Impara Market Analysis with Python | Growth, Marketing, and Customer Insights

Understanding your market is essential for startup success. Market analysis helps you identify competitors, spot trends, and find opportunities for growth. With Python, you can quickly analyze data about your competitors and the broader market, allowing you to make informed decisions. Startups can use Python to compare pricing, evaluate product features, and monitor how competitors are evolving. This approach gives you an edge in identifying gaps and areas where your offering can stand out.


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# Competitor pricing and feature data
competitors = [
    {"name": "AlphaTech", "price": 49, "features": ["API", "Mobile App", "Analytics"]},
    {"name": "BetaSoft", "price": 59, "features": ["Mobile App", "Integrations"]},
    {"name": "GammaWorks", "price": 45, "features": ["API", "Analytics"]},
    {"name": "DeltaApps", "price": 55, "features": ["API", "Mobile App", "Integrations", "Analytics"]}
]

# Calculate average price
prices = [c["price"] for c in competitors]
avg_price = sum(prices) / len(prices)

# Gather all unique features
all_features = set(f for c in competitors for f in c["features"])

# Compare which competitor offers which feature
feature_matrix = {}
for feature in all_features:
    feature_matrix[feature] = [feature in c["features"] for c in competitors]

print("Average competitor price: $", round(avg_price, 2))
print("Feature comparison:")
for feature, available in feature_matrix.items():
    print(f"{feature}: {['Yes' if has else 'No' for has in available]}")

After gathering and analyzing competitor data, summarizing your findings is key to making strategic decisions. Python makes it easy to aggregate and visualize this data, helping you spot trends and outliers. Visual summaries, such as bar charts, can quickly show you how your prices compare to competitors or which features are most common. This clarity supports decisions about pricing, product development, and marketing approaches.


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import matplotlib.pyplot as plt

competitor_names = [c["name"] for c in competitors]
competitor_prices = [c["price"] for c in competitors]

plt.figure(figsize=(8, 5))
plt.bar(competitor_names, competitor_prices, color="skyblue")
plt.title("Competitor Price Comparison")
plt.xlabel("Competitor")
plt.ylabel("Price ($)")
plt.ylim(0, max(competitor_prices) + 10)
plt.show()

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Sezione 3. Capitolo 6

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Suggested prompts:

Can you explain how the feature comparison works in the code?

How can I add more competitors or features to the analysis?

What other types of visualizations can I create with this data?

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# Competitor pricing and feature data
competitors = [
    {"name": "AlphaTech", "price": 49, "features": ["API", "Mobile App", "Analytics"]},
    {"name": "BetaSoft", "price": 59, "features": ["Mobile App", "Integrations"]},
    {"name": "GammaWorks", "price": 45, "features": ["API", "Analytics"]},
    {"name": "DeltaApps", "price": 55, "features": ["API", "Mobile App", "Integrations", "Analytics"]}
]

# Calculate average price
prices = [c["price"] for c in competitors]
avg_price = sum(prices) / len(prices)

# Gather all unique features
all_features = set(f for c in competitors for f in c["features"])

# Compare which competitor offers which feature
feature_matrix = {}
for feature in all_features:
    feature_matrix[feature] = [feature in c["features"] for c in competitors]

print("Average competitor price: $", round(avg_price, 2))
print("Feature comparison:")
for feature, available in feature_matrix.items():
    print(f"{feature}: {['Yes' if has else 'No' for has in available]}")


              123456789101112
            
import matplotlib.pyplot as plt

competitor_names = [c["name"] for c in competitors]
competitor_prices = [c["price"] for c in competitors]

plt.figure(figsize=(8, 5))
plt.bar(competitor_names, competitor_prices, color="skyblue")
plt.title("Competitor Price Comparison")
plt.xlabel("Competitor")
plt.ylabel("Price ($)")
plt.ylim(0, max(competitor_prices) + 10)
plt.show()

Tutto è chiaro?

Grazie per i tuoi commenti!

Sezione 3. Capitolo 6

Market Analysis with Python

1. Why is market analysis important for startups?

2. How can Python help compare competitors?

3. What insight can be gained from visualizing competitor data?

Market Analysis with Python

1. Why is market analysis important for startups?

2. How can Python help compare competitors?

3. What insight can be gained from visualizing competitor data?