Meanwhile, another result on the same page gets clicked on a lot too, but when users click on that one, they stay on the page longer, and don’t even return to the results page to find another result to click on. Nor do they refine their query. Which page is most likely the one that has the better content for that particular search?

Should bounce rate be a ranking signal? Comment here.

Well, being a human, you have the luxury of looking at both pages and making that call. Now, pretend you’re not a human. You’re a computer algorithm tasked with ranking the world’s information for the majority of searchers. While you have over 200 signals that can help you determine which one should rank higher, wouldn’t this be one that could help?

This is not exactly bounce rate, but it’s related. In this case, it is the bounce in the direction of back to the SERP, and while there has been a lot of discussion and argument about whether Google uses actual bounce rate as a signal, it seems pretty likely that they are looking at this specific element of it.

SearchMetrics, after releasing data about the Panda winners and losers in the UK, said, “It seems that all the loser sites are sites with a high bounce rate and a less time on site ratio. Price comparison sites are nothing more than a search engine for products. If you click on a product you ‘bounce’ to the merchant. So if you come from Google to ciao.co.uk listing page, than you click on an interesting product with a good price and you leave the page. On Voucher sites it is the same. And on content farms like ehow you read the article and mostly bounce back to Google or you click Adsense.”

“And on the winners are more trusted sources where users browse and look for more information,” the firm added. “Where the time on site is high and the page impressions per visit are also high. Google’s ambition is to give the user the best search experience. That’s why they prefer pages with high trust, good content and sites that showed in the past that users liked them.”

WebmasterWorld Founder Brett Tabke wrote in a recent forum post, discussing what he calls the “Panda metric“, that “Highly successful, high referral, low bounce, quality, and historical pages have seen a solid boost with panda.”

In a recent video from Google’s Matt Cutts, on ranking in 2011, he talks about increasing site speed, and how this can keep users on your site longer (IE: not bouncing), you can increase your ROI. Speed is a ranking signal. We know that. Speed can reduce bounce rate. Even if Google doesn’t use bounce rate directly, there is a strong relationship here.

A reader (hat tip to Jordy) sent us this link from Matt McGee at SearchEngineLand, posted last June:

Bounce rate and rankings? Matt [Cutts] says Google Analytics is not used in the general ranking algorithm. “To the best of my knowledge, the rankings team does not use bounce rate in any way.” He tiptoed around this question a bit, choosing his words very carefully.

The part about tiptoeing is somewhat intriguing in and of itself, but it’s also important to note that this was nearly a year ago, and the Panda update was not announced until just this past February (and has even been tweaked since then).

We also picked the brain of SEO vet Jim Boykin. We asked Jim how important he thinks bounce rate is. He says, “I think that some aspects of bounce rate are very important in the post-panda world.”

“It’s important to note how Google defines Bounce Rate,” he adds. This is below:

“Bounce rate is the percentage of single-page visits or visits in which the person left your site from the entrance (landing) page. Use this metric to measure visit quality – a high bounce rate generally indicates that site entrance pages aren’t relevant to your visitors. The more compelling your landing pages, the more visitors will stay on your site and convert. You can minimize bounce rates by tailoring landing pages to each keyword and ad that you run. Landing pages should provide the information and services that were promised in the ad copy.”

He also points to how it is defined in Google Analytics:

“The percentage of single page visits resulting from this set of pages or page.”

“Personally, I don’t think that a single page visit is a bad thing. To me, it tells me the visitor found what they were looking for. Isn’t that what Google would want? If I were Google, I’d want a searcher to find the answer to their search on the exact page they clicked on in a search result…not 1 or 2 clicks in. If I were Google, I’d look more at ‘Who Bounces off that page, and returns to the same Google search, and clicks on someone else, and then never returns to your site,’ but I’m not Google, and that’s just my ‘if I were Google’ thoughts”.

Regardless, it can’t be a bad thing to strive to make every page of yours the best page of its type – the solution to the searcher’s problem. At its heart, that is really what the Panda update is about. Really, that’s what search ranking is about in general. Delivering the BEST result for the query – signals aside.

As far as links, while Boykin says it’s “kind of” fair to say that making sure your links point to quality pages can have a major impact on how Google ranks your site post-Panda, he says, “The final solution should be to remove or fix the low quality pages, and thus, all your links would point to ‘quality pages’.”

Again, this should improve bounce rate.

“I think most agree that there’s a ‘Page Score’ or a ‘set of pages score,’ and when that has a bad score, it affects those pages, and somehow ripples up the site,” Boykin adds. “It could quite well be that if you have a page that links out to 100 internal pages, and if 80 of those pages are ‘low quality’ than it just might affect that page as well. A lot of this is hard to prove, but there are some smoking guns that can point in this direction.”

“Bounce rate is important, and yes, many sites that got hit did have a high bounce rate, but comparing this to sites/pages that weren’t hit doesn’t exactly show any ‘ah ha’ moments of ‘hey, if your bounce rate is over 75%, then you got Panda pooped on,’ because the bounce rate Google shows the public is missing many key metrics that they know, but don’t share with us.”

I think the best advice you can follow in relation to all of this is to simply find ways to keep people from leaving your site, before they complete the task you want them to complete. That means providing content they want.

Is bounce rate important in the post-Panda world? Tell us what you think.

"Since search is the primary form of navigation on many websites, it is often the first step a potential customer takes toward meeting his or her online goals – especially on mobile-optimized websites," says Adobe. "When visitors cannot easily find what they are looking for early into their website visit, they may leave to search elsewhere for what they need. As a result, key metrics such as online engagement, conversion, average order value (AOV) and visitor retention can be negatively impacted."

"Every onsite search query is an opportunity," explains Brad Rencher, VP and general manager of Adobe’s Omniture Business Unit. "Each search is a customer telling you what they want, what they are interested in, or what they want to purchase or download. With Adobe Search&Promote, our customers can use search and navigation as a way to dynamically deliver the right content, products and offers to the right visitors. Each search becomes an opportunity to optimize how visitors browse, find, compare and select relevant products and content across screens and devices – driving visitors to convert at a higher rate and leave much more satisfied."

Search&Promote integrates with other products from Adobe’s Online Marketing Suite, such as Test&Target, Recommendations, and Scene7. With Test&Target, Search&Promote users can test search experiences, marketing, or results pages against each other. Recommendations can be integrated into search results, and Scene 7 lets users enhance search results visually. 

Around this time last year, Google launched Commerce Search, also aimed at improving bounce rates for product search on sites. "Search quality is a big factor in changing visitors to buyers online, and in making customers happy too," the company said. "Visitors spend an average of just eight seconds before deciding whether or not to remain on a website, so having a good search tool is important for turning visitors into buyers."

For more helpful information on dealing with bounce rates, watch our interivew with Avinash Kaushik and Brett Crosby from Google from search engine strategies a couple years ago. 

Adobe offers a lot more information about Search&Promote here, with a product overview, success story, white paper, etc.

Do you think your sales could be improved with a better search feature? Discuss here.

"Search quality is a big factor in changing visitors to buyers online, and in making customers happy too," Google says. "Visitors spend an average of just eight seconds before deciding whether or not to remain on a website, so having a good search tool is important for turning visitors into buyers. Google Commerce Search is hosted by and uses Google search technology to make online retail searching both fast and customizable — visitors to your online store can sort by category, price, brand or any other attribute."

"E-store administrators can highlight special products or connect related ones so searching is easier for their customers," the company continues. "Google Commerce Search includes a built-in spellchecker and synonyms so if visitors can’t remember exactly how to spell the particular toy or perfume or anything else they’re hunting for, Google Commerce Search will make some suggestions. Choosing the right one is up to them, though."

This clip will give you a good idea of how it works:

Customization Features

When a user searches for a product, results can be returned in a user-friendly e-commerce-style product page, where they can simply click the item they want and add it to their cart. The site owner has control over how it looks. You can filter results through parameters like category, brand, or price, and you can sort search results by any attribute.

Results can be viewed in either a list view, which is more like a classic search results page, or grid view, which is more like the product page-style. You can control which product categories are promoted at the top or sent to the bottom of results.

Promoting Specific Items

The product also comes with a promotions feature, which lets you easily promote certain items, and cross-sell related products if you choose. It also comes with automatic spellcheck, stemming, and synonyms, so that if a user doesn’t get their query exactly right, it will help guide them to what they’re looking for. I probably don’t have to tell you that any minutia that you can control in guiding customers through the checkout process can be huge for improving sales and decreasing bounce rate. Speaking of that, you can link Google Commerce Search to Google Analytics, making it easy to track various metrics.

Getting it and Keeping it Running

Google says the product can be deployed in days, and that because it is hosted on the Google platform, retailers can scale to meet their higher-demand periods, like holidays, without having to worry about slowdowns or traffic spikes.

"The hosted factor is a key feature in making GSC easy for administrators to use," Google says. "Because there’s no hardware (or software, servers, operating systems, cables, or any other equipment), admins can upload product information to Google Merchant Center and provide a few extra customization parameters – and Google Commerce Search utilizes that product feed to power their website store search."

The same feed you use for Google Product Search can be used for Google Commerce Search. This can cut down on time and tech costs, as the company points out.

Do you think Google Commerce Search boost your web sales? Tell us what you think.

Related Articles:

> Google Answers Bounce Rate Questions

> Is Bounce Rate a Google Ranking Factor?

> Google Talking Bounce Rate Again

Post session remarketing is basically going after individual users who have abandoned your site. "Of all the techniques out there, remarketing in real-time (thru e-mail specifically) is set to grow 152 percent," a representative for SeeWhy tells WebProNews.

"When most people think about tactics to improve website conversion, their first thought is to try a number of steps in the conversion process itself: 1) the need to tune pages 2) refine page layouts, and 3) make discount or promotion offers to boost conversion," says SeeWhy Founder Charles Nicholls. But the company’s research indicates the trend will go more toward targeting the individual.

SeeWhy also points to research from E-Tailing Group, which found that 76 percent of e-tailers ranked "Targeted Email" as the top initiative to improve website conversion. SeeWhy found that:

- Post session remarketing is currently used by 25 percent of Google Analytics Users

- 63 percent plan to deploy session (or website) abandonment follow-up as a technique of increasing website conversion

The overwhelming majority are currently not following up with website abandoners according to SeeWhy’s data. Some say they do within a few days, while others on the same day, and a few even within the same hour.

The majority do seem to think that the current economic environment makes it all the more important to follow up with abandoners. Here’s how respondents prioritize the need to do so:

It would be interesting to see this survey on a bigger scale. Do you follow up with people after they abandon your site? Do you think it’s a good idea?

1) It is really hard to misunderstand. It measures the number of people who landed on your site and refused to give you even one single click!

2) It is available in most web analytics tools, including our own Google Analytics.

3) It is quick and easy to use. Bounce rate will help you understand where and how to make changes on your website in under an hour.

A while back, Kaushik answered some questions from our own Mike McDonald about Google and Bounce rates. He also spoke with him on the subject at a past Search Engine Strategies Conference in San Jose:

Kaushik offers one piece of wisdom in particular that really hits the nail on the head. "Remember, you don’t decide the homepage of your website," he says. "When people search, the engine finds the most relevant page on your site and that’s the homepage. If you have 50,000 pages on your website, you have 50,000 homepages." [emphasis added]

That’s why you need to analyze this data using a web analytics service of some kind. You’ve got to find out the places that are driving the most visitors to your site. This can be done easily in Google Analytics by going to "Traffic Sources" and "Referring Sites."

"In about fifteen seconds you know which sites are your ‘best friends forever’ (BFFs), and where you need to look a tad deeper," says Kaushik. "By identifying the sites that are sending you visitors with high bounce rates, you can investigate the reasons why (the campaigns, the context in which your link is placed, the ads) and make changes to ensure that visitors find what they are looking for when they come to your site."

He offers a second tip, and that is to go to "Content" and "Landing Pages." This is where you will find the bounce rates for all of your "homepages."

There is still debate as to whether or not bounce rate has any effect on your actual search engine rankings, but in the long run, does that really matter? You should be working to reduce your bounces regardless of your search rank. If a viewer bounced, that means they already found your site. That’s what you should be paying attention to.

Although bounce rates received the biggest attention, we would be remiss not to start by quickly listing some signals commonly looked at by information retrieval folks. The two elements include implicit and explicit data (actions and interactions) – examples can include;

Implicit signals

1. Query history (search history)
2. SERP interaction (revisions, selections and bounce rates)
3. User document behaviour (time on page/site, scrolling behaviour);
4. Surfing habits (frequency and time of day)
5. Interactions with advertising
6. Demographic and geographic
7. Data from different application (application focus – IM, email, reader);
8. and closing a window.

Explicit signals

1. Adding to favourites
2. Voting (a la Search Wiki or toolbar)
3. Printing of page
4. Emailing a page to a friend (from site)

Now that we’re past that let’s get a little geeky so those information retrievers don’t shake their heads to hard at us – the terminology. I am as guilty as the next Gypsy of flinging the term ‘behavioural metrics’ about over the last year or so, even performance metrics. If you want to research this more, start by using the term; implicit/explicit user feedback signals – because that’s what we’re talking about.

and thanks to Steve Gerencser for sending the pic

Follow the bouncing timeline

While you can trace the timeline back in the search (blogging/reporting) world many years, it really came home when Search Engine Watch mentioned it (Oct.2008) followed a few months later in a Search Engine Land post. Being the venerable publications that they are, grumblings around the SEO world soon followed. If you go and do some buzz monitoring and searching (which I have), much of the talk began after that. The cracks in the damn began to fissure and this Gypsy was left without enough chewing gum.

So what can we do? Where does one start to truly look for answers as to the potential of such methods being implemented by top public access search engines? It would stand to reason that we begin looking at the information retrieval world itself. Over the last month I have given the benefit of the doubt to the community and gone deeper to find some type of more definitive answer, (list of research papers at the end).

Inherent problems with implicit signals

One thing that became obvious real fast is that the IR world is still not entirely sure of the value for implicit feedback signals as far as how to infer engagement and satisfaction. While there are a long list of problematic areas let’s consider;

1. You save the link for later and continue my search (in Doc let’s say)
2. You found what u needed on the page and went looking for more information
3. You walk away from my browser and leave the window on a page for an hour
4. Multiple users in your home during a given session
5. Open a listing in a new window (when further tracking is unavailable)
6. You found the information in a SERP snippet and selected nothing
7. You were unsatisfied with the page selected and dug 3 pages deeper (unsatisfied, not engaged)
8. Queries from automated tools (like a rank checker) which adds noise to overall data
9. SERP bias – do peeps simply click the top x results regardless of relevance?
10. Different users having different understanding of the relevance of a document (result)

…and on and on. Think about it, some situations can tell the search engine you’re pleased with the results and other times such signals mean nothing. You see, the essential motive is to attempt to assign an emotional evaluation of engagement with the search results. Unfortunately there are too many noisy elements which make this a very difficult task to do effectively.

Noise and confused

It’s widely felt that ‘implicit feedback is more difficult to interpret and potentially noisy’ as noted in – Evaluating the Accuracy of Implicit Feedback from Clicks and Query Reformulations in Web Search (partially funded via grant from Google) – in looking at click behaviour there was indeed a clicking bias based on a few elements;

“….First, we show that there is a “trust bias” which leads to more clicks on links ranked highly by Google, even if those abstracts are less relevant than other abstracts the user viewed.

Second, there is a “quality-of-context bias”: the users’ clicking decision is not only influenced by the relevance of the clicked link, but also by the overall quality of the
other abstracts in the ranking.”

Other research (on click data) looked at how users actually interact with search results as far as bias is concerned. People are often consistent in clicking patterns (clicking top result, second, third) regardless of the underlying data. This means the entire data set can be skewed as not clicking on the 8th result may no necessarily be a vote against the link in the result, but more of an ingrained habit on the part of the searcher.

They summarized;

“Our results show that click behaviour does not vary systematically with the quality of search results. However, click behaviour does vary significantly between individual users, and between search topics. This suggests that using direct click behaviour—click rank and click frequency—to infer the quality of the underlying search system is problematic.”

And also;

“Analysis of our user click data further showed that the action of clicking is not strongly correlated with relevance —only 52% of clicks in a search result list led to a document that the user actually found to be relevant. Attempts to use clicks as an implicit indication of relevance should therefore be treated with caution.” From – Using Clicks as Implicit Judgments: Expectations Versus Observations

Beyond that many of the papers have various elements of implicit user feedback that they felt warranted more study. In short, there is no consensus in the IR community about the validity of these signals – they’re not ready for prime time.

ok. 

The Spam connection

And this my friends, as they say, is the proverbial fly in the ointment. While there is a ton of research and even patents on behavioural metrics, dealing with click-spam has not been addressed in any detail to this point. Many of the papers openly admit they are light in the spam detection area and more research is needed.

 
“A natural question that arises in this setting is the tolerance of this method to noise in the training data, particularly should users click in malicious ways. While we used noisy real-world data, we plan to explicitly study the effect of noise, words with two meanings, and click-spam on ourapproach.” From – Query Chains: Learning to Rank from Implicit Feedback

And that’s just one; it was a common theme among the papers on the topic. This for me goes a long way into understanding that it is premature to suggest search engines that we optimize for are using such signals. There is hope as some tests, as ran by Microsoft, concluded;

“ranking accuracy decreases indeed when more documents are spammed, but the decrease is within a small range. When only a small number of documents are spammed per query, ranking accuracy is only slightly affected even if a large number of queries are spammed.” From- Are click-through data adequate for learning web search rankings?

They felt that such a large percentage of queries are long tail queries that it would be more difficult to effectively disrupt the majority of query spaces (I hear Ralph rumbling some where with that one). But once more, there seems to be a lot more work to be done in this area to effectively combat spam in such a system. To this we add thoughts from a Cornell paper;

“… it might also be possible to explore mechanisms that make the algorithm robust against “spamming”. It is currently not clear in how far a single user could maliciously influence the ranking function by repeatedly clicking on particular links.” From – Optimizing Search Engines using Click through Data – Cornell (pdf)

For me, there simply isn’t enough research or hard data to suggest that the spam issues related to implicit user feedback and click data have been solved. This is a crucial element to the case of them being used today by Google or anyone else.

Not enough, then also try this recent post by your friend and mine, CJ, on Clickstream spam detection or Fantomaster’s Behavioral Metrics and the Birth of SEO Surfbot Nets – let us get to then now shall we?

Getting beyond the geeky; looking to the future

Are we getting somewhere yet? Great… but it’s not all doom and gloom, no need to call the corner just yet. You see, for the most part researchers have been finding some great improvements in search performance; they simply haven’t worked out all the values of such signals nor the spam concerns. In an enterprise environment, where manipulation/spam is far less likely, implicit feedback can be a more useful tool. It is the larger public access environment where spam is far more prevalent that the nut has yet to be cracked.

I stand on my original assertion that this type of approach is best served in a personalized environment. This would be huge in dealing with the apparent issues surrounding spam related issues as it is kinda’ hard to spam ones self you see. This makes personalized a likely candidate for user feedback signals. Either way, it simply hasn’t been solved yet

So what are we left with?? Some noisy signals that are spammable… hmmm… where have we heard that before?

And so now I leave all of this in your capable hands my weary web warriors. If you can go through the research papers listed below (or elsewhere) and find me strong evidence of how they deal with noise reduction and click-spam, then we can discuss it further. That is my challenge to you; because from what is out there, it is not yet viable in a large scale environment.

I submit to you, my enthusiastic optimizers, that bounce rates and it’s implicit feedback brethren are simply not likely to be in Google’s (nor any major search engine’s) current ranking schemes. It is a novelty item at best with potential in a personalized environment.

Care to dispute this? I am more than happy to review any research to the contrary.

Want to know what I think is causing us to see what we believe this to be? You’re just going to have to wait until next week.

 /story..

Comments

First Mike got a response from Google Search Evangelist Adam Lasnik:

If you’re talking about bounce rates in the context of Google Analytics, I’m afraid you probably know as much as I do. I love the product, but don’t know the ins-and-outs of it very thoroughly.

If you’re talking about bounce rates in the context of Google web search and webmaster-y issues, then we really don’t have specific guidance on bounces per se; rather, the key for webmasters is to make users happy so they find your site useful, bookmark your site, return to your site, recommend your site, link to your site, etc. Pretty much everything we write algorithmically re: web search is designed to maximize user happiness, so anything webmasters do to increase that is likely to improve their site’s presence in Google.

Mike also sent a few questions to Matt Cutts, who forwarded them to Google Analytics Evangelist Avinash Kaushik, which produced a nice little Q&A:

Mike McDonald: What is the duration for a single page visit until it is no longer considered a bounce when a visitor leaves?

Avinash Kaushik: Bounce is a Visit level metric.

The definition is simple, if there is a Visit (a session technically speaking) with just one page view in it then it is considered a bounce. IE Someone came to your site, saw just one page, did not other action, left your website.

If you want to get a graduate level explanation about bounce rates and a business / marketing perspective on it here it is:

http://www.kaushik.net/avinash/2007/…unce-rate.html

MM: What if a visitor lands on a page, and then visits another page within 5 seconds and then closes the browser. Is that considered a bounce?

AK: No bounce.

Remember time has no bearing on bounce computations. Just page views. In this case there are two page views in a session. No bounce.

The WAA standard definition of bounce rate, and that of Google Analytics, only considers the page view. Time has no bearing on the equation.

MM: When external links are opened in a new window (i.e. target_blank, rel="external", onclick=… etc) is it considered a bounce? Is there any difference between using HTML and Javascript to open a new window?

AK: Depends.

If in your external link popping strategy you are also sending a "hit" back to GA, as in this strategy….

http://www.google.com/support/googleanalytics/bin/answer.py?hl=en&answer=55527

Then the behavior you describe won’t be considered as bounce because you have just sent a "hit" (a page view really) back to GA.

If in your external link popping strategy you are not sending a hit back to GA then if the person comes to your site, clicks on a link to leave the site, then that is a bounce.

Note that with Event Tracking (advanced AJAX, Flash, Flex, Video etc) released by the GA team Analytics can handle a lot more complex scenarios intelligently. Say if I come to your site. Watch the video you did with me at SES and leave. Most tools would consider that bounce. But if you are using GA and have event tracking for your videos (or 100% flash site) then that won’t be considered bounce. In fact GA will accurately compute how long I stayed on your site, how much of the video I watched etc etc.

MM: If opening external links in a new window is considered a bounce, does it change anything if the user then comes back to the site which has remained opened and then starts browsing around?

AK: See above for first part of your answer. It depends on how you have encoded the external links (with ga tracking or not).

For the second part….

A session in Google Analytics (and pretty much every other web analytics tool out there) is "29 mins of inactivity". So I come to your site. See just one page. Go away to say google or whatever. Come back in 15 mins (or under 29 mins). Do another click. That’s still the same session. No bounce.

Hope this helps.

There is definitely some useful information there. To read the rest of the conversation, or to contribute to it, you can find the thread at Webforumz here.

Web Analytics and SEO

Rand Fishkin at SEOmoz lists web analytics as one of the top seven reasons that companies are investing in SEO during the economic downturn. "It’s sad, but true," he says. "When a downturn arrives or panic sets in, someone, maybe the first someone in a long time, checks the web analytics to see where revenue is still coming in. Not surprisingly, search engine referrals with their exceptional targeting and intent-matching are ranking high on the list."

Word on the street is that web analytics are going to be playing an even more crucial part than ever in traffic heading into the new year. As both Bruce Clay and Matt Cutts talk about in their respective interviews with WebProNews, Google is going to be getting more personalized with search results, and this is going to make traditional SEO strategies more difficult, and perhaps some even obsolete.

We discussed recently how your site’s bounce rate might already play a role in your search engine rankings. SEO Black Hat shared some interesting findings and Google Analytics data regarding this:

Overall Traffic

Google Traffic

Bounce Rates

Obviously it’s going to take some analysis to improve your bounce rate. Avinash Kaushik, author of Web Analytics: An Hour a Day, and Google Analytics group manager Brett Crosby discussed this with Mike McDonald at SES a while back:

Blog Analytics

Blogs have become a big part of business for many companies, and site-owners. Blogs shouldn’t necessarily be treated like other sites when it comes to analytics however. "There are more companies blogging now than ever," says Lee Odden at Online Marketing Blog. "How do you measure results? Measuring the social web is a different thing than what most web analytics packages offer. It can be easy to start a blog, but making it successful requires an excellent feedback mechanism. Blog analytics is one of those sources of feedback to know what’s working and what’s not."

Odden is conducting a poll on preferred blog analytics tools, and so far Google Analytics is ahead by a long shot.

 Kaushik discusses in-depth, six metrics that should be used to measure the success of a blog (as illustrated in his chart below) in a detailed blog post.

Video Analytics

As online video usage continues to trend upward and video search is improved upon, video analytics are going to carry increasing significance as well. It’s going to be interesting to see the strides made in this department over the coming year.

YouTube has already begun offering a tool – YouTube Insight, which is an analytics program for videos that lets users get a look into where and by whom videos were posted. A couple months ago they released a feature for it that allows you to actually pinpoint the specific parts of videos that get viewed the most. This will be a huge metric, particularly when it comes to advertising. "If you give users the tools to attract larger audiences, they’ll create more ad inventory," as Advertising Age noted.

I’m sure there are a number of other video analytics tools out there, and it will be interesting to see what other tools (and metrics for that matter) become relevant.

Analytics Mistakes

Bryan Eisenberg at the Microsoft AdCenter Blog talks a good deal about the seven biggest mistakes of web analytics, which according to him are as follows:

1. Improper Implementation
2. No Goals Setup
3. No Segmentation
4. Paying Too Much Attention to Irrelevant Data
5. Not Setting up Milestone Events Documentation
6. Not Combining Quantitative Data with Qualitative Data
7.  Not Taking Action On the Data

That last one is the one that should really be weighing on your business-minded conscience, and what is likely going to hurt you more than ever in the future. Although the first one will get you on the wrong track right from the start, so taking care of that should be a top priority.

Let me be blunt. Quit screwing around and start taking your analytical data seriously. It just might be the key to your business’s survival as we face not only economic downturn, but a new world of Internet marketing waiting ahead for us. Thoughts?

The report covers global website usage, search engine usage, keyword analysis, top browsers, top operating systems. See the full report here: http://www.visistat.com/web-use-trends-2007.php

Usage Stats:

Average Bounce Rate: 72.8%

Repeat Visitors: 30.4%

Average Visitor Click Path: 3.2 pages

Duration of Average Visit: 2.14 minutes

Search Engine Usage Stats

Comments

1. What is bounce rate?
2. What is the industry standard for bounce rate?
3. What causes high or low bounce rates?

I am going to answer these questions in this post.

What is bounce rate?

Bounce rate is the percentage of visitors who enter a site (or a page) and then leave immediately. Think of a ball (visitor) that is thrown (visits) towards a table (site). It hits the table and bounces back without rolling (visiting any other pages).

Generally, “leave immediately” in the above definition means without going to any other page. However it could also be expressed in terms of time spent on site, say users who spend 5 seconds or less on the site irrespective of the number of pages they view.

Bounce rates are calculated both at the individual page level and at the site level. For an individual page, bounce rate is the ratio of visitors who enter the site from that page and leave without going any deeper, to the total number of visitors who enter the site through that page. In other words it is single page visits/ total entries to the site through that page.
At the site level, bounce rate is simply single page visits/total site visits.

Note: If a visitor enters though a page, refreshes it (manually or via auto refresh such as ESPN score page or MSN money page) but never goes beyond the first page the visit is not counted in the bounce rate. The bounce rate is based on visits, not page views.

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