KaizenAI is the pharma-native AI platform that converts your commercial data into high-impact decisions — from ML-driven call plans and territory sizing to bottom-up forecasting and omnichannel audits.
Platform Approach
Each module is independently valuable but designed to compound — insights from your performance audit feed the tactical plan, which informs sizing and deployment, which calibrates targets.
Omnichannel effectiveness assessment
Analyse how your current omnichannel tactics — face-to-face calls, virtual, phone, approved emails, events — are driving incremental sales for each HCP specialty. Generate ML-powered productive frequency curves with 95% confidence intervals to identify the optimal call window.
From Overview to Rep Fine-Tuning
Deploy an ML-based call plan across four layers: sales projections and channel contribution overview; account opportunities ranked by promotional sensitivity; recommended promotional mix by specialty, channel and tier; and rep fine-tuning with field knowledge feeding back into the model.
Optimal headcount and territory alignment
Use ML-driven promotional saturation curves and profit optimisation to determine the ideal number of reps. Then deploy them with mathematical optimisation across IQVIA® bricks or account-based territories — balancing potential, sales, workload and travel distance across cross-functional teams (Rep, FLM, KAM, MSL).
Territory-centric product prioritisation
Each territory has different sales, potential and market share dynamics. KaizenAI runs product-level ML models for each promoted brand and combines them in a Portfolio Recommender that optimises call allocation across products per territory — capturing incremental growth that uniform call plans miss.
Bottom-up ML forecasting with guardrails
Generate bottom-up ML forecasts integrating account purchasing patterns, tactical plans and seasonal adjustments — with configurable horizons and confidence intervals. Then cascade national targets to territory level using weighted variables (potential, sales, forecast, rep tenure, market share) and guardrails to prevent unfair targets.
Integrated data management & quality
A centralised data management UI lets you upload, inspect and validate all commercial data sources — structures, sales, potential, HCP file, multichannel engagement, events, RTEs and commercial agreements. Over 120 critical errors and warning checks based on pharma business knowledge surface data quality issues before they affect your models.
Use Cases
Each use case is a concrete business question that can be answered with ML. Explore how KaizenAI applies its models in real commercial scenarios.
"While changing field force size, how do we anticipate the impact on sales & market share?"
Effort-Sales Model using Sales + CRM data to generate promotional saturation curves and profit optimisation for each FTE scenario.
"Where should we place reps in an equitable way to achieve a more efficient outcome?"
Assess promotional sensitivity & workload per brick, then run deployment optimisation with configurable constraints: maximise balance, minimise disruption, minimise travel.
"Which specialties are most sensitive to promoting a product?"
Effort-Sales Model per specialty and channel produces incremental sales curves. Identify the productive frequency window and adjust frequency ranges by specialty & tier.
"How should we allocate promotional effort to maximise results?"
Promotional sensitivity curve per covered HCP target combined with effort return by product chart enables data-driven optimal frequency assignment per tactic and channel.
"How to design an optimal call plan to maximise sales growth and attain the sales target?"
Recommended call plan per account based on promotional sensitivity, growth opportunity and call capacity per rep/cycle — with rep fine-tuning and reason tagging.
"How to distribute the promotional effort in customer engagement channels?"
Channel vs. Result Execution Model with constrained optimisation — models sales impact per channel and optimises #calls per rep and cycle while engaging the sales force at HCP level.
"Which product(s) do we prioritise the action plan (and incentives) by territory, to maximise the consolidated result?"
Analyse market share divergence across products and territories. Use opportunity detection maps and variable importance charts to customise the action plan per territory.
"What is the commercial agreements impact by region, territory & hospital?"
ML model analyses discount on sale impact to quantify to what extent pricing agreements are decisive in the sale and the sensitivity of each account.
"How do we make a sales boost to capture a greater business opportunity?"
Exceptional use of sizing, deployment and call plans: increase effort until saturation and concentrate effort in sensitive hospitals or territories with the highest growth opportunity.
"Can we identify the contribution per indication/field team in a multi-indication brand?"
ML model estimates indication-level contribution to incremental sales at territory level. Visualise the balance between indications across territories with penetration bubble charts.
"How can we segment our centres to focus and adapt the account action plans?"
ML clustering allows account archetyping and micro-segmentation based on multiple variables: potential, access type, regional authorisation, promotional sensitivity, #HCPs by specialty.
Real-life Experience
All metrics are from live deployments with real pharma clients — not simulations.
Territories using KaizenAI ML-based call plan vs. BAU across Oncology & Hematology products in Spain.
Incremental sales uplift in Oncology 2 territories using the ML call plan vs. Business-as-usual.
Hematology 1 territories showed +15% penetration index growth compared to non-KaizenAI territories.
ML-based portfolio recommender generated +12.7% incremental sales uplift vs. BAU in a multi-product deployment.
Fine-tuned ML tactical plan consistently drives target attainment above 100% in deployed territories.
In the Spain pilot, 25% of territories used the ML-based call plan — all outperformed BAU territories across all disease areas.
Benefits
Who We Are
We combine deep pharma domain knowledge with cutting-edge machine learning to build and deploy solutions that directly impact commercial performance.
Over 15 years developing and deploying analytics platforms for commercial pharma — from strategy to production-grade ML models.
Competition-proven machine learning expertise in deploying production-ready ML models that actually move the needle on sales performance.
Trusted by a broad range of pharmaceutical companies — from Top 20 global players to innovative mid-size firms — across multiple countries.
Our Clients
From Top 20 global pharma to focused specialty companies — across oncology, rare diseases, hospital, and retail portfolios.
Book a personalised demo and see how KaizenAI's ML models can be applied to your portfolio, territories and commercial teams — with results visible within weeks.