Data science is making its presence felt across domain every day, and the world of search engine optimization is no exception. Data science brings together big data and artificial intelligence in order to analyze and process datasets. It also commonly employs machine learning, which is one of the two types of learning that powers artificial intelligence. Machine learning is a program or system that builds or trains a predictive model from the input data. The system uses the learned model to make useful predictions from new data that is drawn from the same distribution as the one used to train the model. Machine learning is also the study concerned with these programs and systems.
Some years back Google’s artificial intelligence product – Google Brain that works to improve the other Google products by using artificial intelligence, was used to develop RankBrain, which was then rolled out in 2015 to improve the quality of search results that get up with searches on the Google search engine. It was found that about 15% of the queries had never been searched before. So, RankBrain’s job also involved automatically understand these queries so the best results could be delivered.
What is BERT?
Coming back to the present, in 2019, BERT was introduced by Google. BERT is Google’s neural network-based technique for Natural Language Processing (NLP) pre-training. BERT stands for Bidirectional Encoder Representations from Transformers. BERT is the biggest change in search since Google released RankBrain. Simply put, BERT helps computers understand language closer to how humans do. According to Google, BERT helps the system get a better understanding the nuances and contexts of words in searches and then be better able to match these queries with their corresponding results which would be more relevant to the search query. It is also being used for featured snippets across all languages.
One very important thing that SEO professionals need to understand that a website cannot be optimized for BERT or even RankBrain, since they are fundamentally designed for better understanding the search queries and delivering ever more relevant results for these queries. The algorithms behind these processes do not impact how websites get evaluated or even how they get matched to their search queries.
How can data science help SEO professionals?
Data science helps SEO professionals by letting them:
- Visualizing which combinations would have the potential to create the biggest impact
- Understanding the needs of the target audience and build their campaigns accordingly
- Understanding the consumer preferences and the challenges they face
- Identifying the referral sources of converting traffic
- Verifying the various parameters like the loading time, indexing, bounce rate, response errors, and redirects
- Verifying which URLs are the most crawled and which are the least crawled
- Identifying the pages that don’t need to be indexed by the crawlers
- Picking up the sources of unusual traffic
There are some amazing ways in which machine learning and data science can be deployed for search engine optimization:
How can machine learning and data science be deployed by SEO professionals?
- Visualization, etc.
These ways can help SEO professionals save time in their day-to-day operations, as well as, build a business case to help the management make better informed decisions.
The prediction algorithms help prioritize the roadmaps by helping highlight the keywords. The basic idea for these algorithms is that it helps the SEO professionals make a fundamental assumption that if they rank first for certain specific keywords, then what their corresponding revenue would be, which can be reasonably predicted using the prediction algorithms, within a reasonable error margin. This also builds the use case for focussing on specific keywords, compared to others, helping the management make the appropriate decisions.
Perhaps, that one task that takes up the maximum amount of time, but delivers good results if the quality is appropriately maintained and everything is planned & executed properly, then it would be writing content. Good content is extremely valuable and also considerably expensive. The generation algorithms in data science could help generate content automatically based on one’s requirements. If not the entire body of content, then these algorithms can at least help produce an initial editorial framework. Once the process of generating the editorial drafts gets automated, SEO professionals can also go on to semi-automate the internal linking, allowing for highlight the primary and secondary anchor tags manually.
When it comes to labelling images and videos, automation using object detection algorithms can be immensely useful. A prime example of such an algorithm bundle would be TensorFlow, that helps label images as well as optimize all the attributes easily. The automation processes can also have a great positive impact on A/B testing for search engine optimization by using the data obtained from Google Ads, viz., CTR, conversions, etc. This would help the system make better sense of the content, like the title and description of the ads, which performs best and can then be applied to SEO. In this way, the automation processes can be applied to A/B testing, while focussing on content generation, and then updating it constantly based on expected performance.
Data science helps SEO professionals understand the quality of the data at hand, which would directly impact the insights they derive. For this to happen appropriately, SEO professionals would need the right tools, for correctly analysing the data. In this way, data science can help select the right data sources for more accurate analysis and better search engine optimization.
It is quite common today that SEO integrates with various other digital marketing areas, such as, content marketing, CX management, CRO, sales, etc. When this happens, it is highly recommended that the brand not rely on any one particular solution for SEO. It is more about building the right ecosystem then, than perfecting just one particular aspect like a tool or method. When data science is used for better search engine optimization one is able to better integrate methods, tools and practices, which helps draw deeper and more accurate insights from the cumulative data mix.
It is an established fact that backing a brand’s search engine optimization practices with other marketing initiatives makes the SEO initiatives stronger. It is thus essential for SEO professionals to collaborate with different teams, such as, the sales team, developers, UX designers, customer support team, etc. to better optimize for different factors. Data science would help SEO professionals put together a universal set of SEO best practices which can be followed by different teams to work together towards a common goal. They can identify the important dependent and independent variables to better understand how different factors impact the business. Some very useful data for this aspect would be that about the top conversion paths and that about conversations & assisted conversions. To make this happen, SEO professionals would need to have a thorough understanding of the customer’s journey from being a part of the target audience to becoming a prospect and on towards becoming a buyer and a consumer. By doing this, the marketing and SEO activities can be better aligned and the outcomes studied & analyzed better.
When one takes a hierarchical approach to data, one often misses out on important insights. In contrast, using a data visualization approach can help:
- Contrast and compare
- Process large volumes of data at scale
- Accelerate knowledge discovery
- Reveal hidden questions
- Spot common patterns and trends
Data science can be leveraged by SEO professionals while conducting a SEO technical audit, and in return they would surely get interesting insights about the site’s performance and health. This data would further help SEO professionals understand page authority, rankings, number of outbound/inbound links per page, etc. But if the data visualization approach is used, they would be able to draw out information like why some pages perform better in search results while some don’t, and how you can improve in different areas. On-page SEO optimization is a great example of how visualization and data science when combined with good SEO practices can help achieve great results.
The biggest advantage of using data science in search engine optimization is that it eliminates the need and scope for guesswork. With data science coming into play, SEO professionals don’t need to make any presumptions about what could and couldn’t work, and they can be better placed to achieve their goals. It also gives SEO professionals a way to quantify their success, in a field where quantifying most things is quite a challenge.
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