As more and more business are adopting Foursquare as a marketing channel by adding incentives for checking-in, the need to track and analyze this data has resulted in the launch of a Foursquare dashboard.  In order to use this dashboard, you must claim your venue. You do not have to be the person that created the venue to claim it, but you must be available at the venue to answer a phone call from Fourquare and provide details/proof that you are eligible to manage it. You can either manage the account through your personal Foursquare account, or start a new one, as the details of the manager’s account are public.

This dashboard is a collection of venue check-in analytics including stats like:

Key Metrics: (Overview of activity)

- Total check-ins
- Unique visitors
- Percentage of check-ins shared with Twitter
- Percentage of check-ins shared with Facebook
- Gender percentages of check-ins

Top 3 Visitors

12 Most Recent Check-ins

All Check-ins (detailed list)

Time Breakdown (time users are checking in)

See Below:

Currently, there is no way to access the information by a certain week, month, or day, so for now, we are forced to record this data and move into a spreadsheet or other tracking software.

This is a great step in the right direction for a business using Foursquare. We anxiously await more detailed tracking options from Foursquare in the near future.

by Craig Scribner

I measure how people flow through websites.

I figure that if you watch how people walk around the store, you can infer what they want, and change the layout of your store to make things easier to find. I’m always on the lookout for problem spots, where people suddenly do something totally unexpected, which means that the word we keep using doesn’t mean what we think it means.

Wallace Shawn in The Princess Bride

I like to think I’m pretty good at it—but three times in the last year I’ve been hugely embarrassed. In all three cases I presented user behavior analysis to clients, only to have to retract later.

The culprit: robots that look like people.

All three times, the companies I engaged with had implemented some kind of automated monitoring system to make sure their site was working at all times. One was through a vendor, but the other two were home-grown solutions a developer had kind of thrown together on a whim.

One of these scripts was set up to start on the Home page, advance four pages and launch our flash app, then quit. Another one entered the site, picked a product, and advanced through the checkout process to the very last page, and then quit.

Now, companies that offer these services will tell you that they’re operating a such a low level that the trail they leave won’t affect your overall results. But they forget that real people naturally fallout as they advance through your site funnels, but robots push forward with their indomitable spirits (read: they have no spirits).

Imagine you have a 2% conversion rate, and the shortest route from arrival to purchase is 7 steps. Statistically speaking, it’s the equivalent of losing about half of your visitors on each step.

Now think of a robot who looks like a person who’s monitoring your checkout process. His creators told you he’d fly under the radar, and would represent less than 1% of your total site traffic. But since that 1% doesn’t fall out, he will be vastly more detectable the deeper into the site he goes. If he’s programmed to run six of our seven steps, 1 percent on entry becomes 2 percent on the next page, until it’s 32 percent on page 6.

Robots skew the end of the funnel more than the start.

That may sound like a dramatic exagerration, but I promise it’s not. In one of my three cases the robots accounted for 80% of all traffic on the last page of the checkout process. You can imagine what it did to my analysis. I hastily asserted that something had to be technically wrong with the last page of our checkout process, because you just can’t dissuade 95% of the people who get to the last page to quit, even if you try!

Safeguards

Here are some ways I’ve used to detect these Frankensteins (but please let me know if you can think of other good ones).

• Domains/ISP Reports. Traffic from site monitoring companies like Keynote, Gomez, and ObservePoint are easy giveaways. Set up some alerts today, or set up filters to remove that data before it starts pollutingyour reports. Another ISP you should keep a close eye on is that of your own company—although it doesn’t always mean automated traffic. Trend these over time to see if anything jumps out.
• Full Path Reports. If you see that a multi-page path is among your top ten, that should raise your hackles. Trails left by humans always start with single-page visits, and then grow from there. An elaborate, seven-page path breaking into my top ten is what tipped me off to one of these situatoins.

Solutions

If your site monitoring efforts are coordinated with your site analytics, you can pre-configure these automated visits to suppress the analytics call in the first place. But I also noticed that Omniture offers a VISTA rule that will exclude known monitoring programs for you.

Google Analytics has a new Intelligence tool which silently monitors reports you may not even be looking at, and tells you when anomalies occur. But what I’m really looking for is something I don’t think anybody’s created yet: a tool that listens carefully to the normal buzzing around your website, and notices when things seem too normal.

Do you remember the movie Hunt for Red October? A submarine technician had listened so carefully to his sonar that he could detect patterns that were created to sound like the sea, but were really generated from an enemy sub. I don’t think I’ll ever be that good, so I’m hoping that somebody will create a machine that will do it for me!

Sean Connery and Courtney B. Vance in Hunt for Red October

The first step is to determine your objectives.  What are you looking to accomplish?  In my experience, most social media objectives roughly fall into these standard categories:

• Reach- using these channels as an outreach tool to create brand awareness
• Influence- brand building and increasing positive conversation sentiment
• Engagement- communicating with customers directly, building a sense of community
• Conversion- increasing sales, registrations etc.

The next step is to focus on the appropriate metrics that directly speak to these objectives.  There are endless social analytics tools available that will provide many (unnecessary) points of data and really pretty charts…. I recommend staying away from compound metrics and tools that don’t provide a thorough explanation of their algorithms.  Using Twitter as an example, the metrics I would focus on for measuring reach are:

• Followers (Twitter)
• Velocity (Twitalyzer)
• Unique Retweets (Klout)
• Legit Followers (GraphEdge)
• 2^nd Level Network (GraphEdge)

It is also important to remember that you shouldn’t look at social analytics data in a silo.  It is impossible to quantify the value of a social user with social data alone.  By comparing timelines of social data with web data and transactional data, and then tracking against execution activity (types of communication, content posted etc.) it will allow you to identify patterns.  Isolating these patterns will show what types of activity are affecting social media activity and, ultimately, allow you to quantify these deltas.

Facebook is in the process of developing a conversion tracking tool that will provide brands with the ability to determine the exact dollar worth of a Fan.  With the increased need to demonstrate social media ROI, I’m sure we will see more social platforms working to provide this service as well.  However, in the meantime, marketers need to be prepared to provide a holistic, channel agnostic assessment of social media measurement as it relates to all aspects of a brands’ bottom line.

Based on the above definition, one would assume the lower the rate the better, and for the most part that is correct. We typically tell our clients that a bounce rate of less than 45% is ideal for a non e-commerce site, however there are instances (detailed below) when a higher bounce rate is actually a good thing.

1) Deeplinking

Back to our friends at Wikipedia for definition, Deep linking, on the World Wide Web, is making a hyperlink that points to a specific page or image on another website, instead of that website’s main or home page.

This strategy is used not only by search engines when returning results, but also by advertisers who are becoming smarter about understanding audience needs and delivering the exact information they are looking for in one click.

As an example, if I am shopping for a new car on Yahoo! Autos and am interested in a “Green Car” with great gas mileage, I will be served an ad for the Honda Civic Hybrid. Upon clicking the ad, I will be taken directly to the page on the site that talks about the cars gas mileage. In years past, many ads and SEM campaigns defaulted to driving visitors to the site’s home page. As you can see, Deep linking is much more strategic, but it will cause a higher bounce rate because the user won’t have to search the site to get the information they want, which is a better user experience.

2) Links to External Sites

If the goal of your site is to have visitors actually exit your site via a link on your page, then a high bounce rate would be expected, and even desired. A couple of examples of this would be a site where users are asked to log in. If that login directs the visitor to a different site, this would be considered a bounce off your home page.

Another instance of wanting people to exit, would be if you generate revenue based on ad sales from your site. In this case, you want people to engage with the ads and design your site to do just that – a high bounce rate will confirm your strategy is working.

3) Loyal Visitors

Most of us have our favorite sites we like to go to on a regular basis. Mine is runnersworld.com to get the latest news on upcoming races. They always feature this information on a calendar on their home page, so typically I don’t have to dive any deeper into the site to get what I am looking for.

Another example of this would be sites that contain daily horoscopes, financial ticker updates and sporting event scores. An individual will bookmark the page that contains the updates they want, hit it once and exit.

In all of these cases, we are increasing user satisfaction by delivering what is sought, quicker and easier – however, if you have a high bounce rate on a site such as amazon.com, or any other ecommerce or lead generation site, you might want to think about a redesign. I will leave that topic for another day and another blog, because now I must see what races are coming up this weekend.

VML Testing Process

VML Testing Process

1. IDENTIFY – Leverage web analytics about your site to identify high opportunity pages that can significantly impact conversion and revenue. High opportunity pages are those that have high volume of traffic, involved in a conversion funnel, or directly related to conversion on the site.
2. HYPOTHESIZE – Different test ideas are brainstormed about the page to improve KPIs on the page. The more ideas you have, the better. This gives you the best opportunity to test various levers for change.
3. PLAN – Review your ideas internally and come to a consensus on the ‘best ideas’ for testing. Formalize these ideas into a test plan and be sure to include the actual creatives in the test plan.
4. IMPLEMENT – Upon plan approval, implement the plan on the site leveraging technology from a testing platform like Omniture Test & Target, Autonomy Optimost, or Google Optimizer.
5. ANALYZE – After the test has run for a planned duration of time, the results must be analyzed for a comprehensive understanding of the test.
6. REPORT – A formal report should be developed identifying the winning creative as well as comparative analysis from the previous benchmark.

Good luck and Happy Testing!

Both books are considered by many in the #measure community as seminal works in the web analytics practice and profession, suitable for novices and power users alike. Plus, you really can’t argue the price.

Click here to download Web Analytics Demystified and The Big Book of KPIs.

WARNING: You will have to give out your email address for the PDFs, but that seems like a small price to pay.

You can view my results here.

Below is a recent interview with Eric Beane, VML’s Director, Optimization & Analytics

A case appeared in the Kansas City Star on Monday about a man who was tagged as a spammer by Facebook.  This shouldn’t be all that shocking or surprising; Facebook is heavily targeted by spammers.

This case, however, is different. This man wasn’t a spammer. He was just ‘not average.’ John VanPelt (the supposed spammer) was contacted by Facebook and told that “Facebook has limits in place to prevent behavior that others may find annoying or abusive” and that his account would be suspended from posting and/or sending messages.

Why is this important? Why am I even taking the time to write an entire blog post about it? Simple.

The bell curve.

While for some it brings back memories of high school tests, it seems to be popping up in everyday ways in analytics.

VanPelt was suspended for not being an ‘average’ user as defined by Facebook. But, what is an average user? And why do we not like those who fall outside of that average range?

An average user is one that (in an analytics, social media, or life sense) performs in a way we expect. This average user is the largest part of the bell curve and one that we can, with some certainty, predict.

In marketing, we target the bell curve. We talk about average page views, average time on site, average traffic path, and average conversion rate. We worry about the 80% of website traffic and attempt to influence that 80%.

We don’t like the non-average user because we don’t understand them. Sure, we can track them and interpret them, but they don’t fit our mold. We can’t answer the behavior questions with them because they don’t do the same things as our average users.

We target, follow and worry about the 80%.

But, what if we are all wrong? What if we start to worry, target and influence the other 20%?

Imagine if our 10% to the right of the main bell curve are our most dedicated consumers. They visit our site every day, sometimes several times, spending more time than our average user, buying more items and in general helping us succeed. The question, however, is what are doing to target them?

Imagine if Facebook called VanPelt and used him as a resource. Think about it. He is one of your most using users. He not only users your service but makes it a daily, integral part of his life. And yet, he isn’t seen as a resource, he is the annoying possible spammer.

Imagine that your top 10% users received special promotions, special website landing pages, targeted messages and in general were treated as un-average.

Imagine that the top 10% feel as though they are known to the company and are a valuable part of the sales process.

Imagine that we target the 10% and they become our focus.

To some this seems counter-intuitive. Why would you target the smallest group?

You target them because they are your greatest resource. They understand your site better than you do because they use it every day. They help move your products. They open your emails. They join the social communities. They comment on your Facebook page,  they follow you on Twitter, they see your videos on YouTube and they download your mobile app.  They are you advocates, proponents and fans of your product, service or company.

Ultimately, the 10% influence the 80%.

NY Times had a great article Sunday that that talks about how the iPhone and Apple have changed the space of mobile and dominated the mobile app space – it’s worth a read:  http://www.nytimes.com/2009/12/06/technology/06apps.html

I am new to the iPhone world and for the longest time argued that marketers were missing the mark by primarily focusing on it – however after looking at the available data and mobile trends on our clients sites I stand corrected.  On average over 60-70% of all mobile traffic for our clients is iPhone based and for a phone that has less than 8% of the marketplace that is impressive!

Some interesting tidbits from the article:

• There are currently over 100,000 apps for the iPhone – the next closest is the Android at 14,000
• Developers keep 70% of all revenue generated from the App store – that $1 app can make a lot of money
• Apple says it receives more than 10,000 application submissions each week with most become available in the App Store within two weeks