For decades data has grown in importance for businesses of all kinds. With the proliferation of ecommerce, data became table stakes for growing brands, yet many are still struggling with getting this critical data into the best formats to help them truly use the data to make better, faster decisions. In this, the first in a series of data best practice article, we detail the most impactful product data to focus on.
In retail, it’s all about selling products, so it stands to reason that focusing on product hierarchy first is a great place to begin.
What is Product Hierarchy?
A product hierarchy builds relationships between products in a tree structure. This groups products for aggregate analysis and creates relationships between products, groups and siblings at various levels.
An example of an apparel product hierarchy might look like:
- Level 1: Womens
- Level 2: Apparel
- Level 3: Tops
- Level 4: T-Shirts
- Level 5: Graphic T-Shirts
- Level 5: Plain T-Shirts
- Level 5: Striped T-Shirts
- Level 4: Blouses
- Level 5: Button-up Blouses
- Level 5: Peplum Blouses
- Level 5: Ruffled Blouses
How do you determine product hierarchy?
Although there is no set process to create a product hierarchy, there are some rules of thumb you can use.
Align on what you’ll use product hierarchy to do
It’s tempting to think about your hierarchy as the way you always categorize your products, from financial reports to website pages, but that isn’t always the case. Different departments might need a different structure for various use cases. That is okay! Don’t get bogged down trying to agree on a hierarchy that will fit all those use cases. Instead, focus on building the hierarchy in a way that facilitates financial and buying decision making.
How often should you adjust product hierarchies?
A product hierarchy should ideally change very little year over year. If you adjust it too often, reporting (and the resulting decision making) will be challenging. Therefore, focus on defining categories that are unlikely to change that are about the same size in terms of number of SKUs. If you’re wondering if you’ve gone too far, Parker Avery Group shared a great rule of thumb in this snippet from their podcast episode on this topic: https://youtube.com/clip/UgkxYZofJbiFQWEraAczOWcpIEa1gyC60m0i
How many levels should your hierarchy be?
Some of this decision depends on the size of your categories, team and growth plans, but we find that if retailers have a hierarchy deeper than 7-8 levels, it becomes too challenging to plan top-down. The data isn’t meaningful or different enough to warrant the extra complexity the additional levels add.
Another rule of thumb from the Parker Avery podcast is that if your siblings (example Level 5s above) are dramatically different in terms of number SKUs, you’ve probably gone too far. Unfortunately, if your hierarchy is too detailed, you end up not getting very actionable aggregate data and it overcomplicates the planning process.
Even in our example above, there’s a case to be made that Peplum and Ruffled Blouses have gone too far. It might be better to capture detail like cut or sleeve as an attribute instead of as a part of the hierarchy. For more on attributes see our article here.
What are alternate hierarchies?
As mentioned above, different departments may need to think about the hierarchy differently, depending on their use cases. The most common examples are in marketing. If the marketing team wants to put a tab on the website for a holiday, say Mother’s Day, that should not add a new hierarchy value. In contrast, a major holiday where products are created for only the season, like Christmas, might warrant a hierarchy distinction.
How do you manage alternate product hierarchies?
It’s important to select tooling that allows for alternatives to the main hierarchy and the ability to map back to it for the larger reporting. In Toolio, we allow for this for assortment planning and reporting.
Similar to product hierarchy, product attributes help retailers report on groupings of data points to spot trends that help inform new product decisions and demand forecasting.
What is a Product Attribute?
A product attribute is a data point that describes an item. Some attributes are a part of the hierarchy (for example: apparel or tops from the example above), but others will be detailed at the SKU-level (for example: size, cut, color).
How should you manage product attributes?
Unlike product hierarchies, product attributes are easier to bring in and populate, as their use is often in-moment reporting vs. year-over-year reporting. Therefore, changing them on the fly is less complex to do and manage, yet there are still some rules of thumb to keep in mind when working with attributes.
- Consider the level of detail required. We see this most commonly with color where getting down to the detailed color may be important at times, but for other use cases, a color family is more impactful or practical. If there are use cases for both, consider creating two attributes.
- Detail required fields for new product creation. Although attributes are more flexible, they’re only as powerful as the data they hold. If only half of new products have color populated, identifying trends in sales will be challenging at best and erroneous at worst.
- Align system requirements to decisions. Deciding which fields are required for new products is the starting point, then system administrators must ensure these requirements are enforced in the technologies that manage them from start to finish. This is typically done in PLMs and ERPs.
- Identify automation workflows for data population. In the above color example, it’s likely the population of the explicit color is a manual process, but that a system can lump all the appropriate SKU-level colors to the correct value in the color family field.
- Quarterly retrospective. Each quarter, get the teams that use this data together to discuss which data they’re struggling with, what they wish they had and brainstorm ways to resolve. Ideally you have a data steward who can lead and the prioritize changes with the system administrator(s).
In Conclusion: Retail Product Data Considerations
In this article, we delved into the crucial aspects of product hierarchy and attributes in retail decision-making. We emphasized the significance of product hierarchy as a foundation for categorizing and analyzing products, provided guidelines for determining hierarchy levels, and highlighted the importance of avoiding excessive complexity. Additionally, we explored the role of alternate hierarchies and the need for flexible tooling to manage them effectively.
Furthermore, we discussed the value of product attributes in reporting and trend analysis, offering insights on managing attributes with considerations for detail, data completeness, system requirements, and automation workflows. Regular retrospectives and collaboration among teams were encouraged to identify areas for improvement and prioritize data-related changes.
By optimizing product hierarchy and attributes, retailers can unlock valuable insights, facilitate informed decision-making, and enhance their overall retail planning and forecasting processes.