@ikiok eliminating 404 is something that as a marketer I've always dreamt with. Actually having the possibility to do it via an AI-powered tool, makes it even more exciting
Congratulations on your launch
Keep up the good work!
A really useful tool for anyone who's looking to make the switch from Google Analytics to GA4. The ability to easily identify gaps and discrepancies in your data could be incredibly helpful in ensuring a smooth transition. I also appreciate the integration with Google Tag Manager, which could save a lot of time and effort. I'm curious to know if there are any plans to expand this tool to include other analytics platforms, or if it will primarily focus on GA4 migration.
thank you for the insightful review @mashaplotkina_quinky!
this tool will focus only on the GA4 migration and we will release separate tools with different functionalities in the near future!
let us know if you have something specific in mind! ;)
We used it just to underestand if we were taking into account all the metrics and since day 1 has helped us to identify a few gaps we had with the new configuration of GA4! Really easy to use to detect these gaps even if you don't have a technical background.
hey @lucia_pons! thank you for using our app and we are very glad to hear how useful you found it! Do let us know if you have any questions or need any help!
Congratulations on developing a tool that aims to enhance website user experience and SEO by eliminating 404 errors! The integration of no-code and AI to transform Google Analytics data into natural language output sounds promising. As a software developer, I'm curious to learn more about the capabilities and functionalities of your tool.
Could you provide more insights into how your tool identifies and addresses 404 errors? What mechanisms or techniques does it employ to detect and resolve these issues effectively?
Natural language output from Google Analytics data sounds intriguing. Could you elaborate on how your tool utilizes AI to generate this output? Are there specific machine learning models or algorithms employed to interpret and present the analytics data in a user-friendly and actionable manner?
Actionable insights and recommendations are crucial for website optimization. Can you provide examples of the types of insights and recommendations your tool provides? How does it assist website owners or developers in prioritizing and addressing the identified issues?
How does your tool integrate with existing website infrastructure or analytics platforms? Is there a specific setup process or configuration required to start utilizing its features?
As a software developer, I value extensibility and adaptability. Are there opportunities for customization within your tool? Can users fine-tune recommendations or configure specific settings to align with their website's unique requirements?
Congratulations once again on the development of this promising tool. I look forward to learning more about its technical aspects and how it can assist website owners in improving their user experience and SEO performance.
gey @gordon_n and thanks for your thoughtful comment!
"The integration of no-code and AI to transform Google Analytics data into natural language output sounds promising"
Well, then you should definitely our website baresquare.com as our team is dedicated to eliminating manual GA data analysis as much as possible!
Now specifically for 404 Error Hound:
• we use GA data and page titles to understand if a page view was an error or not
• we use our decade-long expertise in web analytics to classify errors in three categories: from paid traffic, from non-paid traffic, from internal traffic (aka internal navigation)
• we feed the top views errors per category into an LLM. Again, we leverage our domain expertise in the prompts. For example, we know that an error during checkout is most likely the most important error from a business perspective. So we distilled our expertise in the promts😉
• every generated alert has the following properties: identified errors, categorized based on the traffic source (which dictates how the issue will be fixed), prioritized errors (based on traffic, which means you are solving issues that affect your customers the most), smart groups of errors which are also prioritized (based on business needs) and finally suggestions for how to resolve the issues (which stems from the category of error - e.g. if it's from paid traffic, you must go to the advertising platform and fix the ads' landing page URL!)
• we have connectors for Google Analytics which enables fast authorization and easy data retrieval
• as this is an MVP, we don't offer much in terms of customizations (great suggestions btw!). But we already have an enterprise-level solution with one of our biggest clients. And behind the scenes, this is also customizable per client. Just not through the UI.
