html
Web scraping e-commerce stuff with Python
Why scrape e-commerce sites anyway?
So, you're thinking about web scraping some e-commerce sites? Great! There are tons of awesome reasons why this can be super valuable. Let's break it down:
- Price Tracking: This is probably the most popular use case. Imagine being able to constantly monitor the prices of your competitors' products, or track price fluctuations for products you sell. Knowing when prices drop can help you grab deals, and understanding competitor pricing strategies gives you a huge competitive advantage. This isn't just for big retailers, either – even small businesses can benefit from effective price monitoring.
- Product Details Gathering: Need to build a comprehensive catalog of products? Manually copying and pasting product descriptions, images, and specifications is a total nightmare. Web scraping lets you automate this, quickly gathering all the information you need in a structured format. This is especially handy if you're building a price comparison website, or simply trying to enrich your own product database.
- Availability Monitoring: Ever been frustrated by constantly checking if a product is back in stock? Web scraping can automate that too! You can set up a script to monitor product pages and alert you when something becomes available. This is crucial for capturing sales opportunities that would otherwise be lost.
- Catalog Cleanup and Maintenance: E-commerce sites are constantly changing. Products get updated, discontinued, or moved. Web scraping can help you keep your own product information accurate and up-to-date by identifying broken links, changed descriptions, or discontinued items on your competitors' sites.
- Deal Alerts and Trend Identification: Want to know when your favorite retailer is having a sale? Web scraping can be used to identify and track deals and promotions across multiple sites. You can also analyze product listings to spot emerging trends and popular items. Analyzing market research data is significantly easier with programatically extracted insights.
- Understanding Customer Behaviour: Okay, this one is a bit more advanced and requires careful consideration of privacy and ethical concerns. But, in theory, you could potentially scrape publicly available reviews and ratings to understand customer sentiment and identify common issues or desired features. Aggregated appropriately and anonymized, this can provide valuable insights into customer needs.
- Lead Generation Data: For B2B e-commerce, scraping product pages can help find suppliers, distributors, and potential partners. Finding relevant contact information, product lines, and client lists of competitors becomes more efficient using data scraping.
In short, e-commerce web scraping empowers you with data. And with data, you can make smarter decisions, optimize your strategies, and gain a serious edge in the market. Think of it as your own automated market research tool.
Okay, so how does it actually work? (A Simplified Explanation)
At its heart, web scraping involves writing a program (usually in Python, but other languages work too) that:
- Requests a webpage: Your program sends a request to the website's server, just like your web browser does when you type in a URL.
- Receives the HTML: The server sends back the HTML code that makes up the webpage.
- Parses the HTML: Your program then uses a library (like BeautifulSoup or Scrapy in Python) to analyze the HTML structure and identify the specific data you're looking for (prices, product names, descriptions, etc.).
- Extracts the data: The library helps you "scrape" or extract the desired data from the HTML code.
- Stores the data: Finally, your program saves the extracted data into a structured format, like a CSV file, a database, or a spreadsheet.
Think of it like this: you're asking the website for its source code (the HTML), and then you're carefully picking out the bits of information you need from that source code. It's like reading a book and highlighting the important passages. Except, instead of reading manually, you're teaching a computer to do the reading and highlighting for you.
A Simple Step-by-Step Example with Python
Let's walk through a super simple example to illustrate the basics. We'll use Python, along with the `requests` and `BeautifulSoup4` libraries.
Prerequisites:
- Python installed on your computer (version 3.6 or higher is recommended).
- The `requests` and `BeautifulSoup4` libraries. You can install them using pip:
pip install requests beautifulsoup4
Step 1: Inspect the Target Website
Before you write any code, take a look at the webpage you want to scrape. Let's say we want to extract the title of the following page: `https://www.scrapethissite.com/`. Use your browser's developer tools (usually accessible by pressing F12) to inspect the HTML structure of the page. Identify the HTML tags that contain the data you want to extract. For example, the title of a webpage is usually located within the </code> tag.</p>
<p><b>Step 2: Write the Python Code</b></p>
<p>Here's the Python code to extract the title:</p>
<pre><code class="language-python">import requests
from bs4 import BeautifulSoup
# URL of the webpage you want to scrape
url = "https://www.scrapethissite.com/"
# Send a request to the URL
response = requests.get(url)
# Check if the request was successful (status code 200)
if response.status_code == 200:
# Parse the HTML content using BeautifulSoup
soup = BeautifulSoup(response.content, "html.parser")
# Find the title tag
title_tag = soup.find("title")
# Extract the text from the title tag
if title_tag:
title = title_tag.text
print("Title:", title)
else:
print("Title tag not found.")
else:
print("Request failed with status code:", response.status_code)
</code></pre>
<p><b>Step 3: Run the Code</b></p>
<p>Save the code in a file (e.g., `scrape_title.py`) and run it from your terminal:</p>
<pre><code>python scrape_title.py</code></pre>
<p>This script will print the title of the webpage to your console. If the request fails (e.g., if the website is down or you don't have an internet connection), it will print an error message.</p>
<p><b>Explanation:</b></p>
<ul>
<li>We import the `requests` library to make HTTP requests and the `BeautifulSoup` library to parse the HTML.</li>
<li>We define the URL of the webpage we want to scrape.</li>
<li>We use `requests.get()` to send a GET request to the URL and store the response in the `response` variable.</li>
<li>We check the status code of the response. A status code of 200 means the request was successful.</li>
<li>If the request was successful, we create a `BeautifulSoup` object from the response content, using the "html.parser" parser.</li>
<li>We use `soup.find("title")` to find the first <code><title></code> tag in the HTML.</li>
<li>If we find a <code><title></code> tag, we extract the text content of the tag using `.text` and print it to the console.</li>
<li>If the request fails or the <code><title></code> tag is not found, we print an error message.</li>
</ul>
<p>This is a very basic example, but it demonstrates the fundamental steps involved in web scraping. You can adapt this code to extract other types of data by changing the URL and the HTML tags you're searching for.</p>
<h2>A Bit More Advanced: Using Pandas for Data Storage and Analysis</h2>
<p>Let's say you want to scrape multiple data points from a product page and store them in a structured format. Pandas is your friend! Here's how you can use Pandas to create a DataFrame from your scraped data. Let's pretend we are scraping products from `https://www.scrapethissite.com/lessons/tables/`, and want product names and prices.</p>
<pre><code class="language-python">import requests
from bs4 import BeautifulSoup
import pandas as pd
# URL of the webpage
url = "https://www.scrapethissite.com/lessons/tables/"
# Send a request to the URL
response = requests.get(url)
# Check if the request was successful
if response.status_code == 200:
# Parse the HTML content
soup = BeautifulSoup(response.content, "html.parser")
# Find all product rows (adjust this based on the website's HTML structure)
product_rows = soup.find_all("tr", class_="team") # Example selector, inspect the website's HTML
# Create empty lists to store the data
product_names = []
product_prices = []
# Loop through each product row and extract the data
for row in product_rows:
# Extract the product name
name_element = row.find("td", class_="name") # Example selector, inspect the website's HTML
if name_element:
product_name = name_element.text.strip()
product_names.append(product_name)
else:
product_names.append(None) # Handle missing data
# Extract the product price
price_element = row.find("td", class_="wins") # Example selector, inspect the website's HTML
if price_element:
product_price = price_element.text.strip()
product_prices.append(product_price)
else:
product_prices.append(None) # Handle missing data
# Create a Pandas DataFrame
data = {"Product Name": product_names, "Price": product_prices}
df = pd.DataFrame(data)
# Print the DataFrame
print(df)
# Optionally, save the DataFrame to a CSV file
df.to_csv("products.csv", index=False)
else:
print("Request failed with status code:", response.status_code)
</code></pre>
<p><b>Explanation:</b></p>
<ul>
<li>We've added `import pandas as pd` to bring in the Pandas library.</li>
<li>We create empty lists (`product_names`, `product_prices`) to store the scraped data temporarily.</li>
<li>We loop through the product rows and extract the name and price from each row, using `row.find()` with appropriate CSS selectors (you'll need to adapt these selectors based on the specific website you're scraping).</li>
<li>We append the extracted data to the corresponding lists. We also handle potential missing data by appending `None` if a particular element is not found.</li>
<li>We create a Pandas DataFrame using the `pd.DataFrame()` constructor, passing in a dictionary where the keys are the column names and the values are the lists of data.</li>
<li>We print the DataFrame to the console.</li>
<li>Finally, we use `df.to_csv("products.csv", index=False)` to save the DataFrame to a CSV file named "products.csv". The `index=False` argument prevents Pandas from writing the DataFrame index to the CSV file.</li>
</ul>
<p>This example shows how you can combine web scraping with Pandas to extract data from a website and store it in a structured format that's easy to analyze and manipulate.</p>
<h2>Important Legal and Ethical Considerations</h2>
<p>Before you start scraping away, it's crucial to understand the legal and ethical implications. Web scraping isn't a free-for-all. Here are the key things to keep in mind:</p>
<ul>
<li><b>Robots.txt:</b> Most websites have a `robots.txt` file that specifies which parts of the site should not be scraped by bots or crawlers. You should <i>always</i> check this file before scraping a website and respect its rules. You can usually find it by adding `/robots.txt` to the end of the website's domain name (e.g., `example.com/robots.txt`). Ignoring `robots.txt` can lead to your IP address being blocked or, in more serious cases, legal action.</li>
<li><b>Terms of Service (ToS):</b> Many websites have Terms of Service agreements that explicitly prohibit web scraping. Read the ToS carefully before scraping a website to ensure you're not violating their rules. Scraping a website in violation of its ToS can have legal consequences.</li>
<li><b>Rate Limiting:</b> Don't overwhelm the website's server with too many requests in a short period of time. Implement rate limiting in your script to avoid overloading the server and potentially causing it to crash. A good rule of thumb is to add a delay (e.g., 1-2 seconds) between requests. This demonstrates respect for the target website's resources.</li>
<li><b>Data Usage:</b> Be responsible with the data you scrape. Don't use it for malicious purposes, such as spamming or creating fake accounts. Respect user privacy and avoid collecting personal information without consent. Only collect the data you need and use it ethically and responsibly.</li>
<li><b>Respect Copyright:</b> Don't scrape copyrighted content (e.g., images, text) and use it without permission. Respect intellectual property rights and ensure you have the necessary licenses or permissions to use the scraped data.</li>
<li><b>Be Transparent:</b> If you're running a web scraper, be transparent about it. Identify yourself as a bot and provide contact information in your script's User-Agent header. This allows website administrators to contact you if there are any issues with your scraping activity.</li>
</ul>
<p>In short, be a good internet citizen. Scrape responsibly, respect website rules, and use the data ethically. Ignoring these considerations can have serious consequences, including legal penalties and reputational damage. Many data scraping services will handle these items for you.</p>
<h2>Choosing the Best Web Scraping Language</h2>
<p>While there are several languages you can use for web scraping, Python is generally considered the best web scraping language, and the most popular, for a few key reasons:</p>
<ul>
<li><b>Rich Ecosystem of Libraries:</b> Python has a vast ecosystem of powerful libraries specifically designed for web scraping, such as `requests`, `BeautifulSoup4`, `Scrapy`, and `Selenium`. These libraries make it easy to handle tasks like making HTTP requests, parsing HTML, and interacting with dynamic websites.</li>
<li><b>Simple and Readable Syntax:</b> Python's syntax is relatively simple and easy to learn, making it a great choice for beginners. Its readability also makes it easier to maintain and debug your scraping scripts.</li>
<li><b>Large and Active Community:</b> Python has a large and active community of developers who are constantly creating new tools and resources for web scraping. This means you can easily find help and support when you need it.</li>
<li><b>Cross-Platform Compatibility:</b> Python is a cross-platform language, meaning you can run your scraping scripts on Windows, macOS, and Linux. This makes it a versatile choice for a variety of environments.</li>
<li><b>Integration with Data Analysis Tools:</b> Python integrates seamlessly with other popular data analysis tools, such as Pandas and NumPy. This allows you to easily process, analyze, and visualize the data you scrape.</li>
</ul>
<p>While other languages like Java, JavaScript, and Ruby can also be used for web scraping, Python's combination of powerful libraries, ease of use, and a large community makes it the preferred choice for most web scraping projects. This doesn't mean the others are *bad*, just that Python offers some particular conveniences. Python web scraping makes a lot of sense!</p>
<h2>Scaling Up: Web Crawlers and Scrapy</h2>
<p>For larger and more complex web scraping projects, you might need to use a web crawler. A web crawler (also known as a spider or bot) is an automated program that systematically browses the web, following links and extracting data from web pages. Web crawlers are typically used to index websites for search engines or to gather data for research or analysis.</p>
<p>One of the most popular and powerful web scraping frameworks for Python is Scrapy. Scrapy is a high-level framework that provides all the tools you need to build and deploy web crawlers. It handles tasks like managing HTTP requests, parsing HTML, and storing data, so you can focus on writing the code that extracts the data you need. A Scrapy tutorial is easily found online.</p>
<p><b>Benefits of using Scrapy:</b></p>
<ul>
<li><b>Asynchronous Architecture:</b> Scrapy uses an asynchronous architecture, which allows it to handle multiple requests concurrently. This makes it much faster and more efficient than traditional synchronous scraping methods.</li>
<li><b>Built-in Support for Handling Cookies and Sessions:</b> Scrapy automatically handles cookies and sessions, making it easy to scrape websites that require authentication or maintain state.</li>
<li><b>Extensible and Customizable:</b> Scrapy is highly extensible and customizable, allowing you to add your own middleware, pipelines, and extensions to tailor it to your specific needs.</li>
<li><b>Built-in Support for Data Export:</b> Scrapy has built-in support for exporting data in various formats, such as CSV, JSON, and XML.</li>
<li><b>Large and Active Community:</b> Scrapy has a large and active community of developers who are constantly contributing to the framework and providing support to users.</li>
</ul>
<p>Scrapy is a great choice for building complex web crawlers that need to scrape data from multiple websites or handle large volumes of data. While it has a steeper learning curve than simpler libraries like BeautifulSoup4, its power and flexibility make it well worth the effort. In some cases, it might be worth paying for data scraping services.</p>
<h2>Real-Time Analytics and Big Data Integration</h2>
<p>Once you've scraped the data you need, you can use it to power real-time analytics and gain valuable insights into market trends, customer behavior, and competitor strategies. By integrating your scraped data with big data platforms and analytics tools, you can uncover hidden patterns and make data-driven decisions.</p>
<p><b>Examples of how you can use scraped data for real-time analytics:</b></p>
<ul>
<li><b>Price Optimization:</b> Monitor competitor prices in real-time and adjust your own prices accordingly to maximize profits.</li>
<li><b>Inventory Management:</b> Track product availability and demand to optimize your inventory levels and avoid stockouts.</li>
<li><b>Customer Sentiment Analysis:</b> Analyze customer reviews and ratings in real-time to identify and address customer issues and improve customer satisfaction.</li>
<li><b>Fraud Detection:</b> Monitor transactions and user behavior in real-time to detect and prevent fraud.</li>
<li><b>Personalized Recommendations:</b> Use customer data to provide personalized product recommendations and offers.</li>
</ul>
<p>By combining web scraping with real-time analytics and big data integration, you can transform raw data into actionable insights that drive business growth and improve decision-making.</p>
<h2>A Quick Checklist to Get Started</h2>
<p>Ready to dive in? Here's a simple checklist to get you started with e-commerce web scraping:</p>
<ol>
<li><b>Choose your tools:</b> Install Python and the `requests` and `BeautifulSoup4` (or Scrapy) libraries.</li>
<li><b>Pick your target:</b> Identify the e-commerce site you want to scrape.</li>
<li><b>Inspect the HTML:</b> Use your browser's developer tools to understand the website's structure.</li>
<li><b>Write your script:</b> Craft your Python code to fetch, parse, and extract the desired data.</li>
<li><b>Test and refine:</b> Run your script and make adjustments as needed to ensure accurate data extraction.</li>
<li><b>Store the data:</b> Save the scraped data in a structured format (CSV, database, etc.).</li>
<li><b>Be ethical:</b> Respect `robots.txt`, ToS, and rate limits.</li>
<li><b>Analyze and act:</b> Use the data to gain insights and improve your business strategies.</li>
</ol>
<p>Web scraping can seem daunting at first, but with a little practice, you'll be extracting valuable data and gaining a competitive edge in no time! Remember to start small, be patient, and always prioritize ethical and legal considerations.</p>
<h2>Ready to take your e-commerce data game to the next level?</h2>
<p>We know scraping can be tricky, so let us do the heavy lifting! Get valuable data and insights without having to write the code yourself.</p>
<a href="https://www.justmetrically.com/login?view=sign-up">Sign up</a> today and unlock the power of e-commerce data!
<hr />
<p>Contact us for any questions: <a href="mailto:info@justmetrically.com">info@justmetrically.com</a></p>
#ecommerce #webscraping #python #datascraping #pricetracking #marketresearch #realtimeanalytics #bigdata #competitiveadvantage #scrapytutorial
<h2>Related posts</h2>
<ul>
<li><a href="/post/e-commerce-scraping-how-to-guide">E-commerce scraping how-to (guide)</a></li>
<li><a href="/post/e-commerce-scraping-without-code-here-s-how">E-commerce Scraping Without Code? Here's How</a></li>
<li><a href="/post/web-scraping-for-e-commerce-here-s-how-guide">Web scraping for e-commerce? Here's how. (guide)</a></li>
<li><a href="/post/web-scraping-for-ecommerce-my-honest-guide">Web scraping for ecommerce: My honest guide</a></li>
<li><a href="/post/e-commerce-scraping-how-to-no-coding-needed-explained">E-commerce scraping how-to no coding needed explained</a></li>
</ul>
</div>
<hr>
<h3 class="mb-3">Comments</h3>
<p class="login-message">Please <a href="/login"
class="login-link">log in</a> to add a comment.</p>
</article>
<!-- Sticky quote widget -->
<aside class="col-12 col-lg-4 order-2 order-lg-2 lg-sticky">
<div class="fixed-quote-widget">
<h2>Get A Best Quote</h2>
<form id="quoteForm">
<div class="input-row mt-2">
<input type="text" name="name" placeholder="Name" required />
<input type="email" name="email" placeholder="Email" required />
</div>
<div class="input-row">
<input type="tel" name="phone" placeholder="Phone" required />
<input type="text" name="subject" placeholder="Subject" required />
</div>
<textarea name="message" placeholder="Message" required></textarea>
<button type="submit">SEND MESSAGE</button>
<div id="quoteSuccess">Thank you! Your inquiry has been
submitted.</div>
</form>
</div>
</aside>
</div>
</div>
<script>
document.addEventListener("DOMContentLoaded", function () {
const form = document.getElementById("quoteForm");
const successMsg = document.getElementById("quoteSuccess");
form.addEventListener("submit", async function (e) {
e.preventDefault();
const formData = new FormData(form);
const data = new URLSearchParams();
for (const pair of formData) {
data.append(pair[0], pair[1]);
}
try {
const response = await fetch("/contact", {
method: "POST",
headers: { 'Accept': 'application/json' },
body: data
});
if (response.ok) {
form.reset();
successMsg.style.display = "block";
} else {
alert("There was an error submitting your inquiry. Please try again.");
}
} catch (err) {
alert("There was an error submitting your inquiry. Please try again.");
}
});
});
</script>
<section class="section latest-news" id="blog">
<div class="container" style="padding-left:50px;">
<div class="row justify-content-center">
<div class="col-md-8 col-lg-6 text-center">
<div class="section-heading">
<!-- Heading -->
<h2 class="section-title">
Read our <span class="orange-txt">latest blogs</span>
</h2>
<!-- Subheading -->
</div>
</div>
</div> <!-- / .row -->
<div class="row justify-content-center">
<div class="col-lg-4 col-md-6">
<div class="blog-box">
<div class="blog-img-box">
<img src="https://images.pexels.com/photos/7988079/pexels-photo-7988079.jpeg?auto=compress&cs=tinysrgb&h=650&w=940" alt class="img-fluid blog-img">
</div>
<div class="single-blog">
<div class="blog-content">
<h6>December 16, 2025</h6>
<a href="/post/simple-ways-to-track-prices-with-web-scraping">
<h3 class="card-title">Simple Ways to Track Prices with Web Scraping</h3>
</a>
<p>Simple Ways to Track Prices with Web Scraping</p>
<a href="/post/simple-ways-to-track-prices-with-web-scraping"
class="read-more">Read More</a>
</div>
</div>
</div>
</div>
<div class="col-lg-4 col-md-6">
<div class="blog-box">
<div class="blog-img-box">
<img src="https://images.pexels.com/photos/39284/macbook-apple-imac-computer-39284.jpeg?auto=compress&cs=tinysrgb&h=650&w=940" alt class="img-fluid blog-img">
</div>
<div class="single-blog">
<div class="blog-content">
<h6>December 15, 2025</h6>
<a href="/post/simple-ways-to-track-prices-with-web-scraping">
<h3 class="card-title">Simple Ways to Track Prices with Web Scraping</h3>
</a>
<p>Simple Ways to Track Prices with Web Scraping</p>
<a href="/post/simple-ways-to-track-prices-with-web-scraping"
class="read-more">Read More</a>
</div>
</div>
</div>
</div>
<div class="col-lg-4 col-md-6">
<div class="blog-box">
<div class="blog-img-box">
<img src="https://images.pexels.com/photos/1089438/pexels-photo-1089438.jpeg?auto=compress&cs=tinysrgb&h=650&w=940" alt class="img-fluid blog-img">
</div>
<div class="single-blog">
<div class="blog-content">
<h6>December 15, 2025</h6>
<a href="/post/simple-ways-to-track-prices-with-web-scraping">
<h3 class="card-title">Simple Ways to Track Prices with Web Scraping</h3>
</a>
<p>Simple Ways to Track Prices with Web Scraping</p>
<a href="/post/simple-ways-to-track-prices-with-web-scraping"
class="read-more">Read More</a>
</div>
</div>
</div>
</div>
</div>
</div>
</section>
</main>
<style>
:root{
--primary:#e85b00;
--secondary:#88ab8e;
--bg:#ffffff;
--text:#1f1f1f;
--footer-bg:#0f1110; /* deep neutral for contrast */
--footer-fg:#e9f1ec; /* soft white/greenish tint */
--footer-muted:rgba(233,241,236,0.7);
--footer-border:rgba(255,255,255,0.08);
--focus-ring: 2px solid var(--primary);
}
/* Smoothness for your flipster bits you already had */
.flipster--flat .flipster__container,
.flipster__item,
.flipster__item__content{
transition: all 400ms ease-in-out !important;
}
/* FOOTER */
#footer{
position: relative;
background: radial-gradient(1200px 500px at 10% -10%, rgba(136,171,142,0.15), transparent 60%),
radial-gradient(800px 400px at 90% -20%, rgba(254,102,0,0.12), transparent 60%),
var(--footer-bg);
color: var(--footer-fg);
}
#footer .footer-accent{
position:absolute; inset:0 0 auto 0; height:4px;
background: linear-gradient(90deg, var(--primary), var(--secondary));
}
#footer .container{ padding-top: 56px; padding-bottom: 24px; }
/* Headings */
#footer .footer-widget h3{
font-size: 0.95rem;
text-transform: uppercase;
letter-spacing: .08em;
font-weight: 700;
margin-bottom: 14px;
color:#fff;
}
/* Brand block */
#footer .brand-wrap{
display:flex; flex-direction:column; gap:12px;
}
#footer .brand-wrap .tagline{
color: var(--footer-muted);
line-height:1.6;
margin: 0;
}
#footer .logo{
width: 220px; height:auto; display:block;
filter: drop-shadow(0 4px 18px rgba(0,0,0,.25));
}
/* Link lists */
#footer .footer-links,
#footer .list-unstyled{
list-style: none; padding:0; margin:0;
}
#footer .footer-links li{ margin: 8px 0; }
#footer a{
color: var(--footer-fg);
text-decoration: none;
opacity: .9;
transition: transform .18s ease, opacity .18s ease, color .18s ease, background-color .18s ease;
outline: none;
}
#footer a:hover{ opacity:1; color: var(--secondary); }
#footer a:focus-visible{ outline: var(--focus-ring); outline-offset: 2px; border-radius: 6px; }
/* Socials */
#footer .socials{
display:flex; flex-direction:column; gap:10px;
}
#footer .socials a{
display:flex; align-items:center; gap:10px;
padding:8px 12px; border:1px solid var(--footer-border);
border-radius: 12px; background: rgba(255,255,255,0.03);
}
#footer .socials a i{ width:18px; text-align:center; }
#footer .socials a:hover{
transform: translateY(-2px);
background: rgba(136,171,142,0.10);
border-color: rgba(136,171,142,0.25);
}
/* Divider + bottom row */
#footer .footer-divider{
margin: 28px 0 18px; border-top:1px solid var(--footer-border);
}
#footer .footer-copy{
color: var(--footer-muted);
margin:0;
font-size:.95rem;
}
#footer .footer-copy a{
color:#fff; font-weight:600;
}
#footer .footer-copy a:hover{ color: var(--primary); }
/* Responsive tweaks */
@media (max-width: 991.98px){
#footer .brand-col{ margin-bottom: 18px; }
}
@media (max-width: 575.98px){
#footer .container{ padding-top: 44px; }
#footer .socials{ flex-direction:row; flex-wrap:wrap; }
}
</style>
<footer id="footer" aria-label="Site footer">
<div class="footer-accent" aria-hidden="true"></div>
<div class="container">
<div class="row justify-content-start footer">
<!-- Brand / Tagline -->
<div class="col-lg-4 col-sm-12 brand-col">
<div class="footer-widget brand-wrap">
<img src="/static/logo-cropped.png" class="logo" width="220"
height="60"
alt="JustMetrically – AI Content & Reporting">
<p class="tagline"><strong>Delivering quality reports and helping
businesses excel</strong> — that’s Metrically’s commitment.</p>
</div>
</div>
<!-- Account -->
<div class="col-lg-3 ml-lg-auto col-sm-6">
<div class="footer-widget">
<h3>Account</h3>
<nav aria-label="Account links">
<ul class="footer-links">
<li><a href="#!">Terms & Conditions</a></li>
<li><a href="#!">Privacy Policy</a></li>
<li><a href="#!">Help & Support</a></li>
</ul>
</nav>
</div>
</div>
<!-- About -->
<div class="col-lg-2 col-sm-6">
<div class="footer-widget">
<h3>About</h3>
<nav aria-label="About links">
<ul class="footer-links">
<li><a href="/posts">Blogs</a></li>
<li><a href="/service">Services</a></li>
<li><a href="/pricing">Pricing</a></li>
<li><a href="/contact">Contact</a></li>
</ul>
</nav>
</div>
</div>
<!-- Socials -->
<div class="col-lg-3 col-sm-12">
<div class="footer-widget">
<h3>Connect</h3>
<div class="socials">
<a href="https://www.facebook.com/justmetrically/"
aria-label="Facebook — JustMetrically">
<i class="fab fa-facebook-f" aria-hidden="true"></i> Facebook
</a>
<a href="https://www.linkedin.com/company/justmetrically/"
aria-label="LinkedIn — JustMetrically">
<i class="fab fa-linkedin" aria-hidden="true"></i> LinkedIn
</a>
<a href="https://www.youtube.com/channel/UCx9qVW8VF0LmTi4OF2F8YdA"
aria-label="YouTube — JustMetrically">
<i class="fab fa-youtube" aria-hidden="true"></i> YouTube
</a>
</div>
</div>
</div>
</div>
<hr class="footer-divider">
<div class="row align-items-center">
<div class="col-lg-12 d-flex justify-content-between flex-wrap gap-2">
<p class>
© <script>document.write(new Date().getFullYear())</script> •
Designed & Developed by
<a href="#" class="brand-link">JustMetrically</a>
</p>
</div>
</div>
</div>
</footer>
<!-- Page Scroll to Top -->
<a id="scroll-to-top" class="scroll-to-top js-scroll-trigger" href="#top-header">
<i class="fa fa-angle-up"></i>
</a>
<!--
Essential Scripts
=====================================-->
<script src="/static/plugins/slick-carousel/slick/slick.min.js"></script>
<script src="https://unpkg.com/aos@2.3.1/dist/aos.js"></script>
<script>
AOS.init();
</script>
<script src="/static/js/script.js"></script>
</body>
</html>