Over the last few years, the amount of product data an ecommerce company needs to be successful has increased exponentially—making it more and more difficult for marketers to meet traditional technical data standards.
A PIM is a software solution whose foundation is a robust centralized repository for inputting, storing, organizing, modifying and disseminating product data. It ensures all product data is uniformly entered and displayed under one roof for your customers.
Integrating your new PIM system in a structured manner ensures you don’t miss out on any important product data. Continue reading for the essential four-step process we follow to keep data cleansed in a PIM.
1. Develop a Classification
As you gather all your data, and before everything is normalized and validated, it’s time to build out a cross-functional classification. What does that mean?
Classification is the organizational structure of the data. If your organizational structure happens to be too complicated or disparate, your end users will suffer. The operational use of your data will also be too complex or disparate for your end users.
This step helps align how the following will be used, managed and updated:
A schema for products.
Attribute category profiles.
Search-engine-optimized product descriptions
Thankfully, your PIM system can help you master all of the above so that it aligns with your products, customers and ecommerce site.
2. Uncover Hidden Assets
Although basic product information is fairly simple to nail down, there is still usually some important data that is hidden away. Oftentimes data that is unstructured; for example, images, videos, and logos.
To ensure you get the most out of your PIM system, consider reaching out to other departments of your organization—such as the marketing, engineering, category and design teams—who you think would most likely have these hidden assets.
3. Define Common Style Guidelines
A style guide will make you and your team's lives less stressful and more productive, as well as make your data fit for purpose. Once you've classified products into categories and filled out attribute values, you'll want to ensure the data conforms to stylistic conventions that help keep the data consistent, uniform and coherent.
This means making determinations for how you want the data to look—from how you format numbers and decimals to how you use brand names in descriptions. Think of style guidelines as a way to enact data quality rules so your PIM data looks and stays clean.
Make sure you internally publish your style guidelines so that any PIM users building out product data stay aware and informed on how the data should be structured.
Bad data is information that is either non-conforming, inappropriate, inaccurate, duplicate, incorrect (e.g. misspelled or misformatted) or incomplete. During this step of the process, it’s important to delete or archive any duplicate, out-of-date, or inaccurate data you see—it’s smart to cleanse your data as you spot issues with it.
In order to scrub your data, we encourage our customers to complete a duplicate and near-duplicate analysis. This can be done with a duplicate analysis tool based on attribute value matches that drive pricing and differentiation.
Set Your PIM Up for Success
Following these essential steps will help you find success within your PIM system and ecommerce site. If you’re looking to reach new horizons and win over more customers, get in touch with our team of technical data experts—we’ll help get you started down the right path.