Sales Forecasting
Predict future sales for different products and regions to test the capability of Al Intisar and view how precise prediction can be for future sales.

Product Performance
Analyze the performance of different products in the market by looking at future trends.

Promotion Effectiveness
Evaluate the effectiveness of marketing and promotional activities.

Data Collection

• Pharma Company provided historical sales data, including product sales, customer information, and market trends for past three years to Al Intisar.

Data Cleaning and Integration

• Data was cleaned and preprocessed to ensure accuracy and consistency.
• Data was integrated from various sources to create a unified dataset for analysis.

Feature engineering

Feature engineering was carried out, Feature Engineering refers to manipulation — addition, deletion, combination, mutation — of data set to improve machine learning model training, leading to better performance and greater accuracy

Model Development

• Multiple predictive models were tested for sales forecasting, such as linear regression, time series analysis, decision trees, neural networks.
• Machine learning algorithms were utilized to develop predictive models for sales forecasting.
• Implemented customer segmentation models based on factors like purchasing history, demographics, and geographical location.
• Analyzed product performance and identify features that correlate with success.

Validation and Testing

• Al Intisar team validated the predictive models using historical data to ensure accuracy and reliability.
• Models were tested on a subset of data to assess their performance and necessary adjustments were made.

Implementation and Integration

• Integrated the predictive analytics models into the existing sales

Sales Forecasting

Predictive Analytics by Al Intisar showed 98.7% percentage precision in prediction after comparing with actual data. Benefits a Pharma Company take from this exercise

Targeted Marketing

By understanding customer segments and their behaviors, the company can tailor marketing strategies to specific groups, resulting in higher engagement and conversion rates.

Optimized Product Portfolio

Identifying underperforming and high-potential products allows ABC Pharmaceuticals to adjust its product portfolio for better market responsiveness.

Enhanced Promotion ROI

Analyzing the effectiveness of promotions enables the company to allocate resources strategically, maximizing the impact of marketing campaigns.

Conclusion

Implementing sales predictive analytics can prove to be a strategic move for ABC Pharma, providing actionable insights to drive revenue growth, optimize marketing efforts, and enhance overall sales efficiency.

This case study demonstrates the practical application of sales predictive analytics and capabilities of Al Intisar in the pharmaceutical industry, showcasing its potential to transform business outcomes through data-driven decision-making.