If you want a quick and easy way to find your own IP address on Google Search, then simply go to your local version of Google and ask the question, “What is my IP address”. Google gives you the answer right at the top of the search results.
How to easily find your IP address on Google search
Do bear in mind, that depending on how your internet is configured and the service you have with your Internet Service Provider (ISP) then you may find that you get allocated a dynamic IP address every time you logon. This normally doesn’t cause a problem for day-to-day internet use, but there may be instances where you need or would prefer a static IP address. In these instances, you can normally ask your ISP to provide you with a static IP for a small fee, or, for businesses you may simply have to ask your local network administrator.
How do I find my IPV4 or IPV6 IP address?
Just follow the process set out above. Search in Google for “What’s my IP address?” or the equivalent phrase in your local language and Google will return the answer directly to you at the top of the search results page. You normally don’t have to worry about the differences between IPV4 and IPV6 addresses – but they are easy to spot as IPV6 addresses are much longer and contain letters and numbers.
Here’s an example of an IPV4 address: 172.16.254.1
Here’s an example of an IPV6 address: 2002:0cb6:84a1:0000:0000:6a1e:0340:7118.
This post is aimed at non-technical users who just want a quick answer to how to find their own IP address. For a more technical explanation of the differences between ipv4 and ipv6 addresses and why we need the ‘new’ ipv6 address block then Wikipedia is as good as any place to start.
We're ready for SGE. Are you?
The rollout of SGE will create unprecedented risks to your hard earned organic traffic, as well as new opportunities to succeed.
You need to be ready. The only question is, whether you want to be ready now or later?
In today's rapidly evolving search landscape, understanding SERP changes across different dimensions is crucial for SEO success. Authoritas' new SERP Comparison module offers unprecedented insight into how search results evolve across time, location, devices, and AI Overview expansions.
Compare SERPs through visual screenshots or ranking tables, analyze temporal changes, track geographic variations, and understand the full impact of AI Overviews on your rankings. Get comprehensive visibility into how search results change and evolve to make more informed SEO decisions.
As Google rolls out AI Overviews internationally, these AI-generated summaries are dramatically reshaping the search landscape and impacting organic visibility. Authoritas now offers comprehensive AIO tracking that goes beyond simple detection, providing deep insights into how these AI-powered SERP features affect your rankings and visibility. Learn how our platform expands and captures complete AIO content, tracks ranking positions across devices, monitors competitor movements, and provides actionable data through detailed exports and integrations. Discover why traditional SERP tracking isn't enough and how you can prepare your SEO strategy for the AI-driven future of search.
As Google continues to evolve its search capabilities, the introduction of AI Overviews (AIOs), formerly known as Search Generative Experience (SGE), has created a huge upheaval for SEO professionals. Understanding the alignment between organic and generative search results has become critical for understanding search intent and maintaining competitive visibility. This post introduces the Authoritas GOA Score™ (Generative to Organic Alignment) and OGA Score™ (Organic to Generative Alignment) as two essential metrics for assessing this alignment and provides practical guidance on how to leverage these insights in your SEO strategy.
Google's new SERP layouts, featuring AI Overviews, are disrupting traditional SEO visibility and rank tracking models. The AI Overviews, now rolling out globally, introduce complex changes that challenge the definition of a top-ranking site. SEOs must adapt their strategies to navigate these significant shifts in search results presentation.
This is the next wave of our in-depth research into the impact Google's new AI-generative results are going to have on SEO. This time we focused on the impact on brand terms, brand + product or service, brand + generic terms and compare the results to generic terms and product or service terms. The SERP is going to change and it's going to have an impact across all 15 markets we studied.
We conducted a study of 1,000 popular commercial search terms to try and develop a picture of the probable impact of Google's new SGE experience on organic rankings and performance. The research depicts a scary picture of potentially dramatic falls in organic visibility for many sites.
Review our latest assessment of how Google's SGE feature is going to change the SERP as you know it. This has implications for rank tracking, content strategy, page optimisation, content quality and more.
If a web page listing is elevated into the featured snippet position, we no longer repeat the listing in the search results. This declutters the results & helps users locate relevant information more easily. Featured snippets count as one of the ten web page listings we show.
Up until last year, Wikipedia truly dominated as the cited source in knowledge panels for brands. The truth is much more complex, of course – Google gets its information from multiple sources and gets corroboration / cross checks that information across multiple other sources before including a brand in the Knowledge Graph.
The quality of the job ad is probably the most significant ranking factor in Google for Jobs. On top of displaying key elements such as a company info, role description, skills and responsibilities, I warmly recommend to add extra layer of information for Google to digest. For example, working hours, salary, benefits, and a more in-depth company information could make the difference between a good and an excellent job ad copy.
MANY of us have seen SEO click through rate (CTR) studies, performed on large data sets, but what can we learn from these, and, more to the point, are they truly representative? Given the ever changing nature of the SERPs – are click-through rate (CTR) studies too crude and limited in their scope to cater for the multi-faceted nature of a typical SERP? And in fact is there even such a thing as a typical SERP anymore?
