Date & Time: Thursday, June 18, 2026 | 12:00 PM ET | 45 minutes + live Q&A
Build best-in-class pricing analytics & optimization capabilities for your brands
In pharma, your ex-manufacturer list price lives in the ERP. Your real net price — after distributor markups, pharmacy purchase price, tender discounts, and reference-pricing caps — lives in a gross-to-net bridge someone rebuilds by hand. Your competitive view comes from IQVIA MIDAS or local panels that divide sales by volume and call it a price. And matching your SKUs to competitors’ across molecule, strength, form, release, and pack size is a manual job one analyst owns. Reconstructing where your price actually sits, per SKU and per market, takes weeks — so most teams do it once a year, for budget.
That gap is expensive, and it rarely looks dramatic. Two products can read identically until you change the denominator: one looks 17% cheaper per pack and turns out 62% more expensive per defined daily dose (DDD) — the only basis on which a prescriber’s cost-per-day comparison is honest. Pack ladders quietly invert. Competitors raise prices faster than you as strength climbs. Inflation runs ahead of your list price, and the cumulative gap never closes. Country teams feel all of it as one sentence: “My prices moved 10%, but my competitors moved 18% — I’m pricing myself out.”
Join Armin Kakas and Enrico Sieni of Revology Analytics for a candid, advanced price analytics session on where pharma pricing actually leaks — and the four-module engine that closes it. Drawing on recent multi-market emerging-market pilots that surfaced 5–20% in net price realization (NPR), we’ll show how DDD-normalized competition, brand-worth scoring, pack-architecture audits, and causal elasticity come together into one reproducible engine — and why reproducibility, not another clever model, is what lets your team run it every cycle and defend every move with your commercial teams.
What You’ll Learn
- Why simple IQVIA-based reporting isn’t pricing analytics — and how a reproducible product-equivalence matrix plus Price/DDD normalization finally gives you an apples-to-apples competitive read across molecule, strength, form, and pack
- How to properly model price elasticities in pharma, and why price is rarely the biggest contributor to volume growth (or erosion)
- How to see the net price realization gap from the ex-manufacturer list through the full gross-to-net bridge — and catch where you’re “pricing yourself out” because competitors moved faster than you
- The Price Pack Architecture (PPA) audit that exposes inverted pack ladders and strength-index leaks before they compound across cycles
- Where Rx inelasticity gives you safe headroom (and where OTC behaves more like CPG) — using causal elasticity to size every move, and to flag where price should be held to defend volume
- How a reproducible engine replaces the one-analyst spreadsheet, so you can run predictive analytics and price optimizations faster across multiple countries and brands – at scale.
Who Should Attend
Pharma pricing and commercial-excellence leaders (global, regional, and country/affiliate); Market Access and Pricing & Reimbursement teams; Brand and Product Managers; and the Analytics & Data Science teams who build the models. The session is grounded in pharma emerging-markets dynamics, and the same engine pattern applies to animal health and the broader life sciences running on IQVIA-equivalent panel data.
