When Should You Use Data Science in SEO?

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When Should You Use Data Science in SEO?

Search engine optimization (SEO) has come along in leaps and bounds over the last decade. The introduction of data science in the past few years, which has only gathered pace recently, is altering the SEO landscape.

In this article, we consider what data science is, how it relates to SEO and whether you should be using it.

What is Data Science?

Data science is a snazzy buzzword which basically incorporates the era of big data (where companies have access to far more information than ever before) and artificial intelligence (machine learning, etc.) with the aim to increase understanding.  

The trick with data science is how it’s used. Certainly, AI and its uses are not a new thing. Just using Microsoft Word as an example, it tries to use AI to figure out what the user wants to say when their words aren’t quite right. Indeed, it’s now capable of appreciating common mistakes and the common corrections needed. Now Microsoft is even looking to be politically correct with auto-correction features within documents to avoid causing offense too.

Google Trends is another example that uses large data sets of previous searches, and by comparing the search volume of different search terms over time, it offers new insights. This is useful to gauge search trends, both historical vs current, and seasonal ones.

Keyword Search Data

Digging deep for excellent keywords to pursue through article marketing and other SEO approaches is a daily challenge for SEO teams. It’s said that data is only as good as its source, but it’s also true that data is only really beneficial when useful information is extracted from it. Overwise, it’s just a mountain of data!

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For companies or SEO agencies looking to find potential search terms to target, they’re interested not just in the estimated monthly search volume, but they also need to see what related terms might be relevant to get a fuller picture. By crunching the data on what other searchers have also looked for and their follow-up searches beyond that, search engines commonly provide “People also asked for” related questions and/or related searches at the bottom of the search query too.

Keyword Difficulties Scoring

Smart use of data science is evidenced in how SEO tool provider ‘Ahrefs’ delivers on the promise of their keyword difficulty (KD) scoring metric for individual keywords.

Ahrefs does the number crunching – it looks at the top 10 search results for a given search query to see what pages/sites are ranking and how many referring domains are pointing to those pages. Its scoring guides SEO practitioners on how difficult it will be to rank a specific keyword and the likely amount of link building necessary to get on the first page of results.

By calculating the referring domains and their relative strength, Ahrefs (via their KD score) provides an indication of how challenging a keyword will be. It’s then possible to use their keyword search and filtering functionality to begin with a seed keyword and filter out keywords that don’t fit a certain criterion. Their lowest grouping is KD 0-10 which is marked as easy to rank for and is estimated to need only 10 referring domains or fewer to achieve a Top 10 ranking.

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While the KD is just one estimate of ranking difficulty which doesn’t count other aspects like on-page SEO, it’s useful to provide a filterable reference point. It also wouldn’t be possible without data science and considerable computing power to stay current with the ever-changing nature of the web.

Conversion Rate Optimization

Conversion rate optimization (CRO) has become more popular as there are now affordable data sets to work from to eventually drive better results.

The idea with CRO is to take the same amount of traffic and do more with it. By better understanding why a sales offer wasn’t successful, or shopping carts get abandoned with regularity, for example, results can be improved.

A-B testing of different designs, sales offers, or shopping cart workflows can deliver useful data points to analyze. The conversion rate – the take up rate of an offer – can be tweaked to get more sales, opt-ins, or another action for the same number of visitors.

Also, the Facebook pixel is one way that site owners use clever computer science to let Facebook identify previous visitors to their site who can be marketed to.

Changing Face of Searches

The release of BERT, one of the recent Google algorithm updates, focused on understanding the searcher’s intent behind a query. Rather than breaking a query down into its component parts, the update wanted to better understand what the goal of the searcher was. By considering what they’re really looking for, better results can be provided.

Only by using machine learning to tackle the difficulties of search engine functionality are search engines able to better appreciate what human searchers want. The use of machine learning via data science really got started in earnest in 2015 with the introduction of Google’s ‘RankBrain’. With it came a clear push towards the use of artificial intelligence to provide improved results over time. The BERT update is one of the fruits of this endeavor.

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Google Provides More Clues

For people interested in how to reach potential customers, advancements in search engine technology due to AI and machine learning have changed their approach. With a move away from using advanced SEO tools, they’re now looking at Google search results for clues about how users are searching for things, what questions they’re asking, and how they’re relating to the topic. As a result, articles are being written that better answer the searchers’ queries and use natural language (not keyword stuffing) to get noticed.

Data science is best utilized when there’s a clear case for it. When insights may be gleaned from search data or statistically significant results based on an analysis of past customer behavior, it’s possible to make changes to move towards better outcomes. It’s usually necessary to know what you wish to discover first and then consider how data can best be analyzed to provide the necessary answers.

Clearly, data science is not a panacea for every difficulty that pains a business. However, it clearly can provide answers to long unanswered questions and in other cases help the business to perform better with the same number of customers or website visitors at a similar cost.