Friday, December 23, 2011

SEO Lesson #2: More Guayaki Yerba Mate

Yesterday we posted on a friend's company who is examining their search engine visibility. The company is called Guayaki and they sell products based on the culture of a plant named Ilex paraguariensis, commonly referred to as Yerba Mate. All the founders of Guayaki have a passion for mate but they also want to be socially responsible. They have pioneered an innovative business model that directly links their customer’s purchases to partner farming communities in their supply chain, enabling fair trade, fair wages and the ability to restore parts of the rain forests from which the Yerba plan grows.

Yesterday we explored how Google's results vastly vary based on where a search is being done from.  This is largely a baseline for understanding where the results are.  Today we will examine what course of action could be taken to elevate their relevancy for various searches.  As always, Technoracle never recommends cheating on SEO.  The rules and systems used by various search engines are very fair and have feedback mechanisms to promote relevant results.  There is no point in coming up #1 for a topic like "horses" if your website is all about golfing.  This serves no purpose and will annoy anyone unlucky enough to find your site.

Having said that, the first thing to do is to try and determine the actual search inventory that is available and what related searches might be useful.  We ran some reports on various systems and determined the following:

The term "yerba mate" has relatively low competition and there are 246,000 global searches and 8,100 local searches per month. The term "yerba" by itself has over 823,000 global searches per month while the term "mate" has 13,000,000 plus. The latter must be heavily discounted since it is a term with a plurality of meanings. People may be trying to find a mate instead of yerba.

The company itself has done well and every month, over 9,900 searches are done for Guayaki. This indicates a good brand presence yet shows us that there is a lot of upside potential.  Stated simply, being able to capitalize on close to a million new eyeballs per month on their website would be potentially lucrative.

There are also mis-spelled variants of "yerba mate" such as "yerbe mata" which are commonly used plus a third word "la" (spanish) used as an article in conjunction with the term. It is possible that capturing this traffic is something that could be of interest.

At this point we are ready to generate a report to present to Guayaki. The company is strong, has a great community and is ready to grow.  There are various techniques that can be used to build search engine traffic but the most proven way is to ensure what you are serving your website visitors is what they are looking for.  Our next step is to use some analytics to understand what the people searching for are hoping to find when they land on the website.  Google Analytics is probably the best tool in the business for this.

On a final note - Merry Christmas to those of you who celebrate it!  This will be our last post until after the holidays.  Peace and blessings and thank you for supporting our blog.

Thursday, December 22, 2011

SEO Lessons: Guayaki Yerba Mate

As part of our ongoing technical work at Technoracle, we like to work with business owners to help them understand search engine optimization (commonly referred to as “SEO”) and provide better results for them. An initial step is often to ascertain the current state however this proves problematic. To explore this problem, we will use the case study of Guayaki. Guayaki is a company using a restorative business model to help provide fair wages to everybody in their supply chain and also restore sub tropical South American rain forests. in fact, it is referred to as the the "Mata Atlantica", or Atlantic Rainforest, deemed one of the top 5 priorities for biodiversity conservation in the world by conservation international.   The Atlantic forest has been reduced to 7% of its original size.They sell products based on Yerba Mate, a substance commonly used and shared by civilizations for centuries and a common stimulant drink. The powers of Yerba Mate are very restorative and we have started consuming it while coding instead of coffee. On a personal note, it seems to be better for productivity than straight coffee or Coke/Pepsi.

Since most people curious about Yerba Mate seem to search for the term “Yerba Mate” or “Yerba Matte” (mis-spelled), they desire a good ranking and currently have it in some areas. The first step was to use our Google Adwords account to ascertain the most relevant search terms and search term volume for any given month. Google however, uses localization and profiling as factors in ranking search results. To illustrate this, we asked several of our networked associates and friends to help do a straw poll on the current rankings. From what we understand, most of them had never searched for this term before so the results were probably more accurate than someone who has already logged several searches for the term and has those searches linked to their profile. This blog post is a summary of some of the results we encountered. While not considered scientifically conclusive, these results may be of interest to others.

Google uses geographical location and Guayaki’s head office is in Sebastapol, CA, USA. Most of their business is in North America. The request was simple. We asked random associates to navigate to and search for the term “yerba mate” and note where any hosted page appears in the results. Here is a random sampling of results:

This is only a small sampling but it shows a pattern of higher results in countries where the product is sold.  We tried to search via however google redirects the browsers to  We consider ourselves tainted as google could note that we (Yerba Mate fans) have visited several times and hence elevate it in my results via any personal google home pages.  Nevertheless, we found it on page 2 in 12th spot.  This was shocking considering one of the principals and co-founders is located in British Columbia and does considerable business here.

So what does this mean? 

Simply stated, search results seen by one person are not necessarily universally shared.    The first step in SEO is to get an accurate read of where your site appears before any attempts to optimize.  This, in itself, is a difficult feat.  Google uses cookies, IP addresses and a host of other mechanisms to determine how to provide you with the most relevant search results.  If you commonly search for your own brand, it is possible it may appear higher in search results on your computer than a computer that has never searched for the same term.  Google, Yahoo and Bing have all stated this policy clearly but have not explained what that means exactly in terms of results.

What would we recommend for Guayaki?

Since the first site that appeared most commonly was Wikipedia, we found a way to link Guayaki to the Yerba Mate page in Wikipedia.  This is done is accordance with Wikipedia’s terms of service and standards.  The page itself claimed sources were required for verification so we added a footnote to show verification that Yerba Mate is in fact sold as an iced beverage in a can.  Be careful about this however and do not try to spam others with links to your page.  Some types of links are not even followed such as blogger comments.  We never advocate trying to hack the system.  The system is set up to govern itself via feedback and respecting terms of use is something we encourage all people to do.  

Having said that, there is a potential that this blog article itself may end up elevating their ranking since it contains links to their site but that is not the intent of this post. 

DISCLOSURE:  David K. from Guayaki is a personal friend.  He has not asked us to post this blog to help with SEO.  We are merely helping him as we would with any other client to understand the SEO landscape.  We have not been paid to post this article.

How does this information help you?

Before you start any SEO project, try a similar grass roots poll to understand where you currently rank and in what geographical areas your brand is ranked higher.  Match this information with your goals.  You may find this is a chicken and egg problem as business in one area may be slow due to the fact no one finds your website or brand via a search in Google, Bing or Yahoo.  If you are trying to build business, approach SEO as a regional endeavor.

If you have any follow up questions on this topic, please don’t hesitate to contact duane at Nickull dot net.  We’re always glad to help.  If you want to know more about Guayaki, check out this video.

Tuesday, December 20, 2011

Data Mapping with Inference and Feedback

We've worked with thousands of companies for most of the 1990's and early Web 2.0 era.  Every Medium to large enterprise has typically struggled with data integration projects. Every new acquisition, system or IT project creates a new integration project. To make matters worse, there are no standard crosswalks for data mapping. This problem is not only epidemic, but increasingly neglected by many enterprises. David Luckham hinted at “IT Blindness” when a company makes incredible blunders that are compounded by false beliefs, often generated by a lack of real data or the inability to process events (both simple and complex). David has developed a set of patterns for solving some of these issues (Complex Event Programming or CEP), yet the events themselves still must be minable for data that can then be integrated.

The Problem:

Data mapping has historically been a rather time consuming practice, often done manually. There are multitudes of issues with data mapping, some of which are dependent upon the context in which instance data might appear. To illustrate this point, let us assume that we could create a single data dictionary of all the terms used in business. This approach has been tried many times with various EDI and XML dialects. Defining a simple data element such as one that would denote the first name of a human being should be easy, correct? The definition itself is not the issue, it is the ability to map it automatically when encountered. The logic of context often makes this hard. Take this data element for example:

Element Name: FirstNameOfPerson 

Type: String64

Description: a string value representing the legal first name of a human being.

We could easily serialize this into an XML element as Duane. Now account for the fact that we must map this data format into a second format that has an element and semantics as follows:

Element Name: PersonFirstName 

Type: String

Description: a string value representing the legal first name of a human being.

It might be easy to figure out that in a vacuum this is pretty straight forward. The challenge comes when the aspect of “context” is added. To illustrate this issue, consider the following data structures:




While both use the same data element for the first name of a person, the semantics (or pragmatics rather) are slightly different based on the hierarchy and context. If both of these appear on the input side, they cannot be mapped to any instance of the PersonFirstName (the second example above) without contemplating the special nature each context brings. The meaning is the first name of a person but the two are not equal. One is the first name of the buyer party and the other is the first name of the seller party. Not immediately apparent is that the instance data set is now also bound to a process (procurement in this case).

The approach of manual data mapping has been around for a few decades. Automating this process is extremely difficult. A processor must be able to account for subtle differences in mapping rules based on a number of things. Even with the best schema and metadata support, exceptions and errors are likely to be encountered.

Computational Intelligence (CI) approach caught my eye the other day. We at Technoracle have studied this problem for a number of years. The CI approach combines an inference engine with a graphical user interface. As input data is encountered, the user interface guides users by uggesting optimal mapping scenarios. Unlike more traditional approaches to auto-mapping that require a significant amount of preparatory work, the inference approach semi-automates some of the work.

Disclosure:  we were contacted by an agent for Contivo to write about their system.  No consideration was paid in exchange for this blog post.  Technoracle reviews technology and does not speak for or make claims as a representative of the companies we highlight.

The approach espoused by one company in particular has caught our eye. Liaison’s Contivo ( builds reusable mappings by associating the metadata with a semantic "dictionary". The method uses an analytics model to parse incoming data, then it references that input against a dictionary that captures and stores mapping graphs. The dictionary is portable and can be leveraged by future transformation maps.

Liaison’s Contivo then establishes an integration vocabulary and thesaurus that may be fine tuned by manual methods. Contivo then leverages the vocabulary and thesaurus to automate data transformation and reconciliation tasks that are traditionally implemented using manual mapping techniques.

Figure – a snapshot of the Contivo Mapping

This approach was the basis for the long term product roadmap in XML Global Technologies, a company co-foundered in the dot com era. Their plan was use the mapping graphs built from their GoXML Transform product (now part of Xenos Group) and store these maps into a metadata Registry/Repository organized using a business ontology so they could be accessed by an entire community of users rather than one single enterprise. This approach made a lot of sense back then and makes a lot today. It also mitigates the issues of changing schemata and EDI vocabularies.

The problem has not gone away. There is a lot of great work being done my companies who can automate the mapping of integration data into known system. Using a feedback loop such as Contivo helps a system evolve over time and can facilitate a much more intelligent approach to solve this problem.

A long term architecture Contivo might consider is to use a social approach to learning via a centralized repository of mapping knowledge. Each of the users systems could continuously update and commit to a central knowledge base that uses the global trade dictionaries and various EDI and XML business dialects alongside a feedback circuit to learn the finer nuances of data translation.

We are left wondering if a standard should be developed for declaring reusable mapping graphs and if so, who should develop it. Many open data initiatives would benefit from this as would those who use the open data.