Pharma Sales Forecasting: Is Your Data Holding You Back?

As of 2018, 90% of pharmaceutical companies rely on third-party data for sales forecasting and product decisions, but many are wasting valuable resources. Poor-quality data leads to inaccurate predictions, which can mean missed opportunities in product development and sales.

Why Quality Third-Party Data is Critical

For pharmaceutical companies, better data means better decisions. By using high-quality third-party data, pharma companies can:

Make better manufacturing decisions based on accurate data
Increase sales through more reliable sales forecasts
Improve product development by understanding real-world patient behavior

How It Works

🔹 Integrate internal sales and advertising data with third-party data on patient demographics, clinical pathways, and buyer behaviors.
🔹 Enhance forecasts by analyzing patient lab results, genetic profiles, and clinical notes to predict demand and trends.
🔹 Refine predictions using advanced analytics like Monte Carlo simulations and cluster analysis.

Real-World Impact: Better Data, Better Results

In a Brazilian budget analysis, pharmaceutical costs for rheumatoid arthritis treatment were overestimated by 463%—leading to inefficiencies in planning. By using better data, pharmaceutical companies can avoid such costly mistakes.

Get the Right Data to Make Smarter Decisions

Pharmaceutical companies that invest in high-quality third-party data have a competitive edge in both sales predictions and product development. Don’t let poor data slow you down—optimize your forecasting with real-world insights.

📩 Want to learn more about how better data can improve your sales forecasts? Let’s talk!

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Live Webinar

Is “Quality” Killing Your AI? Defining Data Fit for Strategic Success

February 18th, 2026 / 1:00 PM EST

Every data investment carries risk unless you know how to measure its “fit” for the mission. Many organizations assume that “high-quality” data is sufficient for AI and analytics, only to discover too late that data fit is the real determinant of success. In this live session from Blue Street Data’s Building with Better Data series, Andy Hannah and Malcolm Hawker unpack why data that works for BI can be dangerous for AI, leading to model failure, wasted spend, and lost trust. You’ll learn how to define, measure, and validate data fit so your models deliver reliable, business-aligned outcomes. Reserve your spot today!