Year Make Model ‑ PCLookup

Develop by Shopify

$19

/ Month

14 Days Free Trial

"Effortlessly find auto parts with Year, Make, Model filters on Shopify."

Customizable design

Csv data upload

Vehicle filtering tool

Search widget display

Compatibility tables

About Year Make Model ‑ PCLookup

Launched Jun 17, 2025

Built by Shopify

Address

7115 Canyon Creek Pt, Colorado Springs, CO, 80922, US

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Description

Year Make Model = PCLookup helps stores show compatible parts, increasing sales and reducing returns

Year Make Model = PCLookUp is a powerful fitment tool for auto parts stores on Shopify. It helps customers find the right parts by filtering products using their vehicle’s Year, Make, and Model. This reduces returns and increases sales. Merchants can import or manually add fitment data and display a search widget and compatibility tables on product pages. The app is easy to use & works with all major Shopify themes.

Help customers find the right parts by filtering Year, Make & Model.
Display fitment tables on product pages to show compatibility.
Easily manage YMM data using CSV upload or manual entry.
Design and layout can be fully customized by our team to match your store.

Pros

Helps customers find the right auto parts using Year, Make, and Model filters, improving the shopping experience.
Reduces returns and increases sales by ensuring compatibility of parts with customer vehicles.
Allows merchants to import or manually add fitment data, providing flexibility in data management.
Works with all major Shopify themes, ensuring broad compatibility.
The app interface is easy to use, making it accessible for merchants without technical expertise.
Supports CSV upload for managing Year, Make, Model data, which simplifies bulk data handling.
Offers a customizable design and layout to align with the store's theme and branding.

Cons

The process of importing and managing fitment data might be cumbersome for stores with limited technical resources.
Customization of the design and layout may require external assistance, which could incur additional costs.
The reliance on accurate Year, Make, Model data may result in issues if the data is outdated or incorrect.