Archive for the ‘Everything’ Category

SEO Job Vacancy – Come work with me and “The Lawyer”!!!!

Posted on June 10th, 2009 • Filed under Everything • 1 Comment

Do you want to work with me? You’re a mad(man/woman)  if you do, but you’ll get to learn from one of “the more experienced and proven” Search guys in the business and will see what enterprise level marketing in the most competitive of industries is all about.

I’m not just looking for the best but for people that are passionate about Search.

You need to have SEO experience, be able to show a proven track record and make a great cuppa!

If  you fit the bill, get in touch with and let’s see what we can work out.

Jason

P.S. “The Lawyer!” is part of the office furniture but the upside is he is bloody good to have in an emergency and his tea isn’t too bad either other than it is rarer than a hen’s tooth :)

 

How to build a Brand Based Search Engine

Posted on May 29th, 2009 • Filed under Everything • No Comments

Are these brands?

Are these brands?

Let’s presume that I was running a search engine and wanted to deliver results that the majority of searchers are happy with.

Hmmm, I  think I would do all of the above. The 1st 3 points are all relatively simple to implement as great people have gone before us and shown us the way to do it but how on earth do you measure “brand”?

It’s a soft term, something that is hard to define, yet, as a business, you know it when you have it and aspire it when you don’t. As a consumer you are aware of it when a product has it and quite often shy away from it if it is missing.

When You search for Cola what would you expect to see?

When you search for cars which would be a “better result” for you ?

When you seek information on spectacles what would you like to see?

Maybe “brand” is the wrong term to use, maybe we should be better by defining it as…

“a product or company that is already within our psyche?”

The one thing that Coke, Rola Cola, Aston Martin, Trabant, Rayban and Dame Edna all have in common with each other is that you mention their name and I am already aware of them, I have a mental image and I have pre determined views about them.

But then I have to work out what is the difference between say Rola Cola and Coke? I don’t know about you but I like Coke and could never stand Rola Cola when it was sold over here in the UK yet I am sure there are many people who prefer Rola Cola’s flavour over Coke. Either way you had to become aware of the product, it had to become ingrained in your psyche, it had to become a “brand” for you to make that 1st purchase.

Let’s look a little deeper, let’s go back in time and ask how these companies’ built their position into our psyches.

In essence everywhere you looked, everywhere you went, whatever you viewed on whatever screen you saw them, and slowly over time they worked their way into your pysche.

But how do you measure those offline unconnected media and incorporate the data to work WITH online links and content as a measure of “brand” ?

The first issue would be to ascertain how many people get to see the messages within the respective media. The second issue would be to recognise what messages and by whom are being pushed forward by the marketers that place the advertising.

Let’s break them down by general category

So now we know who gets to see marketing messages we simply don’t know what messages are out there.

OCR is your friend and so are subtitles (closed captioning) within TV programmes.

TV and Cinema is nothing but a stream of images and sound. Subtitling makes life even easier as raw text data is present and accessible. Print media is nothing but images, and optical character recognition is pretty damn good.

Let me ask you, isn’t it possible to run your theoretically infinite and scaleable resources into running a server farm to process all these images and closed captions looking for mentions of web properties ?

Whern THE becomes WTF

When THE becomes WTF

I admit that OCR isn’t perfect, you sometimes get false positives (WTF or THE ?) but overall it is pretty damn good and more importantly I believe it is more than “good enough” – Perfection may be sought but good enough is exactly that, it delivers a solution to a problem that is accurate enough to deliver acceptable data for our goals. It is “Good Enough”

So let’s recap, in theory ……

But how do we incorporate that raw data so that it can deliver a Brand or Inner Psyche based search engine?

Let’s work the simple angle 1st.

Viewers * Brand Mentions = Psyche Score

Quite simply we could combine the onpage analysis engines to ascertain the theme of a page & site then rank them according to the Pysche Score. In this manner Coke would rank above Rola Cola and Dame Edna would always come below RayBan sunglasses.

The next challenge would be to integrate this with link based algorithms. We would want to do this as we know they work. Yup, they’ve been spammed to buggery and back but overall, spam aside, they work and they work extremely well.

Let’s presume that we have lots and lots and lots and lots of user data. We could gather this via advertising programmes, toolbar useage, clickstream data based on analytics or any number of numerous other data collection methods including running an existing and large search engine. In theory this would allow us to understand and see if there is a correlation in user activity and Pysche mining (Brand) related advertising and touchpoint opportunities (I had to get one bullshit bingo phrase in this post!)

If there was a correlation we would know the power of offline media in comparison to clickthroughs from search, specific search phrases, time on page and many other metrics.

Couldn’t we then weight the power of the offline media accordingly?

Shirley Crabtree - The Big Daddy !

Shirley Crabtree - The Big Daddy !

Do you think the following might be worth testing?

Viewers * Brand Mentions = Psyche Score

Ranking Score = Link Score ^ (Psyche Score * Industry Related Offline Media Weighting)

I believe that this will ensure that larger businesses, essentially BRANDS that have succesfully made it into our inner psyche to rank extremely well and reduce the opportunity for small businesses who operate and market almost exclusively online to compete with the “big daddies” of their industry.

It would ensure that, as long as there was some relevant content on page, any Big Daddy domain name would be able to rank for almost anything. It would also reduce the amount of visible spam as most Big Daddy domain names don’t directly spam. The link spam and content spam would still be there it would simply be invisible to the searcher as so few click past the 1st or 2nd page of results.

So let’s recap over what would be required to implement this…..

Hmmmm, who do you know that might have that?

 

The Guardian Newspaper (and me) on Comment Spam

Posted on May 28th, 2009 • Filed under Everything • 2 Comments

I am pleased to say that in today’s (Thursday 28th May 2009) Guardian Newspaper (print and online editions) I am interviewed regarding a comment spam run by a firm of solicitors.

It highlights the dangers of working with a search marketing company that does not FULLY explain risks – It’s an interesting read and great work by Michael Pollit, the journo who wrote the piece. Check it out at the Guardian website where you will find Are Comment Links Just a Form of Spam

 

Anthropological Search Algorithm

Posted on May 15th, 2009 • Filed under Everything • No Comments

In December 2004 I wrote a paper highlighting how to understand differing human groups. We are nearly 5 years on yet I believe, more now than ever it requires revisiting.
It’s called Widget Chav :)

Widget Chav
Anthropologic Analysis Based on Neck Colour
Wayne & Waynetta Slob
Council Housed, Tower Hamlets London, UK
(Current Address: Private Rented Accomodation, Harold Hill, Essex, UK)
chavs@majorsearchengine.com
Consultant Anthropologist – Dr Onslo Lardarse PHD

Abstract:
In response to a query a search engine returns a ranked list of documents. If the query is broad (i.e., it matches many documents) then the returned list is usually too long to view fully. Studies show that users usually look at only the top 10 to 20 results. In this paper, we propose a novel ranking scheme for users. By identifying the social demographics that a user exists within, a website will be able to tailor the results to those that statistically the user will most want to see, delivering a rewarding experience for both the user and the website operator.

1 Introduction
Hence the ranking tends to be poor and search services have turned to other sources of information besides content to rank results. We next describe some of these ranking strategies, followed by our new approach to authoritative ranking – which we call Widget Chav.
1.1 Related Work
Three approaches to improve the authoritativeness of ranked results have been taken in the past:
Ranking Based on Human Classification: Human editors have been used by companies such as Boo hoo! and Mining for Coal Company to manually associate a set of peoples and individuals with a subset of humanity in the world. These are then matched against the user’s query and visual “gut feeling”  to return valid matches. The trouble with this approach is that: (a) it is slow and can only be applied to a small number of people, and (b) often the classes and classifications assigned by the human judges are inadequate or incomplete. Given the rate at which thehuman race is growing and the wide variation in international usergroups this is not a comprehensive solution.
Ranking Based on Usage Information: Some services such as Almost Hit collect information on: (a) the surfing individual users undertake within search services and (b) the pages they look at subsequently and the time spent on each page. This information is used to return pages that most users visit after deploying the given query. For this technique to succeed a large amount of data needs to be collected for each query. Thus, the potential set of queries on which this technique applies is small. Also, this technique is open to spamming.
Ranking Based on Connectivity: This approach involves analysing the links between people on the assumption that: (a) people in the same demographics are linked to each other, and (b) authoritative people tend to know to other authoritative people.
PeopleRank [Foliate et al 98] is an algorithm to rank people based on assumption b. It computes a query-independent authority score for every person online and uses this score to rank the result set. Since PeopleRank is query-independent it cannot by itself distinguish between people that are authoritative in general and people that are authoritative in the target demographic. In particular a group of people that are authoritative in general may contain a person that matches a certain demographic but is not an authority on the topic of the chavness. In particular, such a person may not be considered valuable within the community of users who breed people within the  ghetto of the userbase.
An alternative to PeopleRank is People Distillation [Also known as Ethnic cleansing, Hitler 1939-1945, Idi Amin 1925 –2003, et al]. People distillation first computes a query specific subgraph of the race. This is done by including people on the query demographic in the graph and ignoring people not in the demograhic. Then the algorithm computes a score for every person in the subgraph based on known connectivity: every person is given an authority score. This score is computed by summing the weights of all incoming connections to the person. For each such reference, its weight is computed by evaluating how good a source of relationships the referring person is. Unlike PeopleRank, People Distillation is only applicable to broad demographics, since it requires the presence of a community of people in the group.
A problem with People Distillation is that computing the subgraph of the race which is on the query group is hard to do in real-time. In the ideal case every person in the race that deals with the query group would need to be considered. In practice an approximation is used. A preliminary ranking for the query is done with group analysis. The top ranked result people for the query are selected. This creates a selected set. Then, some of the people within one or two links from the selected set are also added to the selected set if they are on the query topic. This approach can fail because it is dependent on the comprehensiveness of the selected set for success. A highly relevant and authoritative person may be omitted from the ranking by this scheme if it either did not appear in the initial selected set, or some of the people pointing to it were not added to the selected set. A “focused crawling” procedure to crawl the entire race to find the complete subgraph on the query’s topic has been proposed [Shak et al 99] but this is too slow for online searching. Also, the overhead in computing the full subgraph for the query is not warranted since users only care about the top ranked results.
1.2 Widget Chav Algorithm Overview
Our approach is based on the same assumptions as the other connectivity algorithms, namely that the number and quality of the sources related to a person are a good measure of the person’s quality. The key difference consists in the fact that we are only considering “expert” sources – criteria that have been analysed as having specific purpose of being used collectively by those with a tinge of red in their necks. In response to a new user visit, we first compute a list of the most relevant experts for the potential person. Then, we identify relevant links within the selected set of experts, and follow them to identify neck colour. The targets are then ranked according to the number and relevance of non-affiliated experts that point to them. Thus, the score of a target person reflects the collective opinion of the best independent experts on the query topic. When such a pool of experts is not available, Widget Chav provides no results. Thus, Widget Chav is tuned for result accuracy and not query coverage.
Our algorithm consists of multiple broad phases:
(i) Expert Lookup
We define an expert person as a page that is about a certain topic and has links to many non-affiliated people on that topic. Two people are non-affiliated conceptually if they are from non-affiliated organizations. In a pre-processing step, a subset of the people crawled by a search engine are identified as experts. In our experiment we classified 2.5 million of the 140 million or so pages in “AstaLaVista Baby’s” index to be experts. The pages in this subset are indexed in a special inverted index.
Given an input query, a lookup is done on the expert-index to find and rank matching expert people. This phase computes the best expert people on the query topic as well as associated match information.
(ii) Target Ranking
We believe a person is an authority on the query group if and only if some of the best experts on the query group point to it. Of course in practice some expert people may be experts on a broader or related demographic. If so, only a subset of the relations on the expert person may be relevant. In such cases the links being considered have to be carefully chosen to ensure that their qualifying relationship matches the query. By combining relevant out-links from many experts on the query topic we can find the pages that are most highly regarded by the community of people related to the query topic. This is the basis of the high relevance that our algorithm delivers.
Given the top ranked matching expert-people and associated match information, we select a subset of the links within the expert peoples. Specifically, we select links that we know to have all the query social groups associated with them. This implies that the link matches the query. With further connectivity analysis on the selected relationships we identify a subset of their targets as the top-ranked people on the query topic. The targets we identify are those that are linked to by at least two non-affiliated expert persons on the topic. The targets are ranked by a ranking score which is computed by combining the scores of the experts showing a relationship to the target.
1.3 Roadmap
The rest of the paper is organized as follows: Section 2 describes the selection and indexing of experts; Section 3 provides a detailed description of the ranking scheme used in query processing; Section 4 presents a user-based evaluation of our prototype implementation; and Section 5 concludes the paper.
2 Expert People
Broad subjects are well represented in life and as such are also likely to have numerous human-generated lists of resources. There is value for the individual or organization that creates resource lists on specific groups since this boosts their popularity and influence within the community interested in the topic. The authors of these lists thus have an incentive to make their lists as comprehensive and up to date as possible. We regard these links as recommendations, and the pages that contain them, as experts. The problem is, how can we distinguish an expert from other types of people? In other words what makes a person an expert? We felt than an expert person needs to be objective and diverse: that is, its recommendations should be unbiased and point to numerous non-affiliated people on the subject. Therefore, in order to find the experts, we needed to detect when two people belong to the same or related organizations.
2.1 Detecting Location Affiliation
We define two people as affiliated if one or both of the following is true:
•    They share the same first 5 octets of their longitude and latitude coordinates .
•    The road’s non-generic token in the address is the same.
We consider tokens to be substrings of the address delimited by “.”  (period) or “,” (comma). A suffix of the address is considered generic if it is a sequence of tokens that occur in a large number of distinct hosts. E.g., “Dagenham” and “Louisiana” are names that occur in a large number of our sample set for Chav detection and are hence generic suffixes. Given two locations, if the generic suffix in each case is removed and the subsequent right-most token is the same, we consider them to be affiliated.
E.g., in comparing “22 Acacia Avenue, Dagenham” and “76 Acacia Avenue, Texas” we ignore the generic suffixes “22″ and “76″ respectively. The resulting leftmost token is “Acacia Avenue”, which is the same in both cases. Hence they are considered to be affiliated. Optionally, we could require the generic suffix to be the same in both cases.
The affiliation relation is transitive: if A and B are affiliated and B and C are affiliated then we take A and C to be affiliated even if there is no direct evidence of the fact. In practice some non-affiliated locations may be classified as affiliated, but that is acceptable since this relation is intended to be conservative.
In a preprocessing step we construct a location-affiliation lookup. Using a union-find algorithm we group locations, that either share the same rightmost non-generic suffix or have an IP address in common, into sets. Every set is given a unique identifier (e.g., the location with the lexicographically lowest name). The location-affiliation lookup maps every location to its set identifier or to itself (when there is no set). This is used to compare locations. If the lookup maps two locations to the same value then they are affiliated; otherwise they are non-affiliated.
2.2 Selecting the Experts
In this step we process a search engine’s database of people (we used AstaLaVista Baby’s crawl from April 1999) and select a subset of people which we consider to be good sources of relations on specific demographics. In this instance we have aimed to identify CHAV experts for the following reasons.

CHAVs as a social group are the international holy grail of internet marketing.

Consider the following CHAV stereotype we shall call Sharon.

A fat, ugly, smelly, single, unloved, unintelligent, indebted, gambler woman.

Sharon, would wish to be marketed to by those that sell (as a minimum)

•    Diet pharmacueticals
•    Personal Hygene pharmacueticals
•    Dating Services
•    Marital Aids
•    Soft to Medium Pornography
•    Sexual pharmacueticals
•    Educational material for internet marketing (“How to make a £million in your nightgown” type products)
•    Unsecured Loans
•    Consolidation Loans
•    Debt Management Services
•    Online Casino services
•    Online Bookmaker services

For these reasons and the financial benefits that advertisers would gain from targetting Sharon, we chose the CHAV social group as our target and tailored the final algorithm to include data that is available to our sponsors, which may not be available to other competing search engines.

This is done as follows:
Considering all people with out-degree greater than a threshold, k (e.g., k=5) we test to see if these person point to k distinct non-affiliated locations. Every such person is considered an expert page.
If a broad classification (such as Casuals, Snobs, Drunks etc.) is known for every page in the search engine database then we can additionally require that most of the k non-affiliated persons discovered in the previous step point to people that share the same broad classification. This allows us to distinguish between random collections of links and resourceful, well connected people. Other properties of the person such as level in education can be used as well.
2.3 Indexing the Experts
To locate expert people that match user groups we create an inverted index to map groups to experts in which they occur. In doing so we only index people contained within “key sterotypes” of the expert. A key stereotype is a piece of text that qualifies one or more people in the group. Every key stereotype has a scope within the group format. People located within the scope of a group are said to be “qualified” by it. For example, the Burberry, Piercings, decrepid cars and location score within the expert group are considered key stereotypes.
The inverted index is organized as a list of match positions within experts. Each match position corresponds to an occurrence of a certain stereotype within a key topic of a certain expert group. All match positions for a given expert occur in sequence for a given type. At every match position we also store:
1.    An identifier to identify the type uniquely within the document
2.    A code to denote the kind of type it is
3.    The offset of the word within the type.
In addition, for every expert we maintain the list of people within it (as indexes into a global list of race) and for each person we maintain the identifiers of the key types that qualify it.
To avoid giving long key types an advantage, the number of keywords within any key type is limited (e.g., to 32).
3 Query Processing
In response to a user query, we first determine a list of N experts that are the most relevant for that query. E.g. N = 200 in our experiment. Then, we rank results by selectively following the relevant links from these experts and assigning an authority score to each such page. In this section we describe how the expert and authority scores are computed.
3.1 Computing the Expert Score
For an expert to be useful in response to a query, the minimum requirement is that there is at least one person which contains all the query types in the key phrases that qualify it. A fast approximation is to require all query types to occur in the group. Furthermore, we assign to each candidate expert a score reflecting the number and importance of the key types that contain the query, as well as the degree to which these match the query.
Thus, we compute the score of an expert as as a 3-tuple of the form (S0, S1, S2). Let k be the number of terms in the input query, q. The component Si of the score is computed by considering only key types that contain precisely k – i of the query terms. E.g., S0 is the score computed from phrases containing all the query terms.
Si = SUM{key phrases p with k – i query terms} KnockedLevelScore(p) * FullnessPlumpFactor(p, q)
KnockedLevelScore(p) is a score assigned to the phrase by virtue of the type of phrase it is.
FullnessPLumpFactor(p, q) is a measure of the number of terms in p covered by the terms in q. Let plen be the length of p. Let m be the number of terms in p which are not in q (i.e., surplus terms in the phrase). Then, FullnessPlumpFactor(p, q) is computed as follows:
•    If m <= 2, FullnessPlumpFactor(p, q) = 1
•    If m > 2, FullnessPlumpFactor(p, q) = 1 – (m – 2) / plen
Our goal is to prefer experts that match all of the query types over experts that match all but one of the keywords, and so on. Hence we rank experts first by S0. We break ties by S1 and further ties by S2. The score of each expert is converted to a scalar by the weighted summation of the three components:
Expert_Score = 232 * S0 + 216 * S1 + S2.

3.2 Computing the Target Score
We consider the top N experts by the ranking from the previous step (e.g., the top 200) and examine the pages they point to. These are called targets. It is from this set of targets that we select top ranked perople. For a target to be considered it must be pointed to by at least 2 experts in locations that are mutually non-affiliated and are not affiliated to the target. For all targets that qualify we compute a target score reflecting both the number and relevance of the experts pointing to it and the relevance of the phrases qualifying the links.
The target score T is computed in three steps:
•    For every expert E that points to target T we draw a directed edge (E,T). anchor text qualifies the edge corresponding to the hyperlink.

For each query  w, let occ(w, T) be the number of distinct key phrases in E that contain w and qualify the edge (E,T). We define an “edge score” for the edge (E,T) represented by Edge_Score(E,T), which is computed thus:
•    If occ(w, T) is 0 for any query keyword then the Edge_Score(E,T) = 0.
•    Otherwise, Edge_Score(E,T) = Expert_Score(E) * Sum{query keywords w} occ(w, T)

2.    We next check for affiliations between experts that point to the same target. If two affiliated experts have edges to the same target T, we then discard one of the two edges. Specifically, we discard the edge which has the lower Edge_Score of the two.
3.    To compute the Target_Score of a target we sum the Edge_Scores of all edges incident on it.
The list of targets is ranked by Target_Score. Optionally, this list can be filtered by testing if the query keywords are present in the targets. Optionally, we can match the query keywords against each target to compute a Match_Score using content analysis, and combine the Target_Score with the Match_Score before ranking the targets.

4 Evaluation
In order to evaluate our prototype search engine, we conducted two user studies aiming to estimate the recall and precision. Both experiments also involved three other search engines, namely AstaLaVistaBaby, AlmostHit and Googly, for comparison and were done in August 2005. Note that the current rankings by these engines may differ.
4.1 Locating Specific Popular Demographics
For the first experiment we asked seven volunteers to suggest the demographics of workers of ten organisations of their choice (companies, universities, stores, etc.). Some of the queries are reproduced in the table below:

Alpha Phi Omega    Best Buy    Digital    Disneyland
http://www.dollarbankaccount.com/
Grouplens    Fords    Keebler
Mountain View Public Library    Macy’s    Minneapolis City Pages    Moscow Aviation Institute
MENSA    OCDE    ONU    Pittsburg Steelers
Pizza Hut    McDonalds    SONY    Safeway
Barking Shopping Center    Trek Bicycle    USTA    Vanguard Investments
The same query was sent to all four search engines. We assume that there is exactly one over riding worker demographic in each case. Every time the demographic was found within the first ten results, its rank was recorded. Figure 2 summarizes the average recall for the ranks 1 to 10 for each of the four engines: our engine Widget Chav (WC), Googly (GG), AstaLaVistaBaby (AV), and AlmostHit (DH). Average recall at rank k for this experiment is the probability of finding the desired worker demographic within the first k results.

Figure 2. Average Recall vs. Rank

Our engine performed well on these queries. Thus, for about 87% of the queries, WidgetChav returned the desired page as the first result, comparable with Googly at 80% of the queries, while AlmostHit and AltaLaVistaBaby succeeded at rank 1 only in 43% and 20% of the cases, respectively. As we look at more results, the average recall increases to 100% for Googly, 97% for WidgetChav, 83% for AlmostHit, and 30% for AltaLaVistaBaby.
4.2 Gathering Relevant People
In order to estimate WidgetChav’s ability to generate a good first page of results for broad queries, we asked our volunteers to think of broad topics (i.e., topics for which it is likely that many good groups exist) and formulate queries. We collected 25 such queries, listed below:

Aerosmith    Amsterdam    backgrounds    chess    dictionary
fashion    freeware    FTP search    Godzilla    Grand Theft Auto
greeting cards    Jennifer Love Hewitt    Las Vegas    Louvre    Madonna
MEDLINE    MIDI    newspapers    Paris    people search
real audio    software    Starr report    tennis    UFO
We then used a script to spam each query to all four search engines and collect the top 10 results from each engine, recording for each result the demographic group, the rank, and the engine that found it. We needed to determine which of the results were relevant in an unbiased manner. For each query we generated the list of unique groups in the union of the results from all engines. This list was then presented to a judge in a random order, without any information about the ranks of page or their originating engine. The judge rated each page for relevance to the given query on a binary scale (1 = “good page on the topic”, 0 = “not relevant or not found”). Then, another script combined these ratings with the information about provenance and rank and computed the average precision at rank k (for k = 1, 5, and 10). The results are summarized in Figure 3.

Figure 3. Average Precision at Rank k
These results indicate that for broad subjects our engine returns a large percentage of highly relevant pages among the ten best ranked pages, comparable with Googly and AlmostHit, and better than AltaLaVistaBaby. At rank 1 both WidgetChav and AlmostHit have an average precision of 0.92. Average precision at 10 for WidgetChav was 0.77, roughly equal to the best search engine, namely Googly, with a precision of 0.79 at rank 10.
5 Conclusions
We described a new ranking algorithm for broad queries called WidgetChav and the implementation of a search engine based on it. Given a broad query WidgetChav generates a list of target groups which are likely to be very authoritative on the topic of the query. This is by virtue of the fact that they are highly valued by people in thetarget demographic which address the topic of the query. In computing the usefulness of a target person from the hyperlinks pointing to it, we only consider links originating from persons that seem to be experts. Experts in our definition are from links pointing to many non-affiliated individuals. This is an indication that these people were created for the purpose of recreational procreation, and hence we regard their opinion as valuable. Additionally, in computing the level of relevance, we require a match between the query and the type the expert  which qualifies the link being considered. This ensures that links being considered are on the query topic. For further accuracy, we require that at least 2 non-affiliated experts point to the person  with relevant qualifying stereotypes describing their linkage. The result of the steps described above is to generate a listing of people that are highly relevant to the user’s query and of high quality.
WidgetChav most resembles the connectivity techniques, PeopleRank and People Distillation. Unlike PeopleRank our technique is a dynamic one and considers connectivity in a graph specifically about the query group. Hence, it can evaluate relevance of content from the point of view of the community of authors interested in the demographic. Unlike People Distillation we enumerate and consider all good experts on the subject and correspondingly all good target peopleon the subject. In order to find the most relevant experts we use a custom stereotype-based approach, focusing only on the groups that best captures the domain of expertise. Then, in following links, we boost the score of those targets whose qualifying information best matches the query. Thus, by combining group and connectivity analysis, we are both more comprehensive and more precise. An important property is that unlike People Distillation approaches, we can prove that if a person does not appear in our output it lacks the connectivity support to justify its inclusion. Thus we are less prone to omit good pages on the topic, which is a problem with People Distillation systems. Also, since we use an index optimized to finding experts, our implementation uses less data than People Distillation and is therefore faster.

In a blind evaluation we found that WidgetChav delivers a high level of relevance given broad queries, and performs comparably to the best of the commercial search engines tested.

We have further added to the expert philosophy by including the following criteria utilising specific data that is available to Googly due to acquisitions or product launches.

•    The Googly toolbar tracks where people go, where they visit and how they got there along with all transactions online.
•    The Googly Desktop Search knows what files you have on your computer and enables full integration into the power of the Operating System.
•    The Googly Photo sharing application knows what you, your family and friends look like due to standard and systematic naming conventions by people
•    The Googly Instant Messenger (not currently used as a Googly product but likely to be launched) knows whom you speak to online and how you speak. An example being Slang used and method of placing words in certain order. A Markov chain can be delivered from this data
•    The Googly sattelite picture database understands what your house and home look like, along with
•    The Googly email service knows whom you converse with and the relations in those conversations
Our goal, for this test and for the reasons stated previously was to extend upon the WidgetChav search engines and define a Widget Chav Algorithm. The following variables are gathered from Googly data and combine to deliver a method of giving an accurate scoring of our target demographic

•    A= Burberry Score, gathered through WebCam capture and photo data in Photo software and harddrive. N.B. Fake Burberry raises a higher Burberry score than real Burberry but both add to the overall Burberry Score.
•    B = Location Score, based on Address longtitude and latitude coordinates. Likelyhood of being in a CHAV neighbourhood
•    C= Piercing Score. Certain piercings are more worthy of increasing CHAV Score whereas others reduce it. Multiple face piercings are the sign of a weirdo whereas a belly piercing is an indication of increased chavness
•    D= Technology score. A balance between other factors means that having high technology awareness and owning multiple technical items does not mean that someone is a CHAV. It is only when coupled with other criteria that a CHAV would be identified. It is more important to realise that CHAVs will own every gadget possible (especially the male) as this is why they are in debt
•    E= Smoker score. Pipe or cigar smokers means the person is not a chav whereas cigarette smoking may increase the likelyhood
•    F= Mobile SMS thumb related RSI increases chav score
•    G= Ringtone for mobile phone, chart position
•    H= Describes car in N characters. A non CHAV will describe their car as, “A 2005 Ford Mondeo” whereas a CHAV (Especially the male) will extend upon this. “A 1999 Ford Escort RS Turbo, lowered suspension, extra spoiler, as seen in Max Power.,……..”
•    J= People called Uncle in address book (self explanatory)
•    K= Collective age of non running cars in front and rear gardens multipled by number of vehicles

Using this Googly specific data we can define the likelyhood of being a chav by the following Algorithm, Chav Score

100 – G+ ( B3 C2 ) A + K
E – D

6 References
Lots of boring stuff that I used to put this document together, although most of it came to me whilst analysing and reanalysing Hilltop related data over the last 12 – 18 months.

I suggest you check out

http://en.wikipedia.org/wiki/Chav
http://www.chavscum.co.uk
and http://www.chavscum.com

which are all excellent CHAV resources.

But seriously for a moment. This is a PARODY and data as well as algorithms are probably incorrect if not plain wrong!!

Although I believe it technically possible for a Google to deliver an anthropologic search engine, which can define groups of people I feel this is unlikely.

This spoof is based upon the Hilltop white paper by Khrishna Bharat and designed to show that when you adapt his original white paper and change the relationships from links in web sites to links within groups of people how sinister and dangerous it looks. Under this algorithm almost everyone will be a CHAV

I understand why Google brought the Hilltop algorithm into place and my thoughts can be clearly read at

http://www.logicdiary.com/2004/03/hilltop-in-plain-english.html and

http://www.logicdiary.com/2004/03/website-families-and-their-death-in.html

but I do agree with many people’s concerns that Hilltop is an over extension of Big Brother online. Whether or not G set out to become the definitive resource online they have become so. With this comes responsibility to not inflict undue strain or hardship on the sites they work with to deliver the overall rankings, the SERPs.

It could be said that Google have changed the face of working online for the so called, Mom and Pop stores.

I don’t know the answer I only know the questions and in the meantime I shall continue to try to understand algorithms in a better manner.

Merry Christmas
Jason Duke

The original Widget Chav paper has been archived here. So what do you think, now that we are nearly 5 years on ?

 

Why Jay Deiboldt is a Donkey & GoogleSlapper is a sham!

Posted on May 7th, 2009 • Filed under Everything • No Comments

Jay Deboilt the Google Slapper Donkey!

Is Jay Deboilt a Google Slapping Donkey!

Imagine that you are happily spending the evening with your family, the kids are getting ready for bed and you’re looking forward to a nice dinner once the little ones are in the land of nod. Then, out of the blue, a shrill ring pierces the air – it’s your business phone, the number of which very few people have as it is rarely used and is especially for business emergencies.

You jump up, passing your daughter, with her heavy eyes and bottle of warm bed time milk, to your wife so you can grab the phone expecting al hell to have broken loose.

What follows is an almost verbatim transcript of the conversation that followed:

Me: Hello.

Person : Is that Jason Dyke

I must say that being called a Dyke isn’t the best way to enamour me – I much prefer my real surname of Duke over Dyke every day but let me go on….

Me: Who is this?

Person: I am calling from Jay Deboilt’s office and you are having difficulties making money

I was pissed off with a call starting like this let alone saying I had difficulties making money. I’ll be straight here I could always do with more money but as to difficulties earning it all I can say is my personal tax bill in the 2008/2009 tax year was 6 figures. That’s £ GBP  not $ USD and most of that was paid when £1 was worth $2! Also remember this was TAX, not income and all of that was made due to me taking a website to the very top, around the world for a few single word phrases.

What phrases Jason? I hear you ask – well how does the phrase “poker” grab you? It’s safe to say I have probably “slapped” Google harder than most!

Me: Who is this, where did you get this number from?

Person: You are having trouble making money offline..

Me Interjecting: Please stop talking and tell me where you got this number from

Person: I am from Jay Deboilt’s office and I got your details from our file…. You are having trouble making money..

Me: Woahhhh there. What file?

I was about to remind this “person” that under European Data Protection Laws that any personally identifiable information that is kept about an individual that registration with the Information Commissioner’s Office, a UK government department is a required. I live in the UK, am a UK citizen, was born and bred here and I don’t care if someone who has my personally identifiable information, without my consent is calling from the US.

Person: The file we have on you that says, You are having trouble making money

Me: Stop talking right now and listen to me for a moment

Person: I don’t like your tone.

Me: You bloody called me and YOU’RE complaining to me?

Person: You need to buy GoogleSlapper

Me: (I literally Laugh out Loud)

Person: Are you being rude to me?

Me: Who are you, what is your name?

Person: Hangs up…..

Now I have no idea if Google Slapper is any good or not (I guess not) nor if Jay Deboilt is truly a donkey but  I do know a few things about Jay Deboilt, his business organisation and his business’ processes.

And most of all I know that jay Deboilt doesn’t understand much about Google, despite selling a product that purports to dominate and slap Google’s search engine. I say that Jay doesn’t know much about Google as I am relatively confident that if you search for his name he will need some reputation management soon!

UPDATE – Number 1 for Jay Deboilt – http://www.google.com/search?q=jay+deboilt

Yup Jay, you have a reputation management issue!

END UPDATE

Jay, if you read this, you have my number. I suggest you check the time in the UK then if it’s during office hours you give me a call. I will accept your apology!

 

Amsterdam Affiliate Conference

Posted on May 6th, 2009 • Filed under Everything • No Comments

I’ve just got back from the Amsterdam Affiliate Conference (CAP) and am playing catch up but to keep your interest until I do a more detailed post I thought this might make you smile

(Thanks to Becky Naylor for takin the photo)

To Get the Pot of Gold you can either catch a leprechaun or work with a great SEO!

To Get the Pot of Gold you can either catch a leprechaun or work with a great SEO!

 

AVA RS5 – A Gadget for Geeks…

Posted on April 24th, 2009 • Filed under Everything • No Comments

I about to spend £1000 on a computer. A simple little computer, with no screen, no keyboard, no mouse – just a little slot to take DVDs and CDs.

It isn’t REALLY a server although it calls itself one, as it has a pathetic (in server terms) Atom processor. But what is good about it is that it does one job and one job well.

It’s the only machine I have yet to find that allows you to stick a DVD or CD into it and then automatically rip and streams across the network the movie or music. It runs windows home server (Boooooo!!!)  but  I feel confident I can change that to an operating system worth having (Debian!) but what, if any other physical hardware exists that rips dvds and cds like the AVA RS5 with a front mounted slot, multiple hard disk caddys etc ??

 

Urgent Assistance – Are you a graphics dude or dudette?

Posted on April 23rd, 2009 • Filed under Everything • 1 Comment

If you can assist I will be very grateful.

I am many things but a graphic designer I am not. I have put together a PDF for some printing I need done urgently but it is extremely low quality but it does show the general thought process and layout I require.

I would like someone to take that and urgently turn it around so it’s high quality, print ready. It’s a couple of images and loads of text.
Can you help ?
Thanks

Jason

 

How other people mutate your gene pool!

Posted on April 19th, 2009 • Filed under Everything • 2 Comments

I wrote this article about 4 1/2 years ago and have wanted to revisit it to see how things have changed and how the genetic makeup has been mutated in that time. The reality is things HAVE changed and I will update you on how and where but more importantly of all it seems everyone has forgotten the old news and simply ignore DNA markers in their day to day work.

The old article is still a great read in my opinion and worthy of taking the time to read.

Synopsis:

Or how the Search Engines will commit genocide! I believe that the search engines have no option but to commit genocide but before I go on I should explain my understanding of what genocide means: Genocide is the murder of an ethnicity or the extinction of any group sharing a genetic or ancestral affinity

The Article

Searchers and search engines have a symbiotic relationship. Since time (or the web at least) began search engines have tried to deliver relevant search results to their audience of searchers, aiming to build a long term relationship so they’ll keep coming back to search and search again, rather than have their searchers go and have an “affair” with another search engine.

The searchers will occasionally click on an advert and the search engines will earn an income while the searchers get the valuable information they need and want.

Webmasters learnt that getting high rankings in the search engine position pages meant extra, targetted traffic to their shop, store or informational resource allowing them to convert them into money according to their own business model. Many of these webmasters learnt the art of Search Engine Optimisation and over time many of the results pages became skewed with results the search engines and many searchers found irrelevant to their wants and needs.

The search engines had to act. They NEED the searchers, whereas the searchers don’t NEED the one specific search engine and will happily leave their previously monogomous search engine relationship and will dally with all manner of other search engines until they find a new relationship they are happy committing to (for the time being at least)

Then along came Google and the roles were reversed. No longer did the search engine need the searchers as much as the searchers needed Google! The results were so much better than those they received with every other search affair they had previously been involved with. They were relevant, they were clean, they weren’t bombarded with advertising and most of all all their friends and acquantances said “Have an affair with Google as G is happy to partner with you and – I won’t tell if you don’t!”

The SEOers at first weren’t quite sure what to make of Google but in time they worked her out and decided en masse that there is little point in SEOing for the other engines as Google has all the searchers. And then the SEO game really started :)

Over time Google’s results changed from being extremely relevant to becoming more and more affected by the SEOers. It was like the movies where “If you build it they will come” except this wasn’t about baseball stadiums but inward links.

It became clear that the more links you had pointing to your website that the higher up the SERPs you would be and people built links like links had never been built before.

But Google wised up and started employing a new algo to diminish the importance of links for links sake.

Hilltop came to the rescue!!

But I can hear you saying, “What the hell does this have to do with website genocide ?”

Read on, dear reader and I shall explain all :)

Websites, like people have parentage, but unlike people I believe that it takes 4 to tango!

The 4 parents of a website are:

The Domain name
The IP Address
The Whois Information
The Content

These 4 parents come together and all leave a genetic fingerprint on a website and all of them can be analysed and traced back to form a family tree.

I am going to get a little geeky now and apologise in advance for doing so, but hope it is in plain enough English for you to understand

Every website has a domain associated with it and every domain name points to an I.P. address. There are 2 ways that a domain name can point to an I.P. address :

Either using the http 1.0 or http 1.1 protocols.

The main difference is that http 1.0 domain names all have their own IP address. This means there is a direct, 1 to 1 relationship, between domain name and IP address.

Under http 1.1 many web sites may live under 1 single web address. This means there is a many to 1 relationship, between domain name and IP address.

With the massive growth of the web and other services on the internet it was thought IP addresses would become scarse and so because it is so much more frugal with IP addresses http1.1 became the norm in web hosting.

It is more likely than not that your web sites share their IP addresses with many other web sites without you even realising it.

Now onto the other 2 parents :)

Every time a domain name is registered, at least under .com, .net and .org) information is recorded about the domain owner, technical contact and person to bill. This information is publicly available and is stored in a variety of databases the world over.

Finally there is content. The meat and potatos of a website, the very words that make up its being.

As I said above the search engine’s had a problem and some clever guys thought of a way to help them solve it using an algothey thought of called Hilltop. Hilltop is a very vey very clever idea and many people (myself included) believe that it is active right now in the world’s most dominant search engine.

In essence The Hilltop algo extends the previous Google algos by still using links to ascertain the relevancy of a web page and it’s authority on the subject matter it purports to but it does it in a special way.

It looks for genetic traces between web sites and their extended web site families.

Pre Hilltop it was a standard SEO technique was to build lots of web sites and link from one website to another to another to another to another so that link popularity would build and a websites’ ranking in the SERPs would raise. Hilltop said “Woooahhhhhhhhhhh there SEOer. I don’t think this is fair or right for us, our bank account or our searchers” so we are going to look at the genetics of a website linking to another and throw away those that are geneticly similar.

The original Hilltop Algorithm states that:

“Two pages are non-affiliated conceptually if they are authored by authors from non-affiliated organizations.”

It goes on to state that:

“We define two hosts as affiliated if one or both of the following is true:
They share the same first 3 octets of the IP address.
The rightmost non-generic token in the hostname is the same.

and further says:

“The affiliation relation is transitive: if A and B are affiliated and B and C are affiliated then we take A and C to be affiliated even if there is no direct evidence of the fact.”

They also recognise that:

“In practice some non-affiliated hosts may be classified as affiliated, but that is acceptable since this relation is intended to be conservative.”

I can hear the SEOers jumping up and down shouting that all their hard work on link buildinfg will become defunct unless the links come from an page that is an expert on the subject matter AND not genetically related to their own !!

I believe it gets worse than this as Hilltop only says that a website has 2 parents:

An IP address
and a Hostname

I think that for genetic cleansing to work in the SERPs that Hilltop has been adapted to take into account the other 2 parents I spoke of above, the whois information and the content itself.

Why stop at identifying relationships at IP address and hostname when Whois info is freely available and why not check to make sure that the content a website has is unique and not a copy from somewhere else ?

SEOers can still work to get high rankings under Hilltop by looking to build Expert pages that will link to their target site they wish to raise the SERP ranking for but they’ll have to be careful when they build them.

An Expert page will HAVE TO make sure that it does not have any siblings or distant cousins as the target site.

I’ll give you an example:

Website 1

IP namespace:        1.2.3.4
Generic Hostname:     foobar
Whois Info Line 1:    abc
Whois Info Line 2:    def
Whois Info Line 3:    ghi
Whois Info Line 4:    hij
(all the way to)
Whois Info Line N:    xyz
Content:            fingerprint#12345678

Website 2

IP namespace:        1.3.3.6
Generic Hostname:     barfoo
Whois Info Line 1:    abc
Whois Info Line 2:    abc
Whois Info Line 3:    ghi
Whois Info Line 4:    qwe
(all the way to)
Whois Info Line N:    xyz
Content:            fingerprint#rer6567fdg

Website 3

C class IP namespace:    1.3.3.234
Generic Hostname:     barfoofoobar
Whois Info Line 1:    hgfh
Whois Info Line 2:    kiuo76
Whois Info Line 3:    343
Whois Info Line 4:    fggji
(all the way to)
Whois Info Line N:    56hfghg
Content:            fingerprint#656fdsfdsf

Website 1 is a quarter sibling to Website 2 due to Whois Info
and
Website 2 is a quarter sibling to Website 3 due to IP address
which means that
Website 1 and Website 3 are distant cousins!

In practice this means that if, as an SEOer you wished to construct a set of Expert sites for the niche areas you operate in you MUSt do at least the following.

  • Check the relationship between the web hosting company and specific class C addresses against the target SERP gaining web site
  • Check the relationship between the target SERP gaining web site and the expert hostnames
  • Check the relationship between the target SERP gaining web site and the expert whois info
  • Check the relationship between the target SERP gaining website and the expert content

And you MUST do this for all the websites, domain names, content matching or C class of IP address that score a “HIT” for any relationship as well.

Although this is not impossible I forsee many SEOers saying this is too much work and the genetic cleansing of the SERPs will work!

So what do YOU think of the genetic cleansing in the SERPS and are you checking each of your web hosts for IP relationships on an ongoing basis ?

Jason

Jason Duke is the owner and operator of Strange Logic, the business that helps your business which by using scientific analysis of the search engines and wider online marketing world.

Widget Words is the definitive keyword resource to assist in your search engine marketing and pay per performance campaigns and Widget Reports.com the site that delivers industry specific reports for your target area of business.

Jason can be contacted by email jason@strangelogic.com or telephone +44 7595 924 934 to discuss any of your needs or simply just for an informal discussion about the marketplace and his unique point of view.

 

10 Secrets to Become a Ranking Ranker

Posted on April 15th, 2009 • Filed under Everything, SEO • 14 Comments

There is no feeling like being a world class ranker. Searching for your chosen key phrases on all the major search engines and seeing your site on top makes you a world class ranker. I’ll aim to show you what you can do to help yourself and your business on the way to being a dominant ranker.

I once had a call from the editor of .net magazine asking me to take a new site to the top of the search engines. He wanted the whole of the .net crew to retire from the proceeds, but as well as undertaking the SEO work he wanted me to write an article for the magazine explaining the process.

Once I caught my breath from my belly ripping laughter, I said I would be happy to dispel many of the Search Engine Optimisation (SEO) bunkum and add some clarity to a world full of myth and mystery.

This is what I wrote for the magazine readership:

Despite what you may have heard, there are no secrets in SEO. The web is a very open place and if a site is riding high in the search engine listings, then you can, with enough persistence, get to see why it ranks so well. Replicate it and you can join them

Being Number 1 on the search engines won’t make you rich without either spending money or investment via “sweat equity”. A ranking without an effective business model will just deliver targeted traffic and traffic without monetisation is a drain on your wallet. Bandwidth costs you know!

Yahoo, MSN and even little ol’ Ask are great sources of targeted traffic and that is the key thing when it comes to SEO. You want targeted traffic, people pre qualified by their conscious decision to search for a phrase in their preferred search engine. Do you really care which engine they come from?

So let’s begin the task of taking your site from where it is now, to climbing up the listings. I am going to presume that you already have a site and are happy with its design and layout. I also hope that it isn’t too heavy on any flash, graphics or non textual based content as:

Search engines look for words to incorporate in their index. Words are important, VERY important and if you don’t have them in a format that is visible to the search engine then you are reducing your chances of success.

You may have noticed that I said Content is Queen, rather than the often touted phrase that content is King. The reason is simple, in SEO …

Links, links, links and more links. Links are what makes a search engine find your page and then they combine to rank it higher in the search engine results page (SERP) than any other single criteria.

The little blue text that you click on a page, taking you to a new destination is the single most important aspect in helping your page rise above the competition.

The search engines will be looking to show the most relevant page to a searchers’ query. You can utilise this to your benefit by making sure that each page within your site has a clear and concise topic within your niche area of work. Simply take your time and write more content (refer back to secret number 4) that adds value on your topic of business. Every page that you write gives you the chance to rank for more and more searches phrases, because…..

The long tail is a phenomenon that is spoken about in search to explain how people search. Statistics vary from various studies that have been done over time, but most (over 80%) of searches that are undertaken on search engines are specific, targeted phrases of 3 or more words.

When I build a site the very first thing I do is lay some keyword foundations. The keywords are the phrases that our potential visitors will search for and we hope to rank high on the search results page for.

There are many online resources that assist in helping find what people actually search for starting from a seed word or idea.

I have detailed many of them in the research section but the single most important technique I use is to gather all of our team into a room, with a large pack of blank postcards. We all start from a word and keep extrapolating words and phrases, writing each new phrase on a card, until the floor is full with topics and titles.

These words and phrases become the structure for your site, with a page for every topic. Laying these foundations and allowing them to grow according to what you, your team and your visitors’ think is the key to successful opportunities to rank.

Use the automated tools I have mentioned below to assist but please remember that although automated tools are brilliant, nothing is better in understanding the minds of people, than people.

Make sure that you have stats available to analyse the traffic to your web site.

Most hosting packages offer software that allows you to see where your visitors come from, and those that don’t you can easily use third party packages that will record the information. Knowing what real people actually searched for to come to your site can be an eye opener, and using these to expand upon your keyword pool will open up areas for future visitors to find you.

Knowing that you are getting referrals from the search engines gives a nice warm feeling in your belly and confirms that your previous SEO tactics are providing real and tangible visitors.

Because you have followed the advice above and found the keywords, extrapolated upon them to build a list as long as your arm, make sure that you get the keyword in the title tag and you name that page with the keyword in the filename.

Don’t worry about any other “on page” SEO, such as keyword density, meta tags, this trick or that trick as it is ALL so 1999 and we are in 2009! Just make sure that your chosen keyword or phrase is contained within the title and URL and then simply make sure your content is on topic. The search engines are now more than intelligent enough to understand the semantic relationships between words and phrases so trying to assist them with certain keyword densities is an amost fruitless effort. Leave them to their algorithms and simply enjoy the rewards their efforts can deliver to you.

Special Offers help give you a reason for building content and give people a reason to link to you. It may be 15% day on Thursday but even better would be the route of Link Bait, the technique of getting others to link to you due to controversial or other means. Remember links are king!

Why not offer 75% off to everyone who is over 75 and can bring proof of 4 living grandparents, with 10% off to everyone else ?

My personal favourite and best functioning tip is to think old school. Get on the phone and ASK for a link from sites that link to your competitors. If they link to your competitors why wouldn’t they also link to you?

And my special offer is that I promise that you can and will have a site with increased traffic by following the 10 secrets above, but this offer lasts for today only and no guarantees can be made for tomorrow. To give yourself the best chance of knowing what is required tomorrow, you need to undertake research in the industry of search.

Time spent researching what is happening in the Search Optimisation area, along with the important details of what works and what doesn’t is essential to ensure that your energies are best placed.

As well as various print publications I suggest you spend some time acquainting yourself with the following online resources.

SEOMoz Community – http://www.seomoz.org

Where Rand leads you should follow. Watch out for his Friday specials!

Web Master World – http://www.webmasterworld.com

The world’s largest general topic search and Webmaster related forum

SEO Book – http://www.seobook.com

The definitive constantly updated book on SEO there is. It’s available as a PDF rather than hard copy but there is no better product out there.

Search Engine Roundtable – http://www.seroundtable.com

Keyword Research:

Simply use the best there is out there and forget the rest – Google’s Search Keyword Tool – http://www.google.com/sktool/

The most important thing to remember is that simply reading and not implementing means there will be no more growth of traffic, simply growth of intellect. I suggest a balance to ensure your greatest chance of success.

Targetted Traffic beyond SEO

Garnering targeted traffic via search engines is the single largest route you will find visitors, but it is not the only way. Just like an offline business much of your business will come from referrals and word of mouth marketing. The major difference is that online you can go and find where your prospective customers hang out rather than waiting for them to come through your front door.

Social Interaction and bookmarking sites, such as Digg (http://www.digg.com) Twitter (http://www.twitter.com) ) as well as the myriad of others, not forgetting the forums that are related to your industry can deliver amazing amounts of traffic and all of it targeted.

Try searching at your favourite search engine for (forum industry) remembering to replace industry for your own. Before diving in and delivering a sales spiel, I suggest you sign up, read the existing posts and become part of the community. You should look to nurture a relationship and deliver real value and advice and see sales as an added benefit to credibility you are building with a new group of people.

Links within your profile and, when relevant, within your forum posts, don’t do any harm for your SEO campaign either (See Secret Number 5) and combined with your expert knowledge can the SEO that the forum owner does on his site, gives another route to gaining search positions.

If your site is number one, what better number two position could there be than a forum post where you are explaining the virtues of your company and product?

The Dark Arts

Like many traditional marketing routes SEO also has it’s darker techniques. Often called Black Hat SEO, it generally takes the principles of traditional SEO and pushes the moral boundaries to deliver greater results, at a lower cost and in a much riskier manner.

These techniques can include automated content generation, automated link gathering and tricky redirections from one page to another. Make no mistake about it, if you undertake some of these techniques you will annoy a lot of people, whether that be site owners, searchers or the mass populous of the web.

The search engines themselves publicly frown on these techniques and offer guidance as to what they see as acceptable optimisation.

Google – http://www.google.com/support/webmasters/bin/answer.py?answer=35769

Yahoo – http://help.yahoo.com/help/us/ysearch/basics/basics-18.html

MSN – http://search.msn.com/docs/siteowner.aspx?t=SEARCH_WEBMASTER_REF_GuidelinesforOptimizingSite.htm

But that’s not to say that real financial rewards aren’t delivered. The reality is that if you are prepared to risk the brand that you have built up in your domain name for a short term financial win, black hat SEO can deliver tremendous results, but if you are found to be breeching the search engines guidelines you run the risk of that site being permanently banned from their engine.

Much discussion occurs over the latest and greatest cutting edge techniques for black hat SEO with many of the world’s largest search related earners and entrepreneurs participating in the discussions daily.There used to be only one place to go but now everyone and their dog thinks they are a dark art extremist. Search and you shall find the place you feel most comfortable in.

Please be aware this can lead you to an area of optimisation that can be extremely worthwhile but also potentially dangerous to your site and brand. Don’t say you haven’t been warned!

I Bet you Never Knew this about…

Google: Google was meant to be called Googal, the mathematical number of 1 followed by 100 zeros but a typo meant that the Google spelling stuck!

Yahoo: The name Yahoo as originally settled upon was an acronym for “Yet Another Hierarchical Officious Oracle,”

Bill Gates: Bill famously said, “WWW? Nice toy, but what a waste of time.”

Google Founders: According to the Times Rich List, Larry Page and Sergey Brin are the joint 34th richest people in the world. With a worth of £7.3Billion each

Google: Google are the largest aggregators of personal information online. By default they collect information on everything you do on Google’s sites, and potentially every site that you go to until (by default) at least 2038.

Do you want to know the most important of all tip you shall find?

Make sure you read all the way to the bottom …..

… as if you do you’ll find my phone number which you can use to call and get some one on one advice. Contact me as long as you link to this post http://jason.sh/10-secrets-to-become-a-ranking-ranker-2009-04-15 and keep coming back here to my personal shell – so make sure to subscribe to the RSS Feed

Speak with you soon!

Jason

 

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