Web Analytics Software Applications Web analytics software applications are computer software tools that allow you to capture, analyze and visualize data about your website’s visitors. Here I have picked the top three web analytics software applications which I recommend to all small businesses; they are easy to use and they can help you in tracking and improve your internet marketing so that you can increase your bottom line.

I often speak about Web Analytics Software Applications which are extremely helpful for my field. They provide so many useful details to Web Analytics professionals that help us to understand the behaviors of our audience, and from that we can create content which meets their needs. I’ve found that they help me a lot in my job and I wanted to share them with you!

I’ve been working with a wide range of marketing platforms that help companies gain an insight into what people are doing on their websites. One thing I realised is that Web Analytics software applications have emerged as a pretty important category in the SaaS industry.

Web analytics

What is web analytics

Web analytics is the measurement and analysis of data to inform an understanding of user behavior across web pages.

Analytics platforms measure activity and behavior on a website, for example: how many users visit, how long they stay, how many pages they visit, which pages they visit, and whether they arrive by following a link or not.

Businesses use web analytics platforms to measure and benchmark site performance and to look at key performance indicators that drive their business, such as purchase conversion rate.

Why web analytics are important

There’s an old business adage that whatever is worth doing is worth measuring.

Website analytics provide insights and data that can be used to create a better user experience for website visitors.

Understanding customer behavior is also key to optimizing a website for key conversion metrics.

For example, web analytics will show you the most popular pages on your website, and the most popular paths to purchase.

With website analytics, you can also accurately track the effectiveness of your online marketing campaigns to help inform future efforts.

How to choose a Data Analyst Tool?

How do you find one amongst several Data analytics tools that’s a good fit for your company? Start by considering your company’s business requirements and learning who will be using the Data analytics tools. Will it be used by seasoned Data Analysts and Data Scientists or non-technical users who need an intuitive interface? Some Data analytics tools provide an immersive experience in code creation, generally with SQL, while others are more concerned with click-and-point review best suited for freshers. The Data analytics software should also offer support for visualizations relevant to your business goals.

Consider the ability of Data analytics software to model data. Some support a syntactic and semantic layer or can perform data modeling themselves. If you do not wish to use one that does, you’ll have to use SQL or Data analytics tools like the data build tool (dbt) to model your data before analysis.

Finally, take price and licensing into consideration. Some Data analytics tools charge license or subscription fees, while some Data analytics tools are free. The most expensive Data analytics tools are not always the most comprehensive, and there many robust and free Data analytics tools available in the market that shouldn’t be overlooked.

Now that we have covered what are Data analytics tools and how to choose the best Data analytics software for your business. Let’s explore the popular Data analytics tools of 2021.

3) 25 Most Popular Data Analytics Tools To Know in 2021

To make the most out of the infinite number of Data analytics tools currently offered on the market, we will explore the 25 most prominent Data analytics tools needed to be an expert Data Analyst.

1. R

R is now one of the most popular analytics tools in the industry. It has surpassed SAS in usage and is now the Data analytics tool of choice, even for companies that can easily afford SAS. Over the years, R has become a lot more robust. It handles large data sets much better than it used to, say even a decade earlier. It has also become a lot more versatile.

1800 new packages were introduced in R between April 2015 and April 2016. The total number of R packages is now over 8000. There are some concerns about the sheer number of packages, but this has certainly added a lot to R’s capabilities. R also integrates very well with many Big Data platforms, which have contributed to its success.

2. Python

Python has been one of the favorite languages of programmers since its inception. The main reason for its fame is the fact that it’s an easy-to-learn language that is also quite fast. However, it developed into one of the powerful Data analytics tools with the development of analytical and statistical libraries like NumPy, SciPy etc. Today, it offers comprehensive coverage of statistical and mathematical functions.

Increasingly, we are seeing programmers and other tech folks moving into analytics. Most of these guys are already familiar with Python, and therefore, it has become a Data analytics tool of choice for many data scientists.

3. Apache Spark

Spark is another open-source processing engine that is built with a focus on analytics, especially on unstructured data or huge volumes of data. Spark has become one of the tremendously popular Data analytics tools in the last couple of years. This is because of various reasons – easy integration with the Hadoop ecosystem being one of them. Spark has its own machine learning library, which makes it ideal for analytics as well.

4. Apache Storm

Storm is the Big Data tool of choice for moving data or when the data comes in as a continuous stream. Spark works on static data. Storm is ideal for real-time analytics or stream processing.

5. PIG and HIVE

Pig and Hive are integral Data analytics tools in the Hadoop ecosystem that reduce the complexity of writing MapReduce queries. Both these languages are like SQL (Hive more so than Pig). Most companies that work with Big Data and leverage the Hadoop platform use Pig and/or Hive.

6. SAS

SAS continues to be one of the widely used Data analytics tools in the industry. Some flexibility on pricing from the SAS Institute has helped its cause. SAS continues to be a robust, versatile and easy to learn tool. SAS has added tons of new modules. Some of the specialized modules that have been added in the recent past are – SAS analytics for IoT, SAS Anti-money Laundering, and SAS Analytics Pro for Midsize Business.

7. Tableau

Tableau is among the most easy-to-learn Data analytics tools that perform an effective job of slicing and dicing your data and creating great visualizations and dashboards. Tableau can create better visualizations than Excel and can most definitely handle much more data than Excel can. If you want interactivity in your plots, then Tableau is surely the way to go.

8. Excel

Excel is, of course, the most widely used Data analytics software in the world. Whether you are an expert in R or Tableau, you will still use Excel for the grunt work. Non-analytics professionals will usually not have access to tools like SAS or R on their systems. But everyone has Excel. Excel becomes vital when the analytics team interfaces with the business steam.

9. QlikView

Qlikview and Tableau are essentially vying for the top spot amongst the data visualization giants. Qlikview is supposed to be slightly faster than Tableau and gives experienced users a bit more flexibility. Tableau has a more intuitive GUI and is easier to learn.

10. Splunk

Splunk is more popular than some of the more known Data analytics tools like Cloudera and Hortonworks. It started as a ‘Google for log files’, which means its primary use was to process machine log files data. It has now become much more than that. Splunk has great visualization options, and a web interface makes it easy to use.

11. Microsoft Power BI

Microsoft Power BI is a top business intelligence platform that offers support for dozens of data sources. This Data analytics software allows users to create reports, displays and dashboards and post them. For quick delivery, users may combine a group of dashboards and reports into a Power BI app. The Power BI helps users create and implement automatic models by applying Machine Learning with Azure Machine Learning.

12. SAP BusinessObjects

SAP BusinessObjects provides a suite of Data analytics tools for data discovery, analysis, and reporting. The tools are designed for novice technical users but also for carrying out complex analyses. BusinessObjects incorporates Microsoft Office products, enabling Business Analysts to easily reverse and switch between applications, like Excel and reports from BusinessObjects. It also enables self-service predictive analytics.

13. Sisense

Sisense is a Data analytics software aimed at aiding both technical developers and the Business Analytics process and visualizing all of their business data. It offers a wide variety of drag-and-drop software and interactive dashboards for collaboration. The Sisense platform’s unique feature is its custom in-chip technology, which optimizes calculation to utilize CPU caching instead of slower RAM. This can lead to 10-100 times faster computation for certain workflows.

14. TIBCO Spotfire

TIBCO Spotfire is a Data analytics software that provides natural language search and AI-powered data insights. This is a comprehensive platform for viewing reports for both mobile and desktop applications. Spotfire also offers point-and-click tools for predictive analytics models.

15. Thoughtspot

Thoughtspot is a Data analytics software that allows users to explore Data from various sources through reports and natural language searches. The SpotIQ, its AI-powered system, automatically seeks insights to help users discover trends they didn’t know to search. It also enables users to automatically link tables from various Data sources to break down Data silos.

16. Google Data Studio

Google Data Studio is one of the popular free Data analytics tools for dashboarding and data visualization that automatically integrates with most other Google applications, such as Google Analytics, Google Ads, and Google BigQuery. Data Studio is perfect for those who need to evaluate their Google data due to its convergence with other Google services. For example, marketers could create dashboards to help analyze consumer conversion and retention for their Google Advertising and Analytics results. Data Studio can run with Data from several other sources as long as the Data is replicated first to BigQuery using a Data pipeline such as Stitch.

17. Grafana

Grafana is another free, open-source Data analytics software for monitoring and observing metrics across diverse databases and applications. It offers a real-time view into external processes and warns users when such incidents occur. Grafana is widely used for tracking their applications by tech and DevOps engineers.

18. Redash

Redash is a lightweight and cost-effective Data analytics software for querying data sources and building visualizations. The code is open source, and for organizations that want to begin quickly, an inexpensive host version is available. Redash’s heart is a query editor, which offers a quick interface for requests, schemes and integration management. Search results are cached in Redash, and users can automatically schedule updates.

19. Jupyter Notebook

Jupyter Notebook is one of the robust free, open-source online Data analytics tools that can be administered in a browser after installation using the Anaconda platform or Python’s package manager, pip. It enables developers to generate reports with Live Code Data and views. This Data analytics software supports more than 40 programming languages. Formerly known as IPython Notebook, Jupyter Notebook was initially developed using Python. It enables developers to make use of Python’s wide variety of analytics and visualization packages. The tool has a large group of users who also use other languages.

20. IBM Cognos

IBM Cognos is a Data analytics software for business intelligence with built-in AI tools to show and clarify information concealed in plain English. It has automated Data preparation software to automatically clean and aggregate Data sources, enabling the fast integration and analysis of Data sources.

21. Mode

Mode is a Data analytics software aimed at providing Data Scientists an easy and iterative environment. It offers an interactive SQL editor and notebook environment for analysis and visualization, and collaboration tools for novice users. Mode has a unique Helix Data engine that streams and stores Data from external databases to allow swift and interactive analysis. The Data Analysis supports up to ten GB of data in-memory.

22. KNIME

KNIME is the abbreviation for the Konstanz Information Miner and is a free, open-source Data analytics software that supports Data integration, processing, visualization, and reporting. It integrates Machine Learning and Data mining libraries with minimal or no programming requirements. KNIME is excellent for Data Scientists who do not inherently have proficient programming skills and need to incorporate & process Data for building Machine Learning and other statistical models. Its graphical interface facilitates point-and-click analysis and modeling.

23. Looker

Looker is one of the cloud-based business intelligence and Data analytics tools. It automatically generates Data model to scan Data schemas and connect tables with Data sources. Through an integrated code editor, it allows Data engineers to modify the created models.

24. RapidMiner

RapidMiner is a Data analytics software that caters to all the technology users need, from integration, cleaning to Data transformation before they run predictive analytics and build statistical models. Nearly all this is done by the users through a simple graphical interface. RapidMiner can also be expanded by using R and Python and various third-party plugins available on the organization’s marketplace.

How web analytics work

Most analytics tools ‘tag’ their web pages by inserting a snippet of JavaScript in the web page’s code.

Using this tag, the analytics tool counts each time the page gets a visitor or a click on a link. The tag can also gather other information like device, browser and geographic location (via IP address).

Web analytics services may also use cookies to track individual sessions and to determine repeat visits from the same browser.

Since some users delete cookies, and browsers have various restrictions around code snippets, no analytics platform can claim full accuracy of their data and different tools sometimes produce slightly different results.

Sample web analytics data

Web analytics data is typically presented in dashboards that can be customized by user persona, date range, and other attributes. Data is broken down into categories, such as:

Audience data

number of visits, number of unique visitors
new vs. returning visitor ratio
what country they are from
what browser or device they are on (desktop vs. mobile)

Audience behavior

common landing pages
common exit page
frequently visited pages
length of time spent per visit
number of pages per visit
bounce rate

Campaign data

which campaigns drove the most traffic
which websites referred the most traffic
which keyword searches resulted in a visit
campaign medium breakdown, such as email vs. social media

Web analytics examples

The most popular web analytics tool is Google Analytics, although there are many others on the market offering specialized information such as real-time activity or heat mapping.

The following are some of the most commonly used tools:

Google Analytics – the ‘standard’ website analytics tool, free and widely used
Piwik – an open-source solution similar in functionality to Google and a popular alternative, allowing companies full ownership and control of their data
Adobe Analytics – highly customizable analytics platform (Adobe bought analytics leader Omniture in 2009)
Kissmetrics – can zero in on individual behavior, i.e. cohort analysis, conversion and retention at the segment or individual level
Mixpanel – advanced mobile and web analytics that measure actions rather than pageviews
Parse.ly – offers detailed real-time analytics, specifically for publishers
CrazyEgg – measures which parts of the page are getting the most attention using ‘heat mapping’

With a wide variety of analytics tools on the market, the right vendors for your company’s needs will depend on your specific requirements. Luckily, Optimizely integrates with most of the leading platforms to simplify your data analysis.

CONCLUSION

Google Analytics is the most widely used web analytics software application in our time. The reason behind its popularity is that it is absolutely free. You need not pay a single dime to acquire this application. However, the biggest draw back to this popular web analytics tool is that it gives limited scope when Google itself has so much information to share with you!

Analyze your website traffic in detail from the user’s viewpoint and find out what they do on their visits. Top Web analytics software application recommendations include Google Analytics, Crazy Egg, Clicky, Hotjar and Piwik.