Google Analytics is perhaps the best-known web analytics product and, as a free product, it has massive adoption. Although it lacks some enterprise-level features compared to its competitors in the space, the launch of the paid Google Analytics Premium edition seems likely to close the gap.
$0
per month
RapidMiner
Score 8.9 out of 10
N/A
RapidMiner is a data science and data mining platform, from Altair since the late 2022 acquisition. RapidMiner offers full automation for non-coding domain experts, an integrated JupyterLab environment for seasoned data scientists, and a visual drag-and-drop designer. RapidMiner’s project-based framework helps to ensure that others can build off their work using visual workflows or automated data science.
$7,500
Per User Per Month
Pricing
Google Analytics
RapidMiner
Editions & Modules
Google Analytics 360
150,000
per year
Google Analytics
Free
Professional
$7,500.00
Per User Per Month
Enterprise
$15,000.00
Per User Per Month
AI Hub
$54,000.00
Per User Per Month
Offerings
Pricing Offerings
Google Analytics
RapidMiner
Free Trial
No
No
Free/Freemium Version
Yes
No
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
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More Pricing Information
Community Pulse
Google Analytics
RapidMiner
Features
Google Analytics
RapidMiner
Web Analytics
Comparison of Web Analytics features of Product A and Product B
Google Analytics
8.4
11 Ratings
4% above category average
RapidMiner
-
Ratings
Lead Conversion Tracking
8.110 Ratings
00 Ratings
Bounce Rate Measurement
8.410 Ratings
00 Ratings
Device and Browser Reporting
9.211 Ratings
00 Ratings
Pageview Tracking
9.011 Ratings
00 Ratings
Event Tracking
8.311 Ratings
00 Ratings
Reporting in real-time
7.910 Ratings
00 Ratings
Referral Source Tracking
8.510 Ratings
00 Ratings
Customizable Dashboards
7.910 Ratings
00 Ratings
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
Google Analytics
-
Ratings
RapidMiner
9.5
2 Ratings
13% above category average
Connect to Multiple Data Sources
00 Ratings
10.02 Ratings
Extend Existing Data Sources
00 Ratings
10.02 Ratings
Automatic Data Format Detection
00 Ratings
9.02 Ratings
MDM Integration
00 Ratings
9.01 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
Google Analytics
-
Ratings
RapidMiner
9.0
2 Ratings
6% above category average
Visualization
00 Ratings
9.02 Ratings
Interactive Data Analysis
00 Ratings
9.02 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
Google Analytics
-
Ratings
RapidMiner
8.8
2 Ratings
8% above category average
Interactive Data Cleaning and Enrichment
00 Ratings
9.02 Ratings
Data Transformations
00 Ratings
7.02 Ratings
Data Encryption
00 Ratings
9.02 Ratings
Built-in Processors
00 Ratings
10.02 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
Google Analytics
-
Ratings
RapidMiner
9.0
2 Ratings
7% above category average
Multiple Model Development Languages and Tools
00 Ratings
9.02 Ratings
Automated Machine Learning
00 Ratings
9.02 Ratings
Single platform for multiple model development
00 Ratings
9.02 Ratings
Self-Service Model Delivery
00 Ratings
9.02 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
Google Analytics is particularly well suited for tracking and analyzing customer behavior on a grocery e-commerce platform. It provides a wealth of information about customer behavior, including what products are most popular, what pages are visited the most, and where customers are coming from. This information can help the platform optimize its website for better customer engagement and conversion rates. However, Google Analytics may not be the best tool for more advanced, granular analysis of customer behavior, such as tracking individual customer journeys or understanding customer motivations. In these cases, it may be more appropriate to use additional tools or solutions that provide deeper insights into customer behavior.
RapidMiner is really fantastic to perform fast ETL processes and work on your data as you want, no matter what is the source. You will really save a lot of time when you learn how to use it. You can create mining analysis with several algorithms, and thanks to add-ons, you can apply a lot of techniques. It will not replace a business intelligence dashboard but it allows to create great datamarts for your BI tools. One negative thing is that It's no easy to share your outputs.
I am very impressed at how easily you can work within RapidMiner without much data analytics training. Plus with the help of the crowd, you can see what steps others have taken with their data analytics projects.
Text mining was simple and clean. We used this for our call transcription problem where we didn't have the resources to listen to each call. We needed to qualify each call based on some key phrases.
Our direct mail program was large and not very targeted. Using RapidMiner, we were able to isolate a predictive level we felt comfortable with and decided not to send to anyone below that level. We saved quite a bit of money.
I hope RapidMiner would be the first data science platform that allows data scientists to change the behaviour of a machine learning algorithm that already exists in the repository. For example, I want to be able to change the way a genetic algorithm mutates.
Automatic programming: One day, I hope RapidMiner can automatically generate codes in any 4th generation programming language based on the developed model.
More tutorials/samples needed: Why doesn't RapidMiner becomes the next 'UC Irvine Machine Learning Repository'? Provide real examples and real cases for users to study and understand the best practices in modelling. RapidMiner already has some datasets for a tutorial. Besides the existing samples, I hope RapidMiner can provide more sample data and examples.
We will continue to use Google Analytics for several reasons. It is free, which is a huge selling point. It houses all of our ecommerce stores' data, and though it can't account for refunds or fraud orders, gives us and our clients directional, real time information on individual and group store performance.
Google Analytics provides a wealth of data, down to minute levels. That is it's greatest detriment: find the right information when you need it can be a cumbersome task. You are able to create shortcuts, however, so it can mitigate some of this problem. Google is continually refining Analytics, so I do not doubt there will be improvements
We all know Google is at top when it comes to availability. We have never faced any such instances where I can suggest otherwise. All you need is a Google account, a device and internet connection to use this super powerful tool for reporting and visualising your site data, traffic, events, etc. that too in real time.
This has been a catalyst for improving our site's traffic handling capabilities. We were able to identify exit% from our sites through it and we used recommendations to handle and implement the same in our sites. We have been increasing the usage of Google Analytics in our sites and never had any performance related issues if we used Analytics
The Google reps respond very quickly. However, sometimes they can overly call you to set up an apportionment. I'm very proficient and sometimes when I talk to reps, they give beginner tutorials and insights that are a waste of time. I wish Google would understand my level of expertise and assign me to a rep (long-term) that doesn't have to walk me through the basics.
love the product and training they provide for businesses of all sizes. The following list of links will help you get started with Google Analytics from setup to understanding what data is being presented by Google Analytics.
I think my biggest take away from the Google Analytics implementation was that there needs to be a clear understanding of what you want to achieve and how you want to achieve it before you start. Originally the analytics were added to track visitors, but as we became more savvy with the product, we began adding more and more functionality, and defining guidelines as we went along. While not detrimental to our success, this lack of an overarching goal resulted in some minor setbacks in implementation and the collection of some messy data that is unusable.
I have not used Adobe Analytics as much, but I know they offer something called customer journey analytics, which we are evaluating now. I have used Semrush, and I find them much better than Google Analytics. I feel a fairly nontechnical person could learn Semrush in about a month. They also offer features like competitive analysis (on content, keywords, traffic, etc.), which is very useful. If you have to choose one among Semrush and Google Analytics, I would say go for Semrush.
We tried different data tools and we figured we give RapidMinder Studio a shot as one of our employees had experience with it, and when compared to some of the other tools that we used it was the best fit among the test group that we used. Overall it was a little more fluid and user-friendly.
Google Analytics is currently handling the reporting and tracking of near about 80 sites in our project. And I am not talking about the sites from different projects. They may have way more accounts than that. Never ever felt a performance issue from Google's end while generating or customising reports or tracking custom events or creating custom dimensions
Thanks to the patters that RapidMiner has detected, we have been able to follow clues in the right direction, both for the Protein Interaction Network Analysis and for the Epilepsy Research
Students and participants of the machine learning workshops have learned about this technology and about the tool