This pharma big data case study shows how Hypera Pharma (https://www.hypera.com.br/en/) used predictive CRM intelligence to reduce stockout losses, prioritize field visits, and improve operational efficiency.
Hypera, one of Brazil’s largest pharmaceutical companies, faced high operational costs and limited visibility across more than 90,000 points of sale.
As a result, integrating Close-Up’s Big Data models and predictive CRM transformed decision-making and improved commercial impact.
The Challenge: Stockouts, High Visit Volume, and Limited Visibility
Hypera managed nearly 1,000 SKUs across more than 90,000 retail locations. Field reps spent 20–25 minutes per store, checking around 250 SKUs manually. This created several challenges:
- 277,000+ verification visits per year
- 1/3 of stores received no regular checks
- No visibility into where stockout losses were happening
Managers struggled to understand stockout patterns. They also lacked the ability to track resolution performance and allocate field resources efficiently. Therefore, Hypera needed a data-driven solution to prioritize visits and reduce waste.
The Zero Stockout Project: Big Data + Predictive CRM Intelligence
Close-Up CRM launched a Big Data pilot to automatically detect stockout risks and prioritize stores for field teams. This pharma big data case study demonstrates how the model supported smarter execution.
How It Worked
The model analyzed weekly pharmacy sales for every SKU in every store. It calculated the expected sales baseline and flagged potential stockouts when results fell below a critical threshold. Each product in each location received a four-level risk score, updated weekly.
Because of this structure, the CRM could:
- highlight stores requiring urgent visits
- adjust routes and schedules automatically
- reduce time spent on low-impact locations
Moreover, weekly recalculation allowed the system to adapt quickly as market conditions shifted.
From Data to Action: Predictive CRM in Daily Operations
Big Data only creates value when insights become actionable. Close-Up CRM transformed complex analytics into a clean, intuitive dashboard showing:
- stockout rate
- resolution rate
- sales lost due to stockouts
- trend lines by SKU and territory
Users could filter, export, and prioritize stores instantly. Consequently, managers gained real-time visibility, and field reps no longer relied on manual reports.
To see how these capabilities fit into the broader Close-Up ecosystem, explore the CRM Suite overview, which explains how analytics, workflows, and field execution tools connect: https://closeupcrm.com/crm-suite/
In addition, companies comparing CRM solutions can review the Veeva CRM vs. Close-Up CRM analysis, which highlights predictive capabilities similar to the ones applied in this project: https://closeupcrm.com/veeva-crm-vs-close-up-crm-where-close-up-delivers-clear-commercial-advantage/
Pharma Big Data Case Study: Business Impact
Comparing Jan–Apr 2022 vs. Jan–Apr 2023, Hypera achieved measurable improvements:
- +4.5% increase in stockout resolution rate
- ~75% reduction in average visit time per store
This pharma big data case study shows that every 1% reduction in stockouts adds nearly US$4 million in annual revenue. Furthermore, Close-Up continues to enhance the predictive model and now integrates features like Suggested Activities, which guide reps toward the highest-impact actions automatically.
The Future: Scaling Predictive Intelligence Across Pharma
As AI and data science evolve, predictive CRM becomes a strategic necessity. Companies adopting these tools gain advantages such as:
- smarter allocation of field resources
- stronger commercial execution
- faster decision-making
- reduced operational waste
Most importantly, this pharma big data case study demonstrates how predictive CRM converts raw data into measurable commercial outcomes. Close-Up’s Big Data and CRM teams will continue developing models tailored to pharma’s real-world challenges.
Learn more about Hypera Pharma: https://www.hypera.com.br/en
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