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Why You Need Accurate Product Matching for Digital Retail?

The numbers here indicate the speed of digital commerce in the worldwide marketplace. As per a Statista report, eCommerce would account for a projected 21% of the retail sales globally in 2022 from only 10% before 5 years. By 2025, the online section will reach nearly 25% of retail sales worldwide. In today’s digital landscape, shoppers have become well-informed and carefully compare pricing before making purchase decisions.

Without 99% Accuracy in Product Matching, You Can Miss out on Things Like:

  • Benchmarking against the competitors to provide the finest prices
  • Better product level margins, brand loyalty, and profitability
  • Getting the most precise matches for similar and particular products
  • Saving sustained costs because of inaccurate comparisons and data

Challenges of Product Matching

Most manual product matching methods use 1-2 attributes to recognize product matches. The process may not work as frequently products sold online have missing information, don’t come with UPCs, get different terminology for similar products, or get descriptions or images missing. This method leads to a higher error rate for suitable matches with lower accuracy levels. These matching methods have an additional problem – their incapability to be quick, scalable, and agile. Some most common challenges of precise product matching include the following:

  • Complications matching similar products sold by different sellers under various brand names
  • Depending on only a word or critical image for matching products
  • The inability to reach private-label products that are progressively important
  • Lack of standard language attributes leading to missed or inaccurate matches
  • Lack of unique identifiers – mainly in Fashion, Beauty Products, and Home
  • No way to match products without any UPCs
  • Slight variations of attributes together with imperfect specs

Actowiz Solutions - Best Product Matching at Best Accuracy

The next-generation product matching solution from Actowiz Solutions utilizes Machine Learning for matching data. It is empowered by AI technology and ‘a similarity engine that makes it highly accurate, fast, intuitive, and scalable. It utilizes a three-pronged approach for product matching that helps match products despite lost UPCs, varied nomenclature, inaccurate descriptions, and inadequate product descriptions.


Product Matching: 3 Types

1. Exact Matches

An easy technique to benchmark positioning and prices against competitors is to identify the same products retailed by the competitors and know how they price them in the eCommerce world. Our high-tech crawlers scan the global e-commerce market to deliver all matching products across the competitive landscape to create a winning strategy that converts shoppers during the purchase.


Both products on Tokopedia and Shopee are precisely the same from the same brand; however, Shopee offers lower prices compared to Tokopedia.


2. Similar Matches

There are different variables to get similar matches with your products. An identical match may include specific attributes; for a few, it could be a price range or a product’s visual proximity. If you analyze some variables or get a singular approach, you may miss the close matches that can result in incomplete data to affect the accuracy for a long time.

We have AI-powered data hearing abilities that endlessly monitor websites across the globe to assist you in getting and comparing competitors’ products that are near matches and normalizing the attributes for precise comparison. Moreover, we follow two approaches to similar flag matches to ensure that no close matches are undetected.

Approach to Flag Similar Matches

Approach 1 – Depending on the Product Attributes

Here, one listing of key characteristics is defined for every category in partnership with the clients. No matter if the attributes match or not, the system will flag a competitor’s products as –

Attribute Matching

All the attributes get matched within the suitable threshold

Rejected Because of Attribute Mismatch

One or more attributes need to be matched or inside the acceptable threshold.

The user has to define an applicable pool of applicants (i.e., should an algorithm search for the match on the initial 3 pages or think about all the products?)


We think about the main product attributes which affect a buying decision for flagging similar matches. Both products on BestBuy and Walmart are 55” LED Smart TVs. As all features are the same, we considered that a complete attribute match.

Example.png Walmart-Bestbuy.png

Approach 2 – Shopper’s Workflow-Based Matching

With this approach, an objective is identifying the finest match from the set of products that a shopper might observe while searching for a detailed product on a website.

The approach could be best defined as rule-depending product matching, where an algorithm makes different buckets depending on the defined priorities by a user. It is vital to note that preferences could be customized depending on a client’s business requirements.

3. Variant Matches

Shoppers aren’t comparing only the exact products – they’re also comparing products across different variations in color, size, and quantities, among other factors. Variant matches capture all these factors to optimize the product, tipping odds of winning a shopper in favor.

Both these products on Lazada and Tokopedia are peach-flavored ice tea. As both the products are equal in terms of product type, brand, etc., and the key difference is the pack size, we have considered it a Variant match. Although, Tokopedia is accessible in a pack of 2 while Lazada is in a pack of 1.


Actowiz Solutions Product Matching - A Dynamic Approach

At Actowiz Solutions, we strive to improve product offerings using Artificial Intelligence and predictive analytics. Rather than getting one-time matches for our customers, we apply a dynamic approach for product matching in which we endlessly crawl competitors’ websites to get better, closer, and most applicable matches for products where a variant or similar match is recognized in the first example.

The given workflow breaks the work of the dynamic price approach with Product Matching:




Day 1: Precise match not available, so the system has identified a similar match


We think about main product attributes, which affect the buying decision for flagging a similar match


Day 12: One better and similar match is available; therefore, the system has replaced the past match

Day-12.png OLD-NEW.png

Day 23: A precise match is available; therefore, the system has replaced the past similar matches

Day-23.png OLD-NEW-02.png


As brands and retailers steer the channel-agnostic and post-pandemic retail landscape, the undeniable thing is that the data will drive decision-making in retail channels. Evaluation shoppers will persuade brands and retailers to provide competitive pricing and assortments, with advanced product matching tools to optimize prices, a general requirement across all retail businesses. The quicker companies realize the need for matching product solutions like Actowiz Solutions, the possible benefit they will get and have a better chance of future-proof companies.

For more information, contact Actowiz Solutions now! You can also reach for your web scraping services and mobile data scraping service requirements.


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