For a digital analyst, it’s easy to get caught in a cycle of answering the same basic questions:
- Which channels brought the most traffic?
- How many users completed the checkout?
These and similar questions are needed to report performance, but they provide a superficial understanding of user behavior and only limited help in creating value.
To derive real value from analytics, we need to dig deeper.
We need better questions rather than more data.
The most important (and dreaded) question we should be asking is: Why?
Moving Beyond the Basics
Asking “why” forces us to focus on the motivations, pain points, and decision-making processes behind user actions.
We move from just measuring traffic and conversions to analysis, recommendations and experimentations when we try to understand why users behave the way they do.
Consider these critical questions:
- Why are customers abandoning their carts at step three of the checkout process? Identify the specific friction points (unexpected shipping costs, complicated forms, slow load times etc.) to improve the website, user experience and conversion rate.
- Why is one marketing campaign outperforming another? Understanding message relevance, audience targeting, and creative appeal helps us refine marketing strategies. This is even more valuable than simply reallocating the budget from one channel to another.
- Why do returning customers have a higher AOV? And why do they return? By analysing their purchase history and interactions with marketing touchpoints, we can recommend strategies to maximise customer lifetime value.
Understanding “Why?” enables businesses to move from simply reporting on metrics to making informed, strategic decisions that drive growth.
But, of course, answering these questions is far from easy. Moreover, it is impossible when limiting yourself to GA4 / Piwik PRO / Adobe Analytics data.
The Challenge of Answering “Why?”
Most analytics tools, including GA4, provide a solid foundation of quantitative data. We also get quantitative data from advertising platforms, Google Search Console, and SEO tracking tools.
But numbers alone never tell the whole story.
We must look beyond analytics dashboards and quantitative metrics to develop meaningful insights. We need additional sources of information.
Context: The Power of Qualitative Data
GA4 can show where users drop off, but it won’t tell us why they left. The same applies to advertising platforms: they report clicks, events and conversions but never answer the why question.
This is where qualitative insights come in. User feedback, product reviews, surveys, and session recordings help us to understand users’ pain points, frustrations, and motivations.
For example, heatmaps (from Hotjar, Clarity, and similar tools) might reveal that users struggle to find a call-to-action button. Survey responses might indicate that unclear pricing is a significant problem for SaaS customers.
Sometimes, simply reading the page copy is all you need to understand why users exit the site as fast as possible.
In short, you need qualitative context for your metrics.
Knowledge: External Factors
Data doesn’t exist in a vacuum. Neither does your company and business.
Industry trends, economic trends, seasonality, and competitor activity all influence user behavior.
A drop in conversions might not be a UX issue. It could be due to macroeconomic factors, international trade wars, shifting consumer preferences, or increased competition.
You need external knowledge to ensure your analysis and answers are grounded in reality rather than assumptions and guesswork.
Very often, consultants have limited knowledge of these external factors. This limits our understanding of data and challenges us to learn more about the business environment.
Collaboration: Cross-functional Insights
Digital analytics should not operate in isolation.
Marketing, product, and customer support teams all bring valuable perspectives that can enrich data analysis.
Marketers can provide insights on campaign targeting, product teams can share usability challenges they have identified, and customer support teams can highlight common pain points users report.
But we should not limit ourselves to our traditional data sets. We can also ourselves analyse qualitative data collected e.g. with user surveys.
By doing this, our quantitative data can also be used to validate ideas and hypotheses presented by marketing, product, and customer support teams.
From “Why?” to Hypothesis-Driven Experiments
Concrete answers to “Why?” can be hard to find even with a wealth of data.
However, we can develop hypotheses to test and refine over time.
(Do you remember my CARE model, where R stands for recommendations and E for experiments?)
Example 1: Cart Abandonment
A digital analyst notices that cart abandonment spikes when delivery options are displayed.
Instead of making assumptions, they form a hypothesis:
Simplifying delivery options will reduce abandonment rates.
This hypothesis can be tested through A/B experiments.
We shouldn’t simply jump from analysis to action. Experiments are necessary to validate (or shoot down) our ideas.
Example 2: Mobile Conversion Rate Challenges
Data reveals that mobile traffic has a significantly lower conversion rate than desktop traffic.
Rather than accepting this as a given, analysts develop a hypothesis:
Making the add-to-cart button more prominent will increase conversions.
By implementing changes and monitoring results, businesses can iterate toward better performance.
Formulating our findings as hypotheses makes us more likely to influence website development and marketing tactics.
And this makes our work more satisfying!
Recommend and Experiment!
Developing hypotheses is only the first step. To capitalise on the “Why?” approach, businesses need a culture of experimentation and continuous improvement.
This means that you should
- Test solutions instead of jumping to conclusions, use controlled experiments to validate changes.
- Iterate based on data, refine hypotheses based on real-world results rather than gut feeling.
- Align analytics with business objectives, ensuring data drives improvements rather than just reporting.
Businesses can move beyond vanity metrics by shifting the focus from Which? and How many? to Why?
How to Make Them Click?
When we understand why users click like they do, we can design better websites and user experiences, optimise marketing efforts, and drive sustainable success.
This might sound like I suggest analysts become CRO specialists and advertising strategists.
This isn’t far from truth – we need to provide testable hypotheses and recommendations, not only data and reports for others, so they can base their recommendations and hypotheses on our data.
The world around us changes quickly. Automation and AI replace humans in many tasks, from coding to standard analyses. We need to rethink what digital analytics is all about to stay relevant.
I suggest we focus on creating value instead of collecting data.
To do this, we need to answer questions starting with Why.