Product Pricing Strategy Dashboard

 Product Pricing Strategy Dashboard

1. Introduction


Objective: Develop a dashboard to analyze price elasticity and its impact on sales, enabling informed product pricing strategies.
Purpose: Help businesses optimize pricing to maximize revenue and maintain competitiveness.

2. Project Workflow


1. Problem Definition:
   - Determine how price changes influence product demand.
   - Key questions:
     - What is the price elasticity of demand for each product?
     - How can pricing be optimized to increase revenue?
2. Data Collection:
   - Source: Historical sales data and corresponding price information.
   - Fields: Product ID, Price, Sales Quantity, Date, Region (optional).
3. Data Preprocessing:
   - Clean and organize data for analysis.
4. Analysis:
   - Calculate price elasticity of demand.
   - Identify patterns and trends in sales data.
5. Visualization:
   - Create an interactive dashboard to present findings.
6. Deployment:
   - Host the dashboard for stakeholders to make data-driven decisions.

3. Technical Requirements


- Programming Language: Python
- Libraries/Tools:
  - Data Handling: Pandas, NumPy
  - Visualization: Matplotlib, Seaborn, Plotly, Dash
  - Statistical Analysis: SciPy, Statsmodels

4. Implementation Steps

Step 1: Setup Environment


Install required libraries:
```
pip install pandas numpy matplotlib seaborn plotly dash scipy statsmodels
```

Step 2: Load and Explore Dataset


Load the sales dataset:
```
import pandas as pd

data = pd.read_csv("sales_data.csv")
print(data.head())
```
Explore price and sales trends:
```
print(data.describe())
```

Step 3: Preprocess Data


Handle missing values and ensure data consistency:
```
data.dropna(inplace=True)
data['Date'] = pd.to_datetime(data['Date'])
```
Group data for analysis:
```
grouped_data = data.groupby(['ProductID', 'Price']).agg({'SalesQuantity': 'sum'}).reset_index()
```

Step 4: Analyze Price Elasticity


1. Define Price Elasticity of Demand:
   - Elasticity = % Change in Quantity / % Change in Price
2. Calculate Elasticity for Each Product:
```
def calculate_elasticity(group):
    group['PriceChange'] = group['Price'].pct_change()
    group['SalesChange'] = group['SalesQuantity'].pct_change()
    group['Elasticity'] = group['SalesChange'] / group['PriceChange']
    return group

elasticity_data = grouped_data.groupby('ProductID').apply(calculate_elasticity)
print(elasticity_data)
```

Step 5: Build Dashboard


1. Create Interactive Visualizations:
   - Use Plotly for interactive plots:
```
import plotly.express as px

fig = px.line(elasticity_data, x='Price', y='SalesQuantity', color='ProductID', title='Price vs Sales')
fig.show()
```
2. Develop a Dash-based Dashboard:
```
from dash import Dash, dcc, html

app = Dash(__name__)
app.layout = html.Div([
    dcc.Graph(figure=fig)
])

if __name__ == '__main__':
    app.run_server(debug=True)
```

Step 6: Deployment


Host the dashboard on a cloud platform like AWS, Heroku, or Google Cloud to provide stakeholders with real-time insights.

5. Expected Outcomes


1. Quantitative understanding of price elasticity for different products.
2. Identification of optimal pricing strategies to maximize revenue.
3. Interactive dashboard for real-time pricing analysis.

6. Additional Suggestions


- Incorporate regional or seasonal trends into the analysis.
- Use machine learning models to predict sales based on pricing and other factors.
- Integrate competitor pricing data for comprehensive strategy planning.