Important differences in products and items
The English language can be confusing, at best. Many words we use on a regular basis seem like synonyms, so we tend to use them interchangeably. For example, the words “stalwart” and “stubborn” both mean “unmovable.” But if used correctly in context, the first has a positive connotation and the second has a negative one.
Similarly, “product” and “item” can appear to by synonyms. But in the product data world, there are some important differences.
The differences in products and items
Simply put, “product” is what you market and “item” is what you sell.
Let’s say you work at an apparel company and you’re working to launch a new shirt design. When developing a new product, you start at the “product” level. You decide what product you want to sell: a new shirt. You determine how you’ll describe the shirt on your ecommerce website and downstream channels. You identify the specific features and benefits of the shirt.
These are all product-level attributes. This is what will help convince your end customer to buy your product.
But before you can sell the shirt, you need to establish your item-level attributes. The shirt will come in these colors, patterns, and sizes. These are the specific UPC code(s) and the pricing for each variation.
Product details are more explanatory and item details are more granular.
The business value of products and items
Before you begin a product information management (PIM) implementation, you need to have all of your data at the proper level. For example, if all of your data lives all at the item level, you’re setting yourself up to maintain a lot of data unnecessarily.
But if you can elevate the right attributes to the product level, those data points will remain the same across all items. This will save you time not only in your PIM implementation, but throughout the ongoing maintenance of your product data.
This product vs. item hierarchy normalizes your product data, reducing duplication and the likelihood of manual error. By using product and item data properly, you can decrease the size of your dataset. This makes it easier to generate product data and write marketing copy.
In our experience, most products being sold fit into the product/item model, but there may be some edge cases. For instance, a highly customizable industrial generator that’s being manufactured on an individual basis may not fit the product/item model. Some businesses will have more attributes at the product level; others may have more at the item level. Both of these are OK, as long as the approach is right for that business.
Restructuring your data with products and items
Understanding the product/item hierarchy is a great first step to improving the quality and consistency of your product data. Conforming to this structure will better ensure a smooth PIM implementation. It will also increase the likelihood that your customers get the information they need, which can increase sales. And it will streamline your internal processes, freeing up time throughout the organization to complete more business-critical tasks.
Once you understand the importance of the product/item hierarchy, you’ll need to identify the data sources and system of record (i.e., the authoritative source) for each attribute. Data sources within larger organizations are often disparate and scattered, further complicating an already complex internal workflow.
If you’re just starting out with a product information management (PIM) system, you’re probably inundated with spreadsheets and sorting through lots of data. The saying “paralysis through analysis” is a real symptom, manifested through increased manual data entry leading to a higher probability of data duplication and errors.
If your primary data source is an enterprise resource planning (ERP) system, your product descriptions are most likely going to be at the item level. The downside to that is those descriptions can be inconsistent, with mismatched abbreviations or unclear verbiage. In our experience, ERP data is also not typically enriched for marketing purposes.
Additionally, you should consider where your data needs to go. Technically speaking, outbound requirements shouldn’t affect how you structure your data. But in reality, sometimes they do. For example, legacy websites may need data fed to them in a certain way. Such cases require careful analysis to determine if these requirements can be met via outbound data transformations.
An ideal PIM implementation should provide the company with a revised, more efficient workflow backed by industry-leading software that is custom-tailored to the company in a way that provides maximum value with minimal disruption to existing systems.
Before you can restructure your data into products and items, you must first understand where your data is coming from and where it’s going. This will inform your data cleanup strategy.
Working with a product data consultant
If your current organization structure can’t support a complete product data overhaul, call in the reinforcements. PIM consultants can examine all of your data sources and identify any edge cases that don’t fit the product/item model. (Pro tip: It’s much easier to plan ahead for a third level in your hierarchy, versus trying to add it later.)
If you choose to work with a consultant, figure out as much about your internal data as possible ahead of time. We have seen a direct correlation in our clients doing all of their “homework” and the success of a product data analysis project.
It’s critical to involve the right people in this process. Typical participants in this process include the product team, marketing, sales, ecommerce, data managers, Scrum Masters, ERP managers, and other technical architects. If someone in your company has a hand in your product process, you’ll want to consult them.
Often, when pulling together subject matter experts from throughout the organization, our clients have major “a-ha” moments about their product processes. As each stakeholder starts to see elements of the process that they never knew existed—or they start to see how all of the pieces come together—things start to “click.”
This is another important piece of the data streamlining process. You’ll want everyone on board with the product/item hierarchy, and you’ll need them to understand how this restructure will improve their daily lives.
One of the many advantages of using a PIM is its flexibility. You can change it, bend it, and even break it to fit the correct structure. But PIM works best when it’s set up properly from the beginning.
Having a structured product/item relationship—and a plan in place for maintaining it—sets you up for continued success. Doing this work upfront will save time in your PIM implementation, and it shows the members of your extended product team how everything functions together. Additionally, identifying edge cases upfront will prevent you from having to force them in later.
Success in ecommerce relies heavily upon clean, accurate product data. Submit the form below to speak with one of our digital consultants today.
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