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Manufacturers Best Practices for Managing Ecommerce Product Data

Source of Truth and Various Expectations

32% of manufacturers say limited product data is a top challenge according to a report from Digital Commerce 360. And according to Gartner, 29% of B2B sellers say a top barrier to digital commerce are products that are too complex. As an essential part of successful ecommerce, product data should function effectively within a digital commerce strategy regardless of data complexities. Preparing product data has always been important, but continued ecommerce growth that increasingly focuses on meeting customer expectations means its importance has simply skyrocketed. Buyers must be served product data when, where, and how, to earn the sale.

Multiple tech stacks lead to multiple sources of product data. Without an established source of truth for product data, product naming and organizing inconsistencies are common. Organizing product data must begin by accounting for the different ways product teams, marketing teams, and buyers interact with product data, so that relevant data fields are accessible to those that need it.

The goal is to organize the data. The best way to do this is to prepare data for a specific product information management (PIM) system to maintain consistency. The next step is to upload data to the new source of truth. After uploading the data, organizing it by category and catalog allows for better merchandising. Although managing product data will require the most effort at the beginning of the process, it is ongoing. Establishing a routine to continuously cleanse product data is needed. Business activities will experience increased efficiency and purchasing will likely increase as customers are able to interact with carefully reviewed data. Managing ecommerce product data should be guided by the following best practices:

1. Define the Product Data Model

For the manufacturer embarking on an effort to organize and manage product data, preparation is essential. These are the preparation steps:

  1. Determine the source of truth and centralize
    Centralizing product data is necessary so that multiple versions are not created as a result of product data being managed in different places. In order to centralize product data, a source of truth for all product data needs to be determined. An Enterprise Resource Planning (ERP) system or PIM system may function as the source of truth. Choosing a business system to serve as the source of truth for all product data is easier said than done, especially when a manufacturer’s commerce strategy demands multiple integrations. PIM systems that are built on an extensible platform, like Znode, solve these integration concerns.

  2. Gather existing product data
    Gathering existing product data involves determining the various fields (SKU, name, size, color, instructions, marketing descriptions, shipping details, etc.), imagery of each product, and the current location where product data is stored. A manufacturer will need to determine who creates each piece of data that will be required for the products, and how that data makes its way to the final customer-facing website. It is also important to identify areas where data needs to be created because it currently does not exist. As data is developed and collected, it will be important to define the data flow for future consistency. Important questions to ask during this step include:
    • Do product images all come from a Digital Asset Management (DAM) system?
    • What team is responsible for creating sizing or color specifications?
    • What is the process for naming products?
    • Which data fields need to be associated with each product?

  3. Model the data for the PIM system
    When modeling data for the Znode PIM system, it is important to determine products that are simple, configurable, bundled, or grouped. Most products are probably simple, meaning they have a single SKU. Complex or configurable products are those with multiple variations (different colors or sizes of the same shirt), where each variation is a simple product. Bundled products are simple products sold together that cannot be separated. Grouped products are simple products grouped together that can be separated.

  4. Define attribute hierarchy
    As product data is prepared for Znode’s native PIM system, attributes will need to be selected or created as they relate to the different fields of a product and its display page. Attribute types in the Znode PIM system are Date, File, Image, Label, Link, Number, Text, Text Area, Multi-select, Simple-select, and Yes/No.

    Existing product-specific attributes such as “Shipping Cost” and “Ship Separately” may be associated with other shipping setting attributes in an attribute group. By grouping attributes, it makes it easier for platform administrators when setting up new products or changing details on existing products. Instead of having to dig through the master list of attributes, grouping them together allows admins to quickly find the type of attribute by its grouping.

2. Upload Initial Data

As product data begins to take shape, the next step is uploading initial data to the PIM system. The best practices for this step are as follows:

  1. Load data into templates
    Znode offers several CSV templates through its import engine that data can be loaded into in preparation for the actual upload. Data categories which can be uploaded in bulk to Znode include products, product associations, pricing, inventory, categories, category association, product attributes, zip codes, vouchers, and more. Loading product data into the templates reduces tedious processes of creating or updating each piece of data manually.

  2. Upload data
    Once the templates are completed, it is time to import to the Znode PIM system. Znode’s flexible architecture, which is composed of a completely open set of REST API-endpoints, allows for more complex integration but also speeds up the process for bulk data uploads such as this. Once the data is uploaded, teams will be able view products, attributes, and associated data in the PIM system, and make further adjustments as needed.

3. Make Connections

With product data uploaded to Znode, further set-up with catalogs, stores, and accounts can help bring the product data to life for customers. There are three parts of connecting product data to the customer-facing store:

  1. Set up categories and catalogs
    Organizing product data in the PIM system is often facilitated by the use of categories. Znode allows admins to create categories to group similar products which can then be included in catalogs. For example, a promotional products manufacturer may create a category for drinkware and another for apparel. A manufacturer can develop attributes specific to categories or use default categories. Catalogs in Znode are built from multiple categories of products being brought together and ordered within a hierarchy.

  2. Build stores
    Catalogs of products are the building blocks for stores. A manufacturer with a multi-channel strategy can apply one catalog or multiple depending on the channel and customers. Alternatively, one catalog can be attached to multiple web stores. Znode lets manufacturers personalize ecommerce product offerings to a considerable extent with its unlimited catalog creation and application. The product display store-side is significantly improved with an articulate system of catalogs, categories, and products organized by attributes.

  3. User accounts and profiles
    In addition to personalizing product offerings to specific catalogs and subsequent stores, catalogs may also be associated with specific accounts. This means upon logging in, an account can see only the products in the catalog applied to that account, whereas other accounts or non-account visitors would see a different catalog of products. Similarly, Znode allows admins to set up catalogs for user profiles, meaning a group of similar or associated accounts can view the same catalog.

The category, catalog, store and account application of the product process is made possible with backend data management and Znode’s PIM system. By successfully managing the product display, manufacturers are one step closer to creating an ecommerce experience that will drive conversion more effectively.

4. On-going Review

After completing the process of defining, uploading data, connecting systems and features for product display, product data will require routine review. This includes the fields on the product display pages, and other product data will reveal areas where data is no longer relevant, is incorrect, or is lacking. Identifying new trends in product display or listing or even comparing current data offerings to those offered by competitors will offer opportunities for product data management to shift.

Updating product data organization and details can be an opportunity to solve business challenges and respond to current needs for teams or customers. Create a process for reviewing product data to identify fields no longer relevant or look for gaps that may have developed since the initial upload. Keeping appropriate tabs on the product data in the PIM system will ensure satisfaction for all parties involved.

Product Data Management for Business Performance

Product data itself may not be the issue for manufacturers, but evidence suggests that managing data is a true struggle for manufacturers. Even as business and digital commerce grows, product data can easily become convoluted without the proper practices in place. Multiple sources of truth which lead to inconsistencies and integration challenges are the biggest hurdles manufacturers need to overcome in order to streamline product data.

By conducting a data review or preparation project, manufacturers can define a source of truth for product data while uncovering the necessary elements for each product. This is the starting point for preparing the PIM system and the data for upload.

With a defined product data flow, manufacturers can evolve the digital commerce strategy to meet the business needs of the future. A healthy practice of on-going product data evaluation can reveal areas for development or change, allowing a manufacturer to address new customer needs. Following these best practices for product data management can only improve business performance.

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