Stop visiting by habit. Start prioritizing by evidence.

CRM data, field activity and market intelligence, enriched with insights from over 230,000 external sources. Turning complex signals into clear priorities for every visit cycle.

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Pharma & Healthcare

Every visit priority, with its reasons attached

Pharma and healthcare teams plan their field work in a setting where each visit needs a clear reason and customer structures keep changing. In many organisations, target lists are still set once a year and draw mainly on past revenue, which makes it hard to react to shifts in between. ZopaAI scores pharmacies, practices and clinics on their future potential, using regional demographics, local care structures, competitor activity and your own field data, for Rx and OTC portfolios alike. Every recommendation comes with its reasoning and sources, so it can be discussed openly with Medical, Compliance and the field force. Your team keeps the decision and gets a clearer basis for it.

Frankfurter Allgemeine Zeitung
ZopaAI stands for reliable, scientifically verified AI for use in companies, in order to avoid the problem of AI hallucinations.
Frankfurter Allgemeine Zeitung
Newspaper, Frankfurt

Eight building blocks, usable on their own, together a steering system

All modules are usable on their own or interconnected in a steering portfolio.

View by role

Pick your role, the modules used day to day in that role are highlighted.

Steering

Contact and visit steering

Contact planned by future potential, rather than historical visit patterns. ZopaAI identifies where a visit is most valuable and steers contact frequency and channel accordingly, from in-person visits to inside sales and digital outreach.

  • Contact frequency per account instead of rigid segmentation classes
  • Steering the right contact measure: field visit, inside sales or digital outreach
  • Impact measurement: sales change after contact, per territory
What the output looks like
”Territory South: 38 of 210 accounts generate 64% of the expected potential. Recommended: 18 with in-person visits, 46 through inside sales, the remaining accounts through digital outreach.”

Illustrative wording. Figures and names are not from a real client project.

What changed at an OTC-manufacturer in 6 months.

+11 %revenue uplift at targeted accounts
−26 %time spent on field sales preparation
+9 pplisting depth at targeted accounts

Scope of the case study

Client
An OTC manufacturer with a self-medication portfolio across several indication areas. Presented anonymised.
Territory
Germany, two sales regions, ~ 2000 pharmacies in the target segment.
Implemented
Pharmacy prioritisation, visit frequency planning and per-store cross-sell recommendations.
Period
Six months, rolled out in waves, each measured against a control region on the previous logic.

Full case study as a PDF

Two pages on initial situation, data foundation, methodology, results. Get access to the link.

Presented anonymised. Figures from the client's own project controlling.

In OTC we saw the category shift weeks before it reached our own sell-out figures.
Head of Field SalesOTC manufacturer

Reference from an ongoing project in the pharmacy channel

Questions we hear first

Short answers before your first call.

  • ZopaAI can run completely on external data. Market, prescription, sales or other internal data can be added where available. ZopaAI connects these sources without requiring you to rebuild your existing data infrastructure. Naturally, the combination of internal and external data provides the best results.

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Not another sales pitch, but an honest discussion focusing on your needs. 30 minutes, no sensitive data, no risk, no cost.

  • Industry lead, not a generalist
  • Valuable discussion, honest assessment.
  • Focused on discovering potentials.