There’s a known weakness in browsers which we wrote about in the book. Every time we talked with someone about it, they’d ask us why we didn’t start a company that took advantage of the loophole, and the answer was, well, it’s creepy. The loophole basically lets you see where else your visitors have been on the Internet. Well, it’s now out in the open, in two forms: Beencounter, and Haveyourfriendsbeenthere.

To be perfectly clear, the site won’t show you everything your visitors surf–just whether or not they’ve been to a set of sites you define. Here’s how it works:

1. You decide what sites you’d like to find out about
2. You embed these sites in a hidden portion of the page
3. When a visitor loads the page, the sites that are visited are marked a:visited in the page’s CSS
4. The Javascript in the page can then grab this property of each link and send it back to you

Knowing where a visitor has been can be used for all kinds of things. For one thing, using just a few sites you can guess the visitor’s gender with a good degree of confidence–resulting in more targeted advertising. This isn’t a new idea (it’s been discussed in terms of browser history before). You might also offer a discount to visitors who’ve already checked out your competition.

Haveyourfriendsbeenthere takes advantage of the obfuscation from a short URL to hide what it is, meaning many people will click on it inadvertently. There’s no easy way to fix this without breaking a lot of the history functions that we use when browsing (one user on Reddit pointed out that this flaw has been around since 2002 and there are sites that show your surfing history already).

We figured it was worth talking about it more openly since these two services are likely to make it a pretty mainstream practice, particularly among sites that benefit from demographic targeting.

BTW, clearing your browser history or surfing in anonymous mode will hide your behavior from such tools.

No related posts.

Beth Kanter’s 53rd birthday is today.  Of the many reasons why we’re big fans of Beth, she’s pioneering the concepts of web-based analytics for the non-profit / charity sector.  If you haven’t seen her blog before and you want to deep dive in her thoughts on metrics, start here.

To celebrate her 53rd birthday, she’s using social media to incite change in the world by sending 53 Cambodian children to school.  Here’s the full description of her birthday wish.  Have a few bucks laying around?  Help her out here.  It’ll help kids go to school in Cambodia!  How cool is that :).

It gets better.  She’ll write about the lessons she learned during the campaign (just like we did for the Beers for Canada), and share insights on the metrics and measurement tactics she used to determine what worked and what didn’t.

As always, Beth continues to be a rock solid voice in the world of non-profits & web.  She’s on our A-list.

Happy birthday Beth!

No related posts.

Recently, I was in Israel for a cloud computing conference and some meetings with local VCs. The folks at Gemini, a VC firm, organized an evening with their portfolio CEOs to discuss lean analytics for startups. I concluded the presentation with a list of metrics that a web-based startup should track. I guess they were the right questions; at the end of the evening, Guy Horowitz, my host for the event, said,

“I feel bad for the CEOs of my portfolio companies that aren’t here. Their next board meeting will be miserable.”

Not measuring the right things can be fatal. And VCs are in the business of separating the soon-to-be-dead from the fledgling successes. There’s nothing quite as good at doing this as the cold, hard light of analytics. So here’s the list, with a slide deck and some examples.

Message reach

For many startups, the web is their main marketing medium. Because everything online can be tracked, it’s the perfect platform for accountability. All too often, however, we look at metrics like traffic to the site and number of followers. These don’t matter. What matters is your ability to get a message out to people effectively. If you have a cheap way of reaching many people, that’s a valuable asset.

• Your viral coefficient is perhaps the best example of this. Simply put, it’s a measure of how a message will propagate. CEOs and VCs need to know, on average, for each message they hear, how many people does someone tell?

• One measure of this on open social networks is amplification. How well are messages being amplified? This varies by social network: On Twitter, it’s the number of Retweets; on Reddit, the number of upvotes; on Facebook, the number of people who become fans.

Infrastructure health

Many companies overlook infrastructure health, despite considerable recent evidence that page load times strongly influence conversion rates. For an investor, unhealthy infrastructure is often a sign that the startup lacks operational discipline and can point to deeper problems — hacks, kludges, and the kind of duct-tape improvisation that’s fine for the prototype but won’t do in production.

• Performability, which is a combination of performance (latency) and availability (uptime) is usually enough here. But what you measure matters a lot. You need to track the health of key steps in the business process, such as the enrollment loop or the invite process. If you’re a Software-as-a-Service company, you need this data to resolve disputes with subscribers and prove you’d met any Service Level Agreements you’ve commited to.

Market sentiment

You want to know what your market thinks of you. There are many ways to grab this information, but they boil down into two major categories.

• Voice of the Customer surveys ask visitors what they think when they’re on your site.  You can get it from companies like iPerceptions, Kampyle and Foresee. This is crucial in the early stages of a startup, when you’re trying to decide what business you’re in; it’s also vital when evaluating new features, and can act as a safety net when something breaks because a frustrated visitor has somewhere to vent.
• Community sentiment is a combination of search engines and natural language processing. Several startups such as Scoutlabs try to understand what’s being said, where, and how people feel about a particular topic. It’s a hard task to do well because computers don’t understand sarcasm, but knowing when people are talking trash about your brand online is essential.

User engagement

How engaged are your visitors? Ask yourself: How many accounts have you signed up for, then abandoned? If you were to search your inbox, you’d probably find dozens of password confirmations you’ve long since forgotten about. If you’re running a startup, then the chances are good that you’ll be forgotten too. This is an incredibly painful thing to look at. But if it doesn’t make you want to kill yourself, it’ll make you stronger. You can try out new strategies to engage your community and motivate visitors to return, and see if they’ve worked.

• The best way to show this is to measure the time since the last visit by individual user, on a histogram. Here’s an example.

We like this approach because you can see, at a glance, whether people are becoming more or less engaged, as well as how big your “dead pool” of visitors is. We usually map the two on separate axes because your “dead pool” is probably very big — bigger than you expect. Sorry about that.

Lean analytics

Too often, the web data shared at board meetings doesn’t match the business plan. In the business plan of record, you have some assumptions — conversion rates, content a visitor will share, friends they’ll invite. You need to identify these goals, then report them over and over again, with trends.

• Core goals. What are the 3 or 4 assumptions on which the business hinges? If you can’t explain these — and if you don’t lie awake thinking about them every night — find another person to lead the company. This should be the most important thing in your business. Always know what core tasks  you’re hoping your visitors will accomplish, and why they correlate to business growth. If you want to change one of them, make sure your investors all agree to the change, because you’re effectively changing the business model in which they invested.
• Extended funnel abandonment. Each of these goals has a conversion funnel of some sort related to it. Often, the start of that funnel is off on a social network, where someone hears about your site or product. During the achievement of a goal, a visitor probably has to do things that aren’t on your site — opening an email to confirm an address, for example, or authorizing the site to link to Facebook. Each of these steps towards the goal must be tracked. Check out the Product Planner website for some great examples of these steps, along with metrics to watch for each one.
• Movement towards or away from business goals. At every board meeting, you should know what changes to the product moved you towards or away from a goal. If your goal is to minimize calls to tech support, then what changes helped with that and what didn’t? If the startup can’t answer this question, it’s because it’s not experimenting to see which things affect business goals, and that’s a very bad thing.
• Changes to the funnel. Seeing the funnel is useful; seeing it compared to the past period is essential. If you have an extended funnel, and you can see what changed (”email confirmation bounce rates climbed” or “fewer people clicked on the bit.ly link in Twitter”) then you know where to focus your efforts in the coming weeks.

Business sustainability

Once, companies had to decide whether or not to shut down because they were hemorrhaging money. Today, many web businesses can “hibernate”, spending only a few hundred dollars a month on cloud-based hosting while they wait and try to grow organically. That means there are three trajectories: growth, failure, or sustainable burn. Some key metrics will tell you where you stand.

• Cost per engaged visitor is simply the operating costs of the site divided by the number of visitors. It’s how much it costs to provide service to someone. This is a great metric because you can track it month over month, and understand the impact of hiring or capital expenditure on the site. It had also better be smaller than the revenues and conversion rates you’re seeing, or you’re doomed.
• Peak to average ratio. All sites experience traffic spikes. How much busier is the site at busy times — weekends, seasonal holidays, news events, and so on? What premium do you pay to be able to handle those peaks? And can you decommission well? Lots of people worry about scaling up; fewer remember that elasticity is the ability to shed infrastructure quickly and keep things efficient.
• Minimum sustainable burn. If you had to hibernate, what would the steady-state cost of keeping the site running be? This includes salaries, hosting costs, essential services, and so on. Think of it as a disaster plan. If you’re not seeing adoption, and you’re running out of runway, this is the most important number in the company.

It was a great discussion with Gemini’s CEOs, and a good opportunity to discuss what things other companies are tracking. The deck from the Gemini event is available on Slideshare and embedded below.

If you’re a VC or a group of entrepreneurs who’d like to know more about lean startup analytics, bug Sean or myself on Twitter. We’d love to chat.

No related posts.

Alex Bowyer (@alexbfree) is a research analyst at Bitcurrent in Montreal, where he blogs about emerging technologies and their social impacts, and co-organizes events such as Bitnorth and Enterprise Cloud Summit. He is passionate about using computers to solve human problems in new ways, and all the things that encompasses – user-centric design, productivity, human-computer interfaces and exploring social trends. He used to work for IBM UK, specializing in Voice systems, Java and information management.

In this post, Alex shows us that sometimes, attracting pageviews isn’t rocket science, which you might be forgiven for thinking if you follow this blog regularly:

Unlike my colleagues Alistair and Sean, I’m no analytics expert. But like all bloggers and social media enthusiasts I have an interest in sharing ideas about technology and society, and getting those ideas out to as many  people as possible.

I’ve been exploring ways to get more traffic to my personal blog, and yesterday stumbled upon something quite remarkable. In one day I was able to achieve 2,579 new pageviews, a 1,176% increase in traffic. And all it took was about 30 minutes of effort.

I’ve been using StumbleUpon’s su.pr as my preferred link shortener for some time, because as well as shortening the URL it allows me to contribute those links to my existing StumbleUpon social bookmark feed while also generating some useful stats. I noticed recently that it makes it very easy for you to “thumb up” and review your shortened links, adding them into the StumbleUpon ecosystem:

[For those who are unfamiliar, StumbleUpon is a social bookmarking service that differs from Digg, Reddit or Delicious in that allows you to click a toolbar button and "Stumble" to randomly selected pages, bookmarked by others, from across all the topics you specify an interest in.]

I also noticed that those links I had submitted to StumbleUpon had gained significantly higher amounts of traffic than those that hadn’t. So as an experiment, I went through every single blog post on my blog and “thumbed-up” the site, assigned it to some appropriate tags and stored it in the best fitting topic. (This last step is vital to maximize the chance of interested eyes landing on your page.)

I waited a day, and I think the results speak for themselves:

What was even more satisfying was to see that these were not just new visitors, but they engaged. Most of the new visitors went on to read other content on my blog, as you can see from the reduced bounce rate,

as well as 1,084 new goal conversions (albeit fairly simple goals such as the amount of time spent on the site):

I think that the reason this experiment was so successful is that people clicking that Stumble button are actively seeking “something interesting” – and are very open to new content and ideas, more so than the average web user. This coupled with the fact that StumbleUpon only directs people to pages for topics they have said they are interested in, results in not just more eyeballs, but the right sort of eyeballs.

I would be fascinated to see this approach used on a site with more measurable goals such as product sign-up or purchases*.

* Disclaimer: Stumblers can rate you “thumbs down” as well as up. Your site reputation may be at risk if you don’t keep providing value to those who rely on it.

Related posts:

1. Guest Post: How much is enough when it comes to Voice of Customer?

Google Analytics recently added a new feature, called Alerts. At first glance, it’s an elegant way to show someone when a KPI on their site has changed significantly from what’s expected. It’s baselining, applied to all KPIs — even the ones you’re not looking at.

This is a great idea for folks who forget to check their analytics data, because now they can find out about significant events. It tricks you into being a better analyst. It encourages baselining, segmentation, and thinking about your business. But we think it’s the start of something bigger, once it incorporates the things Google and others know about your online presence.

Details, and some juicy UI mockup speculation, after the jump.

Baselining, even when you didn’t know you should

Beginner web analysts treat analytics as accounting. They use it to report the news, not make the news. It’s only the more advanced analysts that see analytics as a means for optimization, using things like A/B testing to learn whether a change made things better. And to do that, you need a baseline.

The new feature learns what normal is, then shows you deviation. This encourages experimentation: “I tried something new today, and I can see the results.” Google’s already introduced comparative rankings, showing you how you’re doing against others; now, they make it much easier to identify significant changes to your site, even if you don’t know where to look.

Imagine, for example, that you change your website. You don’t see an appreciable shift in traffic volume, so you decide it didn’t have an effect. But hidden in those traffic numbers is the fact that there was an increase in European traffic at the expense of US traffic. The new functionality would show you this, allowing you to tailor content to specific geographies.

Making segmentation easy to try

The new functionality tries to find chunks of traffic that have “broken away from the pack.” It does this for known metrics and segments — such as geographic regions — as follows:

Notice that little “create segment” at the end? It makes it easy to carve out a slice of traffic you should care about, which then means you can start to play and experiment with it. Segmenting traffic is a sign of web analytics maturity, but until recently, it’s been something few people play with. Now, Google Analytics is essentially telling you, “hey, dummy, have a closer look at this.”

You can use custom segments in lots of cool ways–for example, as the analysis above shows, I now know that returning US visitors are more likely to download content from the site, but first-timers aren’t. Once you’ve seen a segment that Google found for you, you’re more likely to create your own because you understand how they work.

Thinking about your business

You can also set up custom alerts within the system to tell you when something’s gone out of kilter. We know lots of companies who use revenue or transactions per second as the first sign that something’s wrong on the website — this is a great top-down approach if you can manage it, because it means everyone in the company is focused on what actually pays the bills.

The new functionality lets you look for specific occurrences even before they happen. Consider @alexbfree’s recent post on Twitter Retweeting, which got picked up by Dave Winer. You can set up an alert to see if Dave sends you traffic:

Overall, these are excellent enhancements to the product. They’ll improve engagement — because the system will tell you when things are happening, rather than waiting for you to log in. They’ll encourage good behaviors like baselining and segmentation. And they’ll also satisfy the less business-centric, more hobbyist segment that just wants to know when the world is thinking about them.

What I really want: a holistic view

It’ll be more useful (and in keeping with the Complete Web Monitoring philosophy) when it includes other kinds of data:

• A timeline of posts created, based on Feedburner statistics or blog history
• A series of Google Alerts showing when some search criteria on the web is met
• A volume of followers or friends obtained through the APIs of social platforms like Twitter, Facebook, and Flickr
• Performance data from synthetic or real user monitoring
• Voice of the Customer feedback through systems like Kampyle

Here’s an example of what that could be like, for a content creator/blogger.

That’s a pretty intimidating amount of information. Most of it, Google already has; some, we’d get from elsewhere. We borrowed concepts from:

• Bit.ly’s historical views (over a longer time period) with a rollover for individual links on a given day
• Google Labs’ News timeline
• The dashboard of Wordpress
• Postrank’s content scoring system (we spent time with these folks this week)
• Feedburner RSS stats
• Email subscription management stats from a mailing list provider
• Moni.tor.us performance monitoring
• Trendistic’s timeline graph of Twitter (with a rollover of Outwit.me’s realtime tag cloud)
• Google Alerts, which come in by mail but could be turned into a timeline with rollovers

Anyone with a bit of time and some spreadsheet know-how can assemble this manually; it could also be done in Greasemonkey with a bit of work, using Google’s new views as the anchor.

Admittedly, this is still “reporting the news” — the real insight comes from observing correlations, such as what kinds of posts increase subscriptions or what news drives follower count. And this is targeted at a specific kind of site (media/community) whereas other businesses more focused on SaaS or e-commerce revenues probably want something that shows productivity or conversion rates.

Unfortunately, there isn’t a lot of money in giving tools to bloggers. We’re a cheap bunch. So while there’s great multivariate testing for online retailers, a content creator has to cobble together many different views and data sources to paint a complete picture.

Related posts:

1. 3 reasons why real time analytics tools are essential

We’re finishing a busy week in New York, with presentations at both Web2Expo and Interop New York. We had a great time running our first Communilytics Boot Camp, and O’Reilly’s bookstore sold out of our book.

The Communilytics stuff was really interesting; we proposed a new “long funnel” model that incorporates both community metrics (such as followers, amplification, and the like) and traditional analytics (conversion rate, checkout value, and so on.) It’s a holistic approach, and we’ll write it up here soon.

We also looked at message propagation in communities a bit. Here’s a clip from the session, which discusses how the combination of Twitter’s formalized Retweet and an understanding of relevance can create “pagerank for humans” in microblogging platforms that share Twitter’s asymmetric-follow pattern.

Completely independent of this, Alex Bowyer over on Bitcurrent wrote a thoughtful piece on how Twitter should have formalized Retweeting, and some of the issues with the current model.

Unfortunately, there’s some strangeness going on between Youtube and Keynote’s video export, so the last 30 seconds of this are clipped. Basically we make the point that this is how to monetize microblog analytics, either by selling sentiment propagation analysis, finding out who influential proponents and detractors are, or knowing where to display ads and to whom.

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1. Twitter New User Survival Guide

We’re in Amsterdam this week, presenting at a Measureworks conference on web performance and optimization and attending a Tweetup.

Our host, Jeroen, told us yesterday that since the introduction of GPSes in Amsterdam, traffic accidents in the narrow-streeted city have risen significantly. Many people are focused on their instruments, rather than looking around them. This made me think of some issues I’d seen with web advertising recently that would have been hard to detect through instruments alone, and underscored some of the shortcomings of a purely instrument-driven analytics approach.

With much fanfare, Microsoft launched Windows 7. By many accounts, it’s a good operating system, despite the widely derided launch parties they tried to encourage (which, to be fair, did get people talking about the launch.) The launch involved a massive online ad buy, as well as a new online store for the company. Two aspects of this launch caught my attention: The differences between regional stores, and the state of video advertising.

The differences between regional stores

Here’s how Microsoft is selling its wares in the US. There are five steps from landing page to checkout.

Note that you can click on the cropped images here to see a larger view of the page from which it came, if you want to better understand it in context.

First, there’s an invitation to get Windows 7 on the home page. Decent, and since I want it, I’ll click it.

Okay, I’m given some options, but the one I want most is to buy the thing. The other options are stuff like a store locator, but since this is 2009 and I have an Internet connection, I’m most interested in buying it now.

I get to choose which one I want — there are three versions. I can see pricing. Great, I’ll click “Buy now.”

I can choose some options (full or upgrade version, download or shipped version.) The defaults are okay, so I’ll click “Add to Cart”.

The cart contents are confirmed, and I can check out. I have some bonus stuff — a chance to second-guess myself on not getting the backup, which increases order cart size; a chance to buy more than one; and a chance to tell someone else about the purchase. This is good e-commerce behavior. But I’m just going to buy one for now.

That was what I expected, and I have an order, right? Well, not if you’re in Canada. Let’s try the same thing on the Canadian Microsoft site.

And now, Canada:

I start by clicking “Get it now” on the home page. This isn’t exactly the same as the image on the US site, but it’s the same text.

Okay, I’ve got the same three versions from which to choose.

The price is higher, which is too bad because the Canadian dollar is roughly 1:1 with the US dollar right now, so this is a 20% “not paying attention to International currency rates” tax. But that’s nothing new on the Internet.

I also notice that the “buy now” button is called “shop now” in Canada. I assume that’s just the result of A/B testing and that Canadians prefer “shop” to “buy” for some reason. So I click it.

Here’s what I get. Wait, what?

Instead of confirming options and putting it in my cart, I see a screen showing me the names of stores.

Let’s be clear: Store information is not what I wanted. There’s nothing on this page saying, “we’re sorry, in Canada we don’t sell products online; you’ll have to go to a store.” Forget for a minute the fact that Canadians can’t buy Windows software online — astonishing as that may be. This is a completely jarring workflow. After what felt like a normal shopping cart pattern, I was suddenly given a page that made no sense. When this actually happened, I tried the workflow three times before realizing what had transpired.

If you’re a web analyst at Microsoft, would you see this? Likely not — you’d notice a large number of exits from this page, and assume you’d done your job of informing visitors where they could pick up a copy of your new product. Instead, you are experiencing huge abandonment.

Relying on your instruments too much

As the Canadian Microsoft store example shows, sometimes your instruments won’t show you something plain and obvious; you have to use common sense. A simple message on the final page saying, “we don’t sell software online in Canada” would alleviate the misunderstanding and make the page successful. Microsoft could even have a link explaining why — and track traffic to that link, to gauge Canadian interest in online purchases, or solicit comments that might mitigate an outcry from Canadians trying to convince Redmond they’re ready to buy products over the web.

Let’s talk about video

Another part of the advertising blitz around Windows 7 is the videos they’re embedding into CTV’s player website. This is a site on which Canadians can watch recently-aired programs (similar to Hulu in the US and the BBC iPlayer in the UK.)

The CTV website isn’t very sophisticated. The most egregious problem with the site is that it can’t interleave commercials and content cleanly. Where Hulu switches smoothly between a program and an ad, the CTV site actually pops out of full-screen mode each time there’s a commercial, requiring the viewer to re-maximize the player.

Just how bad is this inability to handle interactions with online video? When Microsoft advertises Windows 7 on the site, they run a static image for thirty seconds inviting the viewer to click on an ad at right.

That’s completely broken. It underscores just how nascent online video advertising is today. The only way that CTV can let Microsoft segment its respondents is to force them to pop out of full-screen, then invite them to click on a static image which can be imagemapped.

To do video advertising properly, advertisers will soon demand:

• A referring tag showing where on the screen the visitor clicked (in case there are several visual components)
• The timecode within the ad (in case the ad covers several features.)
• The programming information (what show was it?) and other facts that can be used for demographic targeting

This stuff is available in more advanced analytics and video delivery platforms. But the fact that Microsoft had to waste 30-second video clips on a static image shows just how far we still have to go before video advertising delivers on its promise.

No related posts.

Jonathan Levitt has spent the last 5 years as a pioneer in the voice of customer analytics space. Through his speaking, writing, and evangelism, he was instrumental in legitimizing voice of customer analytics at a time when traditional web analytics still dominated the online business intelligence conversation. Jonathan has worked with world leading brands like Bank of America, Verizon, Dell, Procter & Gamble, Ford, and Reebok and has been featured in several industry publications including 1to1 Magazine, ClickZ, DM News, and MediaPost.

One of the best sources of business intelligence for companies of any size is raw Voice of Customer data.

This is particularly true for start-ups, where early, frequent, and consistent interaction with customers is critical to getting off the ground. The more customer-centric your decision making processes are from day one, the more likely you will get to the next stage in the development and maturation of your business plan.

This explains the recent growth in the selection of free and low cost Voice of Customer collection tools. User Voice, Kampyle, Survey.io, 4Q Survey (disclaimer: I helped conceive and build 4Q) — all of these are examples of popular Voice of Customer collection tools that can provide site owners with a pipeline of cheap and actionable visitor-sourced insights.

Once you put on the VoC practitioner’s hat, however, questions about respondent count size inevitably come up. Simply put, you need a way of knowing how much data is enough.

At what point can you act on the findings coming through your shiny new tools, with full confidence that you have collected a representative sample of your audience? If you’ve been running a User Voice customer feedback tool for 3 weeks and you’ve only collected 20 respondents, is that enough to act on? These are certainly agonizing questions for a data-centric marketer.

Now’s the time to start glancing over enviously at the big sites, because they don’t have this problem. The laws of probability are such that feedback from 500 respondents is usually enough to deliver reliable data at even the strictest confidence intervals. A big site like Dell.com can pull in 500 respondents within a day or two; at that clip, statistical significance comes through in a heartbeat.

But since your traffic generation muscle isn’t likely to match Dell.com’s anytime soon, I’ll let you in on a little secret: for small, startup websites that want immediate answers to their questions, the size of your sample almost doesn’t matter.

Here’s why. Representative feedback sampling requires a known population that is relatively stable and doesn’t fluctuate all that much–basically, a predictable population that will yield reproducible results. But the visitor bases of small, startup websites are anything but stable, especially if the websites are in a voracious traffic acquisition mode. The reality is that the composition of their online audiences is constantly shifting, which seriously undermines any effort at scientific VoC measurability.

This is one case where directional data can be just as powerful as representative data. I’m not saying you should blow up your website and start from scratch because of 1 piece of negative feedback, but you don’t need more than 20-25 pieces of feedback to really get started. So, forget about the science and the stats, and focus instead on segments of visitor discontent.

Look for repetitions and common patterns in your feedback; group similar items together and focus on sectional site optimization . If you notice two or three pieces of feedback that are eerily similar, then chances are your respondents are surfacing a real issue that’s resonating far deeper in your growing visitor base.

Voice of customer research can be a wonderfully responsive early warning system for a small website owner. Don’t get caught up in obsessing over respondent counts. If you’ve got 25 or so pieces of real visitor feedback at hand, you can go a long way in constructing a visitor-centric website experience that will help your website to grow and flourish.

Jonathan Levitt

Blunt – The Conversation Agency

Related posts:

1. brilliant example of surveying users about a feature before it is built
2. Guest Post: How I increased traffic 1,176% in 24 hours

There’s been alot of talk lately on the idea of prototyping and demonstrating a product to solicit feedback.  The catch?  The product isn’t actually built.  This goes hand in hand with the MVP concept practiced by lean startups.

I was on BackType today, and I just witnessed an awesome example of this concept in action.

BackType is a search engine that indexes millions of comments across social media platforms and lets you query for particular topics that interest you.  Cool, right?  I performed a search query for “analytics”, and the resulting page had a small tab called “Trends”.

Curious, I clicked on it and was brought to this page:

Drat, it’s not released yet!

Brilliant!  BackType has given me an excuse to come back and check to see if the tab is active.  Even better – they were able to collect my expectations before the feature has even been released (or built!).

This is product management crowdsourcing at its finest.

Well done, team BackType.

Related posts:

1. Guest Post: How much is enough when it comes to Voice of Customer?

We had a good discussion about performance and its impact on KPIs like analytics and conversion with Strangeloop this week. Here are the slides, available for download or viewing, on Slideshare.

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