Atomic Data makes search engine dominance possible

This is the third installment in a series that blends website architecture, data structures, and SEO marketing into a collaborative design pattern continuing from Part 2 - Best Business Practices for Product Catalog Data Structures - Speed vs Flexibility 

Many e-Commerce projects begin with an existing brick and mortar store that has decided to go online.  This means that certain data models and business processes can be inherited from the legacy business processes of a non-online environment. 

If you were going to open a physical, brick and mortar store, you would generally design the store based on "Customer Paths", meaning you would examine the vector that a customer would take upon entering your store so you could direct them along the shortest path (in certain cases) to where they were trying to go to find the product that they wanted.  Many websites are designed along a similar path but the application of brick and mortar strategies to websites may not be the most effective.

Take for example the concept that an apparel store is designed along the Customer Path strategy of Departments, Aisles and Shelves.  An apparel store would generally have a Ladies department, with a Shirts Aisle and a Tank Top Shelf.  It would make sense from a Customer Path perspective to have (female) customers enter, segment them by Gender as they walk to the Ladies department, further segment them by Type as they walk to the Shirts Aisle, and further segment them by Type as they scan the Tank Tops Shelf.

This seems to work in practice, but only as long as you can only have a single store.  Take a customer now that is female but instead wants the Nike Shirts section.  Your demographic segmentation Customer Path does not cater to them properly and so the Customer is forced to scan through all shelves that have Shirts in order to find the Shirts that match the Nike Brand.  You can see how relying on a fixed hierarchy limits your store planogram and structure in a very singular manner.  To experiment with alternate Customer Paths, you would be forced to do a hard store reset, or you could experiment with alternate locations... perhaps a Nike Store which would provide a Brand-based alternative for the Brand-conscious customer.

Imagine now a website where instead of a fixed store with a rigid, hierarchical structure of Departments, Aisles and Shelves, you had a completely dynamic store that could be rebuilt in an instant and individually for each customer that entered for their own, private shopping experience.  Imagine also, those fixed Aisles and Shelves full of product, which instead of sitting in fixed placements, when a Customer entered the store the entire inventory was tossed into the air, only to fall back in the precise order that the Customer wanted to see them in upon entering.  This is no fantasy in an online e-Commerce website where this type of flexibility is possible.

Let's take a look a the Customer Path options open to an e-Commerce Apparel customer:

If you recall the Product to Property Mapping diagram shown in Part 2 - Best Business Practices for Product Catalog Data Structures - Speed vs Flexibility, you will see some of the same Property mappings in the above diagram.  These help to illustrate the product being mapped within the data model along the Customer Preference Paths instead of a fixed hierarchical model that a traditional brick and mortar store operator might follow.

For example, a customer that may be more interested in Tour de France could be immediately segmented in a store with inventory sorted by the Event Property first.  Then, if the customer was interested in the Brand Property next, the inventory would be tailored to suit by showing Nike merchandise.  Finally as the customer settled on a Tour Property related Product with UCI Pro Tour branding, the final product match is easily found because the inventory re-sorted itself to match the preconceived desires of the newly arrived customer.

Similarly, a customer that was more interested, at the time, in Lance Armstrong and then Tank Tops and then a color selection of White, could follow the Customer Path of Player / Type / Color.

You can see how the model continues.  Take some time to evaluate your own design process when you created your categorization model for your e-Commerce storefront.  Think about the process you went through as you decided on the model and see if you were trying to adapt a brick and mortar model to one that could have been conceived with an online presence in mind from the start.  If so, this may help guide you along a fresh look at the construction of a new categorization schema for your online e-Commerce catalog.

The series continues in Part 4 - Best Business Practices for Product Catalog Data Structures - SEO Path Aliasing

This is the fourth installment in a series that blends website architecture, data structures, and SEO marketing into a collaborative design pattern continuing from Part 3 - Best Business Practices for Product Catalog Data Structures - Customer Paths.

It may seem counterintuitive to discuss search engine optimization (SEO) techniques in the midst of a conversation about data structures, architecture diagrams and in-store plan-o-grams, but it can directly relate to your choice of data models.  As we discussed in the previous article, it is important to structure your website to conform with the needs of entering customers in a way that segments them properly so they find the things that they were searching for.  Part of this is anticipating what a customer is going to want before they enter your store. 

When dealing with search engines, there are two customers to contend with... the "Natural" search engine... and the "Paid" search engine.  These two customers are very important to understand and to distinguish and need to be treated with a deference and distinction from the "real" customers that frequent your online store.  The complexity arises to some degree because these two "customers" happen to be "ghost shoppers".  You never know when they are going to arrive and they generally float through your store much like a customer would, but they are searching for every product on every shelf in every aisle and in every department... all at the same time.  The complications continue because you want to manage what the ghost shoppers can and cannot see so they don't memorize portions of the store that you don't want reported on the search engines.  This may come across as elemental theory to an SEO expert, but in the context of blending SEO concepts, architecture and data structure modeling, it illustrates one aspect of the equation.

Imagine now that you are a search engine, whose job is to find, identify and classify billions of e-commerce pages throughout the Internet with the primary objective of finding pages that are considered "relevant."  I quote the term "relevant" because what that precisely means changes with the breeze and the whim of arcane departments of voodoo at the various search engine optimization firms.  With that said, you want to look at a natural search engine as a stream of water pouring into your website.  This stream is going to remember whatever it touches, so you want to ensure that it finds the things that you want it to see.  You also need to consider the diffusion of the stream of water as well.  Don't let the natural search engine stumble across pages like "Privacy Policy" or "Terms & Conditions" as that won't deliver any tangible benefit for you.  In similar fashion, on your landing pages you should try to structure your site so the links that are the most compelling draws for the majority of natural searching customers should be setup to receive the largest stream of natural search "attention." 

You also need to anticipate every possible combination of keywords that would be used to "land" on any given destination.  Lets take a look at the SEO Path Aliasing diagram to illustrate that:

We have already covered Customer Paths but sometimes the proper "path name" doesn't match an actual English phrase.  This means that the combinations of words that make sense for categorizing a mix of products may not make linear sense for a keyword search.  Our diagram above illustrates this with the green path of "Ladies / Nike".  There may not be many customers that would enter that phrase in a search, but it may be a logical progression as they navigate through a website.  This is where Aliased Paths come in.  In our example, the Aliased Path for "Ladies / Nike" could be "Ladies Nike Apparel"... sure this one is a bit of a stretch...  I'm not sure how many actually type in the word "apparel" but you'll need to work with me on this one.

You will note that this path is identified as "overridden".  In smaller e-Commerce websites, it may be a simple matter to manually go through each Customer Path and identify the possible Aliases but in far larger catalogs this quickly becomes a daunting task.  It doesn't mean that overridden Path Aliases aren't an important part of configuring your catalog categorization scheme, but you can, for the most part, rely on the auto-generated Path Aliases for many of the Customer Paths in your catalog.  Take the path "UCI Pro Tour / Tank Tops" which easily converts to an English text keyword search of "UCI Pro Tour Tank Tops". 

Note also our attempt to focus the "stream" of the natural search flow throughout the various Customer Paths.  Many search engines respond to a setting within the hyperlinks of a "NOFOLLOW".  This mechanism gives you some measure of control over which links you allow the natural search "probing" to find.  You will note how the various Customer Paths are identified as NOFOLLOW for those paths that we want the search engines to pass on as they traipse through our pages.  This poses another logistical issue in a large-scale e-Commerce website which we will address in the next segment, Part 5 - Best Business Practices for Product Catalog Data Structures - SEO Weighted Auto Mapping