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Apply data science in SEO data to win and succeed in the digital era !
SEO is fiercely competitive so it’s no longer enough to rely on the standard techniques that everyone has mastered. One needs to use data science techniques to unearth hidden opportunities that will give you the edge. There is a four-step framework for applying data science to your SEO data and transforming it into actionable marketing insights.
1. Decide on your data sources
Data insights can only be as good as our data sources. Where should we look?
Descriptive analytics tools like Google Analytics, Google Search Console, SemRush, Ahrefs, heatmaps and technical audit tools are strong contenders. With SEO getting more complex and integrating with other areas of digital marketing such as CRO, content marketing, CX management and ultimately sales, relying on one (or a few) of these solutions is no longer enough.
How many data sources are necessary to get better SEO insights? The answer will depend on your current setup and post-adoption goals. We need to see the areas where our visibility is limited and which data sources contain the answers you need. The next step needs to be then build a good harvesting engine/pipeline for those sources and to prepare your data for analysis.
2. Data science can help align SEO with other marketing initiatives
Our SEO gets stronger when backed by other marketing initiatives. One team cannot optimise for all the 50+ search ranking factors without close collaboration with other specialists such as developers, UX designers, sales and customer support teams. Data science helps us figure out a universal set of SEO best practices every team can apply and adhere to.
To understand what actions matter the most for our business, we need to track the ever-changing relationships between the dependable and independent variables.
An event is a variable, a sales offer, a campaign or another activity is something we can easure.  With data science, we can pinpoint the relationships between different campaigns (or individual actions) executed and attribute their results to some KPIs (e.g. higher conversion rates).
To get an understanding of how SEO impacts other channels, we need to  analyse the following data:
  • Conversions and assisted conversions. The latter will help you identify the channels that don’t directly generate the conversions but play a part in the process. We need to understand if a customer discovered our website via organic search, browsed the products and later typed in the URL directly to make a purchase, or converted from a remarketing FB ad.
  • Top conversion paths.  Our data shall give us more insights into how users interact with your website and other channels before becoming a lead or placing an order.
By gaining a deeper understanding of your customers’ journeys, we can create stronger alignment between all the marketing activities you deploy and attribute the results to individual campaigns with ease.
3. Focus on stories, not numbers
Apart from picking the right data sources/tools, we also need to pay attention to the right metrics. This needs to be analysed also. For instance, if we search traffic from Canada that may seem like an SEO win, we need to see if traffic of any value for a business operating solely in the UK, The answer is clear no.
We need to focus on tracking the metrics directly. We need to type our URL to specific KPIs – such as those reflecting conversions, repeat business, higher customer engagement etc. Make sure that you keep a strong focus on quantifiable, actionable metrics, not the vanity ones.    
In addition, it’s important to look beyond the SEO campaign numbers and dwell more on what drives those results. Insights are not just good data summaries. They are stories, explaining certain behaviours your customers are exhibiting and their correlation with your marketing campaigns.  
SemRush suggests focusing on some metrics to create effective measurements:

4. Use data science techniques to visualise
Numbers in spreadsheets could be hard to understand for decision-makers. And by looking at your data hierarchically, we can miss an important story hidden between the lines.
Data visualisations could help us to:
  • Accelerate knowledge discovery
  • Compare and contrast
  • Spot common trends and patterns
  • Digest large amounts of data at scale
  • Reveal questions that would otherwise be missed
Here’s a real-life example from our team. We used data science during an SEO technical audit and received a lot of insights about the client’s website health and performance. We understood all about the page authority, number of inbound/outbound links per page, rankings and a multitude of other factors.  However, it didn’t provide a clear answer as to why some pages performed better in search results, while others lagged behind.

We need to visualise the website’s internal link structure and estimate the overall domain authority of each page on a 1-to-10 scale. This would be similar to Google, we could immediately see the areas for improvement and take proactive action.

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