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The Product Matrix: Why You Will Love Normalized Features in SKU-Level Data

January 10, 2023
traqline Author 5 mins read
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Normalized Features

In today’s competitive market, merchandisers, buyers, and marketers need data-driven market intelligence in order to meet increasingly aggressive business goals. 

SKU-level insights are an undeniably effective option. 

However, available SKU data varies from non-existent to overwhelming, and confidence in accuracy is vital to steering businesses in the right direction. When dealing with multiple retailers, brands, attributes, and model years, one key consideration when ensuring the accuracy of the product matrix is the normalization of product features. 

For consumer durables, TraQline SKU Metrix™ delivers an accessible product matrix from our database, allowing for easy comparison and evaluation of product features and SKU data. 

Read on for a closer look at the value of normalized product features. 

What are normalized product features?

“Normalizing” a product line means scrubbing and cleaning a dataset in order to establish similar nomenclature across product attributes” and “features”.

This allows for an analyst to create groupings of product models  – regardless of variance in labeling of features or attributes – for comparison and analysis.

Product Features vs. Attributes

 A product feature is a description of a particular function that a model performs or possesses. For example, “color” is a feature.
A product attribute is a description of that feature’s state. For example, “stainless steel” is an attribute of the “color” feature. 
Classifying as a feature or an attribute is subjective on the part of the data aggregator – what’s key is that each is consistent and the same nomenclature is used across all products.

While this can be an effective marketing strategy, it can also complicate the analysis of product matrix data. 

Product normalization overcomes this by stepping back from branding and categorizing features and attributes under standardized names. As a result, products can be easily sorted, compared, and analyzed with an accurate and actionable marketplace-wide perspective. 

Does feature normalization really make a difference?

Normalized features are essential in gleaning accurate, comprehensive insights from market research. When exploring product matrix data, the ability to sort and compare SKUs by features that are aligned across the industry is critical – enabling merchandisers, buyers, and marketers to make more informed and impactful data-driven decisions on pricing, branding, purchasing, and more. 

Feature normalization examples within Major Appliances

To best illustrate the value of normalized product features, let’s dive into some examples of the variances you can find in the major appliance industry. 

Notably, within each example, there’s a clear depiction of how the naming of each feature aims to position each brand differently, building the perception that there is a unique benefit to consumers. As mentioned, the normalization process removes this layer of marketing conflation to align features. 

[Shown above/right: SKU Metrix “Compare SKUs” Dashboard ExportFor every major appliance model, SKU Metrix aligns hundreds of features and appearance traits across all brands and models.]

Front-Load Washers: Steam Cleaning Options

Washing machines have a range of potential features, with newer units now offering steam cleaning.

  • LG identifies this feature as “TurboSteam™ Technology.”
  • Samsung labels this feature as “Multi-Steam Technology.”
  • Whirlpool calls this feature as the “Wrinkle Shield™ Option with Steam.”
  • Maytag marks this feature as a “Wrinkle Prevent Option w/ Steam.”
  • Kenmore identifies this feature as “Accela Steam™ Technology.”

In systems that automatically group SKUs by feature name, each attribute would fall into a different category on the product matrix. Within a tool like SKU Metrix, the normalization process would instead accurately group these major appliance products within a “Steam” category, allowing for more accurate comparison.

French Door Refrigerators: Door-in-Door Feature

In the french door refrigerator market, one unique feature among certain manufacturers is the door-in-door option. As the name suggests, this feature offers an extra layer within the fridge door that lets consumers easily store and access their most commonly used items.

  • Samsung calls this feature the “Food Showcase Fridge Door.”
  • Kenmore calls this feature the “Grab-N-Go™ Door.”
  • LG calls this feature “Door-In-Door® with CoolGuard™ Panel.”

By name, these features all seem like different capabilities, however, SKU Metrix groups all of these under the “Door in Door” feature umbrella – better highlighting the inclusion of this feature.

Cooktops: WiFi Technology

We all know someone who forgetfully leaves on their cooktop. The latest cooktop models offer a solution: WiFi connectivity that can check the cooktop status, pair with an AI assistant, and even remotely turn the cooktop off. 

  • LG calls this feature “ThinQ® Technology.”
  • GE calls this feature “WiFi Connect.”
  • Samsung calls this feature “WiFi + Bluetooth Hood Connect.”

By normalizing this feature under “WiFi Connectivity,” SKU Metrix provides a more accurate overview of smart technology in the cooktop market. 

Unlock Industry-Leading SKU Data for Major Appliances

These examples only break the surface of the value SKU Metrix™ delivers through normalizing product features to give businesses an unmatched overview of the major appliance market. 

Pair this unparalleled product matrix software with our TraQline consumer insights tool and Hybrid POS™system to grow your understanding, raise your competitive advantage – and leave an impact on your market.