The average modern-day buyer compares prices from thousands of products and
hundreds of online retailers from various e-commerce websites to decide the
affordable product to buy easily. As a result, retail sellers must change product
prices too often to stay in the race and earn the maximum possible profit.
Recognizing, classifying, and matching desired products is the primary step to
comparing prices across available websites. But, there is no standard process to
represent products across these websites leading to more complexity.
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
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
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
● 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
Alternatively, we use APIs to integrate internal analytics or deliver sheet-based
reports regularly, depending on client choices.
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.