End customer self-service has grown dramatically during the last decades due to the increasing adoption of ecommerce. When customers shop online they need a lot of information about the product they intent to buy. One of the pieces of information they need is an image of the product. The image helps customers to understand if it is the intended product they are going to buy and helps with quickly differentiating among a range of products.
Unfortunately the most common image around on web shops is the “image coming soon”.
Completeness is a huge problem in Product Information Management (PIM) as examined in my previous post called Multi-Domain MDM and Data Quality Dimensions. A missing product image is a classic completeness issue for product master data.
As a web shop you can collect a product image in several ways, namely:
- Take the image yourself
- Get it from the manufacturer
The former approach is cumbersome and usually only used for selected products for a special purpose of use. The latter one is far the most common. When you deal with many products and constant new on-boarding of products, you want to have a uniform and automated approach to collect images along with all the other product information needed for the specific product category.
A clumsy variant of the latter is scraping it from your manufacturer’s website or even your competitor’s website. Or having someone far away doing that for you.
The better way is to start sharing product data and digital assets, including product images, within the ecosystems of manufacturers, distributors, retailers and end users. Stay tuned. A service for that is coming soon 🙂
as you rightly pointed out, completeness is one of the pivotal requirement for product information.
However, I suppose the need for differentiation, especially for retailers, is a growing concern which basically means standardization is the “villain” 🙂
I am involved in many opportunities in retail and I see how going through the process of either enhancing the pictures scraped from the web or received from suppliers e.g. by adding metadata to impact SEO, etc. or photo shooting (internal or outsourced) is very much the only way to get to the following equation: Product = Emotion.
Sharing, as you suggest, will facilitate the sourcing of information but this is half of the story.
With growing sympathy for your work,
Thanks for commenting Michele.
I certainly take your point about that standardization can be the enemy. In a PIM implementation I am currently working with, we have defined a scoring model for product data completeness. While the lower levels are standardized, the top levels deals with differentiation per category and at the very top-level differentiation from the market. As you point out this is about variation of digital assets, SEO capabilities and also how products relate.
Now back to the first half of the story being product data sharing in the ecosystem of manufacturers, distributors, retailers and large end users. The service called Product Data Lake that I am working with is unlike most other services for that purpose not based on standardization. If two trading partners use the same standard, that is fine and makes things easier and you can still utilize the automation features of the Product Data Lake along with all the other product data that is not standardised or standardised in a different way.
I am very keen to learn more about your “product data lake” project. Will read what you have written so far as soon as I have a bit of time 😉