What is Web Analytics
It is not just an analysis process, but also about putting yourself in the user’s shoes, frequently measuring KPIs, collecting data and producing reports based on user navigation and interactions, whether on a website, app or any other platform that the user has access to your business.
How we use it
It is important to deeply understand the user for a good digital marketing strategy. This involves analyzing behavior, journey, devices, channels and habits, as well as demographic aspects. Data collection is essential for improving marketing strategy, evaluating the effectiveness of campaigns, optimizing the sales funnel and measuring return on investment.
Furthermore, web analytics analysis helps identify navigation problems, improve content and provide a more fluid and relevant experience for the user. The use of descriptive and predictive analyzes based on navigation data allows both the understanding of the past and the prediction of future behaviors, which contributes to personalization, optimization of user journeys and expectation of results.
If you do an analysis, you will fit into one of these two classes!
The two main types of analytics are: descriptive analytics and predictive analytics.
Descriptive analysis involves understanding what happened in the past, such as drops in access, conversions and user behavior. Be an explorer! This helps you understand the history and results of past campaigns.
The most common analyzes to be explored on a daily basis in web analytics are drops in access and conversions, which can be caused by several possible causes, such as problems in input channels, changes in campaigns, availability of products/services, technical aspects, usability , and impact of competition.
User behavior analysis: The importance of monitoring user behavior is extremely important, including elements such as screen scrolling, time spent on pages and click/chat usage. The building of user habits over time also stands out.
Techniques: Heat maps, click and mouse movement maps and eye movement, to understand users’ interaction and interest with the website.
In general, each scenario has its own complexity to analyze and there is a need to monitor a variety of factors to understand and optimize the user experience.
Predictive analysis uses historical and behavioral data to help project the future, using statistical modeling and artificial intelligence. These analyzes are essential for taking informed actions based on browsing data, improving the effectiveness of strategies not only in digital, but in various areas of the company.
It is not always easy to translate effective actions just through the customer journey, companies usually have a comprehensive mix of products, several channels and in many cases isolated systems that do not talk to each other. An example presented to help with more strategic actions is the analysis and construction of a Lifetime Value (LTV) model, which represents the monetary value of the customer’s relationship with the company.
When building this model, several variables are considered with navigation data, behavior and transactional data, etc. It is very important to map variables relevant to the business in a way that is adequately reflected in the model. As a result, by using LTV strategically, it is possible to understand and meet customer needs over time and contribute to actions in different areas of the company.
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