ML Product Matching: A Key Factor to Retail Success
There are more than 2 million online retailers in the US. With that scale, how can retail businesses and consumer goods manufacturers keep up with product matching? Manually tracking attributes across the web isn’t realistic; the problem with manual product matching is that the process is expensive, error prone, and tedious.
Fortunately, retailers and CPGs can opt for an automated product matching process since machine learning has created serious efficiencies.
What is Product Matching?
Product matching is a form of e-commerce data analysis and the process of matching exact or similar products across various sources.
With machine learning, you can successfully collect product data across multiple sources and attributes. Your product matches should capture images, ratings, product descriptions, pricing variants, unique product codes (UPC, ASIN, etc.), size, availability, flavor, related products… and more. This deep view helps retailers and consumer goods brands understand their competitors, trust their strategy, and identify ways to improve.
Retail businesses use product matching for price benchmarking, assortment gap identification, promotion monitoring, and an overview of the competitive landscape. Retailers leverage product matching to compare catalogs and identify assortment and price differences.
Product matching is a great tool for brands because they can investigate how their items are presented across retailers and ensure policy and standards are met. Plus, brands apply this information for pricing intelligence, promo comparisons, and assortment gaps.
Exact Match Example
In the images below, you can see that the same Once Upon a Farm snack pouch is listed on various retailer websites. Notice that the example on the left is out of stock.
Similar Match Example
For a similar product match, the items are closely correlated, but not defined as exact. Look at the images below of vanilla, unsweetened almond milk. Whole Foods private label and an H-E-B product. While the brand, descriptions, and price vary – the actual product is the same – vanilla unsweetened almond milk.
What are Benefits to Product Matching?
It’s a data-driven world and you’re expected to stay ahead of the curve. A winning strategy starts with effective product matching.
Product matching is critical to retailer and brand success because it helps the organization recognize their market position and optimize the customer experience (specifically related to consumer preference drivers like price, availability and assortment).
6 ways you’ll benefit from ML product matching:
+ Identify assortment gaps and overlap
+ Price products competitively
+ See out of stock / availability across market
+ Compare competitor catalogs
+ Evaluate product performance
+ Make faster, data-driven decisions
What are Product Matching Challenges?
And while there are plenty of benefits to accurate product matching, it’s not simple – it’s a challenge for retail businesses and manufacturers of all sizes.
In our omnichannel world, we have more so many buying options… a quick search on our phone lets us quickly compare prices for pretty much any product we want to buy. Yes, some products are sold at the same price, with the same images across retailers (but that’s not always true).
Think about it like this… you have to buy a new bag of dog food. You head online to make a purchase. First, you check your local pet store – the dog food might have one picture, price, and description. Then, you check Walmart and find a different price, a different picture, and a different description. Now, you look at Target to find more variation. Accuracy is the biggest challenge – retailers need to match all of these dog food listings even though they’re marketed in different ways.
+ Poor data collection methods and a non-uniform process creates poor data quality and inability so scale product matching.
+ Retailers like Amazon famously adjust their pricing multiple times a day which makes manual matching repetitive.
+ It’s more complicated to match “similar” especially private labels.
+ Geographies might change their assortment offering.
All of this to say… you have to trust your data collection process and your product matching requirements. With machine learning, you can increase your coverage with confidence.
We recommend that retailers and brands regularly monitor their data through product matching to stay competitive. That means you can…
+ Understand intraday market price positions
+ Identify pricing and promotion opportunities
+ Monitor MAP or UPP
+ Identify out of stock risks
+ Use historical data to track trends
We’re proud to offer retailers granular, comprehensive product matching results so that you can fully explore your competitive landscape.
Bungee Tech has data on more than 1 billion products sold across hundreds of retailers. With our patented machine learning process, product matching is more automated and less time-consuming. A faster more effective approach which means faster more profitable decisions for you.
If you’re ready to take advantage of high-quality data collection and product matching, reach out to our team today. Our solution will to relieve your biggest headaches so you can focus on growth. It’s time for you to have real-time visibility into competitive catalogs and optimize your strategy.