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.