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July 29, 2007

Three Bloggers Sound Off: What is a Search Engine?

Over the past couple of weeks, I've been participating in a fascinating email discussion about Web Search with two of the leading bloggers in this space: Charles Knight, editor of the popular Alt Search Engines blog (a member of the Read/WriteWeb blog network) and Kaila Colbin of the VortexDNA blog. We quickly realized that a starting point for any discussion about search engines was to first come up with an answer to the question: What is a Search Engine?

What we found was that although we agree broadly, we have different views about the specifics; the overall debate provides a great framework for any discussion about web search. Charles is planning to run our three posts as a series on the Alt Search Engines blog this week:

  1. First, a rant from me on "What is a Search Engine"?
  2. Next, a great piece by Kaila on "What is Not a Search Engine?"
  3. Finally, Charles writes a definitive piece about "What is an Alternative Search Engine?"

I'll put up a link on this blog once the posts are up. Charles and Kaila are amazing bloggers, so it should be well worth checking out!



Survey: The Future of Web Search

In this latest poll on the Software Abstractions blog, we would like to ask readers about the Future of Search .

Specifically, which features do you see as the most important ones for Web Search in the future? Vote now and let us know!

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Update: This survey is now closed. You can view the results of the survey here.


July 26, 2007

Top 8 Reasons to Implement Tracking and Measurement for your Web Site

With the easy availability of full-featured, free online services like Google Analytics, an increasing number of companies are implementing Web Analytics tools for their web sites. Many of these implementations still focus on older metrics like page views and click-throughs. Although conversions and overall traffic levels continue to be important, today's Web Analytics tools - which have come a long way from the "Log Analyzers" of ten years ago - can provide a rich set of Business Intelligence metrics that allow web site managers to drill down to much greater depth in understanding user behavior.

In this article, we identify the core Business Objectives for a Web Analytics implementation.

Web Analytics Objectives

1. Traffic Measurement and Analysis
One of the primary goals of web analytics is to measure traffic quality, volume and engagement (e.g. time spent on site ).
Traffic can be segmented by source, into three categories:

  • Referrals from other web sites and from marketing campaigns
  • Free (organic) traffic from Search Engines, which depends on the quality of content and on SEO tactics
  • Paid (AdWords) traffic from Search Engines

2. Measure and Optimize the Effectiveness of Conversion Paths
This is the usual definition of web analytics; it includes an audit of key navigation paths, calls-to-action, pricing, discounts and the power of the overall sales pitch. You can also measure the effectiveness of individual web pages.

3. Test and Optimize Conversion Enhancement programs
Closely related to the previous item, this goal is all about enhancing revenues from a current buyer, by optimizing upsell and cross-sell strategies. It answers the questions: "How well do you encourage users to buy more? How often do you get repeat buyers?"

4. Measure "Bounce Rates"
This is one of the simplest and most important metrics, yet it is often neglected. This metric focuses on discovering which pages on the site cause users to drop off. This is a critical point of analysis - it can highlight deficiencies in the web site, navigation or content.

5. Gauge the Effectiveness of External Marketing Campaigns
Web site traffic analysis can provide valuable feedback about external Marketing campaigns: by source, medium (email, web page, print, blog feed) and campaign. It can be used to test various forms of advertising.

6. User Segmentation
A site audience is rarely completely homogeneous; it is usually made up of several distinct constituencies. A highly effective way to enhance the user experience is to segment users into groups by behavior and re-orient the site for each group, with special navigation and emphasis.

7. Day-parting
This is segmentation based on external factors not related to the user herself; e.g. time of day, geography, referring source, organic vs paid, and so on.

8. Drive Design and Usability Decisions
Many companies make site design decisions "blindly", without measurement or testing. However, instead of trying to guess what users will like, we can directly measure the effectiveness of various web design and usability decisions, individually and together, using multi-variate analysis and A/B testing. This means that we can improve a web site in a systematic, measurable way, while minimizing any adverse impacts.

Conclusion

As we've seen above, Web Analytics provides powerful capabilities to improve online sales and analyze user behavior. Which of the objectives in this list do you use? Do you see any that are missing from this list?



July 11, 2007

Coming Soon: Software Abstractions Surveys

I'm planning to introduce polls periodically into upcoming posts on this blog. As a test - call it Survey 0 - I've created the multiple-selection survey below.

The results of this survey will be used to provide more content of interest to you, the reader of this blog; so you might as well take 2 seconds and vote!



A future post will comment on the results of this survey.



Dispatches from Searchnomics: Google's Avinash Kaushik on Advanced Web Analytics

[Things have been busy lately; I'm finally catching up on some older posts I've been meaning to get to ...]

I've written before (see: Google Analytics - take two , Bounce Rate ) about Avinash Kaushik - Author, Blogger and Google Evangelist. He's a recognized authority in the relatively new field of Web Analytics; his blog, Occam's Razor, is one of the highest-rated blogs on this topic. In his trademark style, Kaushik gave a highly entertaining and very informative presentation on Advanced Web Analytics at the recent Searchnomics Conference.

In his talk, he focused on Seven Tips that you can use Now! to implement and improve web analytics for your web site.

1. Join PALM! (People Against Lonely Metrics)
Kaushik began with the key point that one should never look at the data or chart for a single metric in isolation; when studying analytics data, one should always look at multiple metrics together [otherwise the metric gets lonely!]. For example, when looking at how many users viewed a single page or how many bounced - you want to look at those together, along with the site average, delta changes and indexed performance. Indexing metrics, especially, highlights the items that are working particularly well or badly. In this way, you can turn the metrics into actionable information.

2. Give your data Context
Associated with the first point, Kaushik suggested that you should look at all your data in context. You can compare any given metric or set of metrics to either the average values for your site as a whole, or to the same metrics during a prior time period. He used Google Trends as an example, showing how you can compare two companies at different points in time.

3. Segmentation Rocks!
He pointed out that a web site audience is rarely completely homogeneous; instead, it is composed of many users who can be broken down into different segments. It is important to partition the data into segments according to the type of user. For example, segments can help you compare adword results vs organic results; if the bounce rate for adwords for a given segment is high, you are effectively losing money!

Most important: web site improvement actions are always implemented for specific segments, never for aggregates.

4. Enter the Matrix: It's a Multi-Dimensional World
Since real-world marketing proceeds along multiple dimensions - not just web site traffic, but also Emails, Ad words and Campaigns - it's important to analyze the data using multiple metrics along all these dimensions, in order to optimize your spending and conversion rates.

5. Think Looong Tail. Everything.
Kaushik explained that all of these Analytics graphs - Clicks, Visitors and Budget Spend - have something in common: a large head and a long tail. Similarly, when looking at Keywords: the expensive, branded keywords are all in the large head, while keywords characterized as generic/category/"early bird" are all in the long tail.

Typically, according to Kaushik, a lot of the AdWords spend goes into the head; but if you have good SEO implemented, you should be highly-ranked in the organic results anyway, so there should be no need to bid on it. On the other hand, long tail keywords, which are usually a lot less expensive, are used by end users very early in their buying process; if you want to focus on new customer acquisition, these are precisely the users you would want to target.

A critical insight: Web Analytics should not just provide reports, it should provide solutions to business problems.

6. Web Analytics 2.0 is Qualitative
The key questions Web Analytics should be trying to answer, for each user segment, are: What is the user here to do? How easy or hard was it for her to do it? And, finally, was the user's problem solved?

The bottom line is to consider the Customer Intent, by looking at a variety of metrics. Looking at Conversion Rate alone will not be sufficient to do that.

7. Web Analytics is Testing! 
Experimentation and Testing is an integral part of Web Analytics; otherwise, decision-making degenerates to using HIPPO - the "Highest-Paid Person's Opinion". E & T is your one powerful weapon against HIPPOs: you take the best ideas everyone has, and run them through tests. Let the data show you which ones are the most effective and which ones are not.

Kaushik pointed out that Google has a Multi-Variate Testing tool called the Google Website Optimizer, which allows you to test multiple ideas at the same time without the need for any IT changes.

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Related Reading
Search meets Web Analytics at Searchnomics Conference  by Jeremiah Owyang, where Jeremiah does an excellent job of covering an equally useful Web Analytics presentation by Eric Peterson of Web Analytics Demystified.



July 07, 2007

Powerset - a new model for venture-funded startups?

In his latest blog post, Steve Newcomb, COO of Powerset, talks about the openness displayed by his company at their recent Powerlabbers meet. At the risk of being boring - radically disagreeing with him would have been so much more interesting - I think that's a fair statement. All of the Powerset people who spoke to the assembled group, as well as those with whom I chatted afterwards, were genuine and open when talking about the technology and the company. There were several direct challenges, thinly disguised as questions, directed at the senior Powerset folks; I did not see Newcomb dodge a single question.

This type of open and direct conversation with bloggers and the tech media is refreshing. So far, Powerset has shown a mastery at managing the blogosphere - both to create buzz (see my earlier post for a comparison of search engine traffic vs buzz ) and to disarm the over-heated hype. Coupled with their Powerlabs community for product development (similar to those pioneered by Dell, Omniture et al), does this represent a new approach to building venture-funded startups in Silicon Valley: the "open kimono" approach compared to the "ultra stealth" approach?

July 05, 2007

Bounce Rate: Rich Web Analytics from a Simple Metric

Avinash Kaushik is at it again. The Web Analytics guru, blogger and now, Google Evangelist, recently wrote a fascinating article over on the Marketing Profs Daily Fix blog. In the article, entitled Bounce Rate: Sexiest Web Metric Ever? , Kaushik examines a fairly simple and uncomplicated measure: Bounce Rate, and shows us how it can be useful in a variety of ways to analyze web site traffic and to improve the conversion rate.

He defines bounce rate and highlights its importance, as follows:

In a nutshell bounce rate measures the percentage of people who come to your website and leave "instantly".
...
Bounce rate measures the quality of traffic you are acquiring, and if it is the right traffic then it helps you hone in on where/how your website is failing your website visitors.

For the time- and attention-challenged, here is a very quick summary of his main points for measuring bounce rate:

  • Find the definition of Bounce Rate for your Web Analytics tool and measure it for your web site as a whole (as a baseline).
  • Measure the bounce rate for your traffic sources.
  • Measure the bounce rate of your PPC campaigns.
  • Measure the bounce rate of your top-trafficked pages.

Interestingly, in one of the comments for the post, he highlights specifically how Google Analytics can be used to track this data.

Overall, Kaushik's article is a comprehensive look at the uses of this type of data. This is especially useful because Bounce Rate is a fairly simple metric that is offered by most WA tools, including Google Analytics (which is free!). If you are interested in Web Analytics, then I recommend checking out the original article at Marketing Profs and the abundant comments on the post.

Further Reading:



July 01, 2007

Looking for Googhoo!

My friend Ashkan Karbasfrooshan recently wrote a fascinating article wondering whether Google should buy Yahoo!. In an email, he wondered about my position on this topic, so I thought I would take a whack at it, speaking as an ordinary investor. [On the other hand, if you're serious about investing, you should ditch my article and read Jeff Matthews' series of long articles about his pilgrimage to Omaha to observe the world's greatest investor - yes, he (Jeff) isn't making it up!]

The Upside

For what it's worth, I think there are good reasons why Google should consider this idea seriously:

1. Stock price: In my opinion, Google is overvalued and Yahoo! is undervalued at this point - making it the perfect time to buy market share (and traffic) at a low price.

 

 

2. Traffic: Yahoo! still outdoes Google in pure traffic. And the quality of the traffic is intrinsically different. The predominant portion of Google's traffic comes from its flagship search engine site. Is this traffic defensible? Since the cost of switching is trivial for individual users, there's no lock-in and little loyalty. Remember Alta Vista? How long before someone comes up with a better mousetrap [i.e. search algorithm]?

At the same time, Yahoo! has really broad, deep content, which makes it far more defensible IMHO. Content really is king!

 

 

3. Mind share: Google and Yahoo! are still neck-and-neck in terms of user mind share. Microsoft is a somewhat distant third, however the folks from Redmond are far from out of the race. A combined G/Y! company that offers powerful algorithms (Google search), rich applications (Google Apps) and wide-ranging quality content (Yahoo! web properties) would present Microsoft with a formidable competitor indeed!

 

 

The Downside

1. Culture: Looking from the outside, I imagine that the cultures of these two companies are quite different!
2. Anti-trust concerns: Will the DOJ cause hang-ups on issues of search monopoly?
3. Financials: Will the numbers work? I have no idea - I'm no corporate deal-maker. (Ashkan's post throws some light on this issue.)

A rant about Google's search

Is Google peaking in Search? A couple of search events this week made me consider this possibility.

First, Google's Marissa Mayer gave a wide-ranging talk about the Future of Search, highlighting eight initiatives that Google is focusing on to improve search. All of these search improvements seem to be incremental - honestly, which of these eight are truly revolutionary?

Second, I had the opportunity to attend the blogger/tech-media event at much-hyped search startup Powerset. By implementing natural-language processing techniques, Powerset has the potential to improve the relevance of search results radically by matching the semantics of the content in its index with that of the query. This is a great example of an approach that, if effective, will certainly offer more than incremental improvement!

It remains to be seen if a Google-killer will emerge soon in the world of search; if history is any guide, we should see long periods of incremental development, punctuated by sudden, unexpected, discontinuous changes.

Conclusion:

I for one, would welcome our new search overlords from Googhoo!



[Disclosure: I have no positions in either Yahoo! or Google.]


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