Data Science in Search Engine Optimisation and Usability and Behaviour analysis

By integrating this behavior with data on search keywords and search engine rankings, we can discern whether the users who land on your site exist for a relevant reason. We examine the path they take to better comprehend how they navigate through the site and identify any issues experienced throughout their journey.

All this information feeds into a comprehensive 360-degree process that evaluates the SEO strategy, page content, and even the site layout and navigation. Aiming not only to attract users but to attract the right users and ensure they have the best possible experience.

We integrate several data sources and tools via our own API, which interrogates these resources.

Using a data-driven approach, employing AI and machine learning models, we generate our own reports and gain insights into search and site performance. These insights then inform the other disciplines.

Site development doesn’t end at launch. Instead, we view it as the start of an ongoing journey that benefits both the client and customer by creating the right connections.

The data we collect and process is delivered as visual reports or a live dashboard displaying real-time activities, and we proactively pinpoint actionable events.

To determine the efficacy of a page or content piece, we could implement strategies like A/B testing. This involves presenting one version to half the users, and another version to the remaining users. We then monitor their behavior to determine which version is better suited to them.

Our approach to analyzing site traffic and user behavior is unique. We utilize data science and programmatic methods of mathematical statistics to view the situation from different perspectives. We'll check if values fall outside a permissible range (the confidence interval) and determine whether this change is critical.

When conducting A/B tests, it's essential to consider concepts like statistical power, sample size, confidence interval, and statistical significance. Statistical power is measured in percentage and determines how likely the test will show a difference between two given options. Another aspect to consider is statistical significance. It assesses the likelihood that a test result didn't happen by chance. The ideal level of significance (or, confidence) in A/B testing is 95%. Meaning, the probability of error (the P-value) lies within the remaining 5%. The statistical significance of a test depends on the confidence intervals and the area of their intersection. Confidence intervals are the range of values within which a population parameter will fall, given a certain confidence level, and a larger sample size indicates how stable the test results are.

We handle the technical aspects of processing and mapping data, providing you with a user-friendly report that's easy for marketing departments or web managers to understand and digest.