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Using AI To Optimize Voice Search To Improve Your Website Content

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Using AI To Optimize Voice Search To Improve Your Website Content


A rapidly increasing number of internet users use voice search to seek content on the internet.

Businesses can involve AI in SEO processes to make their content more visible in such search results.

All around the world, people are increasingly multitasking to save time. As their hands may be occupied with another task, individuals may simply call on Siri, Alexa, Cortana or any other intelligent voice assistant to get specific information about something or to perform a different—but equally significant—task simultaneously. Certain studies have found that 48% of netizens may prefer voice search mechanisms over the standard process for general web searches. Optimizing SEO for voice searches can be tricky because spoken language contains more regional inflections and nuances that need to be factored into the process. The use of AI in SEO optimization can resolve this problem. Already, AI is a handy technology for content ideation and curation. Involving technologies such as Natural Language Processing and AI in SEO optimization enables your business to create content that will perform well in voice search result visibility.

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AI in SEO: Existing Content Optimization

Normally, keyword phrases are generated by in-house content evaluators before businesses create brand-new content. Then, the content published on their website is built around such phrases. This process allows such content to be visible and highly ranked in typed search results. In addition to that, for voice searches, AI and NLP scan the content that is already created before suggesting SEO-related modifications. The involvement of AI in SEO involves the analytics of thousands of web pages containing similar types of content. Based on the SEO performance of reference datasets, AI recommends specific keywords to be used for the purpose. After incorporating the AI-based modifications, the content becomes much more voice search voice SEO-friendly.

AI in SEO: Keyword Extraction and Topic Discovery

As stated earlier, AI can perform data analytics to find the SEO-related keyword phrases in a given piece of content. Also, such analytics can be used to find SEO-related information from other websites and content forums. These analytics enables businesses to “extract” keywords based on the SEO performance of external content sources. Additionally, AI scans the market trends to recommend topics to businesses to create brand-new content. In short, AI makes it easier to make currently published content on your website more SEO-friendly and create content in the future using such keywords.

The involvement of AI in SEO optimization for voice searches can be much deeper than these two points. Thus, making investments to incorporate AI-based tools and applications for the purpose is highly useful to improve the visibility of your digital content.



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TECHNOLOGY

How Businesses Can Automate Root Cause Analysis (RCA) With Machine Learning

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How Businesses Can Automate Root Cause Analysis (RCA) With Machine Learning


In the event of a severe incident for your business, you need to analyze what exactly changed (the root cause) to understand its impact.

Using machine learning for root cause analysis can help identify the event that caused the change quickly and easily.

Things can sometimes go wrong in your business’s daily operations. It can be a minor issue, such as a system outage lasting for a couple of minutes. Or it can be something severe as a cyberattack.

Generally, such events result from a chain of actions that eventually culminate in the event. Identifying the root cause is the best way to solve the issue. But manual root cause analysis takes time and often doesn’t provide the exact cause of a mishap. Using machine learning for root cause analysis can automate the process, helping identify the underlying cause quickly and with higher accuracy.

Power of Machine Learning for Root Cause Analysis

To understand why an issue occurred, you need to identify the root cause. But root cause analysis can often be complex and provide inaccurate results. Using machine learning for root cause analysis helps solve this issue.

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Log Analysis

Using machine learning for root cause analysis can help zero in on the exact location of the problem. You don’t have to scroll through infinite logs to identify which components were impacted and when. The machine learning program can automatically and quickly find the root cause by analyzing a given log data set. 

Moreover, the machine learning program can even predict future incidents as more and more data is available. The program compares real-time data with historical data to predict future outcomes and warns you of any unwanted incident beforehand. This will help improve your incident response, reduce downtime and improve productivity.

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Benefits of Using Machine Learning for Root Cause Analysis

There are many benefits of using machine learning for root cause analysis. It can help teams take the right action at the right time, minimizing your losses. Some of the benefits are discussed below.

Reduces Costs

The cost of solving the issue is reduced as your teams don’t have to guess and work around blind spots. Machine learning tools locate the exact line of code responsible for a performance issue, and your team can start working on fixing it right away.

Saves Time

The time spent fixing the issue is significantly reduced as it helps solve business pain faster by locating the cause quickly and accurately.

Provides Long-Lasting Solutions

Machine learning tools provide a permanent solution for your problems and foster a productive and proactive approach.

Grows Your Business

Using machine learning for root cause analysis helps improve the efficiency and productivity of your organization, which eventually leads to business growth.

 

No system is perfect. Incidents will happen, no matter what. But what you do afterward is in your control. Root cause analysis should be done as soon as possible. Using machine learning for root cause analysis not only improves your incident response, but over time, it can also help prevent incidents from happening in the first place.



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