The Complete Guide to Web Scraping for Digital Shelf Success

The digital shelf is the online version of the physical shelf you see in supermarkets, hypermarkets, or department stores. When you sell online, you display your products via the digital shelf. In simple words, those product listings on ecommerce marketplaces and or your e-store websites will be considered as the digital shelf. Products that rank on search engines or the ecommerce marketplace are more likely to sell and therefore in the online retail space, the digital shelf is critical to determining your sales success, product exposure, product visibility, and competitiveness.

How does one achieve that? By better product descriptions, better than competitor product listings, and better meta description and tags for your products. This is possible if you scrape digital shelf data from online marketplaces for analysis. This data can be used to revamp every aspect of the digital shelf to attract buyers' attention, including product listings, pricing, SERP ranks, and reviews.

Web scraping, an automated extraction of data technology, allows e-retailers to collect real-time product data, and product descriptions, specifications, inventory data, and pricing data from rivals, e-commerce sites, and marketplaces.

However, many e-retailers do not know how to scrape digital shelf data while adhering to legal and ethical guidelines. Also, what are the best methods and techniques for scraping this data? Therefore, we have created a detailed guide to web scraping for digital shelf success.

Understanding Digital Shelf in the Retail Industry

In retail, the digital shelf refers to a product's online presence across many digital channels, such as Search Engine Results Pages (SERPs), web pages, e-commerce markets, and social media.

It covers every aspect of how products are shown online, including descriptions, photos, reviews, and availability, to influence consumer purchasing decisions.

The digital shelf includes the following components:

Pictures

Images assist online buyers in visualizing the goods they're looking at in ways that no other method can. This material provides several perspectives or views of a product, simulating what visitors might feel if they picked it up and touched it.

Price

Both online and in-person buyers place a great deal of importance on an item's price, therefore it must be simple to locate on the digital shelves. To prevent customers from being disappointed if the price increases the next time they come to the website, it may be preferable if you let them know when the offer or promotion finishes.

Product Description

What are the dimensions of the item? What color is it? Are there several sizes available? Product details contain keywords, product titles, descriptions, and store information.

Inventory details

Knowing that there aren't many things left in stock creates a sense of urgency like nothing else. Inventory levels are a crucial component of any digital shelf, whether your goal is to present your consumer with useful information or to encourage a purchase.

Reviews and Feedback

Previous customers' feedback is very important. A buyer's journey also takes into account a brand's response to feedback.

What is Digital Shelf Web Scraping?

Digital shelf web scraping is the process of extracting product data such as descriptions of products, their images, reviews, metas, specifications, inventory details, etc. for analysis purposes. The technique of collecting large volumes of information from the internet and storing it in a database for later use and analysis is called web scraping, sometimes referred to as data mining. Depending on the needs, a range of technologies, tactics, and outsourced techniques can be used to perform retail web scraping.

Beautiful Soup and Scrapy are popular tools and methods for Python-based custom solutions. Puppeteer and Selenium are excellent for manual and automated web scraping techniques on dynamic, JavaScript-heavy websites. One option for outsourcing is working together with a web scraping service provider that can design and manage scraping systems tailored to specific goals.

Web Scraping digital shelf involves extracting the below type of data from ecommerce marketplaces:

• Product Name
• Description of the Product
• Product Pictures
• Product Cost
• Reviews and Ratings from Customers
• Availability of Products & Offers
• Categories and Specifications
• Best Seller Details
• Quantity

Steps for Web Scraping Digital Shelf Data:

Step 1 : Determine Goals:

Add the precise details you require, such as market trends, competition prices, product availability, and customer reviews. To make sure that the search is in line with your business plan, establish specific objectives to direct your scraping activities.

Step 2 : Select a Platform and Web Scraping Tool:

Select a tool based on your needs and level of technical expertise.

• Web Scrapers
• Web Scraping APIs
• Automated Crawlers
• Python Scripts
• Jave-based Scraping

For easy tasks, Python libraries like Beautiful Soup or Scrapy are effective. For websites that are dynamic, headless browsers capable of handling JavaScript-heavy content include Selenium and Puppeteer. Structured data can be provided by APIs without copying.

Hire Web Scraping Services for Digital Shelf Optimization

If you are not tech-savvy in using scrapers or developing scripts for scraping ecommerce web data, then hiring web scraping services such as Scraping Intelligence is the best way to digital shelf data extraction. Web Scraping companies provide custom data scraping solutions, APIs, mobile app scraping services, etc. for extracting digital shelf data from ecommerce websites. Scraping Intelligence also has automated crawlers for specific ecommerce marketplaces.

Step 3 : Develop and Implement:

Develop or modify scripts tailored to the websites that you wish to target. To evade detection, use IP roaming or a proxy. Instead, utilize a cloud platform to implement scraping operations and add a delay between searches to mimic human behavior for scalability and reliability.

Step 4 : Ethical Compliance:

Comply with the site's conditions of service and only copy information that is available to the public in line with legal and moral obligations.

Step 5: Data Analysis and Cleaning:

After gathering the data, clean the raw data to get rid of mistakes, superfluous information, and duplicates. For ease of analysis, store in a structured format, like a relational database or CSV file.

Web Scraping's Advantages for Digital Shelf Growth

E-retailers or ecommerce businesses can analyze the digital shelf data in many ways as below:

Descriptive analysis

Descriptive analysis sums up and interprets past data to grasp what occurred before and current product information patterns.

Example: An online seller examines different parts of their digital shelf data from the past six months:

• Product Ratings and Reviews:

  They find that products with great pictures and full specs tend to get better ratings and more reviews.

• Product Description Analysis:

  When they look at what product descriptions say, they see that items with clear short, and benefit-focused descriptions (around 300-500 words) sell better. They also spot that products using bullet points to show main features and listing tech specs do better in searches and have fewer returns.

• Image Analysis:

  Products with many sharp high-quality images, including real-life shots and views from all angles, get more people interested and buying.

Cognitive Analysis

Cognitive analysis or sentiment analysis uses AI and machine learning to process and understand big chunks of unstructured data, like customer reviews, and social media comments.

Example: An online shopping platform uses natural language processing to look at thousands of customer reviews across different product types. This analysis shows common issues often-mentioned features, and new trends in what customers want helping the retailer to tweak their product lineup and marketing plans. An online shopping platform uses natural language processing to look at thousands of customer reviews across different product types. This analysis shows common issues often-mentioned features, and new trends in what customers want helping the retailer to tweak their product lineup and marketing plans.

Prescriptive Analysis

Prescriptive analysis does more than just predict future outcomes. It suggests actions to take to get the best results.

Example: By looking at competitor prices, stock levels, and past sales figures, a prescriptive model offers the best pricing plans for different items. It might suggest cutting prices on slow-selling products to lift sales or hiking prices on popular items during busy times to boost profits.

Diagnostic Analysis

Diagnostic analysis aims to understand why certain events or trends happened by taking a close look at the data.

Example: An online electronics store sees a sudden dip in sales for a specific smartphone model. Through a deep dive into digital shelf data, they find out that a rival just launched a similar model at a cheaper price and with better search result visibility. This knowledge allows the store to tweak its pricing and enhance its product listing optimization.

Predictive Analysis

Predictive analysis uses past data and statistical methods to forecast future trends and results.

Example: By looking at seasonal patterns, search data, and past sales trends, an online store predicts which types of products will likely see higher demand in the next few months. This helps them to improve their stock management making sure they have enough of popular items and don't overstock on products that might not sell well.

How to Use Digital Shelf Data for Improving Product Visibility?

Getting customers to see correct product information is the first stage to conquering the digital shelf.

Identify your best goods and allocate resources to prioritize your digital shelf strategy by:

Tracking Product Exposure

Before making a purchase, customers look at the features of the items and read reviews on the first page. Apart from price, consumers put the top importance on product photos and titles. Using digital shelf data analysis, make sure that product descriptions and photos are correct and consistent on all platforms. Your listings won't rank well on the online shelf unless they are optimized.

Identifying & Ranking the Best Items

An important first step in gaining the digital shelf is understanding which categories you rely on to expand and which product generates the most income online. You can allocate resources effectively if you are aware of your best-selling items. Knowing your priorities will allow you to concentrate on capturing the attention of your product and maintaining its high visibility on the digital shelf.

For instance, it's probably time to launch a new advertising campaign or boost spending on current ones if your most popular product fails to sell as well as it typically does although its search rank hasn't changed. In order to get a new product moving and selling, it will require additional funding and staff members up front.

Keeping Prices Competitive

A lot of customers are searching for products that meet their budget or offer the best value.

You should maintain competitive prices or provide a price match option if it fits with your business plan and you're selling the same goods as your competitors. You can't compete in the online marketplace without a specialized platform to analyze trends and modify prices.

With digital shelf data, it's easy to monitor the prices of competitors for comparable and identical products and modify your prices to capture the largest possible portion of the market. By regularly gathering pricing data, they can immediately adjust their pricing strategy to preserve competition and maximize profits.

Using a Focused SEO Approach

Although your product page must include the appropriate SEO components, you won't always rank on the very first page of search results on Google. High-quality photos, videos, 360-degree spin pictures, and a strategy to drive customers to the pages that sell products are all necessary to keep them at the top. It's essential to understand all the small details to maximize product SEO. Digital shelf data will help you improve your SEO game.

Knowing Market trends

Product data is often used by businesses to conduct market research, which includes social listening, gathering data on popular items, seasonal demand, and customer preferences.

These insights might be used to introduce discounts or enhance your current products. It makes it easier to keep an eye on stock levels, modifications to product variants or forms, and the removal of particular items. This information aids in stock-level optimization by preventing overstocking or stockouts.

Final Words

By implementing web scraping techniques, you can stay on top of the digital shelf. It will help you find and list the right products, keep prices competitive, improve SEO, monitor shopper sentiments, etc. For digital shelf optimization, which is a continuous activity, web scraping is crucial since search engine algorithms and consumer trends are always changing.

This implies that the best keywords for increasing product visibility now could not be as strong in the future. Therefore, to stay competitive, regular digital shelf monitoring and product listing updates are essential.

Scraping Intelligence is a provider of web scraping services that specializes in the extraction of high-quality publicly accessible data in easy-to-read formats. We help you maintain your competitive edge in the cutthroat e-commerce market. We provide services for scraping product data from major platforms like Amazon, eBay, and other online marketplaces, helping businesses stay competitive in the digital retail space.

Find out how our digital shelf data scraping can improve your online performance and spur the expansion of your ecommerce business. Get in touch with us now!