Well folks, we’re at it again. The month by month the journey continued Monday into Tuesday night to semantify the hallowed templates of bestbuy.com. One of April’s goals: to enhance machine understanding of Best Buy’s considerable product offerings while retaining human searchability and readability. After long wait, we have deployed code to the search templates to establish a human-readable and machine-parseable front-end API.

Many moons ago (even before all this RDFa goodness), we established a URI scheme we call “shop URLs”. Basically it’s an easy way to pass a search term in a URI and get a visual list of up 50 products our search appliance considers relevant. However, when you have a catalog of 400K+ products, simple visual results may not be the best or most efficient way to sort through the cruft and get at what you’re looking for. Enter stage left our friendly machine helpers: Search Engines, Parsers and Aggregators — this deployment activity is focused on feeding you! We’ve deployed step one of enabling a solution to product visibility and discovery issue by unleashing the result data in RDFa (with GoodRelations, Dublin Core, FOAF, Google Ratings vocabs) for maximum machine parseability.

After all this grandeur and hype, I’m hoping you’re still interested in how it works. You may point your eyes and parsers here:

http://www.bestbuy.com/shop/search+term*

* Please note, due to marketing and business considerations, some of the more popular terms may redirect you to a dataless “category page”. To get a RDFa-enabled result, simply append a * to your search term, e.g., http://www.bestbuy.com/shop/ipods* (how dare those marketing people stand in the way of good data!)

Let’s dive deeper with a quick example. So I’m a bit eclectic and looking for a thermometer online. I would like to see results of the “thermometers” from bestbuy.com, plus pass the data to my machine friend, an application I am building to help me make the right product choice.

First I type access my human-friendly representation using a “shop URL” directly in the browser:

http://www.bestbuy.com/shop/thermometers

Which results in a human-readable web page:

human-readable shop url

Looks like I have 15 product offers that match and are available via bestbuy.com or in store. Excellent.

I’m going to take that same URI and pass it on to my machine helper who just wants the data, no fluff. Let’s say we’re working with RDF/XML…on the surface, the 15 product offers may appear like this:

rdf extract from shop url

Expanding an individual offer yields the following data-rich result:

expanded data extract of shop url

So endeth the second phase of sematification. Make sure and leave your API keys at home, this search data is all open! Tune in for more later this week, I will be discussing another one of April’s goals, expanding RDFa markup to Best Buy’s product detail pages.

I’ve had the good fortune of sharing the possibilities, power, and my personal vision of the semantic web with a number of audiences in the past couple of months. This has also given me a great deal of time to think deeper about how we can utilize the massive amount of unstructured data that exists now on the web. There’s a lot of beneficial data out there, information companies can ingest and use in machine learning, and data that should be openly shared externally and made available for both humans and machines to access and distill.

While brainstorming new ideas for my next go at an interesting presentation, I concocted a very simple “strategic formula” that I believe all business and organizations could leverage when it comes to the Semantic Web, Linked Open Data and, well, just data in general. It looks a little something like this:

So what do these spheres mean? Anyone who sells something or provides a service that people use should be looking for as much exposure as they can get on the web. The external data sphere represents human and machine readable data that you’d want everyone to access. One of the primary vehicles gaining popularity on the web is RDFa, a way of utilizing richly annotated HTML to deliver data to machines while retaining the rich visual web human users have become accustomed to. There are also markup techniques like Microdata that do a similar job, allowing us to enrich HTML utilizing semantic vocabularies like GoodRelations to create virtual representations of real world physical objects. Search engines like Yahoo! have been taking advantage of rich data markup techniques for years, and Google has built RDFa, Microdata and Microformats support into their Rich Snippets initiative. The great thing about “front-end” semantic markup techniques is with a little additional knowledge and tools, it allows countless numbers of HTML devs to create a very rich web of data by simply adding data annotations to their HTML, essentially making the entire web an open and queryable database or API for us to extract knowledge from.

On the other side of the spectrum, most businesses have proprietary or sensitive data that they would not want to expose, but could still utilize internally for business benefits. This is where non human-readable semantic data technologies like RDF/XML would be useful. Companies could build internal apps that query a large amount of data that they posses, but typically don’t utilize. What if I could mash up internal data like product margins, inventory levels, along with store trend data and the “sentiment of the web” and start asking it questions? I can see benefits that touch every aspect of the business, from extremely contextual consumer and associate-facing product recommendation engines to merchandising tools that automatically determine trends and adjust product levels across the enterprise, even down to the region or individual store level, with limited human involvement.

Combining these external and internal data structures will result in insights — a necessary resource needed by all companies simply to survive in the current extremely competitive landscape. Data-driven insights are device, platform and trend agnostic, meaning they can easily be utilized and deployed to any new app, operating system or device. With the online space rapidly transforming into a “splinternet” of device types and methods for consuming and producing data, a solid base of semantically structured and linked data will be key to the next generation of successful enterprises.

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:

More