What’s the Role of AI-Powered Pricing Intelligence with Product Matching in eCommerce Business?

What is Wrong with Already Existing Pricing Intelligence Solutions?

There are many challenges in the eCommerce market with incumbent solutions. One of the biggest challenges for them is to work promptly. In essence, it's like losing track of the process of finding value-driven information that helps retail sellers get an advantage over competitors.

Here are the different types of solutions available in the market:

Internally Developed Systems – Retailers create solutions that cannot match products and often depend on manual data collection. These solutions lead to significant business operations, maintenance, and update challenges since inexperienced professionals have developed them.

Web Scraping Solutions don't carry capabilities to normalize data or match products. It's a huge struggle to scale them to manage high-volume data during promotional events. They also need to catch up in delivering actionable insights.

DIY Solutions – These solutions need manual data entry and research. They are expensive, hard to scale, and inaccurate. They need human intervention to a great extent with descent efforts.

What’s the Role of AI?

We have designed a competitive pricing intelligence solution to help retail sellers get competitive benefits by offering timely, correct, and actionable pricing analytics by enabling product matching in bulk. Product Data Scrape provides access to retailers with detailed pricing data on billions of competitor products often when they ask us.

Our technology solution mainly includes the following.

1. Data Collection

At Product Data Scrape, we consistently extract data from various sources across the internet with high accuracy. Being an experienced company in this field, we can collect vast data and train product matching platforms with customization.

Our data includes points from billions of products and geolocations with many retail verticals. These datasets have a hierarchical arrangement of information depending on retail taxonomy. At the base level, there is subcategory data; at the top level, we have product info, line title, description, and other data fields. Our data scraping systems and machine learning models help us create labeled datasets for essential information using proprietary tools.

2. Artificial Intelligence for Product Matching

We perform product matching via a unified platform that uses image and text identification capabilities to accurately spit the same SKUs across selected eCommerce products and stores. We also classify products based on their features and design a normalization layer based on different image and text attributes. We take the help of an ensemble of deep learning models to computer vision problems and NLP specified to us to retain the domain.

The processing of text data involves internal and deep pre-trained word integrations. We have customized word representation techniques line BERT, ELMO, Transformer, and state-of-the-art to capture profound text with better accuracy. A self-attention mechanism helps the correlation between the word description and the question.

Data processing of images begins with object detection to split the interest of a given product. We then explore deep learning models like Inception-V3, VggNet, and RedNet, which we have trained with the help of labeled images. Next, we use multiple preprocessing techniques like face removal, background removal, skin removal, and image quality improvement, extracting signatures from images using deep learning and ML-based algorithms to find products from billions of listed products uniquely.

Finally, we distribute billions of product images to several stores for quick access and to facilitate searches on a vast scale within milliseconds and maintain high standards of quality and accuracy with the help of our image-matching engine effectively.

3. Using Human Intelligence for Finishing Touch!

In cases where the accuracy or performance score of AI-based models matters, we have a dedicated team of Quality Assurance engineers to verify the output.

Our team performs the following activities.

● Find out the reason why the confidence score of machine-driven models is low.

● Confirms the correct product matches from selected products

● Find or develop a way to encode this knowledge in a rule to feed it back to the AI-based algorithms so that these models will perform better.

Using the above steps, we have developed a feedback loop that improves itself; by its very nature, it performs better over a specific duration. This process mainly allows us to match products from a large pool of websites and categories at scale quickly and with high efficiency and accuracy. The system has the knowledge and database of over half a decade of operations, and it will be challenging for anybody to repurpose it.

4. Data Visualization with Actionable Insights

After finishing the matching product process, we collect prices at any frequency allowing retail sellers to optimize their product pricing daily. Our SaaS-based web portal consisting of reports, dashboards, and visualization, helps to consume pricing insights.

Alternatively, we use APIs to integrate internal analytics or deliver sheet-based reports regularly, depending on client choices.

Conclusion

Natural, affordable, and time-saving product matching processes based on AI models help grow business to a significant scale. Contact Product Data Scrape to learn more about the process, web data scraping services, and more.