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Why You Should Stop Measuring Site Satisfaction

Ok, so you shouldn't stop measuring how your visitors feel, but I maintain it is far more beneficial for you to measure site dissatisfaction.

It is a well-known fact that consumers are far more likely to discuss and share a bad experience than a good one. In fact, most estimates put the number at 12, angry consumers will tell 12 people about a bad experience they have, and you're lucky if they tell one about a good experience.

Additionally, in today's technology hungry world, bad news can spread faster, and wider, than ever before. Consumers are no longer just telling their family and friends, they are sharing with complete strangers. Bottom line is, bad reviews hurt business, so the more angry customers you have the worse off you are.

I could go on-and-on expounding on the many perils of a bad consumer experience. But suffice it to say, angry customers are far more detrimental to your business than satisfied ones. So why then do a majority of companies focus only on the satisfied portion when measuring site satisfaction?

I believe it would make more sense to focus on the unhappy, or on-the-fence customers, and figure out what it would take to convert them to satisfied. No matter how you slice it, this would lead to increased revenue.

Thoughts? Comments? I'd love to hear what everyone else thinks!

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“Half the money I spend on advertising is wasted; the trouble is I don’t know which half.”

- John Wanamaker

But it doesn't have to be that way!

I just came across a very interesting take on the importance of consumer data. Dedric Choi, at iMedia, wrote an article in which he explains:

• The kinds of data that matter vary widely from advertiser to advertiser
• With the rise of real-time ad exchanges, data become at once more necessary and more powerful
• More information allows for more precise targeting and increases the chances of finding specific attributes that lift performance

I, like Choi, felt the pain of advertisers and publishers alike in the situations he illustrated:

How many times have you worked with a client and found that its idea of the target audience for the brand or product is significantly different from the actual audience? Similarly, how often have you looked at data around a "targeted" media plan and found that less than 50 percent of impressions were served against your target? Or you were told a site indexed 80 percent female, making a great fit for your brand, only to find 40 percent of your impressions were served to teenage boys.

Crowd Science understands that this is a day-to-day struggle for so many advertisers and publishers. That is why we created a demographics platform product from the ground up to address this problem.

Check out the article here: The Growing Importance of Consumer Data

WSJ.com Considers Blocking Google

Lots of talk today about Rupert Murdoch's suggestion that News Corp might start blocking Google from premium properties, like WSJ.com, in an effort to drive up subscription-based revenues.

Hitwise shows that Google and Google News account for 25% of WSJ.com's traffic. Bill Tancer, general manager for global research at Hitwise, also commented on the behavior of those visitors:

According to Experian Hitwise data, over 44% of WSJ.com visitors coming from Google are "new" users who haven't visited the domain in the last 30 days.

And concludes that it might be a mistake:

While Mr. Murdoch makes some strong points in his Sky News interview regarding the plight of the news industry and the perils of making all content free, as clickstream data demonstrates - blocking Google could isolate the Journal from potential new online subscribers.

He may well be correct. But I'm not sure we have enough evidence to be certain.

Like Mike Hudack, I think there is a counter argument here about there being much greater value (in terms of lifetime revenue) in the non-Google visitors. Anyway, it's nearly 2010. The decisions about content being free were made way back in the mid 2000's. It's healthy for the big content providers to experiment.

BTW, if anybody from News Corp is reading and would like help in figuring out what else distinguishes the Google visitors from the non-Google visitors, give me a shout, Crowd Science can help.

Hybrid is the New Black - T.S.Kelly

Hybrid is the New Black

Part-time Crowd Scientist, T.S. Kelly, recently penned a post on his blog, The Media Strategist, that explores the "mixocology" going on in today's audience measurement marketplace. It's called Hybrid is the new BLACK -- which happens to be one of T.S.'s favorite sayings -- and it's definitely worth a read.

Continuing Debate Over the Problems with Optin Surveys

Some really interesting research out of Stanford just landed in my RSS reader. This blog examines - and summarily refutes - challenges to a study finding that non-probability Internet sampling delivers poorer quality data than probability sampling conducted on the Internet or telephone (RDD).

The study, whose roots began in 2004/5, with further investigation in 2009, shows that non-probability Internet samples, which would refer to opt-in samples such as those from Internet panels, yield results of significantly lower accuracy than RDD or probability Internet samples. This holds true even when weighting of the data from opt-in samples was conducted in an attempt to make the sample more representative.

The current blog addresses various questions regarding the currency, relevance, and validity of these findings – with the answers to these questions unwavering in their support of the original findings. For example:

• The initial findings from 2004/5 still hold true in 2009, in spite of any potential changes in either Internet or telephone sampling methodologies.
• The differences between the probability and non-probability samples were significant.
• Findings from this investigation do not rely on cost-prohibitive or specialized methodologies.
• Differences in results between probability and opt-in samples were not due to a lack of proper balancing or weighting. In fact, while no balancing was applied to the two probability samples, balancing and weighting were applied to the non-probability samples but did not eliminate the error typically found in opt-in sample surveys.

We're (obviously) big cheerleaders for probability Internet sampling here at Crowd Science (it's at the core of our Research Platform), so it's good to see some further evidence from the academic community.

Check out the blog here: The Numbers

Check out the original research here: Comparing the Accuracy of RDD Telephone Surveys and Internet Surveys Conducted with Probability and Non-Probability Samples