free for 7 days.
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