Let’s face the facts, it’s a tough job to be a retailer these days. Competition is fierce, customers are demanding, and product margins are razor thin. Just when retailers finally get that product into a customers hand and out the door, it can come marching right back into the store as a return. In fact, studies estimate there are tens of billions of dollars worth of product returned back to retailers, and very small percentage of those are actually defective. This means that brick and mortar retailers have plenty of fully functional open box products gathering dust on shelves and are missing an opportunity to get these units back into the hands of customers.

All of our local Best Buy stores are challenged with returned products. Our physical stores can be silos of beneficial product data, especially when it comes to the availability and reduced price scenarios presented by open box products. Up to this point, our open box items have not been openly displayed on the web — we tend to focus on new, unopened products, leaving an huge unmet opportunity at the store level to increase web visibility to returned products.

While this seems like a large problem to tackle, we have found a forward-thinking way to increase the visibility of open box items at our local stores using the power of open source software and open front-end semantic data standards without employing traditional marketing tactics to push individuals toward these products. Earlier this month, we began rolling out the capability for store associates to contribute to the web of data while increasing visibility to their local open box products through a simple WordPress plugin and RDFa templating mechanism. Each Best Buy store is empowered via their local store WordPress blog (background here) to enter the SKUs of the open box products they have in their inventory. The plugin fetches the relevant product data using Best Buy’s Remix API and the user is prompted to enter the open box price and a reason the product was returned. With one last click, the user saves the data and the product is published to the store site, is made available to the semantic web through front-end RDFa templates and auto-generated XML sitemaps.

There are some interesting features, techniques and potential outcomes of this work that are worth discussing:

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There is a good amount of chatter about the semantic web out there, but not a ton of concrete, working examples. I decided to put our Best Buy data to work and publish BBY SKUs in RDFa, using the GoodRelations e-commerce ontology. As I see it, simply publishing the RDFa is not an issue — the challenge is to apply real-world style and structure to the code to make it both machine and human readable. I’m trying to answer the question: is the RDFa model flexible enough to allow Joe Web Developer to successfully publish valid structured data while satisfying the desires of his design, business, and marketing counterparts?

I’m pleased with the first round of results, ~460K worth of “next-gen” product detail pages. Take a look at some choice example SKUs from the Best Buy product catalog:

• Music SKU: http://products.semweb.bestbuy.com/y/products/9312861/
• Movies SKU: http://products.semweb.bestbuy.com/y/products/7590289/
• Software SKU: http://products.semweb.bestbuy.com/y/products/9509686
• Games SKU: http://products.semweb.bestbuy.com/y/products/9346442/
• “Hardgoods” SKU: http://products.semweb.bestbuy.com/y/products/8967228/

Interested parties can get a full URL list here (txt, gz), or split up into list 1, list 2, and list 3 (txt).

Thanks to: Martin Hepp, Andreas Radinger, Alex Stolz, Yahoo! Searchmonkey, Jason Galep (design guidance), and Best Buy Remix.

I have been fortunate enough to work with some great people out there working for open web and semantic standards. However, I am reluctant to use identify myself with my current employer in this work, as it can sometimes foster negative connotations. On multiple occasions I have been either directly or indirectly warned of “forcing” standards, or creating them for profit or company gain.

While there are inherent benefits to adopting these data standards for ecommerce sites, and thus my company and industry, I am constantly reminded of a simple fact: I am a consumer too, and I deserve a better experience when searching for products. This is why I advocate for better semantic data practices on the web. To illustrate my point, I need only look back to this past weekend. My wife and I are needing new kitchen appliances, but have a non-standard (measuring from today’s modern standards) area to fit a refrigerator and free-standing range. While we’ve solved for the range, I spent a great deal of time tracking down specs for a handful of refrigerators to see if they fit in within our limited height constraints. To my surprise, I found different height measurements for every commerce site I visited, for the same refrigerators. This prompted a visit to the store, where the dimensions on the retailer’s site didn’t match what was physically on the floor. Frustrated, we were sent spiraling toward square one in our quest for new appliances.

With more participation of retailers, manufacturers, and service providers in the semantic web, these gaps surely would close. I envision a time where publishing good semantic data in an hproduct microformat or product RDFa enables applications to capture data and organize it — providing clarification to inconsistent data, offering a larger view of product reviews, and giving consumers better pricing and service options from a wider variety of stores.

Until that day comes, I am still looking for a black 25+ cubic foot side-by-side refrigerator with bottom-mount freezer that is Energy Star qualified and will fit into a 67 1/2 inch high space. Or a good contractor to remodel our kitchen.