Has Your Business Unlocked the Full Potential of Web Analytics?

The Pivotal Role of Web Analytics in Modern Marketing

Web analytics emerged as a powerful tool for business owners, enabling them to extract vital insights from their online activities. As of 2023, a notable 71% of businesses have embraced the digital realm by establishing at least one website, a movement significantly influenced by pandemic-driven changes. This trend underscores the ever-growing significance of web analytics in today’s marketing world.

Within the realm of marketing, web analytics acts as a treasure trove of information. It helps you distinguish between the usual stuff, such as seeing the performance of organic and paid traffic, and more specific things, such as highlighting your most effective landing pages. You can also meticulously track every session, view, and even pinpoint things like blog posts that captivate audiences and rank prominently for specific keywords. In short, it’s the navigational tool steering your business’s digital endeavours.

Building on the foundation of web analytics, the term “marketing analytics” has emerged. This can be described as the application of data analysis techniques to extract marketing and customer insights, aiding in informed decision-making. Whether it’s web analytics, social media analytics, or other forms, as long as they have the element of “analytics” in the realm of marketing, they all converge under the broad canopy of the “marketing analytics” term.

To truly grasp the advantages of web analytics and gain a competitive edge, it’s essential to delve into the core of analytics: statistics and data visualisation. Statistics form the bedrock of analytics, offering tools and techniques to interpret data and derive meaningful insights. For instance, take a look at some of these statistical techniques we found commonly used in web analytics:

• Mean (Average): This is your go-to metric for a quick snapshot of performance. For instance, calculating the mean of organic sessions on your blog’s landing pages offers a bird’s-eye view of your content’s traction. It’s a straightforward way to gauge overall engagement and identify trends.
• Median (Middle Value): When your data is a mixed bag with some outliers (like a few blog posts driving exceptionally high traffic and some significantly low), the median comes to the rescue. It gives you the middle ground, ensuring these outliers don’t skew your perspective. This is crucial for making balanced, data-informed decisions about your content strategy.
• Standard Deviation: When your data exhibits a normal distribution, the standard deviation becomes a crucial tool for understanding the spread of your data. Consider this scenario: You are tasked with evaluating the performance of all your blog posts. You focus on URLs following the pattern /companyABC.com/en/blog/ and successfully gather data on the number of sessions each post has received over the past three months. After compiling this data, you calculate that, on average, each blog post receives 60 sessions and a standard deviation of 3. The average gives you a baseline for comparison. The standard deviation, in this context, will help you understand how much the session count for each blog post varies from this average of 60 sessions, so you can answer questions like “How many landing pages perform below, above, or around the average of 60 sessions”, provided that your data exhibit a normal distribution. If you want to know more about standard deviation, we recommend reading about the 68-95-99% rule!
• Linear Regression: This technique helps you understand things like how different traffic sources (like organic search, paid ads, and direct traffic) impact your website’s revenue. Essentially, it quantifies the contribution of each traffic type (independent variables) to your overall revenue (dependent variable). This insight is invaluable for prioritising marketing channels and optimising your budget for the best ROI.

Data visualisation, on the other hand, has recently emerged as a critical part of web analytics. In the past, its value was often overlooked, seen merely as a cosmetic touch for reports or news articles. Now, it’s recognised as an essential tool for marketers and web analysts, transforming complex data into clear, digestible insights. Analysts are often faced with the dilemma of making data visualisation easy to understand without compromising its accuracy or its ability to capture as many relevant data points as possible. That’s why asking the right questions before making any type of visualisation to showcase the result of an analysis and choosing the correct type of visualisation are as important as the analysis itself. 

Alberto Cairo, in his book “The Truthful Art: Data, Charts, and Maps for Communication”, beautifully explains how he views data visualisation: “Any visualisation is a model. And good models abstract reality while keeping its essence at the same time. It means models that are truthful, accurate, informative, and understandable”.

The Case for Experimentation – A/B Testing

Having delved into the core concepts of analytics, we can now appreciate the true significance of web analytics in modern marketing. This goes beyond mere number crunching or the passive tracking of various web metrics. It’s about translating your website data into actionable insights that can significantly enhance your web performance. In this context, the key lies not just in gathering data but in understanding its relevance and application.

For example, imagine you’re planning to conduct an experimental A/B test using Google Analytics 4 (GA4) data to improve the conversion rate of a registration form by placing it on specific high-traffic landing pages of your website. After identifying the pages with the highest traffic, you aim to determine whether strategically placing your registration form on these pages would increase its conversion rate.

Before diving into the metrics and analysis, it’s essential to clearly define and understand your business objectives and the metrics you will need to achieve those objectives. This involves selecting your web analytics platform (in this case, GA4), identifying the key metrics to track (such as the past and current event conversion rate for the form submission, page traffic, and total traffic that comes from specific traffic sources), establishing the desired outcome (higher conversion rates), setting a timeframe for the test, and deciding on the specific statistical techniques to be applied, such as hypothesis testing and confidence interval analysis.

The initial step also includes ensuring access to the necessary data. You should begin with an exploratory data analysis (EDA) to understand trends and patterns in historical data. This step is crucial for establishing a baseline against which you can compare the results post-experiment. In GA4, this may involve reviewing reports on user engagement, traffic sources, and event completions and setting up appropriate event tracking for the form submission if it hasn’t already been done.

The preparatory steps are essential for a successful experimental test, serving as a foundation before delving into the analytics. This preliminary work ensures you have a clear understanding of your objectives, preventing you from getting lost in endless cycles of analysis. It’s also crucial to have a well-defined plan for what actions and decisions you’ll take based on the results of your analysis.

Understanding Your Web Analytics Platform Features and Metrics

In the current era of web analytics, the data and metrics available largely depend on the analytics tools used and the needs that you want to address. For example, as of 2022, Google Analytics is the most popular platform, boasting over 28 million active users. Currently, approximately 37.9 million websites utilise Google Analytics for their web analytics needs. GA4, the current version of Google Analytics, has the most up-to-date features that comply with global data privacy standards, including a full IP anonymisation feature with some machine learning capabilities. (Click here to read our previous article about how to navigate the rise of GA4!)

Once you successfully set up and implement GA4 on your website, one of the key areas to focus on is the “Life Cycle” collection in the reports section, which offers a holistic view of the user journey on your website. This section provides various metrics such as user acquisition, engagement, average engagement time, page views, and sessions. 

In the Acquisition section of the Life Cycle collection, you have the “New users” metric, which is broken down by traffic source (or channel group). This allows you to understand how many new users came to your website from each traffic source. Knowing this information can help you prioritise and optimise your marketing channels to attract even more active users to your website. However, this depends on your specific business goals and objectives. GA4 also offers the flexibility to filter this data by traffic source and other parameters.

A particularly notable, though sometimes underrated, feature of GA4 is the ability to export these data points outside the GA4 ecosystem in various formats like CSV. This functionality enables further analysis using tools like Python and its analytics libraries, allowing for the creation of customised visualisations tailored to your specific needs and the application of statistical techniques, which vary depending on the insights you seek to uncover.

Given the vast amount of information and metrics available on even a single web analytics platform, it is easy to become overwhelmed by analysis paralysis. Therefore, the key to effective web analytics is to have clarity of purpose—to know what specific information you need, to understand its importance, and most critically, to determine how you will use these insights to make tangible improvements to your website. This approach ensures that web analytics is not just a formal exercise in digital marketing practice but a strategic tool for driving meaningful progress in the digital landscape.

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:

Collect

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.

Aggregate

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.

Integrate

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

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.

Discover how our Analytics experts can transform your Data into actionable insights

At Bridge, we are a step further from data analysis; we transform the insights that we got from the analysis into a roadmap for your digital success. Our team of web analytics experts is equipped with the latest web analytics tools and techniques to help you make sense of your web data. Imagine not just observing but truly understanding the core of your website’s performance and the digital footprints of your customers—that’s the clarity we bring to your digital strategy.

We also go beyond analytics. We blend advanced analytics with insightful market research, statistical modelling and robust business intelligence (BI) reporting. This unique combination ensures that every aspect of your business’s digital presence is not just understood but optimised for performance.

But it’s not just about numbers and graphs. We believe in making analytics accessible and actionable for our clients. Our expert data storytellers will guide you and help you understand the story behind the numbers and how you can translate it into actionable business strategies. 

Contact us today to find out how we can help you and your team translate your data into powerful insights to drive growth.