Building a Python Dashboard for Amazon Deal Insights
For Amazon businesses and market analysts, staying ahead of the competition requires real-time insights into product performance, pricing trends, and seller activities. Building a custom dashboard using Python and the SellerMagnet API allows for in-depth competitive analysis, optimized inventory management, and comprehensive market research. This post guides you through building such a dashboard, focusing on practical use cases and leveraging the power of SellerMagnet API.
Why Build a Custom Dashboard?
Off-the-shelf solutions often lack the flexibility to focus on the specific metrics that matter most to your business. A custom dashboard provides:
- Targeted Insights: Tailor the data displayed to your specific needs.
- Real-Time Monitoring: Get up-to-the-minute information on product performance.
- Competitive Advantage: React quickly to market changes and competitor strategies.
- Scalability: Adapt the dashboard as your business grows and your data needs evolve.
Prerequisites
Before you begin, make sure you have the following:
- Python 3.6+ installed.
- A SellerMagnet API key (available at Try Free).
- Required Python libraries:
requests,pandas,plotly, anddash. Install them using pip:
pip install requests pandas plotly dashStep-by-Step Guide
1. Setting Up the Python Environment
Create a new Python project and install the necessary libraries. Ensure your API key is stored securely as an environment variable or within your code (handle with care!).
2. Fetching Amazon Product Data using SellerMagnet API
The foundation of your dashboard is the data from Amazon. SellerMagnet provides several key endpoints for this purpose. Let's start with fetching product statistics using the Amazon Product Statistics endpoint.
import requests
import pandas as pd
import plotly.graph_objects as go
import dash
import dash_core_components as dcc
import dash_html_components as html
# Replace with your SellerMagnet API key
API_KEY = "YOUR_API_KEY"
# Product ASIN and Marketplace ID
ASIN = "B08N5WRWNW" # Example: Playstation 5
MARKETPLACE_ID = "ATVPDKIKX0DER" # Example: Amazon.com
def get_product_statistics(asin, marketplace_id, api_key):
url = "https://sellermagnet-api.com/amazon-product-statistics"
params = {
"asin": asin,
"marketplaceId": marketplace_id,
"api_key": api_key,
"graphs": "true" # Request graphs for historical data
}
response = requests.get(url, params=params)
response.raise_for_status() # Raise HTTPError for bad responses (4xx or 5xx)
return response.json()
product_data = get_product_statistics(ASIN, MARKETPLACE_ID, API_KEY)
if product_data["success"]:
print("Data fetched successfully!")
print(product_data["data"]) # print all data to check schema and values.
else:
print("Error fetching data:", product_data.get("errors", "Unknown error"))
Example Response:
{
"success": true,
"data": {
"asin": "B08N5WRWNW",
"amazonPrice": 49900,
"bestSellerRank": 1,
"buyBoxPrice": 49900,
"buyBoxFulfillment": "AMZ",
"buyBoxSellerIdHistory": [
[
"2024-01-01T00:00:00",
"Amazon"
]
],
"salesRankHistory": [
[
"2024-01-01T00:00:00",
1
]
],
"trackingSince": "2023-01-01",
"graphs": {
"priceTrend": [
{
"timestamp": "2024-06-14T00:00:00",
"price": 49900
}
],
"rankTrend": [
{
"timestamp": "2024-06-14T00:00:00",
"rank": 1
}
]
},
"metadata": {
"category": "Video Games",
"lastUpdated": "2024-06-14T12:00:00"
}
}
}
3. Building the Dashboard Layout with Dash
Dash is a Python framework for building web applications. Create a basic layout to display key metrics and graphs.
app = dash.Dash(__name__)
app.layout = html.Div([
html.H1(children='Amazon Product Dashboard'),
html.Div(children='''
Real-time insights into Amazon product performance.
'''),
dcc.Graph(id='price-trend-graph'),
dcc.Graph(id='sales-rank-graph')
])
4. Populating the Dashboard with Data and Graphs
Use the fetched data to create interactive graphs. This example uses Plotly to visualize the price and sales rank trends.
@app.callback(
[dash.Output('price-trend-graph', 'figure'),
dash.Output('sales-rank-graph', 'figure')],
[dash.Input('asin', 'value')] # dummy, there is no input on this example
)
def update_graph(asin):
# Extract data for price trend
price_data = product_data["data"]["graphs"]["priceTrend"]
df_price = pd.DataFrame(price_data)
df_price['timestamp'] = pd.to_datetime(df_price['timestamp'])
# Create price trend graph
fig_price = go.Figure([
go.Scatter(x=df_price['timestamp'], y=df_price['price'], mode='lines')
])
fig_price.update_layout(title='Price Trend', xaxis_title='Date', yaxis_title='Price (cents)')
# Extract data for sales rank trend
rank_data = product_data["data"]["graphs"]["rankTrend"]
df_rank = pd.DataFrame(rank_data)
df_rank['timestamp'] = pd.to_datetime(df_rank['timestamp'])
# Create sales rank graph
fig_rank = go.Figure([
go.Scatter(x=df_rank['timestamp'], y=df_rank['rank'], mode='lines')
])
fig_rank.update_layout(title='Sales Rank Trend', xaxis_title='Date', yaxis_title='Sales Rank')
return fig_price, fig_rank
if __name__ == '__main__':
app.run_server(debug=True)
5. Running the Dashboard
Execute your Python script. Dash will provide a local URL (usually http://127.0.0.1:8050/) to view your dashboard in a web browser.
Advanced Use Cases
1. Competitive Price Tracking
Use the Amazon Product Offers endpoint to monitor competitor pricing. This allows you to dynamically adjust your pricing strategy.
# Fetch product offers data
def get_product_offers(asin, marketplace_id, api_key):
url = "https://sellermagnet-api.com/amazon-product-offers"
params = {
"asin": asin,
"marketplaceId": marketplace_id,
"api_key": api_key
}
response = requests.get(url, params=params)
response.raise_for_status()
return response.json()
offers_data = get_product_offers(ASIN, MARKETPLACE_ID, API_KEY)
Analyze the offers_data to identify the lowest prices, fulfillment methods (FBA/FBM), and seller ratings. Visualize this data in your dashboard to quickly assess the competitive landscape.
2. Inventory Management
Combine product statistics with offer data to estimate sales velocity. Use the Amazon Product Estimate Sales endpoint to estimate monthly sales and make informed decisions about inventory levels.
# Fetch estimated sales data
def get_estimated_sales(asin, marketplace_id, api_key):
url = "https://sellermagnet-api.com/amazon-product-search-estimated-sells"
params = {
"asin": asin,
"marketplaceId": marketplace_id,
"api_key": api_key
}
response = requests.get(url, params=params)
response.raise_for_status()
return response.json()
sales_data = get_estimated_sales(ASIN, MARKETPLACE_ID, API_KEY)
Integrate this data into your dashboard to display current inventory levels, estimated sales, and reorder points.
3. Seller Reputation Analysis
Use the Amazon Seller Review endpoint to monitor the feedback and ratings of your competitors. This helps you understand their strengths and weaknesses.
# Fetch seller review data
def get_seller_reviews(seller_id, marketplace_id, api_key):
url = "https://sellermagnet-api.com/amazon-seller-review"
params = {
"sellerId": seller_id,
"marketplaceId": marketplace_id,
"api_key": api_key
}
response = requests.get(url, params=params)
response.raise_for_status()
return response.json()
# Assuming you have the seller ID from the product offers data
SELLER_ID = offers_data["data"]["buyBox"]["sellerId"]
reviews_data = get_seller_reviews(SELLER_ID, MARKETPLACE_ID, API_KEY)
Display seller ratings, review counts, and recent reviews in your dashboard for a comprehensive view of seller performance.
4. Product Discovery and Market Research
Leverage the Amazon Search and Amazon Bestsellers endpoints to identify trending products and explore new market opportunities.
# Search for products
def search_amazon(query, marketplace_id, api_key):
url = "https://sellermagnet-api.com/amazon-search"
params = {
"q": query,
"marketplaceId": marketplace_id,
"api_key": api_key
}
response = requests.get(url, params=params)
response.raise_for_status()
return response.json()
search_results = search_amazon("headphones", MARKETPLACE_ID, API_KEY)
# Get bestsellers in a category
def get_amazon_bestsellers(category_id, marketplace_id, api_key):
url = "https://sellermagnet-api.com/amazon-bestsellers"
params = {
"category_id": category_id,
"marketplaceId": marketplace_id,
"api_key": api_key
}
response = requests.get(url, params=params)
response.raise_for_status()
return response.json()
bestsellers = get_amazon_bestsellers("electronics", MARKETPLACE_ID, API_KEY)
Present the search results and bestseller lists in your dashboard to identify promising products and categories.
Conclusion
Building a Python dashboard for Amazon deal insights empowers businesses with the data-driven decision-making capabilities needed to succeed in a competitive marketplace. By leveraging the SellerMagnet API, you can gain real-time access to critical product data, monitor competitor activities, optimize inventory management, and identify new market opportunities.
Start building your custom dashboard today and unlock the full potential of Amazon data with SellerMagnet!