The challenge
In the ecommerce landscape, consumer healthcare companies face challenges in pricing their products effectively, as pricing decisions must account for a wide range of factors, such as fluctuating demand, profitability, and inventory constraints – all while protecting brand reputation.
One of our healthcare clients faced significant hurdles in optimizing pricing on their ecommerce platform. Pricing adjustments were primarily based on intuition, with minimal visibility into the impact of these changes. Additionally, there was a disconnect between analytics and actual price changes, leading to inefficiencies and missed opportunities.
To address these challenges, we developed a Proof of Concept (POC) for a machine learning-based price recommendation engine. The goal was to enable data-driven decision-making and provide automated, actionable pricing recommendations to support the company’s ecommerce strategy.
The Solution
The solution combined advanced data integration and predictive modeling to deliver a tailored recommendation engine that seamlessly connected analytics with pricing operations. The project was implemented in two key phases:
- Data Integration and Machine Learning Model Development
We integrated diverse datasets from the client’s operations to create a robust foundation for the recommendation engine, including:
- Net Sales Data: To analyze revenue and sales patterns.
- Ad Spend: To assess marketing impact on pricing and sales.
- Conversion Rates: To identify pricing’s effect on purchase likelihood.
- Inventory Data: To account for stock levels in pricing decisions.
Leveraging advanced machine learning techniques, we built models to predict optimal pricing strategies based on historical data, market dynamics, and customer behavior. The engine generated specific recommendations for price adjustments across SKUs, designed to maximize revenue and profitability while maintaining competitiveness.
- Visualization and Interactive Insights
The price recommendation engine was embedded into an intuitive Power BI dashboard to ensure ease of use and accessibility for the client’s decision-makers. Key features included:
- Interactive Visualization: Users could explore pricing recommendations for individual SKUs with just a click.
- Performance Metrics: Historical performance data and the impact of previous price adjustments were made instantly accessible.
- Actionable Analytics: Clear and prioritized recommendations for price adjustments were provided, enabling faster and more informed decision-making.
Impact & Results
The implementation of the price recommendation engine delivered tangible business benefits, transforming the client’s pricing processes and outcomes:
- Fully Connected Datasets: Unified and enriched datasets provided a comprehensive view of sales, margins, prices, and other key metrics, enabling smarter decisions.
- Actionable Insights: The integration of the engine with Power BI empowered stakeholders with outcome-oriented visuals, enhancing the link between analytics and strategy.
- Enhanced Responsiveness: Decision-makers gained the ability to quickly assess past performance and implement recommendations in real-time, significantly reducing lag in price adjustments.
- Increased Automation: Frequent, automated insights eliminated manual analysis bottlenecks and streamlined the pricing workflow.
Conclusion
Through this project, the Consumer Healthcare Company successfully transitioned from a manual, intuition-based pricing approach to a more structured and data-driven process. Leveraging machine learning enabled the client to respond quickly to market dynamics to achieve a competitive edge in ecommerce pricing.