Product Matching

How to Get Accurate Results
Comparison shopping nowadays is highly prevalent amongst customers. In fact, price monitoring engines are a great way to put your items in front of numerous consumers and that’s why businesses need to make sure that the price that they set for an item is competitive in comparison to their rivals (price is extremely important for 81% of customers).
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Competitive data crawling

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What are the pricing strategies of rivals?

First, retailers need to collect as much information as possible about their competitors. For instance, hone in on the pricing strategies of rivals. Additionally, examine the competitive pricing history as well as what is currently trending.

Pricing strategy

The sequences of data collection

For this, retailers have to get the most accurate competitor data. There are two sequences possible here.

  1. Matching team uses the results of the website full crawl, the process of withdrawing all data (the whole website catalogue) about the products which is afterwards kept in the database
  2. If you’re looking for specific items on a website, first product matching is performed to compare your items with the competitors' and then a regular crawling is carried out for these goods in particular in order to obtain information about the prices (match particular products on the competitors’ website and afterwards let the parsers look for the prices on regular basis).
Competitive Data collection

Matching difficulties

Some problems that retailers face when trying to match items:

  • Different item names
  • The data received is incomplete
  • The images aren’t the same
  • Prices are drastically different

If the data matched isn’t accurate, then that can heavily influence decisions in a negative way. For instance, poor assumptions and useless choices could be made, resulting in a decrease in profits. Therefore, the depth of the comparisons, the rate of zero prices, the number of errors, and many other aspects should be taken into account in order to make sure that the quality of the data collected is high.

Issues of product matching

Visual and automatic matchings

There are two types of product matching: the automatic one and the visual one. Combining them is the best way to get highly qualitative data.

Data comparison
How does automatic matching work?
Competera Product Match expert

Automatching speeds up the work of the visual matching team by automatically categorizing the goods. Using deep learning technologies, the product matching software is able to find an item on competitors web sources quickly and with a high degree of accuracy.

First we have to classify an item by examining its title (brand, model, name, etc.), image and some particular keywords in the description. It gives us probabilistic assessment of the item belonging to a particular class.

Case A. This class of product is absent on competitor’s website.

For example, if we’re looking for an electric kettle and the competitor is not selling kettles on his website, the matching software will know about it.

Case B. This class of product is there

The matching software will narrow down the funnel and compare all kettles on the website to the one in question basing on its title, price, description, technical characteristics, color, and image (here the neural networks are of great help). First the software sweeps away the kettles which have dissimilar titles and characteristics.

As a result of previous measures, we get the kettles which have the same category and similar titles. Now we need to apply the sophisticated models, e.g. comparing the product titles with the help of neural network, making the evaluation of price proximity (the more the price is different, the less is the probability of match).

When we determine that the prices, the titles are similar, we use deep machine learning to compare the pictures when it’s possible (if the quality of picture is good enough).

For every item, we build metrics to define the probability of match. If one of the chosen items is extremely similar to the product in question, we realize it’s a 99% match and we don’t need it to be checked by humans. Though, some of those items are to be checked in order to make sure the automatching algorithms work fine.

If the match probability is less then 20% we conclude that the item was not found on the competitor’s website.

If out of the chosen products there are 3 similar items, we assign the task to the Product Matching Team.

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How does visual comparison help?
Competera Product Match expert
Visual matching of data helps improve the results of automatic matching, also it is used in some industries where it’s impossible to match products without a human intrusion (like jewelry, fashion, etc.). On top of that, a visual comparison is also utilized because you can’t depend solely on a robot since it occasionally can’t even get into tightly protected sites.
Our team of automatchers is highly experienced through years of work. It has large capacity (50K+ matches per day) and a sophisticated motivation system with bonuses for the best performing matchers. Also, we have our matching database which is constantly replenished with new product matches to enlarge the capacity of Competera.
With the mixed approach to product matching, we developed a complex algorithm of search and multi-stage verifications which assure the highest data quality necessary for retailers to make the right pricing decisions.
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