Pharma has spent years trying to personalise content. The bigger opportunity may be learning before personalising.
Hyper-personalisation has long been presented as the future of pharmaceutical engagement: every healthcare professional receives the right information, in the right format, at the right moment, shaped around a deep understanding of their needs.
It is attractive, and difficult to make work. Pharma does not usually lose relevance because the ambition is too small. It loses it in the layer between strategy and execution: the handover from strategy to content, from content to channel, and from digital interaction to the next field conversation.
True one-to-one engagement needs more than AI. It needs high-quality data, connected platforms, governance, content architecture and customer understanding. Consent, lawful basis, use of online identifiers, and the treatment of any inferred health data shape what can be done in practice.
Many companies have moved beyond broad market segmentation. Campaigns are adapted by specialty, therapy area, and account type or channel preference. Some teams are beginning to use behavioural data in omnichannel programmes. Still, the gap remains: knowing who an HCP is does not mean knowing what they need now.
“Knowing who an HCP is does not mean knowing what they need now”.
That gap shows up after the click. A physician reads adverse-event management content, then receives a general disease-awareness email. A specialist spends time with post-congress updates, then lands on introductory material. A field representative starts a meeting without the digital context that could have changed its first five minutes.
These are ordinary losses of relevance, repeated across campaigns, channels and markets. The industry often responds by asking for more data: more attributes, more systems, more AI. But hyper-personalisation does not have to arrive fully formed. It can be learned. Every interaction can reveal something useful about an HCP’s current professional need.
That is the missing middle: not a replacement for segmentation, not yet one-to-one engagement, but a practical operating layer between the two.
Why traditional segmentation reaches its limits
Segmentation remains essential. Specialty, therapy area, account type and prescribing behaviour all provide useful starting context. The problem is that these attributes are stable while information needs are not.
Two physicians may sit in the same specialty and still be looking for different things: mechanism, pathway guidance, or safety material after a difficult patient conversation. The specialty has not changed, but the immediate need has.
HCP information behaviour is frequent and task-led. In a survey of 511 healthcare professionals, 88.5% searched for medical information either daily or several times per week. Common questions involved dosing, drug-drug interactions, adverse events and safety, guidelines and disease-state information.
For pharma, the lesson is commercial as much as educational. The right content has less value if it arrives at the wrong point in the journey. A separate study of US HCP preferences found that consumption and sharing of publication content continues to evolve, with changes apparently accelerated by the pandemic.
Specialty is useful. It is just too blunt to carry the whole personalisation strategy.
Progressive discovery, not perfect profiles
The practical route forward is not collecting every possible data point before engagement begins. It is learning as engagement unfolds.
An opening email may reveal which patient population attracts attention. A content choice may show whether practical guidance is more useful than theory. Repeated engagement with a disease area may suggest an active clinical focus. A preference for short video over a scientific review may say more about time pressure than scientific interest.
Individually, these are modest signals. Together, they create a richer picture than specialty alone.
This does not require hidden inference or ever-expanding customer profiles. It uses transparent, consented interactions with approved content to understand what the HCP is showing interest in now. Each interaction answers a small question, and the next interaction can respond.
Traditional segmentation assigns HCPs to groups based on stable characteristics. Progressive discovery lets those groups evolve as new information becomes available.
The missing middle
The missing middle is the operating space between static segmentation and true one-to-one engagement. Most organisations will not achieve complete customer understanding before communication begins. They do not need to.
Meaningful improvements in relevance often come from making sure the last interaction changes the next one. A physician who repeatedly chooses practical treatment guidance does not need more introductory disease education. A specialist who engages with clinical evidence may benefit from deeper scientific content. Someone returning several times to adverse-event management is signalling a current priority.
None of this requires the organisation to know everything about the individual. It requires the organisation to learn something useful after every interaction.
The missing middle gives pharma a way to move towards hyper-personalisation without waiting for a perfect single customer view, complete data integration, large-scale AI deployment or traffic volumes big enough to support complex machine-learning models.
Modular content makes learning usable
Progressive discovery only creates value if the content can respond.
Traditional campaign assets are built as finished pages, fixed email journeys or one-off local adaptations. Every new audience creates another asset, approval cycle and localisation burden. The model scales badly.
A better approach is to stop treating the page as a single finished object. The page becomes a set of approved content blocks: theoretical blocks such as mechanism, study design or clinical rationale; practical blocks such as patient identification, dosing, pathways, adverse-event management or implementation guidance.
The value sits in controlled recombination. A team can adjust the balance between theoretical and practical content, alter complexity, and create different experiences for different microsegments without rebuilding the whole asset each time.
Approved content lives longer, duplication falls, local adaptation becomes safer and MLR review becomes more predictable. Governance also becomes clearer because every module can carry its scientific basis, approval status, intended audience, channel rules, expiry date and permitted combinations.
The ABPI Code sets standards for promotion to health professionals, interactions with health professionals, and information provided to the public and patients. A modular model does not soften those requirements. It makes them easier to operate.
Privacy by design, not profiling by default
The language around behavioural data needs discipline. Pharma should be cautious about importing consumer-retail models into HCP engagement. The trust context is different, and the explainability threshold is higher.
The useful signals are often modest: what was read, ignored, revisited, completed, requested or used as a route to another piece of content. They can inform whether the next interaction should be more practical, advanced, concise, visual or better suited to another channel.
The ICO’s direct marketing guidance makes clear that personal information can include online identifiers such as cookie IDs, IP addresses and advertising IDs. The ICO’s direct marketing checklist also states that explicit consent is required where special category data, including health information, is used for direct marketing purposes.
Those obligations do not disappear because the signal is digital. The test is simple: can the company explain why this HCP received this next content experience? If the answer depends on a black box, the model is not ready. If the answer is based on consented behaviour, approved content logic and transparent rules, the case is stronger.
What leaders should measure
Many omnichannel dashboards still reward activity: opens, clicks, impressions and time on page. Useful, but limited. They rarely answer whether engagement is becoming more relevant.
A missing-middle model needs measures that show whether the system is learning. Are HCPs progressing from broad education to deeper evidence? Are content blocks being matched to observed interest? Are field teams receiving digital context they can use? Are approved assets being reused without multiplying approval burden? Are campaign learnings improving the next deployment?
These questions move the discussion beyond omnichannel theatre. They connect personalisation with commercial productivity, medical quality and operational control.
Where to begin
The best starting point is usually not the biggest data programme. It is a high-value campaign with existing content, consented engagement and a clear HCP journey.
Break the page into blocks. Define which blocks are theoretical, practical, and introductory or advanced. Add the metadata needed to govern use. Decide which behaviours create a microsegment. Track how HCPs move through the experience. Adjust the next touchpoint based on what the previous one revealed.
This does not require every tool to be fully integrated before progress begins. It does require content, data, channel planning and governance to be treated as one operating system.
The boardroom question
The missing middle matters because it is where most pharma companies can make progress now. It asks them to build towards hyper-personalisation through better segment intelligence, better content architecture and better use of behavioural feedback.
The fastest movers will not have the grandest personalisation vocabulary. They will be the ones that can prove the last interaction changed the next one.
That is the question worth taking into the next brand review, digital steering group or executive committee: when an HCP interacts with your content today, what changes tomorrow?
References
¹ Information Commissioner’s Office. Plan direct marketing. Direct marketing guidance. Available at: https://ico.org.uk/for-organisations/direct-marketing-and-privacy-and-electronic-communications/direct-marketing-guidance/plan-direct-marketing/
² Hermes-DeSantis ER, Hunter RT, Welch J, Bhavsar R, Boulos D, Noue MA. Preferences for Accessing Medical Information in the Digital Age: Health Care Professional Survey. Journal of Medical Internet Research. 2021;23(6):e25868. doi:10.2196/25868. Available at: https://www.jmir.org/2021/6/e25868/
³ Bessler JB, Fazzone W, Sladicka N, Ruth A. Understanding US healthcare provider preferences for consumption of publication content: opportunities to leverage omnichannel approaches. Current Medical Research and Opinion. 2023;39(9):1271–1277. doi:10.1080/03007995.2023.2240116.
⁴ Association of the British Pharmaceutical Industry and Prescription Medicines Code of Practice Authority. ABPI Code of Practice for the Pharmaceutical Industry 2024. Operative 1 October 2024. Available at: https://www.pmcpa.org.uk/media/r0anf5ya/2024-abpi-code.pdf
⁵ Information Commissioner’s Office. Direct marketing checklist. Available at: https://ico.org.uk/for-organisations/direct-marketing-and-privacy-and-electronic-communications/direct-marketing-checklist/
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