- Problem statement:
Our beverage company offers a variety of SKUs, but current pricing strategies lack optimization for profit and market share. We need a data-driven approach to:
- Identify optimal price points for each SKU.
- Understand how price changes for one SKU impact sales of others.
- Develop pricing strategies that consider competitor pricing and market trends.
- Approach:
- Data collection: Gather historical sales data including volume, price, promotions, and SKU attributes. Combine this with competitor pricing data and market research on consumer preferences.
- Exploratory Data Analysis (EDA): Analyse trends in sales volume, price elasticity, and correlations between SKUs using tools like:
- Correlation analysis: Identify how price changes in one SKU affect sales of others.
- Time Series analysis: Uncover seasonal trends and patterns in sales data.
- Modelling: Develop pricing models using techniques like:
- Demand forecasting: Utilize algorithms like:
- Exponential smoothing: Effective for short-term forecasting with stable demand patterns.
- ARIMA (Autoregressive Integrated Moving Average): Suitable for time series data with seasonality and trends.
- Price optimization: Implement algorithms like:
- Linear regression: Establish the relationship between price and sales volume.
- Random forest regression: A more robust method for complex relationships with multiple variables.
- Model validation and testing: Evaluate the accuracy of the models using historical data and conduct A/B testing to validate the effectiveness of price recommendations.
- Solution:
The outcome will be a data-driven pricing strategy delivered through a user-friendly Power BI dashboard:
- Recommended price points: Each SKU will have an optimal price based on model recommendations.
- Price bundling strategies: Identify opportunities to bundle SKUs at discounted prices using clustering algorithms like K-Means to group related products.
- Dynamic pricing models: Develop frameworks using techniques like:
- Rule-based pricing: Set automated price adjustments based on pre-defined conditions (e.g., competitor price changes, promotional periods).
- ML models: Implement algorithms that continuously learn and adapt pricing based on real-time data.
- Power BI dashboard: Visualize key metrics and insights for user consumption, including:
- Interactive charts showing price elasticity for each SKU.
- Sales performance comparisons across different pricing strategies.
- Competitor pricing trends and analysis.
- Business impact:
- Increased profitability: Profitability in premium category segments increased by 1.8% with optimised pricing and product positioning.
- Category market share: With Competitive pricing strategies, category market share in Premium & Super Premium Categories increased by 2.1% & 0.9%, respectively.
- Data-driven decision making: The company gained a deeper understanding of customer behaviour and market dynamics, allowing for more informed pricing decisions in the future.
