Menu
Back to Blog

Use Web Scraping to Increase your Ads ROI

Published by Anis Gandoura - stabler.tech CEO & VP of Engineering on
advertisements

Section 1: Data-Driven Advertising with Web Scraping

Understanding the Role of Data in Ad Campaign Success

In the modern digital advertising landscape, data is the backbone of successful campaigns. Advertisers rely on accurate, up-to-date, and relevant data to make decisions about targeting, messaging, and budget allocation. Web scraping provides the ability to gather large volumes of public data in real-time, empowering advertisers to base their strategies on actionable insights.

For instance, consider a company launching a new fitness tracker. To maximize ad performance, they need to know:

  • Who their target audience is and their purchasing behavior.
  • Competitor pricing and promotional strategies.
  • Trending keywords and popular platforms for advertising.

By scraping e-commerce platforms, social media, and industry-specific forums, advertisers can uncover these insights without relying solely on third-party analytics platforms.

Web Scraping as a Key to Unlocking Actionable Insights

Web scraping transforms scattered online information into a structured and analyzable format. Tools such as Python libraries like BeautifulSoup and Scrapy allow businesses to automate this process effectively. Here's an example:


from bs4 import BeautifulSoup
import requests

# URL of the target website
url = "https://example-ecommerce.com/trending-products"

# Send a request to fetch HTML content
response = requests.get(url)
soup = BeautifulSoup(response.content, 'html.parser')

# Extract product names and prices
products = soup.find_all('div', class_='product-item')
for product in products:
    name = product.find('h2').text
    price = product.find('span', class_='price').text
    print(f"Product: {name}, Price: {price}")

In this example, a company selling fitness gear could identify trending products and adjust their ad campaigns accordingly.

Types of Data Relevant for Advertising Campaigns

Not all data is created equal. For advertising, the following types of web-scraped data prove most valuable:

  • Demographic Information: Gather age, location, and interests from publicly available profiles and reviews.
  • Keyword Trends: Scrape search engine results or hashtags to identify the most searched topics in your niche.
  • Competitor Data: Analyze competitors' ad copies, landing pages, and promotional offers.
  • Product Reviews: Extract reviews from e-commerce sites to identify what customers love or dislike about products.

For instance, by scraping an e-commerce platform, you can uncover trending keywords used in competitor product titles. These insights can directly inform your ad copy, boosting its relevance and click-through rates.

Hands-On Example: Scraping Amazon Product Reviews

Let’s say you want to improve your ad copy by understanding customer sentiment. Here’s how you can scrape Amazon reviews:


import requests
from bs4 import BeautifulSoup

# Amazon product URL
url = "https://www.amazon.com/product-reviews/B08XYZ123"

headers = {"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64)"}
response = requests.get(url, headers=headers)
soup = BeautifulSoup(response.content, 'html.parser')

# Extract reviews
reviews = soup.find_all('span', {'data-hook': 'review-body'})
for review in reviews:
    print(review.text.strip())

By analyzing these reviews, advertisers can incorporate recurring positive themes into their campaigns or address common complaints to differentiate their products.

Section 2: Enhancing Ad Targeting through Web Scraping

Collecting Audience Insights for Precision Targeting

Effective ad targeting starts with a deep understanding of your audience. Web scraping allows you to gather data about audience demographics, preferences, and online behaviors from various platforms, including social media, forums, and review sites. For example, scraping publicly available data from Twitter can reveal trending hashtags and common interests among your target audience.

Consider this Python script for scraping trending hashtags from Twitter:


import tweepy

# Twitter API credentials
api_key = "your_api_key"
api_key_secret = "your_api_secret"
access_token = "your_access_token"
access_token_secret = "your_access_secret"

# Authenticate with the Twitter API
auth = tweepy.OAuthHandler(api_key, api_key_secret)
auth.set_access_token(access_token, access_token_secret)
api = tweepy.API(auth)

# Fetch trending hashtags
trending = api.trends_place(1)  # '1' indicates worldwide trends
for trend in trending[0]['trends']:
    print(trend['name'])

This data can help tailor ad campaigns by focusing on the interests and language patterns of your audience, ensuring higher engagement rates.

Identifying Market Trends to Refine Campaigns

Markets evolve rapidly, and staying ahead requires up-to-date knowledge of trends. Web scraping enables real-time tracking of market movements, such as emerging products, new technologies, or shifting consumer preferences. For example, scraping Google Trends can provide insights into rising search queries in your niche.

Here’s an example of using Python to extract trending searches:


from pytrends.request import TrendReq

# Initialize Google Trends API
pytrends = TrendReq()

# Get trending searches
trending_searches = pytrends.trending_searches()
print(trending_searches.head())

Using this data, advertisers can create campaigns that capitalize on current interests, ensuring relevancy and increased click-through rates.

Leveraging Competitor Ad Analysis for Strategic Advantage

Understanding your competitors’ advertising strategies can provide a significant edge. Web scraping allows you to analyze their ad copies, CTAs, and landing pages to identify gaps or strengths. Platforms like Facebook Ad Library or Google Ads transparency reports offer publicly available data that can be scraped for analysis.

For instance, the script below shows how to scrape competitor ad data from Facebook Ad Library:


import requests
from bs4 import BeautifulSoup

# Facebook Ad Library URL
url = "https://www.facebook.com/ads/library/?active_status=all"

headers = {"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64)"}
response = requests.get(url, headers=headers)
soup = BeautifulSoup(response.content, 'html.parser')

# Extract ad titles
ads = soup.find_all('div', class_='ad-title')
for ad in ads:
    print(ad.text.strip())

By analyzing this data, you can refine your ad strategies, adopt best practices, and create unique value propositions that resonate more effectively with your audience.

Hands-On Example: Scraping Landing Page Data

Another powerful use case is extracting content from competitor landing pages to identify their focus areas and pain points addressed. Here’s how you can scrape key elements from a landing page:


from bs4 import BeautifulSoup
import requests

# Competitor landing page URL
url = "https://competitor.com/landing-page"

response = requests.get(url)
soup = BeautifulSoup(response.content, 'html.parser')

# Extract headlines and CTAs
headline = soup.find('h1').text
cta = soup.find('a', {'class': 'cta-button'}).text

print(f"Headline: {headline}")
print(f"CTA: {cta}")

With this information, you can ensure your ads and landing pages offer unique benefits that stand out from the competition.

Section 3: Optimizing Ad Spend with Dynamic Pricing and Bid Management

Dynamic Pricing Insights for Budget Optimization

Dynamic pricing allows advertisers to adjust pricing strategies based on market demand, competitor pricing, and other external factors. Web scraping can play a critical role in collecting real-time pricing data from competitor websites, enabling businesses to make informed decisions about their pricing strategy.

For example, let’s consider an e-commerce company selling electronics. By scraping competitor websites, they can monitor product prices and adjust their pricing dynamically to stay competitive. Here's a Python script that demonstrates how to scrape pricing data:


from bs4 import BeautifulSoup
import requests

# Competitor website URL
url = "https://competitor.com/category/electronics"

response = requests.get(url)
soup = BeautifulSoup(response.content, 'html.parser')

# Extract product names and prices
products = soup.find_all('div', class_='product-item')
for product in products:
    name = product.find('h2').text
    price = product.find('span', class_='price').text
    print(f"Product: {name}, Price: {price}")

By integrating this data into their pricing algorithms, businesses can optimize their ad budgets by promoting the most competitively priced products and focusing on high-margin items.

Real-Time Monitoring of Ad Performance Metrics

Tracking the performance of ad campaigns is essential for ensuring a high return on investment (ROI). Web scraping tools can gather performance data from ad platforms, including impressions, clicks, conversions, and cost-per-click (CPC). This real-time data can inform decisions about which ads to scale and which to pause.

For example, a Python script using the Google Ads API might look like this:


from googleads import adwords

# Initialize the AdWords client
client = adwords.AdWordsClient.LoadFromStorage()

# Define the report query
report_query = (
    "SELECT CampaignName, Impressions, Clicks, Cost "
    "FROM CAMPAIGN_PERFORMANCE_REPORT "
    "WHERE Impressions > 0 DURING LAST_7_DAYS"
)

# Fetch the report
report_downloader = client.GetReportDownloader(version='v201809')
with open('campaign_performance.csv', 'wb') as output_file:
    report_downloader.DownloadReportWithAwql(
        report_query, 'CSV', output_file
    )
print("Report downloaded.")

This script extracts performance data from Google Ads campaigns, allowing advertisers to evaluate ROI and reallocate budgets for maximum impact.

Automated Adjustments to Maximize ROI

Automation is key to optimizing ad spend at scale. Web scraping can be integrated with programmatic advertising platforms to automate bid adjustments based on real-time market conditions. For instance, if a competitor lowers their prices, automated scripts can increase bids on high-performing keywords to maintain ad visibility.

Here’s an example of automating bid adjustments using scraped data:


import requests

# Scrape competitor pricing data
url = "https://competitor.com/pricing"
response = requests.get(url)
prices = extract_prices(response.content)  # Custom function to extract prices

# Adjust bids based on competitor pricing
def adjust_bids(prices):
    for keyword, price in prices.items():
        if price < current_price[keyword]:
            increase_bid(keyword)

adjust_bids(prices)

This approach ensures that your campaigns remain competitive while optimizing ad spend for the highest ROI.

Hands-On Example: Monitoring Google Ads CPC

Monitoring cost-per-click (CPC) for targeted keywords can help advertisers adjust bids dynamically. Here’s an example of scraping CPC data from Google Ads reports:


from googleads import adwords

# Initialize client and fetch keyword performance data
report_query = (
    "SELECT KeywordText, AverageCpc "
    "FROM KEYWORDS_PERFORMANCE_REPORT "
    "WHERE CampaignStatus = 'ENABLED' "
    "DURING LAST_30_DAYS"
)

# Save to CSV for analysis
with open('keywords_performance.csv', 'wb') as output_file:
    report_downloader.DownloadReportWithAwql(
        report_query, 'CSV', output_file
    )
print("Keyword performance data downloaded.")

Analyzing this data allows advertisers to focus ad budgets on keywords with the best ROI potential.

Section 4: Advanced Strategies and Tools for Scaling Ad Campaigns

Integrating Web Scraped Data with Programmatic Advertising

Programmatic advertising relies on automation to buy and manage ad placements in real-time. When combined with web-scraped data, it becomes a powerful tool for scaling ad campaigns effectively. For instance, integrating competitor pricing and audience sentiment data can help refine ad targeting and bidding strategies in real time.

Here’s an example of how to use scraped data for programmatic advertising:


import requests
import json

# Fetch audience sentiment data from scraped reviews
sentiment_data = scrape_sentiment_data()  # Custom function for web scraping

# Push data into the programmatic ad platform API
api_url = "https://adplatform.com/api/update-targeting"
headers = {"Content-Type": "application/json"}
response = requests.post(api_url, data=json.dumps(sentiment_data), headers=headers)

if response.status_code == 200:
    print("Audience targeting updated successfully.")

With this integration, advertisers can dynamically adjust their campaigns based on real-time insights, ensuring maximum efficiency and scalability.

Tools and Platforms for Efficient Web Scraping

Several tools and platforms can simplify the web scraping process, making it accessible to advertisers without advanced coding skills:

  • BeautifulSoup: A Python library for parsing HTML and XML documents.
  • Scrapy: A powerful web crawling and scraping framework in Python.
  • Stabler: A no-code web scraping tool suitable for non-technical users.

These tools allow advertisers to collect and process data at scale, whether it’s for competitive analysis, audience insights, or real-time campaign adjustments.

Best Practices for Data Integration into Marketing Workflows

Successfully integrating web-scraped data into your marketing workflows requires strategic planning and execution:

  • Data Cleaning: Use libraries like Pandas to clean and structure raw scraped data.
  • Data Visualization: Leverage tools like Tableau or Matplotlib to create actionable insights from the data.
  • Automation: Set up automated pipelines using tools like Apache Airflow to ensure data flows seamlessly into your marketing systems.

For instance, an e-commerce company can automate data collection and use visualization dashboards to monitor ad performance and adjust strategies in near real-time.

Hands-On Example: Automating Data Pipelines

Here’s an example of automating a data pipeline to integrate web-scraped data into a marketing dashboard:


from airflow import DAG
from airflow.operators.python_operator import PythonOperator
from datetime import datetime

# Define scraping function
def scrape_data():
    # Custom scraping logic
    pass

# Define DAG
default_args = {"owner": "airflow", "start_date": datetime(2024, 12, 23)}
dag = DAG("data_pipeline", default_args=default_args, schedule_interval="@daily")

# Define tasks
scrape_task = PythonOperator(task_id="scrape_data", python_callable=scrape_data, dag=dag)

scrape_task

This setup ensures that data is updated daily and seamlessly integrated into the marketing workflow.

Conclusion

Web scraping offers advertisers a wealth of opportunities to optimize and scale their ad campaigns. From collecting audience insights and monitoring competitors to implementing dynamic pricing and automating workflows, the potential applications are vast. By integrating web-scraped data into programmatic advertising and leveraging the right tools, businesses can achieve unparalleled precision and efficiency.

As digital advertising continues to evolve, staying competitive will require leveraging all available data sources. Web scraping is not just a tool for gathering data but a strategic asset for enhancing ROI and driving sustained growth. With the hands-on examples and tools provided, advertisers are well-equipped to take their campaigns to the next level.

Back to Blog
By using this website, you accept our Cookie Policy.