A quick introduction to Attributes
Seelk Studio's Attributes give you to power to organise and accurately compare your Amazon product groups against each other. With the ability to organise products into exclusive groups, each Attribute allows you to compare and contrast your product groups to better analyse your Amazon business performance. When you compare Attributes with tags, you will quickly understand that, Attributes (unlike tags) contain product groups that are mutually exlusive, meaning for each Attribute, each product can only exist inside a one group at a time. Therefore, any comparison between groups is entirely accurate, and as a business you can now easily know where you need to take action most.
🤓 If you need a more lengthy introduction to Attributes, don't hesitate to read the following articles:
→ What are Attributes and how do they work?
→ How do I create an Attribute and group my products?
Renku's "Clothing Type" Attribute how-to
Renku, our favourite Norwegian clothing company, is an Amazon Vendor in both 🇫🇷France & 🇩🇪Germany. This business quarter they are trying to get to grips with how they are performing across different clothing styles of products and have decided to create an Attribute to analyse this inside Seelk Studio.
As we already know, Amazon only allows you to see sub-groups according to Category and Brand. In this case, Renku's products fall into the Categories: Men, Women, Boys and Girls.
N.B. "Categories" can be considered as a type of Attribute, as your products can only exist in one category at a time.
If we focus on Renku's 🇫🇷Amazon France catalog, they have a total of 120 products. The breakdown of the number of products per Category is as follows:
- Men (95)
- Women (15)
- Boys (5)
- Girls (5)
For Renku, this level product grouping and analysis by Category is not enough. They need to group products in different dimensions according to their business needs - for example, according to a clothing style, rather than a simplistic categorisation according to gender!
Amazon business performances according to clothing style is everyday analysis and without an easy way to group your products according to your needs, you will end up having to import your data manually and applying your own custom grouping.
How Renku's built their Clothing Style Attribute
Using Seelk Studio's Attributes, Renku was able to organise their products according to Clothing Style, which they broke down into the following groups:
- Slim (24 products)
- Casual (34 products)
- Snow (17 products)
- Custom (10 products)
- Sleek (14 products)
- Formal (25 products)
- Comfy (6 products)
As you can see all of the 120 products in their 🇫🇷Amazon France catalog are contained in the Clothing Style Attribute, each product existing inside a distinct group.
The products targetted in each one of the groups was part of a separate efforts to improve product listings and after a few months Renku was able to easily compare which group was contributing the most new reviews, global sales and overall ranking.
Clothing Style inside Seelk Studio's Catalog
Here we can clearly see which group is suffering the worst in terms of Content, Offers and Reviews. Looks like "Slim" needs a lot of work to get up to part with Sleek, Formal and Comfy products.
Clothing Style inside Seelk Studio's Business Intelligence
In Seelk Studio's Business Intelligence app Renku's can now use "Group by" to see Clothing Style and then order according to Contribution to Growth (%) to see which products are contributing negatively according to the previous year.
To learn how Attributes enable you to calculate contribution to growth across different groups, read the following article: