The Omnichannel Measurement Problem in Pharma

Every pharmaceutical company in 2026 describes itself as "omnichannel." It's in the strategy decks. It's in the investor presentations. It's in the job descriptions. The word appears so often in pharma corporate communications that it's lost almost all meaning — which is convenient, because most companies can't actually measure whether their omnichannel strategy is working.

Having designed measurement frameworks, customer engagement scores, and content effectiveness tools for pharmaceutical operations across multiple markets, I can tell you from direct experience: the gap between omnichannel ambition and omnichannel measurement capability is enormous. Not because the data doesn't exist, but because the organisational structures, incentive models, and analytical approaches that most pharma companies use were designed for a single-channel world. Bolting "omnichannel" onto a fundamentally channel-centric operating model doesn't produce omnichannel measurement. It produces multi-channel confusion with better PowerPoint slides.

The State of Pharma Omnichannel in 2026

Let's be honest about where the industry actually is, rather than where the conference presentations claim it is.

Most pharmaceutical companies have invested heavily in omnichannel technology. They've deployed Veeva CRM and Veeva Vault for field force management and content distribution. They've built HCP portals with personalised content journeys. They've implemented marketing automation for email campaigns. They've invested in remote engagement platforms that emerged during COVID and never went away. Some have added AI-powered next-best-action engines that recommend which channel to use for which HCP.

The technology stack is impressive. The measurement capability is not.

Ask a brand director to quantify the ROI of their omnichannel investment and you'll get one of three responses. The first is silence — they genuinely don't know. The second is channel-level metrics dressed up as omnichannel measurement: "our email open rate is 32%, webinar attendance grew 15%, rep call frequency increased by 8%." These are activity metrics that describe what happened in individual channels without connecting them to each other or to business outcomes. The third — and most dangerous — response is a sophisticated-sounding but fundamentally flawed attribution model that claims to have solved the omnichannel measurement problem by assigning credit to individual touchpoints in a multi-touch journey. More on why that's problematic in a moment.

Why Traditional Metrics Fail in an Omnichannel Context

Channel-Level Metrics Are Necessary but Insufficient

I'm not saying email open rates don't matter. They do — as operational indicators of channel health. If your email open rate drops from 32% to 18%, something is wrong with your email programme. But an email open rate tells you nothing about whether that email contributed to a physician's decision to prescribe your product, attend your advisory board, or recommend your treatment to colleagues. It measures reach, not impact.

The same applies to every channel-level metric in common use. Webinar attendance measures interest, not behaviour change. Rep call frequency measures activity, not influence. Portal login rates measure curiosity, not commitment. These metrics are useful for optimising individual channels. They are useless for understanding whether your integrated engagement strategy is actually driving clinical or commercial outcomes.

The problem compounds when you try to aggregate channel-level metrics into an "omnichannel" view. Adding up email opens + webinar attendees + rep calls + portal visits gives you a number that means nothing. It's like calculating a restaurant's quality by adding up the number of meals served, the Yelp reviews, and the parking spaces. The units don't combine meaningfully.

Attribution Models Are Philosophically Flawed for Pharma

In consumer marketing, attribution models — last-touch, first-touch, linear, time-decay, algorithmic — have been refined over decades. In pharma, they don't work, and not just because of data limitations.

The fundamental problem is that pharmaceutical customer engagement doesn't follow a linear funnel. An HCP doesn't move neatly from "awareness" to "consideration" to "prescription" through a sequence of touchpoints you control. Their prescribing decisions are influenced by clinical evidence they read in journals you didn't place, peer conversations at conferences you didn't sponsor, patient requests you can't track, formulary decisions made by committees you have limited access to, and personal clinical experience that accumulates over years.

Within this complex web of influences, your omnichannel touchpoints — the rep visit, the email, the webinar, the portal content — represent a fraction of the total information diet. Claiming that a specific touchpoint "caused" a prescribing decision is not just analytically questionable. It's epistemologically suspect. And building your measurement framework on attribution models that make this claim will lead to bad strategic decisions — over-investing in whichever touchpoint the model arbitrarily credits, while under-investing in activities whose influence is real but harder to measure.

Engagement Is Not a Proxy for Behaviour Change

This is the insight that I think the industry is slowest to accept. A highly engaged HCP — one who opens every email, attends every webinar, downloads every resource, takes every rep meeting — may simply be a voracious learner. They may be deeply engaged with your content and completely unchanging in their clinical practice. Conversely, a physician who attends a single advisory board and has one deep conversation with an MSL may fundamentally change their treatment approach based on that interaction.

Engagement quantity and behaviour change are not the same thing. Any measurement framework that treats them as interchangeable will systematically overvalue digitally visible interactions (which are easy to track but often low-impact) and undervalue high-touch, high-influence interactions (which are harder to track but often decisive).

What Actually Works: A Four-Layer Measurement Framework

Based on experience building these systems for pharmaceutical operations, here's what a credible omnichannel measurement framework looks like. It's not a single metric. It's a layered analytical approach that connects engagement data to business outcomes through intermediate indicators.

Layer 1: Customer Engagement Scoring

The foundation is a composite engagement score that captures four dimensions: depth (how meaningfully is the HCP engaging?), breadth (across how many channels?), recency (how recent is the engagement?), and trajectory (is engagement increasing, stable, or declining?).

This is not a simple activity count. It requires weighting interactions by their significance — an advisory board participation is worth more than an email open, and a detailed MSL discussion is worth more than a webinar registration that never converted to attendance. The weights should be calibrated against outcomes data, not set by committee assumption. I've seen companies where the engagement scoring weights were decided in a workshop by brand managers. This is like calibrating a medical device by asking the marketing team what the readings should be.

A well-built CES gives you a single, interpretable number for each HCP that reflects the depth and quality of your relationship. But it's a leading indicator, not an outcome measure. Which is why you need the next layers.

Layer 2: Journey Analytics

Journey analytics maps the actual sequence of touchpoints each HCP encounters — not the idealised journey you designed in a workshop, but the messy, non-linear, cross-channel reality of how real physicians interact with your brand.

This is where most companies hit a wall, because it requires stitching together data from multiple systems — CRM data from Veeva, digital engagement data from your web analytics, email interaction data from your marketing automation platform, event attendance from your event management system, medical affairs interactions from your MSL CRM. The data exists, but it lives in silos with different identifiers, different timestamps, and different levels of granularity.

When you do connect the dots, you start seeing patterns that channel-level analytics completely miss. You discover that a specific sequence of interactions — say, an MSL visit followed by a targeted email followed by a webinar — is associated with much higher engagement progression than the same touchpoints in a different order. You discover that HCPs who engage across three or more channels are dramatically more likely to change their clinical behaviour than those who engage heavily in just one channel. You discover that the timing between touchpoints matters as much as the touchpoints themselves.

Layer 3: Content Effectiveness

This is the layer that most pharma companies skip entirely, despite spending millions on content production. Which content assets actually drive engagement progression? Which ones are consumed but have no measurable impact? Which ones are never consumed at all?

Content effectiveness measurement requires connecting content interaction data (which piece of content did the HCP engage with, for how long, and what did they do next?) to engagement trajectory (did their engagement level change after consuming this content?). When you can answer these questions across your content portfolio, you can make informed decisions about content investment — rather than the current approach, which in most pharma companies is to produce content based on what brand managers think is needed, with minimal feedback on what actually works.

The findings from content effectiveness analysis are consistently humbling. Most pharma companies are producing 3-4x more content than their customers actually consume, and the content that drives the most engagement is rarely what the marketing team predicted. Short, clinically focused content consistently outperforms long-form brand messaging. Content co-created with KOLs consistently outperforms content developed internally. Peer-generated content consistently outperforms company-generated content. These are patterns that only become visible when you measure content effectiveness systematically rather than relying on production volume as a proxy for impact.

Layer 4: Outcome Correlation

The ultimate question: does engagement drive business results? This layer connects engagement patterns to commercial and medical outcomes — prescribing behaviour, formulary adoption, clinical protocol changes, HCP advocacy.

I'm deliberately using the word "correlation" rather than "attribution" because I think intellectual honesty matters. In a complex, multi-influence environment like pharmaceutical prescribing, claiming causal attribution for individual touchpoints is analytically dishonest. What you can credibly do is demonstrate that HCPs with higher engagement scores, across more channels, with more recent interactions, are significantly more likely to prescribe your product, adopt it earlier in their treatment algorithm, and recommend it to peers. This doesn't prove that your omnichannel strategy caused these outcomes — but it provides strong evidence that it's associated with them, and that's sufficient for strategic decision-making.

The companies that do this well use cohort analysis rather than individual attribution. They compare clinical behaviour across engagement segments — high engagement vs. low engagement, multi-channel vs. single-channel, increasing trajectory vs. declining trajectory — and look for statistically significant differences. When they find them, they have a credible evidence base for continued omnichannel investment. When they don't, they have an honest signal that the strategy needs rethinking.

The Organisational Problem Is Bigger Than the Technical Problem

I've left the hardest part for last. Everything I've described above is technically achievable with data and tools that most pharma companies already have. The reason it doesn't happen is organisational, not technical.

Omnichannel measurement requires data from commercial, medical, digital, and field force functions. In most pharma companies, these functions operate with different KPIs, different budget lines, different reporting structures, and — critically — different incentives. The commercial team is measured on market share. Medical affairs is measured on scientific exchange. Digital is measured on engagement metrics. Field force is measured on call frequency. Nobody is measured on the integrated customer experience across all of these functions.

Until the measurement framework is aligned with the organisational structure — shared metrics, shared dashboards, shared accountability for customer outcomes — the data integration problem will keep being treated as an IT project rather than a strategic transformation. I've seen companies spend millions on data integration platforms without changing a single incentive or KPI. Unsurprisingly, the data got integrated but nobody used the integrated view, because their individual function's dashboard still showed the metrics they were rewarded for.

The fix is not another technology purchase. It's a governance change: creating a cross-functional measurement framework with metrics that matter to every function, dashboards that every function trusts, and accountability that rewards customer outcomes rather than channel activity. That's not an analytics project. It's an organisational design decision. And it requires executive sponsorship from someone who has authority across commercial, medical, and digital — which, in most pharma companies, means it requires the CEO or COO to care about it personally.

Until that happens, most pharma companies will continue describing themselves as "omnichannel" while measuring themselves as "multi-channel." The PowerPoint slides will look great. The strategic clarity will remain elusive.

If you're trying to build an omnichannel measurement framework that actually works — or fix one that doesn't — let's talk.