Guarantee the best results for product data extraction
When businesses operate in a competitive environment it is imperative to know what their competitors are charging in real-time and this can be hard to keep track of. For any data driven organization, implementing a solution that automatically extracts product data from websites in real-time and at scale, is indispensable to stay ahead of the competition.
Setting an automatic process for product data extraction can be a powerful tool for data driven businesses of all sizes. You can extract specific products information including offers, price, currency, and availability. Provided that the data extraction process is able to identify the key attributes of a product, it can then use this information to create reports with insights into the behavior of a particular product.
For this to work, it is key to have the right procedures in place that guarantee the best results for your automation product extraction projects. By doing so, your organization can better understand how consumers are using a specific product, foresee any necessary adjustments to improve the user experience and as a result, increase sales and demand.
In this article, we'll explain not just everything you need to know to get started, but also show you how to guarantee the best results when working with product data extraction.
What is automatic product extraction?
Automatic product extraction (APE) is a technology that can help businesses better understand their market and gain a competitive advantage through product data analysis. In this post we'll explore the many benefits that APE can offer.
The goal of product extraction is to obtain the key attributes of a particular product, starting with the URL of a webpage featuring a single main product. Within this a ‘product’ is considered to be virtually any type of consumer good. As we already mentioned, the product URL is the first step, so for this case, we will not consider the task of crawling or finding it on the website.
The use of this technology can help businesses obtain the key attributes of products, which can then be used for competitive advantage. Businesses can free up time and resources so that they can focus on more important tasks.
With product data extraction, businesses can see their competitor pricing for the same product and then analyze whether they should implement any changes to their current pricing strategies. This data can help organizations discover exactly where they can cut costs to improve their margins, while still being able to meet customer demands.
Ensure accurate product extraction and article comparison
When applied effectively, automated product extraction can help you collect and analyze data from a variety of sources, and hence boost business efforts such as market research and pricing intelligence. Automatic product data extraction processes can be a highly effective way to improve your understanding of consumer behavior and help you make better decisions about the products that you sell.
With that in mind, extracting content from product pages can be more challenging than extracting content from news articles or blog posts. This is because product pages often contain a lot of information, and the websites themselves can vary significantly in terms of their appearance and how they actually work.
Another consideration is that a product page often contains not only the main product, but other related products as well. Moreover, many attributes can be missing sometimes, even the price, and the extraction system must not pick a price from another product on the same page.
Here is a basic example of the initial fields:
One example is to look for specific keywords or phrases that may be associated with the product, and parsing these out as well. Secondly, it can sometimes be useful to use search engines to identify specific terms or phrases related to products on a particular website.
Diligent extraction of key product attributes
An example of how this can be done is by feeding each of these solutions with a carefully selected set of real-world product page URLs, with the objective of evaluating which approach yielded the best quality results in terms of extracting these key product attributes.
This means that if some systems had custom per-domain extractors, they wouldn’t be active, so the extraction quality might be affected. With that said, the key terms to keep in mind here are Price Availability (whether a product is in-stock or out-of-stock) SKU (Stock Keeping Unit).
The data categories to be extracted must also be clearly defined in your product data API when extracting products. This way you can outperform the competition by effectively extracting all the product data fields you need from SKUs/GTINs/MPN to stock availability, reviews, and more.
Measuring Data Quality
F1 is widely used as an objective measurement of extraction quality, as it accommodates cases of commonly occurring attributes as well as rare ones.
It is a measure of data quality that combines recall and precision. It is imperative to keep in mind when handling product data extraction projects.
In this context, the recall of a particular attribute - such as product price or SKU - is the ratio of correctly identified attribute values relative to the total number of attribute values in the dataset.
Precision, meanwhile, expresses the ratio of correct predictions for some attribute, relative to the total number of predictions for this attribute.
An F1 score is thus expressed as the harmonic mean of recall and precision.
Evaluating Data Quality
Automated product extraction is a great time-saver, but like anything else in life, it should be handled with care. It is even better if you can work around deploying intelligent automation to improve results. Nevertheless, before implementing such an automated solution at scale, it’s important to first evaluate the data quality of your product data extraction projects.
5 important evaluation steps:
- Use evaluate.py script, with Python 3.6+ and tabulate dependency
- Set F1 as the main metric
- Each attribute can have multiple ground truth values
- Maximum one predicted value
- All systems predictions set under dataset/output
Keep in mind that these metrics don’t just measure your results but also give you pointers on how to improve in future projects. So be diligent, constantly check your work and ask for feedback in order to get better insights into your data quality.
Product data extraction can be quite challenging due to the large volume of data that needs to be processed in order to extract the key attributes of a particular product. This is why it is important to have an automatic process in place for product data extraction, in order to manage complex queries and efficiently extract the information that suits your needs.
Taking it a step even further to a use example in the business world. The application of AI and ML in such processes can help maximize operational efficiency of Customer Relationship Management (CRM) to increase accuracy and efficiency in complex decision-making situations. Provided that the data entered into the CRM is clean, reliable and well-structured, artificial intelligence can be used to improve predictions of customer behavior. It enables faster decision making by optimizing company resources and avoiding wasting time trying to understand what customers actually want.
Automatic data extraction for products and listings will help quickly obtain the right data and generate reports based on reliable information. As long as you first ensure you are working with reliable information, you can then proceed to apply automated data extraction procedures to gather the information. From there, you will be able to create reports and data analysis that provide valuable insights into specific products, consumer behavior and your specific industry as a whole.
In summary, it all boils down to ensuring you are working with reliable information that is relevant within your context or industry.
Get in touch to see how we can help you with product data extraction.