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Can SEO be Made Predictable?

05 Oct

Search engine optimization is quite a complex thing a reason many businesses hire SEO expert teams to handle their SEO activities. When a user places a search query, search engines use powerful algorithm to display the pages that have the best match of the query. So the big question is, "How do search engine determine the pages they are going to display against a query?"

Google and other search engines use algorithm designed using advanced data to carry out search engine optimization activities. Using that kind of data, the algorithms are able to provide the results they find. The question as to whether or not it is possible to predict SEO, the answer is yes. Businesses and SEO experts can predict search engine optimization by applying advanced data science.

What's Behind Search Algorithms

Search algorithms check at multiple attributes in different parameters to give a definitive rank irrespective of the queries users place for search. To give meaningful results from a search and to rank pages in the most accurate way, engines evaluate an array of parameters that involve interpretation of the query, quality and depth of content, and user experience of a particular page.

The engines look at the intention of placing a query and what the user is really looking for. Engines also check whether the webpage they are displaying answers the query asked by the user in the correct manner. In addition, engines look at how easy it is for a user to find the information they want and whether the page loads quickly and provides a seamless searching experience.

Businesses that need to rank higher in SERPs need to consider SEO expert services since these professionals know what search engines look for when indexing and ranking websites and webpages.

Other parameters and attributes that search engines use to determine the ranking of pages in search results are reputation of domain or brand and expertise, trustworthiness, and authority of the domain in which the content or information is published. Practically, SEO involves value addition to content and improvement of the quality of pages. It also involves enhancing search friendliness through use of technical improvements like loading speed and ease of finding the information in a page.

Without understanding the main parameters that work in search algorithms, SEO experts and website owners would continue to struggle to optimize their websites for search in a consistent way.

That being said, it is actually possible to predict SEO, but the main challenges are understanding the limitations faced by SEO experts and businesses in measuring as well as reporting and having case studies for SEO.

Solving the Challenges Faced in Identifying and Assessing Search Parameters

  • Many enterprise search engine optimization tools including browser extensions have been introduced that carry out pretty a good job to report the performance of SEO metrics such as traffic, rank, and back links. Some of these tools are Google Analytics, Google Search Console, and Screaming Fog - these are used as technical tools for SEO. Tools used for keyword research are SEMrush, Google Keyword Planner, and Ubersuggest. For link research, the tools are BuzzSumo, Majestic SEO, and Ahrefs.
  • While the enterprise SEO tools work well, they fail in combining important SEO metrics and putting them together to help in determining search performance. In order for SEO expert teams to deliver appropriately and get good results, they need to understand the challenges experienced in search and how to deal with them.
  • Without having key SEO metrics working together to provide a holistic view of internet search performance, it makes SEO professional to make their decisions intuitively - this is an approach, which may hinder consistency and scalability in search performance.
  • Another challenge is that there are just too many metrics but too few insights in search performance. While it could be possible to have all the data elements put together within a single place, it may not be possible for humans to sift through them and pick out the meaningful action items that they want.
  • SEO experts also find it difficult to find a balance between identifying the right target keywords, right content, and right optimization efforts. For instance, you may have a website that has multiple pages with same theme and external backlinks or keywords, but the best links may not be well optimized for the best target keywords.
  • Also, there are conflicts of interest that may arise between different business units in determining optimization priorities. When there is no mechanism to pin point the right optimization efforts capable of producing the greatest impact on ranking, it makes the work of optimizing a website for SEO search and ranking a difficult thing.
  • Other challenges that are seen in search engine optimization are unreliable standards for benchmarking the number of clickthroughs. There is also lack of a business case studies that can help build investments into SEO data science.

Building Scoring Models for Predicting SEO

To help solve the problem of predicting SEO, there are different approaches that can be applied. SEO professionals need to identify critical data integration methods and variables. Many of the SEO platforms fail to combine all variables and metrics in SEO in one place. With data modeling and use of skill, it is possible to begin having a mechanism to predict SEO.

Read the Blog: How Can Low-quality Product Images Decline Your Sales?

SEO professionals can use basic data warehousing and SEO tools to be able to integrate and combine different metrics. For example, for the content, they can use things like frequency of phrase or word usage, exact or partial matches of phrases and keywords. In terms of link data, they can use aspects like relevancy of links in relation to a target page or percent of no-follow and do-follow links. When you automate these metrics, it is possible to have a continuous and reliable benchmarking process that you can use to determine and measure many other SEO tasks and results. Another thing to do is build algorithm scoring models. The scoring model helps classify problems.

Besides building a scoring model, there is need to have strategy and simulation in which case a platform for improving the models is set up. An environment where SEO pros interact and work together the same way developers do can help in bringing up actionable insights and tackling challenges together. It also helps assess the impact of a strategy before it is implemented into the SEO environment.

 

Conclusion

Ideal predicting SEO is possible, but at this moment, there lacks the mechanism to do it. Data science needs to be incorporated and the dedicated SEO expert teams need to input their knowledge and work together to ensure that search engine optimization does not remain a nightmare to businesses and SEO experts.

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