In-depth Analysis of Soft Data and Hard Data: How Residential Proxy Can Optimize Data Crawling

In the era of big data, companies and research institutions are increasingly relying on data to drive decision-making, however, not all data is the same. Data can be divided into different categories, the most common of which are soft data and hard data. Understanding the difference between these two types of data and how to effectively collect and use them is crucial for companies that want to stay ahead in a highly competitive market. This article will delve into the concepts of soft data and hard data and introduce how to crawl data through residential proxies.

What Is Soft Data? What Types Are There?

Soft data refers to qualitative data that cannot be expressed by precise numerical values. This type of data usually involves people's emotions, attitudes, opinions and behaviors, and is often presented in non-numerical forms such as text, sound, video, and pictures. Soft data plays a vital role in analyzing and understanding social phenomena, market trends, user experience, etc. There are mainly the following main types:

• Text data : This is the most common type of soft data, usually in the form of written or oral text, such as articles, books, interview records, user reviews, social media posts, etc. For example, customer feedback in product reviews, opinion analysis in news reports, and conversation records in interviews.
• Audio data : includes various sound records, such as voice messages, podcasts, music, phone recordings, etc. These data are usually used to analyze emotional expressions, intonation, language habits, etc.
• Video data : Contains visual and auditory information, and can provide richer context than text and audio data, such as user-uploaded product unboxing videos, recordings of internal company meetings, and consumer reactions in advertisements. Video data can be used to analyze behavioral patterns, emotional expressions, environments, etc.
• Image data : includes static images, photos, charts, etc., which are often used to analyze visual information such as brand logos, product displays, advertising creativity, etc.
• Behavioral data : It involves the action tracks and usage habits of users or consumers, such as click records, shopping paths, browsing history, etc. These data are usually obtained through the user's interactive behavior on the network platform.
• Sentiment data : mainly refers to the user's emotional response obtained by analyzing text, audio, video, etc. It is used to understand the user's emotional inclination towards a certain brand, product or service. For example, the emotional inclination analysis of brands on social media, the emotional expression of users in comments, and the emotional feedback in customer satisfaction surveys.

Soft data can help companies gain a deeper understanding of consumer needs, emotions, and behaviors, thereby making more accurate market positioning and product decisions.

What Is Hard Data? What Types Are There?

Hard data refers to quantitative data that can be expressed by precise numerical values and statistical methods. This type of data is highly objective and verifiable, and usually comes from direct measurement, recording or calculation. Hard data is often used in financial analysis, market research, business operations and other fields to help companies make decisions based on facts and data. The following are the main types of hard data :

• Financial data: This is the most common type of hard data, usually in the form of precise numerical values, involving the company's income, expenditure, profit, assets and liabilities, etc. This data is usually recorded and managed through the company's financial system. For example, the company's quarterly income report, cost expenditure table, profit margin and cash flow statement, etc.
• Market data: Numerical information related to market activities, including sales, market share, price index, inventory level, etc. These data are the basis for market analysis and forecasting. For example, product sales volume, changes in market share, commodity pricing strategy and inventory quantity.
• Demographic data: Information about population size, structure, and distribution, usually used for market research, social research, and policy making. For example, data on population age structure, gender ratio, education level, and income level.
• Operational data: covers the internal operational activities of the enterprise, such as production efficiency, equipment utilization, order processing time, etc. These data help enterprises optimize operational processes and improve efficiency. For example, the hourly output of the production line, equipment operating time, order delivery time and employee work efficiency.
• Web analytics data: This includes information such as website visits, click-through rates, bounce rates, and conversion rates. These data are collected through web analytics tools and used to evaluate the performance of websites or apps. For example, the number of daily visits to a website, the length of time users stay on the site, the number of clicks on a specific page, and the conversion rate of ads.
• Sales data: It refers to the sales activities of the company, including sales volume, sales quantity, return rate, etc. These data are usually recorded in the sales management system and used to analyze sales performance and market demand. For example, monthly sales revenue, return rate, sales quantity of different products and customer order data.
• Scientific experimental data: data from the experiment and measurement process in scientific research, including experimental results, measurement values, statistical data, etc., used to support scientific hypotheses and theoretical verification. For example, effect data in drug trials, laboratory measurement results, and precise measurement data in physical experiments.

Hard data plays an important role in decision-making, performance evaluation, trend forecasting, etc. Enterprises can understand market trends, optimize operating strategies, and improve financial performance through the analysis of hard data.

What Is the Difference between Soft Data and Hard Data?

To better understand the difference between soft data and hard data, we can compare their characteristics in multiple aspects.

Soft data and hard data each have unique advantages and application scenarios, but in modern business and research environments, the two are often used in combination. For example, in market research, soft data can provide in-depth insights into consumers, while hard data can verify the universality and accuracy of these insights. By integrating soft data and hard data, companies and researchers can make more comprehensive, in-depth and reliable decisions.

How to Collect Soft and Hard Data?

There are different methods for collecting soft data and hard data. The collection of soft data usually requires a flexible approach because it usually exists in an unstructured form. Here are some common methods for collecting soft data:

• Questionnaire survey: Obtain consumers’ opinions and attitudes by designing a questionnaire with open-ended questions.
• Social media monitoring: Use social media analytics tools to capture user comments and feedback on the platform.
• Focus Group: Bringing together a group of people with similar characteristics to discuss a specific topic and gather their opinions on it.
• Customer feedback: Collect user feedback through customer service systems, emails, or online reviews.

The collection of hard data is usually carried out in a structured way. The data is accurate and quantifiable. The following are some common methods for collecting hard data:

• Database records: Record and manage various types of data information, such as financial data, sales data, inventory data, etc., through the company's internal database system.
• Sensors and IoT devices: Collect numerical data about the environment, production equipment, user activities, etc. through sensors or IoT devices. Sensors can monitor and record various physical quantities such as temperature, humidity, pressure, etc. in real time.
• Website and application analysis: Use website analysis tools to collect user behavior data, including clicks, dwell time, conversion rate and other data.
• Public data sources: Utilize statistical data released by governments, research institutions, and other public sources. These data often include demographics, economic indicators, health data, etc.

How to Optimize Crawling of Soft and Hard Data?

There are significant differences between soft and hard data in form, structure and source, so different technologies and tools are involved in the scraping process. Using a combination of suitable tools and residential proxy services, it can be effectively extracted from various data sources. Required information.

Crawling soft data – social media data

1. elect the platform: Decide on the social media platform you need to scrape, such as Twitter, Facebook, Reddit, etc.
2. Get API access: Most social media platforms provide API access interfaces, which require registering a developer account and obtaining an API key.
3. Write crawling scripts: Use Python combined with libraries such as Tweepy or Scrapy to write scripts, and set crawling keywords, time range and other parameters.
4. Data processing: The captured data may contain irrelevant information and needs to be preprocessed and cleaned, such as removing stop words, deduplication, and sentiment analysis.

Here is the sample code for scraping Twitter data using 911 Proxy and the Tweepy library:

import tweepy
import requests
from requests.auth import HTTPProxyAuth

# Set up your Twitter API key
api_key = "your_twitter_api_key"
api_secret_key = "your_twitter_api_secret_key"
access_token = "your_access_token"
access_token_secret = "your_access_token_secret"

# Set 911 Proxy information
proxy_username = "your_proxy_username"
proxy_password = "your_proxy_password"
proxy_host = "your_proxy_address" # For example: the IP address of the 911 proxy
proxy_port = "your_proxy_port" # For example: the port of the 911 proxy

# Configure Tweepy and proxy services
auth = tweepy.OAuthHandler(api_key, api_secret_key)
auth.set_access_token(access_token, access_token_secret)

# Using a proxy to make requests
proxy = {
"http": f"http://{proxy_username}:{proxy_password}@{proxy_host}:{proxy_port}",
"https": f"http://{proxy_username}:{proxy_password}@{proxy_host}:{proxy_port}",
}

# Configure proxy authentication
proxy_auth = HTTPProxyAuth(proxy_username, proxy_password)

# Tweepy API Example
api = tweepy.API(auth)

# Search Tweets
keyword = "rproxy residential"
tweets = api.search(q=keyword, lang="en", count=100)

# Print the tweet content
for tweet in tweets:
print(tweet.text)

Crawling hard data - database data

1. Connect to database: Use Python to connect to the target database and execute SQL queries to extract the required data.
2. Data export: Export the queried data to local files or other data processing platforms.

The following is a sample code for crawling MySQL database data using Python combined with a residential proxy :

import pymysql
import requests
from requests.auth import HTTPProxyAuth

# Set 911 Proxy information
proxy_username = "your_proxy_username"
proxy_password = "your_proxy_password"
proxy_host = "your_proxy_address" # 911 proxy IP address
proxy_port = "your_proxy_port" # 911 proxy port

# Configure proxy information
proxies = {
"http": f"http://{proxy_username}:{proxy_password}@{proxy_host}:{proxy_port}",
"https": f"http://{proxy_username}:{proxy_password}@{proxy_host}:{proxy_port}",
}

# Set up proxy authentication
proxy_auth = HTTPProxyAuth(proxy_username, proxy_password)

# Use the proxy to send a request to test whether the proxy works
response = requests.get("http://example.com", proxies=proxies, auth=proxy_auth)
print(f"Proxy test return status code: {response.status_code}")

#MySQL database connection configuration
db_host = "your_database_host"
db_user = "your_database_username"
db_password = "your_database_password"
db_name = "your_database_name"
db_port = 3306 #MySQL default port

# Connect to a remote database (via SSH tunnel or VPN with proxy configuration)
connection = pymysql.connect(
host=db_host,
user=db_user,
password=db_password,
database=db_name,
port=db_port,
cursorclass=pymysql.cursors.DictCursor,
)

# Execute SQL query
with connection.cursor() as cursor:
sql_query = "SELECT * FROM your_table_name LIMIT 10"
cursor.execute(sql_query)
result = cursor.fetchall()

# Print query results
for row in result:
print(row)

# Close the database connection
connection.close()

Summarize

Both soft data and hard data play an indispensable role in the strategic decision-making of enterprises. Whether it is capturing consumer emotions and behaviors or analyzing precise market and financial data, these two types of data provide unique perspectives and insights. By capturing and utilizing these two types of data through residential proxy services, enterprises can obtain the required data more conveniently and efficiently, thereby standing out from the competition.