• really poorly most local business use (or don’t use) local search
• really abysmal local results currently are
• really easy it could be to improve results

Really!

So let’s address these issues individually:

Why don’t local businesses participate?

Because they don’t know any better and don’t believe that this applies to them. Whether you are a small business or Fortune 100; chances are you do need local search.

Let’s use a practical example. Assume I decide to cook Pad Thai for dinner and require ingredients. If I query, “Fish Sauce Morrisville, NC 27560” in G-maps, then ideally I will find local retailers who sell it. IMO, an outstanding opportunity for any business that sells what I need. My decision is going to be based on locale, store reputation and potentially price.

And that leads me to issue #2

Why are the results are crap?

Here’s what Google Maps gives me:

All restaurants…and completely irrelevant. It appears to be pulled from Web Search and then ordered by location. This doesn’t help me.

How can they fix it?

A feed. Forget limiting local search to services. Increase it to include products. Stores keep inventory electronically. Do you think for a moment that your local supermarket doesn’t have some idea of how much Ben & Jerry’s Cherry Garcia they have on hand (give or take a pint)? Why couldn’t they list the inventory in an XML or Excel file and submit it to Google the same way we do with shopping engines or Google Base?

Data Fields could include:

• Product name
• Product skew
• Product description (optional)
• Product image (optional)
• Store location(s)
• Store phone number
• Hours of operation
• Product price!!! (hey, there’s a novel concept)

And if you tie that to Google Maps, all of a sudden, you have something useful for both big box and small businesses. Additionally, because Google Maps is tied to location rather than PR relevance, content, metadata, etc. the user gets to decide based on:

• How far they are willing to drive
• How much they are willing to pay
• Their previous experiences with that brick and mortar
• Google Coupons from the manufacturers versus the retailers

Overall, this seems to be a remarkably inelegant, yet simple solution for what I believe to be a very common problem?

Do you agree? What am I missing here?

Well, it is if you’re a music fan.

Musicovery is an interactive web radio station that allows you to choose music based on your mood. Why is it dangerous? Because it’s linked directly to iTunes!

So this morning when I arrived in a dark mood, it seemed perfectly reasonable to buy a copy of O’ Fortuna.

You have to love the simplicity. Since music is so often linked to emotion, it makes perfect sense to tie it to an impulse buy.

Kumar Ramanathan is the CTO of Relona, a developer of Calculus Intent-Based Search Algorithm. Kumar took a few minutes to discuss Relona’s purpose and capabilities:

SEL: How did Relona come about?

KR: By late 2001, during the middle of the dot-com bust, I was convinced that search would continue to be the most important application on the web. So I spent 3 months trying to create a new search-algorithm that would be a significant improvement over existing technologies. The result of that effort is the “Perfect Search” algorithm. I distributed a paper about this algorithm to about 30 leading researchers in information retrieval around the world, and the response was very encouraging. About 20% of them wanted to join our advisory board. Perfect Search is a learning algorithm and eventually, over time, it learns from user feedback and converges towards near-perfect results. But it can work only if installed in a site with the kind of traffic that Yahoo or Google get. It couldn’t be used by a startup.

In 2005, I started work on an algorithm that would be an immediate improvement over existing search-engines, without relying on user-feedback or other social search techniques. This would have to be an algorithmic search which outperformed existing engines. The result of this effort is Relona Calculus.

So Relona now has two search algorithms - one a social learning system, and the other a pure algorithmic refinement system.

SEL: What would you consider to be Relona's primary purpose?

To help users find the information they need with the least effort.

SEL: Relona currently appears to be a "filter" on Yahoo results for the purpose of refinement. Are there applications for the software if MSN, Ask or Yahoo chooses not to integrate?

The Relona Calculus algorithm has wider applications than something like PageRank which really only works on the web. Our algorithm can be used for enterprise search, job-search, and a number of other applications where link-analysis cannot be used. However, the market for which we bring the most value is web-search. An improvement in quality of Yahoo's search-engine can produce a vast increase in its market-cap, in the range of billions of dollars. So this is where we are focused for now.

SEL: You had mentioned that you felt Relona could help MSN, Ask or Yahoo overtake Google. Do you feel that improvements in these long-tail-query SERPs are enough to increase any of these engines' market shares?

The difference in performance between Google/Yahoo/Ask/Microsoft is very small for the most popular queries. Most users will find it difficult to say that any engine does better than the other. But on long-tail queries, the difference is obvious.

A search-engine that performs well on long-tail queries will be perceived in the user-community as the "best". Since there are no switching costs, users tend to migrate to the engine that they consider "best". It is a matter of perception. Winning in the long-tail will help Yahoo/Ask/Microsoft become known as the technical-leader and the change in perception will automatically lead to gains in market-share.

In addition, Yahoo reported that queries are getting longer. So what is “long-tail” now may become mainstream soon. In 1998, the average search-query was 1.2 words long. By 2006, it had grown to 3.3 words.

SEL: How does Google's Universal Search affect Relona's offering?

Google's Universal Search is a way to integrate results from multiple narrow verticals (maps, books ...) into the main SERP. The difficult problem here is figuring out if the results from the vertical are really pertinent to the user's query. I am very excited by this development because Relona's Calculus algorithm does really well in this aspect. Given a query, we can very accurately figure out the relevance of any vertical database. Once we have a score about the relevance of the vertical, figuring out where to insert it within the main SERP is straightforward. So I see Google Universal Search as the validation of an additional opportunity for Relona. We can either help Google do this better, or we can help Google's competitors respond with a better universal search. [Here’s Kumar’s blog post concerning this topic]

SEL: Are there plans to increase Relona's capability to include images, audio or video?

Relona performs well on Audio and Video, but the market is still in its infancy. If you look at the success of YouTube and compare it to the failure of Google-Video, the difference was not in the search-algorithm. YouTube did better simply because it had a larger database of videos to select from - not because its search algorithm was better. We don't plan to enter such markets until it matures to the point where the algorithm can really make a large difference. But there are other markets such as job-search where we might look for partnerships.

SEL: Could you tell me how Relona differs from Ask3D?

Ask3D is somewhat similar to Google’s universal search. Instead of embedding the results from other vertical databases (images, video, encyclopedia) Ask places them on the right-hand-side. But since there are more verticals than space to fit them in on the side-bar, Ask has to decide which ones are more relevant for the query. Relona can help Ask make a more accurate choice of verticals.

Interestingly, Ask3D doesn’t enhance the results in any meaningful way if you enter long-tail (complex) queries. But for shorter queries, the snippets from Wikipedia are really useful.

SEL is proud to offer interviews from Search Leaders to our readers. If your company has something to say, please shoot me a note at jeremy.swiller at thinkpartnerhip dot com.

J.P. sent me a great post this morning describing the intricacies of PageRank. Most of this wasn’t news to us. In fact, it was nice to see more scientific proof to what we’ve said for years. Here are a few things that we learned:

1. PR(A) = (1-d) + d(PR(t1)/C(t1) + … + PR(tn)/C(tn))—It’s the PR formula.
2. With regard to ranking popularity, there is weight given to site age and duration, but this doesn’t transfer to PageRank.
3. Wikipedia links don’t improve PageRank automatically (update: but pages which extract information from Wikipedia might improve PageRank). This puzzled us because Wikipedia uses nofollow. So automatically or otherwise, I don't see a site getting new link juice from Wikipedia anytime soon.
   

Ultimately, I’m still a believer that PageRank is Green Pixie Dust. Screw the green bar! Worry about the context and relevance of your links, not the volume.

And stock in Pfizer and Lilly plummet as their respective sales of Viagra and Cialis are cut in half.

Considered “the worst of the criminal spammers,” Robert Alan Soloway was arrested and indicted for 35 counts of wire fraud, mail fraud, money laundering, identity theft and other charges.

Sadly, I don’t believe this will put a huge dent in the email spam problem for a few reasons:

1. There are plenty of others to take Soloway’s place. Many of our clients are approached repeatedly by these types of organizations. There’s no shortage of people willing to lie about a list’s double opt-in status.
2. It takes too long to chase spammers down. This loser was at the top of the list and it’s taken this long for any meaningful action to be taken. In fact, he owed $17 million from a couple of 2003 lawsuits that he didn’t bother to pay.
3. The FTC only operates in the US. How many spam emails to you receive in Asian dialects? I don't understand them, but my guess is that they are Chinese proverbs that when translated mean something like, “I’m going to spam you until your server crashes. By the way, could I interest you in a low-interest mortgage?”

One spammer down, several to go.