Web Scraping for Retail Optimization Using Jupyter Notebooks on Adobe Experience Platform (AEP)

Introduction: What is Web Scraping?

Web scraping—a term that many of you might already be familiar with—is the process of automating the extraction of data from websites. For those who are new to this concept, it serves as a quick refresher: web scraping allows organizations to efficiently gather unstructured data from the web and transform it into actionable insights. In today’s digital age, where data is the cornerstone of strategic decisions, web scraping offers unparalleled opportunities to optimize operations, enhance customer experience, and drive competitive advantages.

What is Data Science Workspace (DSW) in AEP?

Data Science Workspace (DSW) is a core service within Adobe Experience Platform (AEP) that empowers data scientists and analysts with advanced tools for machine learning (ML) and artificial intelligence (AI). Key benefits of DSW include:

• Pre-built ML recipes: Examples include Product Recommendations and Retail Sales Predictions, powered by Adobe Sensei.
• Seamless AEP integration: Utilize data within AEP to enhance customer profiles and deliver actionable insights.
• Flexibility: Adapt pre-built models, import existing ones, or develop custom ML models from scratch.

Retail Use Cases for Web Scraping and Scrapy

Use Case 1: Competitive Pricing Analysis

Retailers often implement dynamic pricing strategies by comparing product prices with competitors. Scrapy can help extract real-time pricing data to inform these strategies, ensuring competitive pricing that maximizes sales and profits.

Use Case 2: Optimizing Product Recommendations

An outdated or invalid recommendation engine can harm user experience. Scrapy can identify out-of-stock or discontinued products, ensuring only valid recommendations are shown, even if daily product feeds are delayed.

Note on Personalization:

In addition to improving user experience, this approach can help retailers stay competitive. By ensuring that only relevant and in-stock products are recommended, retailers can build trust and loyalty among customers while also keeping pace with competitors selling similar products. Personalization powered by accurate product data helps differentiate your offerings and can lead to higher conversion rates. Additionally, by dynamically displaying competitive pricing for identical products sold by other retailers, businesses can strengthen their position in the market, driving both customer satisfaction and revenue growth.

Demonstration of Use Case 2: Updating Product Recommendations

One of the key challenges faced by a book retailer is ensuring the recommendation engine doesn’t display products that are "out of stock" or "discontinued." This is critical, especially when daily product feeds, the primary source of data, are not delivered for several days. Here’s how Scrapy in AEP’s JupyterLab can address this challenge:

Step 1: Install and Import Required Libraries

Install the following Python libraries:

• Pandas: For creating dataframes to store and analyze the scraped data.
• Requests: To make HTTP requests.
• Scrapy.http.TextResponse: To parse response objects efficiently.

import pandas as pd
import requests
from scrapy.http import TextResponse        

Step 2: User Input for Pages to Scrape

Allow the user to specify the total number of pages to scrape. For demonstration purposes, we’ll scrape just one page.

page_url = 'https://www.example.com/products?page=1'
response = requests.get(page_url)
response_obj = TextResponse(response.url, body=response.text, encoding='utf-8')        

Step 3: Extract Product Data

Fetch details like Title, Price, Stock Status, and Ratings using CSS selectors. Use a template URL to fetch data from multiple pages if needed.

products = []
for product in response_obj.css('div.product-item'):
    products.append({
        'Title': product.css('h2::text').get(),
        'Price': product.css('span.price::text').get(),
        'Stock': product.css('span.stock-status::text').get(),
        'Rating': product.css('span.rating::text').get()
    })

# Convert to DataFrame
product_df = pd.DataFrame(products)        

Step 4: Save Scraped Data to Dataset

Select a dataset in AEP and save the scraped dataframe for further analysis.

product_df.to_csv('scraped_data.csv', index=False)        

Step 5: View Scraped Data

Use the "Explore Data in Notebook" feature in AEP to visualize the saved dataset. This dataset provides information on out-of-stock or discontinued products, enabling the recommendation feed to exclude these items.

Similarly, Customer Journey Analytics (CJA) can be used to visualize deeper insights derived from scraped data, such as:

• Competitor price trends.
• Sentiment analysis of customer reviews.
• Product recommendation dashboards.

Thus enhancing the overall customer experience and decision-making process.

Use Case 3: Sentiment Analysis for Customer Reviews

Scraping customer reviews from competitors' sites or forums allows retailers to perform sentiment analysis. This helps in understanding customer preferences, pain points, and motivations, which can be used to refine marketing strategies and product offerings.

Conclusion

Web scraping is a cost-effective, scalable solution for gathering real-time data to optimize customer experiences and improve decision-making in retail. When combined with Adobe Experience Platform’s Data Science Workspace, the potential for personalized and data-driven strategies becomes limitless.

Guidelines for Ethical Web Scraping

• Review the website’s terms and conditions.
• Obtain user consent and establish agreements where necessary.
• Check the robots.txt file for restricted pages.
• Avoid scraping websites requiring login credentials.
• Adhere to GDPR, CCPA, and other data protection regulations.

Disclaimer: For demonstration purposes, I used a website that explicitly permits web scraping and complied with all relevant terms and conditions.

References

• Scrapy Documentation
• Adobe Experience Platform Data Science Workspace Overview