Current Issues Facing Clickstream Analytics QA For many years, clickstream analytics quality assurance has meant that engineering and QA resources have to manually spot-check clickstream beacon calls for errors or unexpected data values.    Some clickstream tools also have no data transformation layer,...

In an ideal world, every organization would connect their offline and online data successfully. However, for most organizations today, the goal of connecting offline and online data is simply out of reach due to a plethora of issues. Some of...

In today’s world, many companies rely on their successful decision-making by using data. Turning data insights into action is key, but in order to make changes from those insights, it’s important to visually format that data. By visually formatting data,...

Many people like the idea of having a data-driven culture, but they don’t know where to start in creating one for their organization. It’s just like any philosophy. The idea sounds amazing, but implementing it is a completely different story....

In many analyses, users are segmented into two categories: those who are engaged with a specific aspect of your digital property and those who are not. The goal is to discover how or why those users convert. However, by doing...

When using an app or exploring a website, I’m sure you’ve passed some serious judgment on the user experience of that digital property. Whether you were trying to order food, get quotes on some insurance, or swipe to find your...

This week we released our eBook, A Guide to User Journey Analysis. This comprehensive guide shows you how to use the Cognetik User Journey Analysis Model, which is a user-centric approach that eliminates the need for isolated product analyses. In...

What Is User Engagement & Why Measure It? Whether you are new to analytics, or just need a refresher, user engagement refers to how an individual responses to a digital experience. That experience may be via desktop, tablet, mobile, or an...

There is surely a lot of buzz about artificial intelligence (AI), machine learning (ML), and data science (DS). Views vary widely from what people are doing with AI now and what could be done in the future. The practitioners of...