Lessons from our conversation with Dave Anderson, VP of Product Marketing at Contentsquare
Every company claims to care about customer experience. But when it comes to digital experience specifically – what users actually see, feel, and do on a website or app – there’s still a massive blind spot.
As Dave Anderson put it in our recent conversation, “Everyone thinks they’re delivering a great digital experience. The truth is, they’re not measuring it properly.” Most companies still rely on legacy analytics models that surface fragmented data and fail to connect it meaningfully to actual customer behavior. The result? Misguided decisions, siloed strategies, and missed opportunities.
The Problem: Analytics Without Empathy
Traditional web analytics platforms were built for counting things – clicks, bounces, scrolls – not for understanding people. Marketing teams look at one dashboard. Product managers another. UX teams something else entirely. But very few of these tools reveal how the experience actually feels to the end user, or how different functions can act on that experience together.
This fractured view makes it nearly impossible to diagnose root problems in the customer journey. One team might celebrate a campaign’s CTR, while another struggles with cart abandonment. Everyone’s technically “right” – but nobody’s seeing the full picture.
The Shift: From Metrics to Meaning
What’s emerging now is a deeper, more product-centric approach to digital optimization. Instead of just tracking isolated KPIs, leading teams are asking better questions: 
- Where are users getting stuck – and why?
- What signals do their actions give us about intent or confusion?
- Which moments in the journey matter most?
And most importantly: How can we make this insight immediately useful to everyone – without a degree in data science?
This is where visual analytics enters the scene. Rather than presenting tables of stats or heatmaps in isolation, new platforms overlay behavioral data directly onto the digital experience itself. Teams can see, in real time, how users are interacting with specific elements – like a checkout button, a product carousel, or a piece of content.
It’s not just easier to interpret. It reframes the entire conversation around action.
Case in Point: Clarity That Drives Results
In our discussion, Dave shared examples of how companies are turning visualization into performance. One retailer increased call-to-action visibility by 50% simply by reordering page elements. Another improved checkout conversion by 10% by identifying unexpected friction during a key step.
These aren’t dramatic redesigns or costly tests. They’re small, evidence-based optimizations driven by clearer insight into actual user behavior. In short: clarity → alignment → action.
Cross-Team Translation Layer
One of the most compelling advantages of visual analytics is its power to unify teams. When product, UX, and marketing professionals can look at the same screen, see the same friction points, and agree on what needs fixing, organizational politics give way to shared priorities.
It stops being about whose metric matters more, and starts being about what the customer actually needs.
As Dave put it, “It’s simple, it’s visual, and it gives people the design direction they can make decisions on.” That simplicity matters. Especially in environments where speed and consensus are hard to come by.
What’s Next: Predictive + Prescriptive
Looking forward, AI will accelerate this shift from observation to orchestration. Visual tools are beginning to evolve into advisors – flagging friction before it becomes a problem, suggesting layout changes, and even surfacing revenue opportunities.
Imagine logging into your dashboard and seeing a prompt like:
“Move this product higher on the page – based on intent signals, it will likely outperform.”
That’s not just analytics. That’s automated insight, tailored to your goals and surfaced without a data analyst.
Why This Matters: Making Analytics Accessible Again
Ultimately, the success of any analytics tool comes down to this: can someone in product marketing use it without instruction and immediately find value?
If the answer is yes, then insight becomes a shared language – not a privilege of the data team. That’s how companies become truly customer-centric. Not by tracking more – but by making better sense of what they already have.
