Web data strategy: the missing link between engagement and impact

Across the industry, data capture has become a ritual. Dashboards are filled, reports are sent, but few can connect what’s measured to what matters. Page views, bounce rates and content downloads might be captured, yet few teams can explain what those numbers mean for behaviour, brand impact, or business value.

The issue isn’t technology, it’s translation. Data is collected in abundance but rarely structured, tagged, or connected in a way that reveals actionable insight.

As Tom Botting, Managing Director at Forge DC, put it during our recent London roundtable:

One leader recently shared with me that their team achieved a tenfold increase in engagement on a key digital platform, yet the result barely registered internally. The business couldn’t link the uplift to an immediate commercial outcome, so it wasn’t recognised as value.

That contrast is stark when compared to industries like retail or OTC, where the commercial connection between engagement and outcome is direct. In those sectors, a signal of intent, a search, a repeat visit, or an abandoned basket directly informs marketing action, personalisation, and investment. The link between engagement and revenue is visible and therefore prioritised. In pharma, where attribution is harder to prove, performance often slips down the agenda.

It’s a fitting analogy. The industry has plenty of fuel, but the engine isn’t firing. Until teams connect the pipes, align on purpose, and start refining what they already have, momentum will remain out of reach.

Pharma’s digital ecosystem is complex. Multiple markets, agencies, and technology stacks operate semi-independently, creating gaps in tagging, taxonomy, and tracking that make even simple comparisons difficult. But it isn’t just a technical challenge, it’s cultural and organisational too. Success is often defined by delivery, not performance, with little attention paid to outcomes once a project launches. Different functions value different metrics, so what counts as success depends on who you ask. Without shared goals, there’s no shared accountability.

You can’t fix data by fixing one thing. It’s a system problem, not a spreadsheet problem. And until data is treated not as a by-product but as the engine that powers digital performance, organisations will keep mistaking motion for progress.

The good news: there’s a way forward. At the roundtable, Tom shared his Web Data Strategy: Path To Action framework, a practical roadmap for teams who want to transform their websites from static channels into connected engagement engines.

It’s built around six foundational steps, grouped into what to do now, next, and later:

NOW

A. Content taxonomy – Apply structure and meaning to every asset and interaction so engagement can be analysed by campaign, theme, or journey phase.

B. Consistent tagging – Ensure all sites and channels use the same taxonomy so data can be stitched together and compared without fragmentation.

NEXT

C. ID resolution – Link web activity to known HCP profiles by resolving identities across devices and platforms to create a connected view.

D. Unified customer profile – Combine behavioural and attribute data to form longitudinal profiles that highlight intent and readiness.

LATER

E. Audience strategy – Define and segment audiences based on unified profiles to identify customer signals and opportunities.

F. Personalised experiences – Activate insights across web, email, field, and events to deliver relevant, timely, and measurable engagement.

What is the signal?

Describe the specific action, behaviour, or data point that could indicate intent, engagement, risk, or opportunity. For example, the data shows an individual repeatedly visiting a dosing calculator page.

How would we tag and track it?

Define what information or metadata you’d record about this action. What did they look at, how many times, how recently? How would you capture it and connect it to a person — for instance, tracking visits on the website and linking them to a known profile if possible. In this example, that behaviour could be tagged and connected with an individual user ID.

How would we use it?

Explore how that signal could be leveraged across different use cases: to trigger personalised content, to enrich analytics and attribution models, or to enhance segmentation and next-best-action logic. In this case, we might infer that the user would benefit from connecting with an MSL or sales rep to help them work through specific cases, or perhaps trigger the delivery of a short video about dosing.

This is where data maturity begins. It’s not about collecting more data, but connecting what you already have. The point isn’t reporting, it’s recognising signals, defining meaning, and responding intelligently.

A mature web data strategy turns measurement into movement. It connects what’s happening online to what happens next. The organisations that are winning with data aren’t those collecting more of it, they’re the ones connecting it better.

They’ve tuned the engine, not just added more fuel. They know what drives performance and when to accelerate. They can see not only what users do, but why, and what to do next.

For everyone else, the recommendation is this: stop drilling new wells and start refining the ones you already have. Because in this landscape, data is the fuel, but without alignment, accountability, and activation, the engine won’t run.