Data Science in Search Engine Optimisation and Usability and Behaviour analysis

Data Science in Search Engine Optimisation and Usability and Behaviour analysis

We gather data from user interactions on the platform, including heatmaps to gauge where they spent most of their time, their origins, browsing patterns, and overall behaviour.

By amalgamating this behavioural data with information on search keywords and search engine rankings, we can discern if a visitor landed on our site purposefully. We scrutinise the route they took to comprehend how well they navigated our site and identify any obstacles they encountered in their journey.

This information feeds into a comprehensive, 360-degree assessment of our SEO strategy, page content, and even the website's layout and navigation. It's a perpetually evolving process, endeavoring to attract not just any traffic, but the right kind of traffic whilst ensuring the user has the best possible experience.

We link up with several data sources, amalgamating these with assorted tools and our own API, which connects and examines these tools.

Using a data science-focused approach and our proprietary AI and ML models, we generate reports and insights on search performance and website effectiveness. These insights then reinform other disciplines.

Website building doesn't end at launch. We believe the launch should be the starting point of a fresh journey that yields benefits for both the client and the customer by forging the right connections.

The data we compile and process is presented as visual reports, or a real-time activity dashboard, making it possible to proactively identify actionable occurrences.

Should there be ambiguity regarding page or content performance, strategies like A/B testing can be deployed, where users are presented with two versions and their behaviour monitored to ascertain which is most intuitive or suitable for them.

Unlike conventional analytical approaches to website traffic and user behaviour, our application of data science allows us to examine the situation from various angles, using programmatic methods of mathematical statistics. We determine if values fall outside a permissible range (the so-called confidence interval), and whether this deviation is critical, and if any immediate action is necessary. For instance, if today's yield was less than the same day last week, but not below this month's average, then no drastic action may be required.

While conducting A/B tests, it's important to consider concepts like statistical power, sample size, confidence interval, and statistical significance. Statistical power, measured in percentages, decides the test's likelihood of exhibiting the difference between the two options under consideration.

Another factor is statistical significance, establishing how likely a test result is a matter of chance. The optimal confidence level in A/B testing is 95%, leaving a 5% scope for error (the so-called P-value). The test's statistical significance depends on the confidence intervals and their intersection area. Larger confidence intervals and sample sizes reveal the stability of the test results.

We manage the nitty-gritty of data processing and significance mapping, ensuring a user-friendly report for marketing departments or web managers, making the data we provide easily digestible and comprehendible.