Implementing a Web Analytics Framework for Data-Driven Decision Making
We strongly advise implementing a web analytics framework if you’re new to integrating web analytics into your web strategy. For instance, consider exploring the six steps for the analytics effectiveness framework outlined in ‘Google Analytics Breakthrough: From Zero to Business Impact’ by Feras Alhlou, Shiraz Asif, and Eric Fettman, which provides valuable guidance that can be adapted for use with GA4 and other web analytics platforms. The framework consists of the following steps:
This stage encompasses planning objectives, data gathering, and initial exploratory data analysis (EDA). In the planning objectives phase, stakeholders involved in the analytics process collaborate to define objectives and key results (OKRs) as well as key performance indicators (KPIs) that will be monitored and evaluated. An example of an objective could be to increase organic traffic to blog posts and improve their ranking in terms of relevant keywords. Corresponding KPIs might include the number of organic sessions and views originating from Search Engine Results Pages (SERPs).
In the data gathering phase, you need to gather data about your website visitors and their interactions with the site on a specific timeline. You can use a web analytics tool like GA4 to track relevant user interactions and site metrics. Additionally, at this stage, you also need to ensure that your data is accurate and reliable before proceeding with analysis. This may involve removing incomplete or inaccurate data as well as converting the data into a consistent format if necessary.
Within the EDA phase, you want to get a general summary of your data. Usually, this can be done by utilising visualisation methods, taking some descriptive statistics, or looking for patterns, anomalies, and outliers in your dataset.
Once your OKRs are established and you have a clear understanding of your data structure, it’s essential to tailor your data collection to align specifically with your organisation’s needs. This involves carefully filtering and segmenting your data to focus on relevant aspects. For example, if your goal is to grow traffic primarily through organic channels, you should configure your analytics to specifically track and analyse organic traffic, filtering out non-organic sources to maintain a clear focus on your strategic objectives.
Additionally, aggregation can also mean consolidating all metrics and data points into a single, coherent dataset. This can be achieved through various methods, such as integrating key metrics into a unified visualisation dashboard by utilising tools like Looker Studio.
Is a single web analytics platform sufficient to meet all your data needs, or is there a necessity to integrate insights from one platform with another to obtain a comprehensive view? This again comes back to the objective that you wanted to achieve on the first step.
Visualisation plays a crucial role in effectively communicating the insights derived from your web analytics data to your audience. Rather than solely relying on basic screenshots from your web analytics platform, it’s beneficial to create custom visualisations that are more targeted and informative. For example, you can design visuals that highlight the top 5 blog posts in terms of organic sessions and views or illustrate which landing pages have consistently garnered the highest organic traffic and associated ranking keywords over a period of several months.
Interpret and Act
In the dynamic world of all types of analytics, we are very familiar with the four progressive stages of analytics, each unlocking new dimensions of insight and strategic value.
Descriptive Analytics: This foundational stage is your compass in the data landscape, offering a clear view of what has happened and what is unfolding. Here, the focus is to answer questions such as what has already happened and what is currently happening. You can ask and answer questions like “How much organic traffic have we received so far for the past year?” or “What is our current growth traffic rate on our service landing pages?”
Diagnostic Analytics: Think of this as detective work in your analytics quest. Building on the narrative crafted by descriptive analytics, diagnostic analytics dives deeper, dissecting the ‘whys’ and ‘hows’ of past events. It’s a blend of art and science, using data to unravel the tapestry of factors and influences that have shaped your business outcomes. At this stage, you can drill down to a more granular level, asking questions such as “What contributes to the spike in organic search in June and July?” or “Does the increase in the number of ranking keywords have any relationship to the amount of organic traffic we received over the past few months?”
Predictive Analytics: Here’s where we turn foresight into a competitive edge. Predictive analytics is the crystal ball of analytics, employing historical patterns and sophisticated algorithms to forecast future scenarios. Various regression-based techniques and time series forecasting methods can be applied at this stage.
Prescriptive Analytics: This goes beyond predicting future trends or outcomes (as done in predictive analytics) by recommending specific actions and strategies. It leverages machine learning algorithms and automated optimisation techniques to analyse large datasets. The goal is to not only forecast future scenarios but also to provide actionable insights and recommendations that can actively shape those outcomes.
Throughout each stage of your web analytics journey, it’s crucial to integrate your ‘intuition’—what we prefer to call ‘domain knowledge’—with the analytics process. Think of domain knowledge as a bridge, connecting the insights from your data with the understanding you have of your customers and business, informed by your experience. Your expertise is key to making sense of the analytics results you obtain, enabling you to translate these findings into actionable insights effectively.
It’s essential to skilfully apply your domain knowledge across all stages of analytics. This involves not just interpreting the results but also playing a critical role in formulating the right questions, setting meaningful objectives, selecting appropriate statistical methods, and ultimately translating these insights into informed business decisions. Leveraging domain knowledge in this way ensures that the data-driven insights are not only accurate but also relevant and practically applicable to your specific business context.