A recent study posted in AdAge would have you think so. The study, conducted by an agency called 360i (direct link to the data here) examined 1800 tweets and determined that only 12% of updates posted to Twitter mentioned a brand – and for most of those, the brand was Twitter itself. The implicit conclusion: no one is talking about your brand on Twitter, so brands can direct their attention elsewhere.

I’m certainly not going to argue that Twitter is anywhere near as significant as other channels, and it is true that many marketers use Twitter like a big “easy button” for the Internet. Pushing it tends to give you immediate feedback, which is gratifying, but also tends to summon other marketers.

I’m not willing to go completely down the road of irrelevance based upon this study, however, for one important reason. The AdAge article states that, “after spending six months going over a statistically significant sample of 1,800 tweets,” the researchers were struck by how mundane most were. It is true, by the way, that 1,800 tweets would give you a statistically significant sample – I know, given the metric tonnage of tweets the service throws off every hour that it seems like a small number, but (cue Sly Stallone voice) Sampling Is Da Law.

The problem with the sample isn’t the fact that the N=1800. 1800 respondents would be fine if you sampled them all at once. If I drank from the firehose right now and just grabbed 2000 truly random tweets, you could actually draw some valid conclusions with a 2-3% margin of error on the total. However, that isn’t what this study did – instead, they looked at 1800 tweets over a six month time period. If we assume that they were spaced out regularly, that’s 300 a month, or roughly 10 per day. Even if we accept one month as a valid sampling time frame, what this study gives you isn’t one 1800-person sample, it’s six 300-person samples. Bolloxing them all together gives you a big number, but introduces a longitudinal bias into the data that’s pretty much a non-starter as far as I’m concerned. We weren’t talking about BP six months ago, for instance. And we aren’t really talking about Toyota now.

That’s about as far as I’ll go here to disparage the data, but it does beg the question – why so few? Harvesting tweets is, as far as I know, essentially free and relatively painless. Why not analyze 50,000 of tomorrow’s tweets and see if you get the same result? Yes, it’s work. That’s kinda what makes it worth doing.

Is Twitter Irrelevant For Brands? is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

Here’s a stat I’d love to know – what percentage of retweeted links on Twitter are never actually read by the retweeters? So many of the current crop of Twitter influence/trust measuring services place a great deal of weight on the “retweet” – perhaps more than is healthy. Some may factor retweets in as just one component of their algorithm, but then use other third-party Twitter measures (that are also largely based on retweets) as other components, which may actually compound the error. What I don’t know, though, is if any of these services measure the “blind” retweets – those links passed along without being actually read by the retweeter – and if those particular retweets are treated differentially.

The fact is, just as there are multiple reasons why someone might retweet a link, there are equally numerous reasons why someone might retweet a link they didn’t actually click on. Some of these are innocuous enough (they might have already read it, for instance, as Matt Ridings reminded me), while others may be purely sycophantic. Some retweets stem from a genuine trust in the original source; others are simple gamesmanship. That a retweet – especially of unread content – implies influence of some kind is probably axiomatic; however, since the motive for a retweet can’t be parsed by a machine, the degree to which a blind retweet implies trust can’t really be discerned with an acceptable degree of certainty.

So, here is my open question to you, dear readers. Is a retweet of a link I did click on qualitatively or quantitatively different than a retweet of a link I didn’t click on? I’d especially love to hear from anyone in the Twitter grade/level/clout measuring business. I’m open minded about this – convince me in the comments. The floor is yours…

The Blind Retweet is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

This report claims it can, and the New York Times ran with it. Once that happened, the Twittersphere started tweeting it (presumably, making Twitter users “happy.”) Anything to this? Well, I’d point out that as of earlier this year, only about 7% of Americans were even on Twitter. The data was also collected from 300 million Twitter messages stretching back to 2006. The first time we ever added Twitter usage to our tracking surveys was in late 2007 – when Twitter usage was essentially a rounding error and not even representative of .1% of Americans. So this data, while colorful and interesting as a snapshot of Twitter, can hardly be billed as representative of the mood of the nation.

Still, I’m not going to beat up what is a fascinating look at the mood of persons who have updated their status on Twitter (which is not the same thing as Twitter users.) Where such a study becomes interesting to a researcher like myself would be if a similar chart could be constructed of the actual “National Mood,” and then compared to this data. If you had both datasets, then you would not only have the true National Mood (you don’t need the Twitter data for that), you would also have a valuable means of calibrating the tendencies of Twitter users by examining where they differ, and where they resemble, the 90+% of Americans who don’t use Twitter. For instance, our last data on Twitter users revealed that they were more optimistic about a U.S. economic recovery – but is that due to their own economic circumstances, or are Twitter users actually generally more optimistic people? For social media researchers, that is pretty valuable stuff.

So, “pulse of the nation,” as the article claims? No, but pulse of Twitter, yes. Comparing the two would be a seriously interesting study, but one I’m in no mood to conduct.

Can the U.S. Mood Be “Inferred Through Twitter?” is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

Last night, I attended a minor league (AA) baseball game in North Carolina (where I live) and was struck by how active the team was on various social media platforms. Before the game even started, they were showing pictures of the “Facebook Fan Of The Game” from their fan page, which I later learned had well over 3,000 members – not too shabby! I was also struck by the number of times the PA announcer asked fans to “join the team’s Facebook Fan Page and follow us on Twitter” for news, discounts and special promotions.

This is a team that is doing everything correctly, right down to their social media calls-to-action. Yet, I was struck by this phrase – “join the team’s Facebook Fan Page and follow us on Twitter” and how neatly it encapsulates the huge usage disparity between Facebook and Twitter that we observed in Edison’s recent research on Twitter Users in America. Some of the people who’ve studied this report expressed surprise that there was such a tremendous difference between the percentage of Americans using Facebook, 41% at the time of our report, and those using Twitter, which stood at 7%. Yet awareness for both social media services was equally high – roughly 87-88% awareness for both amongst all Americans (online and off.) That there is a disparity is not a shocker, given Facebook’s considerable head start. Why, however, given nearly equal and ubiquitous awareness, does actual usage of Twitter trail so significantly behind usage of Facebook?

I think one answer lies neatly within the example provided by this minor league team’s promotion. The phrase “follow us on Facebook and Twitter” is repeated so often that it begins to resemble what my friend Dennis Clark calls “chickenorfish” syndrome: when the flight attendants rush down the aisle with their carts, robotically asking everyone “wouldyoulikechickenorfish,” commoditizing both and making neither particularly appealing.

In the case of “followusonfacebookortwitter,” almost everyone who maintains a social profile online is on Facebook, so if that is sufficient to get news, discounts and special fan promotions from the team, why would the average American bother with Twitter? Yet Twitter is a different dog, and its asymmetric nature cries out for differential treatment by businesses from their Facebook fan pages.

The team, I want to emphasize here, is really doing nothing wrong, and just about everything right. It isn’t their responsibility to teach people how to use Twitter, or encourage its adoption – it’s their responsibility to engage fans, and be everywhere they are. Ultimately, the responsibility rests with Twitter itself – the company – to reach out to the many media outlets tacking Twitter onto their “chickenorfish” promotions, crafting exclusive offers, and making the benefits and differential uses of Twitter for businesses not only crystal clear, but of value. Location-based networks like Foursquare and Tri-Out, along with online coupon providers like Groupon, are already doing this now at the local level.

I’ve noted this before, but it is incumbent upon Twitter to clearly articulate the unique value, usage and benefits of their service, before teams like the one I saw last night begin to craft their own differential strategies using Facebook for engagement, location-based services for sales promotions, and Twitter is left as the odd man out.

By the way, I hope you are enjoying my humble efforts here at BrandSavant – look for even more to come in the near future, including some great guest posts and an updated “news” section to keep you apprised of the latest social media research. Don’t miss out – subscribe today to have BrandSavant neatly deposited in your RSS feedreader or email box. And thank you.

This post originally appeared at the Edison Research blog.

The Twitter – Facebook Disparity is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

A short thought for “Follow Friday“: spend a little time searching out people who take you well outside your comfort zone. I wrote more about the Twitter “self-selection bias” a while back, but to put it plainly, if you are only following people you agree with, sooner or later Mr. Social Darwin will catch up with you. I was reminded of this while reading the comments to Tamsen McMahon’s thought-provoking and oddly controversial piece, “Clique Clique…BOOM” over on the excellent Brass Tack Thinking blog she cowrites with Amber Naslund.

I won’t recapitulate Tamsen’s piece here, because you will go read it as soon as you finish this, right? What I appreciated about the piece was the level of constructive debate in the comments, from people who took issue with Tamsen’s stance on the “clique.” One notable dissenter, Mark Schaefer, made an interesting point regarding what he termed “the blogger’s pack”: the social web is an economy of favors.

Mark’s meaning is clear: prominence on the social web is driven by a simple mechanic: you scratch my back, I’ll scratch yours. You promote my e-book, I’ll give your online course a favorable review. And so on. Do you agree with this? I think in many ways it’s empirically true, but only if your horizon is short.

So, here’s what I think (I wrote this there, and I’ll repeat it here). In the short term, he might be right. In the long term, the social web is an economy of ideas. The difference, for the individual, is backing the right horses. You are who you retweet.

So, this “Follow Friday,” promote someone with a lasting idea, regardless of their “clique.” Not only will it be good for your soul, it’ll be good for society. Mr. Social Darwin guarantees it.

Is The Social Web An “Economy Of Favors?” is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

The short answer is: a lot. If your social media efforts are strictly tied to tweeting out coupon codes, then you have a pretty direct and reliable measure of the efficacy of your efforts: simple conversion. For the rest of us (and, that’s pretty much 99% of us), online measures are not going to capture the full impact and value of social media for your organization. There are lots of smart folks in social media monitoring and sentiment analysis trying to crack the ROI nut, trust me – but mining unstructured data alone will never truly quantify the value of online engagement to offline sales.

For instance, Southwest Airlines (SWA) has a notable presence in social media, particularly with their Twitter account at @southwestair. A lot of people talk about them online, and a lot of the chatter about SWA (particularly in comparison to some of their U.S. competitors) is positive. Buying air travel, however, is not a spur-of-the-moment decision for most people (myself excluded ) There is likely to be considerable distance between the point of influence – being favorably predisposed to SWA through their social media interaction – and the point of purchase. In the intervening time, SWA is also likely to run some kind of sale or promotion, and while it may have been that promotion that actually prompted the purchase, it may also have been Southwest’s online behavior that put them into the purchaser’s consideration set.

See where I am going with this? If the offline sale gets the “credit” for conversion, the efforts expended in social media – however important – get little or none. Parsing out the impact of cross-channel media on purchase behavior is a bit of rocket surgery, but well within the purview of a competent CMO – provided, of course, the right inputs are available. If you are only measuring your social media efforts by mining unstructured online data (monitoring, sentiment analysis, etc), then you may be capturing enough to track reputation, or the health of your brand on the social web, but you aren’t tracking enough to make the connection to purchase behavior, particularly in longer sales cycles.

This isn’t to say that you shouldn’t be monitoring or mining this data online; it’s an essential input to the process. You do, however, need to augment it with offline inquiries. This may mean commissioning additional online or offline research projects (depending on where your transactions actually happen), or perhaps adding a few social media indicators into the data you already collect, but the bottom line is that this isn’t really a mystery. It’s done every day by marketing departments all over the world, for other channels and media.

Here’s what prompted me to go down this particular rathole today: I am currently working on a project to measure the impact of a campaign that will target visitors to minor league ballparks across America. The campaign, which has a variety of components, hasn’t run yet, but we are measuring today, in the actual ballparks, before a single tweet, billboard or radio ad has begun. By conducting offline pre-engagement measures at those ballparks, we can sample the fans and develop a reliable baseline for our client. Later, after the campaign has run, we’ll conduct the exact same measures, in the exact same way. Our client will know precisely what worked and what didn’t work (online and offline), because we will have the apples-to-apples, before-and-after analysis to determine what components were successful, and what aspects didn’t perform.

Pretty easy stuff, really: measure before, measure during, and measure after. Measure online, but also measure offline. Works for out-of-home media, works for TV and it will work just fine to quantify the value of your Twitter account. Mining online data can give you a snapshot of what people think about your brand or product on the social web, and tracking this data might even give you a sense of how these perceptions change over time. For most brands, however, the actual behavior change occurs elsewhere. It might happen in an Amazon shopping cart, at a car dealer, or even at a local ballpark. When you can sync your online monitoring efforts with offline measures, one calibrates the other – and the true ROI of social media can be measured, understood and appreciated. For my friends in the social media space, that is what I want for you.

What You’re Missing By Measuring Social Media ROI Online is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

Karl Havard posted a provocative article this week on Econsultancy entitled “Social Media Monitoring: Time To Say ‘Sod It’?” (for my non-Anglophile friends, “sod it” is a nicer way of saying “F@%k it.” Just so you know.) His contention: as more and more people join the social web, the task of monitoring and responding will become more and more onerous, and ultimately unscalable. After all, how many conversations can you possibly have at once? At what point does responding to “everything” become unsustainable?

I can’t speak to the potential tactical burdens of the growth of the social web, but my suspicion is that as more and more middle-of-the-bell curve folks join in online conversations, the ratio of people talking about brands will diminish, not increase, and the corresponding burden on a company’s customer service/social response teams will not grow linearly, but begin to taper off. Besides, the best way to conserve resources on the reputation management/customer service side of things is not to stop listening, it’s to make better stuff, right?

In any case, I don’t think companies today have any obligation to listen and react to every conversation online, let alone in a future where everyone who is going to be on the social boat is all aboard. Social media monitoring does, however, have to prove its worth beyond mere tactical interaction in order to evolve and become more central to the theory of the firm. In order to do that, social media monitoring has to graduate from “fire-fighting” app to a reliable source for consumer insights. Today, monitoring enables companies to find and respond to people having issues with the widgets they sell; tomorrow, it will enable companies to design better widgets.

The only way that social media monitoring can make this leap is if companies can trust the data they see as representative and reflective of reality. That’s a little dicey today – the high percentage of Twitterers having brand conversations is less indicative of the actual prevalence of those conversations online than it is the size of Twitter’s fishbowl. When you don’t know who isn’t on the social web (and who isn’t having brand conversations), you can’t model non-response bias. When you can’t model that, then you can’t reach any projectable conclusions from what you do hear on the social web. Do the 5-6 people piling on your brand on a message board speak for 500? 50,000? Just themselves?

When more and more people join the social web and, perhaps, engage in brand conversations, then yes, the burden of listening to each and every conversation will increase. But sampling those conversations, which is what a competent CMO or market research analyst would do, will get more reliable and representative. The more “regular” people chatting about brands online, the more representative a sampling of those conversations will be – and the more important social media monitoring will become as it graduates to genuine social media research.

So, to sum up, more is not worse. More is better. And the more people participate in the social web, the more important it will be to have ears to listen. The key is to be able to see the forest, and not just the trees. Or the sod.

Is Social Media Monitoring Worth The Trouble? is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

When I publish this blog post, a plugin will dutifully tweet you all that I’ve written something new here on Brandsavant. You’ll get a tweet with the title of the post, and some kind of shortened link. Later, I’ll check my tracking dashboard, and see how many of you clicked on the URL. I’m partial to Argyle Social, but you might use Bit.ly, Hootsuite, even Google. In any case, you probably do the same thing, sometimes without even knowing it, all over the social web (not just on Twitter.)

It’s an ego thing, I admit. There isn’t much of a business reason for me to track this post, for instance. I like to see how I did. I bet you do, too. I surely didn’t need to, though. Yet I find myself looking at my link tracking dashboard every day, glancing over my “hits” and “misses,” the links you clicked on, and the links you didn’t. When you don’t click, I feel bad. When you do, I get a little charge. Same for you, maybe?

I wonder, though, if we are all tracking a little bit too much. When I share a URL with you, there are three components: the headline, my own credibility, and of course the link itself. Sometimes I write a catchy headline, and a post “does well.” Most of the time, I write a pretty average headline, and the clicks it generates are based largely on my own credibility – whoever clicked, probably did so because they trusted me to share something of value.

What’s interesting about our addiction to URL shortening, and all of the helpful social media clients we use to share our links that do the shortening for us, is that the third component – the link itself – no longer carries any inherent trust. If I share a link with you about the oil spill, and the link is clearly from cnn.com or the New Orleans Times-Picayune, the link itself is a visible clue that I’m passing along something potentially more trustworthy than if I passed along a link from funnyoilspilljokes.com. The actual link is a component of trust – one more clue that you should (or shouldn’t) click on something I share. But, because we are all addicted to tracking (and even if we aren’t, all the social media clients we use assume we are), all of our links look the same: http://crap.ly/crap.

Yes, I know that some Twitter clients will extrapolate the original link from a shortened one and show the full reference. I also know that most people don’t use these clients, and anyway we are using shortened links everywhere, not just on Twitter. I am also aware that Twitter’s new link shortening service will also show the original link, but again – if we are addicted to tracking – that link will also be a crap.ly/crap link. The real question is, can we give up our addiction to tracking, even for pithy little posts like this one, so that our status updates aren’t continual streams of dri.vl?

I’m going to continue using link shorteners to track some things for business reasons, but maybe lay off them a bit for all the personal/fun/purely social stuff I share. I guess where I am going with this is that if you see that the URL I am sharing is from a trustworthy source to you, then you don’t have to solely rely on my credibility to decide whether or not you click that link. And if you do click that link, and you do find it useful, then maybe you trust me a little bit more. But if you didn’t click on the link because its source was obscured (and thus not a trust component), have I lost a tiny opportunity to build trust with you for the next time I want to share something? What do you think?

Finally, if I’m being honest, I posted this on Twitter with a trackable link. I hope you clicked – it makes me feel good. No, I’m not an addict. Yes, I can quit anytime. This is the last one, I promise.

Trust And Our Addiction To Link Shortening is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

Yesterday I read a critique of a new study that represented itself as empirical research on an aspect of social media. The critic noted that he knew the study would be crap as soon as he saw the word “empirical” in the title.

It is true that much violence has been done to the truth in the name of empirical research. However, empirical research is not “anecdotal” research. Empirical research is observation, pure and simple. When minorities in the US sought to prove housing discrimination, it wass empirical research that provided the hard evidence as to whether there were, in fact, disparities in home ownership. The same is true for demonstrating bias in credit or mortgage decisions. In fact, the first step in showing any bias whatsoever is to first observe whether or not there even is an actual disparity. Empirical data allows the would-be arguer to determine if there is any factual basis for further exploration.

Therein lies the key to properly slot empirical research – empirical research cannot stand in for that “further exploration.” Drawing conclusions based upon empirical research is, by definition, inductive reasoning, and likely flawed. But the fault there lies in the researcher, not the research. Empirical research by itself does no violence to the truth.

So, before blowing off the results of any study as “merely empirical research,” don’t forget some of empirical research’s greatest hits, like penicillin, the Census, the 24-hour clock and the movement of the planets. How they move, and why, are likely more philosophical discussions. How far they moved, and when – this is the purview of empirical research. The key is – did the study imply some kind of conclusion that fallaciously confounded correlation with causality? Or did the readers of the study make that mistake through inference? In either case, the fault lies with a human, but the empirical research itself is likely blameless.

The Value of Empirical Research is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

A recent post on Search Engine Land highlighted a small survey of businesses currently advertising on Foursquare. The headline for this post, “Only 10% of Business Would Pay For Foursquare” sits squarely above a gigantic pie chart showing the big difference between 90% and 10%. The article draws the conclusion that this 10% “willing-to-pay figure has to be somewhat alarming” for Foursquare. This might be true, and I don’t really want to knock that conclusion in this space. Yes, 10% is a small number.

Keep in mind, however, that only 7% of Americans are even aware of location-based services. That’s the small number that Foursquare and others in the space might really be thinking of, because that’s a fixable number. That’s a number that will grow, and when it grows, the import and business reach of these location-based networks will grow with it. Which will, in turn, cause the first small number noted in this post to grow as well. If awareness of Foursquare were as high as Facebook (88% of Americans), then they might have cause for alarm, because that would be a clear sign that they were truly in danger of being marginalized.

As a guy who makes his living telling stories with numbers, I’ll close with this thought: if the survey quoted in the Search Engine Land post were really projectable to all businesses (it isn’t, but work with me here), then 10% of all businesses would pay for Foursquare. I’d take that business in a heartbeat. BMW and Lexus both have market shares of about 2% in America. There is big money in small numbers.

The Big Money In Small Numbers is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

The past few years have not been kind to commercial aviation. Flying is a significantly greater pain in the keyster than it used to be, and a year of freakish weather hasn’t helped much. When I moved from NYC to greener pastures, I knew there would be tradeoffs–sure, my mortgage is now less than I used to pay to park my car, but flights are a lot less convenient and I now fly a lot of “regional jets,” or as I like to call them, “sh*ttyjets.”

Still, there are tremendous benefits to using an airport that doesn’t require a train to get from the parking lot to the terminal. You may not be able to move to a less congested airport as I did, but there are a couple of other tricks you might find useful.

1. Join an Airport Lounge. This, to me, is not a luxury, but a necessity. $300-$400 just to have a bar and a cushy chair? Au contraire, mon frere. It is upon this little tip that the rest of this post depends. First of all, you needn’t put all your eggs in one basket, airline-wise; I belong to Priority Pass, which lets me use a bunch of different lounges in every city I travel for just $400.00 a year. If you don’t travel as often as I do, you can get a cheaper membership that allows 10 visits plus the ability to purchase more access (at 25 bucks) as needed for only $250 bucks.

   Why is this worth it? Consider what $25.00/visit will get you:

   Wi-Fi ($9.99)
   A nice adult beverage ($8.50)
   An outlet for your laptop that doesn’t involve you stretching your cord across the jetway (pricele$$)
   Quiet (1 Meeeelleeeeon dollars)

   Here’s the thing–if you travel ten times this year, you will be delayed at least six times. You might even be really delayed. This causes stress. You are stressed for two reasons: 1) you will be late to your destination, and 2) you are helpless, out of the office, and unable to be productive. Can’t help you on #1, but I can work like the dickens in a nice quiet lounge. In fact, there is nothing I need to work that I can’t get in an airport lounge. If you are stuck for two hours in an airport, it is astonishing what you can get done with your phone, a desk, reliable wi-fi and some peace and quiet. Plus, if you are at the end of your workday, enjoy a nice adult beverage while you catch up on emails and summaries. Now, isn’t that worth $25?

2. OK–so you got your lounge membership. Now your day just became incredibly flexible. My number one delay-beating tip is to always take the first flight of the day where possible. For my frequent trips from RDU to PHL, that amounts to a Southwest flight at 6:10 am. Unthinkable, you might say–but that flight leaves on time, every time. The later the morning gets, the more airports like LAX, EWR and ORD get stacked up, even on sunny days. If there is bad weather anywhere along the flight corridors to Newark, incoming planes go into this funky chicken dance called SWAP (Severe Weather Avoidance Plan) which increases the distance between planes in the area even if your weather is perfect, and you aren’t even going to Newark. EWR is the devil.

   Also, not as much happens weather-wise at 5 or 6 am. If you travel in the southeast in spring/summer, you can pretty much set your clock around having a boomer around 4 o’clock, which snarls up the rest of the day. Getting out while it is still cold and dark means that all those happy air currents are just as sluggish and lethargic as you are, so they aren’t yet causing trouble in the mosh pit of the upper atmosphere.

   Finally, just as the roads get busier from 7:30-8:00 on, so too do the runways. If you leave on a 9 o’clock flight, I can pretty much guarantee you that you will hear these words from the cockpit: “Ladies and Gentlemen, we are currently number 27 for departure, so we’ll be shutting down the engines for a while. We’ll update you on our estimated blah blah blah…” Yuck. Leave at 5:30, and all you will hear is “Flight Attendants please be seated,” which is pure magic (unless you hear it at 32,000 feet).

3. What if you can’t get a flight that early? For example, maybe the best or only flight you can catch doesn’t leave until 9:30. Simple–pretend it leaves at 6 anyway. Let me tell you, getting to the airport in any city at 8 o’clock is a dicey proposition. Then once you get to the airport, you have to go through screening with every other Tom, Dick and Harriet trying to catch their flight as well. So, your commute gets longer, and the wait to get through security gets much longer. Go through security at 6 am, on the other hand, and you will sail right through.

   C’mon, Tom, you aren’t seriously suggesting I go to the airport 4 hours early to catch my 10:00 flight, are you? Well, yes…I am. That is part of being a grown-ass businessperson. You aren’t going to the office anyway, so no one will even know you did it. Instead of spending an extra 45-60 minutes in traffic/security/hell, you are spending it in the nice cushy lounge you paid for in #1, above, getting ahead.

   Now do you see what 25 bucks just bought you? An hour or two of stress-free, productive time. So when your flight does get delayed, or you have to circle above Miami for 45 minutes to wait out a storm, you started your day off like a champion.

4. Finally, Mitch Joel and Chris Brogan are big fans of this Eagle Creek wheeled carry-on. I am not, because I don’t get to fly in full-size, grown-ass jets as much as they do. Try sticking any wheeled carry-on in the overhead compartment of an RJ or (my favorite) a Dash 8, and watch the looks you get from your flight attendant. Sure, you can gate-check, but that’s what everybody does – which means you’re waiting for your bag one way or the other. Get yourself a Red Oxx Skytrain or a Tom Bihn Tri-Star (these are not affiliate links, and I love these bags.) Lose the wheels and frame of a wheeled carry-on and you can carry more stuff in less space. Try it for yourself – these bags are black holes. I take one plus a small laptop bag on two-week trips to the middle east and can sail through from Embraer to A380 without a hiccup.

Happy trails, crappyjet traveler.

The Small Market Road Warrior is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

If you are thinking about introducing a new product, or making some tweaks to an existing product, there are a variety of ways to use market research to help hone your offering and optimize pricing. You might want to use qualitative research to determine why a customer might or might not be attracted to your product, and you might use quantitative models to determine pricing, or feature sets. If, however, your goal is simply to determine whether or not your customers will actually buy a given product, there is no better predictor of behavior, than behavior. In other words, if you want to find out if there is interest in a given offering, one of the best things you can do is just offer it to a sample of your customers, and see if it works, or if it needs to go back to the drawing board.

If you are fortunate enough to have a reasonably large email database, then testing a product with a sample of that database is a pretty sound way to proceed, especially if you have multiple offerings that you wish to compare. This allows you to try different combinations of price and features with various segments of your database and see which ones have legs, and which ones need to go back to the drawing board.

Some of you may have access to robust email marketing suites or other marketing automation tools that can help here, but if not, here is one way to get the most out of this kind of quick and dirty email marketing “research.” The key is in proper sampling (isn’t it always?) You will want to ensure that you are getting a truly random sample of your email database for this to really be of use as decision support – if you take the first 100, or the last 100 names, for instance, you are imposing a bias on your efforts by using a disproportionate number of early (or late) adopters to your product or brand. Truly random sampling means that every member of your database has an equal, non-zero chance of being selected.

So here is the official BrandSavant way to do this yourself:

1. First, you need to have your email database dumped into Excel, so that every record (person) is on a different row. Pretty much any list manager you might use probably exports to Excel, or at least to .csv, which Excel reads nicely.
2. Insert two columns at the beginning of your database (in other words, add blank columns “A” and “B” to the left of your first column, shifting the first column of your database to “C”.)
3. In A1, enter RAND() into the formula box.
4. Highlight A1 and then click and drag your cursor straight down column A until you get to the bottom of your database. Every cell in column A should now have a random number between 0 and 1.
5. With the random numbers in Column A highlighted, click Edit->Copy (or control-c) and then click in cell B1. Under Edit, select Paste Special. Paste as “Values.” This puts a static copy of each random number in column A into a corresponding cell in column B. This is important, because the numbers in Column A are not fixed and will regenerate every time you do anything. The numbers in Column B are static and can be sorted, which is what you are going to do next.
6. Under Data, select sort, and then sort by column B, ascending (alternatively you can click in cell B1 and click the A->Z button, if your toolbar has that.)
7. Voila. Your database has now been randomized. You can select the first 100 names and send them your test offering, and send other versions to records 101-200, 201-300, etc.

Now, 100 is a smallish sample by academic standards, so you are really looking for big disparities in response. But if you are doing A/B testing, at least you can tell yourself that you’ve made a solid effort to equalize the samples for Offer A and Offer B. And you get a BrandSavant official stamp of approval. Attaboy/girl.

Quick And Dirty Email Product Testing The Right Way is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

Some years back, I attended a talk by Dean Hachamovich, who heads up the IE team at Microsoft. Dean recounted what he saw at the time as the three phases of the Internet. We began with “Browse,” when we all got our first AOL accounts and search wasn’t very good. We killed a lot of time this way.

Then, search got a whole lot better, and we didn’t have to rely on vertically-scrolling monstrosities of portal pages to find what we needed. This new era, the “Search” phase, made things much more efficient, as directed information-gathering replaced much of the inefficient browsing we used to do for information.

I think I saw this talk back in 2005, which is when Dean indicated we were on the cusp of the next, third phase of the Internet – “Subscribe.” Though he saw search continuing to be the dominant paradigm in the near term, he believed that continuing development of RSS and other tools would empower consumers to search once, and subscribe to those searches to get information, which would be an even more efficient way to interact with the Internet than Search.

A funny thing happened to that model in the past couple of years, however: social media. About half of all Americans have a profile on one or more social media sites, and while subscribe remains one of the dominant paradigms, it has morphed from subscribing to information streams into subscribing to people.

I thought about these models today when I saw a tweet from Jeff Jarvis about Bit.ly’s recent growth, and how more Bit.ly links were decoded in May (4.7 Billion) than Google links to publishers (about 4 Billion). It struck me that when I click on a Google link, it’s because I searched for something. I knew exactly what I wanted, and I’m good at search. A lot of us are, now.

What I do when I click on most Bit.ly links, however, is something entirely different. If you think about the URL-shortened links you typically click on, I bet you’ll find that you come across these the same way I do – on social media platforms, through some kind of sharing mechanism. In short (pun intended), you found them while browsing.

In truth, what advances in search and subscribe have really done for us is to give us better filters for browsing. We like to browse. In fact, we need to browse. True, the ability to search for and find exactly the information we need is crucial. But what about the information we don’t need – or better, don’t yet know that we need? My wife the scientist tells me that the only difference between basic science and applied science is that basic science just hasn’t been applied – yet. Making serendipitous connections, seemingly at random, is how many breakthroughs really happen. What precedes great discoveries often isn’t “Eureka!” but rather, “that’s funny…”

In my world, we call this “Undirected Knowledge Discovery.” Data Mining. You might call it “browsing.”

Long live the browse.

Back To The Browse is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

Over at the day job, I recently shared a pretty remarkable piece of data – the percentage of Americans who use social networks and check those social sites/services multiple times daily has gone up from 18% of social networkers to 30%. Big jump!

This on its own would be an interesting little factoid – even a significant one – but when you pair it with another finding, you see that a simple graph of that data point actually obscures the true impact of this growth. Not only has the percentage of social networkers who use their sites/services daily grown, the actual number of Americans who use social networks at all (the base number for the frequency finding, in other words) also grew dramatically, from 34% of all Americans to 48% year over year. I go into a little more detail about this over at the Edison blog, but essentially both the numerator AND the denominator of this little equation have grown.

Here’s that finding with the actual projected number of Americans who check social media sites and services multiple times daily superimposed:

So 18% to 30% is 67% growth – if the base number were flat. With the growth in the base (number of social networkers) AND the percentage of those Americans who use those sites multiple times daily also growing, the projected number of Americans engaging in habitual social networking has actually more than doubled.

More on this (and on our latest study of Americans’ social networking behaviors) over at the Edison blog.

One Number Raises An Eyebrow, But Two Tell A Story is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

Maybe that’s a little strong. Here’s what I do take issue with, however – misrepresenting an impressive-sounding sample size as a valid study. Today I got a pitch about a “social media study” that purported to have a sample size of over 2,000, which if done correctly would have a margin of error of just +/- 2.0%. I won’t dignify this particular study with a link, but a closer inspection of the methods revealed this:

Sample size: 2322 persons 6-54, Error +/- 2.0%
Persons ages 6-54, 55% male/45% female
Address-based sampling
The study was conducted between January 15 and April 30, 2010.
Data was accumulated through in-person interviews, telephone interviews, social media focus groups and web surveys.

See, if you saw the results from this study and didn’t bother to scrutinize the methods, you might be fooled into thinking you had a good piece of research to write about. Sometimes, sample size can fool you, as it does in this case. Consider:

1. The data was accumulated over almost four months. This introduces a longitudinal bias into the data. Think opinions have changed about BP over four months?

2. The data was collected multi-modally, using a series of qualitative projects all cobbled together. You can only really claim to have a given sample size for a survey if it is the same survey. Much of this data appears to have been quantitative questions asked in multiple small groups, and then simply crunched together. If the sample really was 2300 persons surveyed in one mode (phone, web survey, etc) then you could claim that the margin of error is +/- 2.0%. If you just add together a bunch of tiny, unrepresentative qualitative samples, all you are really doing is compounding error. A typical focus group, for instance, might have 10 respondents. The margin of error on a 10-person survey is, I dunno, about +/- 99% You can multiply those tiny samples together all you want – they don’t get any better.

3. 55% Male/45% Female is another way of saying “convenience sample,” or, “this is who showed up for the focus groups.” The US population is actually slightly more female.

4. Where were these interviews conducted? It would be pretty hard to do a nationally representative sample using in-person interviews and focus groups. Certainly you’d make Southwest Airlines pretty happy. There is surely an unstated, unknown geographic bias. Probably a pretty sizable one.

5. What’s a “social media focus group?” If such an animal exists, it needs some esplainin’.

Basically, you can’t claim this sort of “survey” is representative of anything, even if the sample were 10,000. What’s worse, you can’t characterize the sample at all, so you can’t even refer to the data as “amongst respondents to this survey.” Which survey?

So, this is a rare airing of sour grapes here on BrandSavant, but my purpose here is to get you to think about numbers when you see them. Surveys like this are done all the time – it’s up to you to ask the tough questions before you report on the results or – heaven forbid – act on them.

Tomorrow, a more positive post.

Sometimes I Hate Market Research is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

Doesn’t get as much press as Facebook or Twitter, but LinkedIn keeps growing and growing. Some new stats from the day job here: Are You LinkedIn?

LinkedIn User Base Grows 300% In Two Years is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

We are still, I would argue, in the infancy stage for social media monitoring, particularly as a research or strategic input. Monitoring for possible “tactical interactions,” such as opportunities for customer service or pre-sales questions, is immensely valuable, however. It strikes me that there are enormous opportunities even with tactical communications to model and segment responses, but the “modeling” one would have to do is likely unique to each brand, product or service.

Still, opportunities abound, if you’re willing to do a little work. One potentially powerful tool to make use of involves longitudinal data. Currently, much of the data thrown off from social media monitoring tools can be trended in aggregate (number of packaging problems over time, number of prospects per month, etc.) but a trended study of tweets is not exactly the same thing as a longitudinal study of tweeters twitterers people using Twitter.

Consider: trending the direction of sentiment over time (assuming this is done with sound methods you can trust) may be an excellent way to monitor the general zeitgeist of your brand – but what about the opinions of individuals? A simple measure of brand mentions, as I’ve discussed before, can be a random walk, but measuring an individual change in sentiment, or a movement from awareness to consideration to purchase, could be an immensely useful metric – albeit a thorny problem to solve.

The answer lies in modeling, and potentially in combining server data with survey data. We may not be able to accurately model an individual’s behavior, but if we can place an individual with some reasonable sense of certainly into a given bucket (“Aware of product but not looking to make a purchase in category,” “Aware of product, in the market for category but negatively predisposed to brand,” etc.) then we can make more sense of the data being thrown off by social media monitoring platforms.

This would be a significant project for a brand or even for one of the monitoring players out there, but it would involve taking an initial pass at some kind of natural language identification of the character of social media messages, placing users into tentative “buckets” based upon those messages, and then reaching out with a survey instrument to persons within each of those buckets to hone and clarify those buckets into actual behavioral clusters of people along the purchase continuum (or even the post-purchase continuum). What this vision would enable is the formulation of more accurate segment-based responses for brands, and the ability to measure the effectiveness of that messaging.

Imagine the results: a social media monitoring platform identifies, say, 1000 people who are in the market for a given product but are negatively predisposed towards your brand. A/B testing could send samples of those individuals differential messaging, and longitudinal tracking of those users could identify which messages were more effective at moving individuals from “Aware-but-reject” to “Aware-and-considering.” Again, we don’t want to get caught up in the actual individual, but if we can to some degree of certainty (through a combination of survey research and unstructured data) place them in one segment or another, a longitudinal view of the data could track the effectiveness of your monitoring and response efforts at moving people from one bucket to the next (ideally closer to purchase or complete satisfaction).

What do you think? Science fiction? Or already being done? What are some of your ideas for strategically tracking the messengers in addition to the messages?

Longitudinal Social Media Monitoring is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

Naomi Dunford wrote such a ridiculously awesome post on Ittybiz yesterday that I felt compelled to bring it to your attention. In it, she described a “dilemma” one of her clients has – how to capitalize on the number of Twitter/Facebook followers she has amassed. In other words, what’s the point of all those followers if you can’t monetize the audience. There sure are a lot of would-be social media gurus out there who give “conceptual” advice about this (“Be transparent! Help people! Join the conversation!”) but little in the way of brass tacks.

If you have a boatload of followers, and you are selling something, then sell that thing to those followers. Sounds easy, right? In practice, however, not so straightforward. Naomi bemoans the lack of actionable, tactical resources on how to actually do something with all those followers, and she’s not wrong. It strikes me, though, that this is really just a modern manifestation of a far older business problem.

I have 3,000 followers on Twitter. I don’t deserve that many, and most of them probably followed me for default, passive reasons rather than an active wish to engage with me specifically. Be that as it may, there they sit: 3,000 followers. What do I do with those followers? In my case, not much. Giving me thousands of Twitter followers would be like giving me 3,000 business cards, which would also be wasted on me.

I’m not much of a social networker, because I’m not much of a networker, period. I’m crap at it. I don’t feel bad about this – I have other gifts. But I’m not the guy who works the room at conferences, or the first guy you call to make a connection with someone else. I’m no Keith Ferazzi. I’m OK with that. I know some master networkers, and I am in awe of their ability to keep names/faces/details straight and to make meaningful connections with and between so many people. I’m not that guy. This is why I will always need to either work for or work with that guy (or gal) to fully realize my potential. Many of you, I bet, are in the same boat.

I really think that, once we get past the tools, master networkers are master networkers and brilliant salespeople are brilliant salespeople, online or off. A lot of the people selling social media consulting are good with the tools, but if you are like me what you really need is help with the actual sales and/or networking. The latter skills should be high in your criteria for choosing a partner to work with online. The best thing you can do if you have a big follower list and you are not sure how to monetize it is to find someone to help – not to do so carries a tragic opportunity cost – but don’t get caught up in “tool talk” when you vet potential partners. We all know a few of the real Masters Of The Rolodex out there – you know what to look for. Anyone can learn the tools.

The Value Of Followers is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

Sometimes, what is blandly presented as “fact” can also prove to be a sophist trick, or at the very least not the whole story. This is certainly true of statistics, and one of the more commonly maligned pieces of statistical data is “margin of error.” This is not a blog about statistics, but if you are a marketer, you’ve undoubtedly been presented with marketing research data and been given a whole series of descriptive statistics about that data, including margin of error. Here’s what you need to know to interpret that piece of information in the real world of business decision support.

Imagine that you are looking at a pre-election poll, and the results indicate that Candidate “A” has 52% of the respondents’ support, and candidate “B” has 48%. Your very next question should be, “what is the margin of error?” Let’s say that the margin is +/- 3, which is about what you get with a 1000-person sample at a 95% confidence level. What that means is that 95 times out of 100, Candidate A would get between 49% and 55% of the vote, and candidate B would get 45% to 51%. This is a fact, yes, but it’s often used in some pretty cringeworthy ways. If you learn about this poll from a supporter of Candidate “A,” you are likely to hear it reported as “my guy is winning.” If you hear about this poll from a supporter of Candidate “B”, however, you can guess what they’ll say: it’s within the margin of error. In other words, statistically, it’s still anybody’s race, right? It’s entirely possible that a race that polls 52% to 48% could end up 49% to 51%, with Candidate “B” winning, so the argument from margin of error basically maintains that if a race is within the margin of error, that it’s anybody’s race.

Now, technically, that is within the realm of possibility, but it’s a gross oversimplification of what margin of error really is. The Candidate “B” supporter sees the race as a crapshoot, but if I’d bet a dollar on the frontrunner in every 52 to 48 race I’d ever seen with a 3% margin of error, I’d be a centimillionaire. Because here’s the truth – if the sampling is competent, Mr 52 is probably gonna beat Mr 48 almost every time.

The folks who make the argument from margin of error typically fall into two camps. The most benign practitioners of this sort of argument are those who have what I would call an academic knowledge of statistics and margin of error, but not necessarily a practitioner’s view. In other words, they may know from an academic perspective that a 52 to 48 race could end up as a 49 to 51 race, but they may not see these types of surveys enough to know that it generally goes the way of Mr 52. That’s innocent enough. But on the other side, you have the folks who have a dog in the fight somehow. Either they support the underdog, or they have a vested interest in a given number being higher or lower than reported – doesn’t matter which. The important thing is that they are emotionally invested somehow in the reported data not being right.

It is in those cases that we see the most sinister uses of the argument from margin of error, because what you are getting is, in fact, spin – even though on the surface it’s couched in statistical fact.

Let me give you an example that puts this more in perspective for a marketer. Say I surveyed 200 male Smurfs, and found that 20% of them cheat on their wives (in reality, this number is much higher, but work with me here.) If I report this number, there are bound to be Smurf-lovers out there who either don’t believe this, or have some kind of emotional investment in this not being true. With a sample of 200 (at a 95% confidence level) we have a margin of error of about 7%, so this number is likely to fall between 13 and 27. So, our Smurf-lover maintains, this number is meaningless–it could be 13, it could be 27, so it’s a bogus result. You just can’t tell. In other words, because this number could be anywhere between 13 and 27, it’s silly to even come out with a number like 20–you’re just throwing a dart, right?

Here is where that isn’t exactly true. The argument from margin of error either doesn’t understand probabilistic sampling, or is trying to willfully misrepresent probabilistic sampling, in the guise of presenting an apparent “fact.” What this argument would have you believe is that the range of answers is a box – let’s call it a fish tank. Inside that fish tank we have 15 numbers swimming around (13 to 27, inclusive) and if you sample that fish tank to get a number, you reach your hand in and pull one out – it could be a 14, it could be a 23, it could be any of those numbers, right?

But that’s not how margin of error works. Again, with proper sampling, the probability of pulling a given number lies along a curve, not in a big fish tank. The way to think of this is to imagine that the 20 I reported as the number of Smurfs who cheat on their wives is the center pole of a tent. So the tent is highest at the number 20, and the fabric curves down until it gets to the very edges of the tent, where it’s staked to the ground, at numbers like 13 and 27. If you were to fill that tent with red rubber balls (and I don’t know why you would…), what you would see very quickly is that most of the balls either touched or were just a few balls away from that big center pole – they’d be piled up from the ground to the very top of the tent close to our center pole at 20. Way out at the edges, near the 13 and 27, there is only room all the way around that tent for one ball.

So most of the balls in the tent are a lot closer to 20 then they are 13 or 27, and if you reached into that tent to grab a random ball 100 times, not only would that ball be between 13 and 27 95 times, it would be between, say, 17 and 23 the majority of the time. You don’t have an equal chance of pulling a 13 as you do another 20, because most of the responses are going to be closer to the middle of the tent than they will be to the edges.

So when you hear someone say that due to margin of error, you just can’t tell what a given number really is, that’s factually true because descriptive statistics are estimates of a population. But again, it’s a lot smarter to bet with the number than it is to bet against it (especially if you have tracking data and/or corroborative data from other research.) Which is not only the law, it’s bad news for Smurfette.

Fishtanks And Tentpoles: A Rant About “Margin Of Error” is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

Last night I got a particularly hamfisted pitch on Twitter for an automated sentiment tracking service. Apparently, they picked up that I had mentioned a brand, and sent an unsolicited reply that gave me the number of positive and negative mentions of that brand online along with a link to click for more information.

(FYI – I call this sort of solicitation the “Eavesdrop Pitch.” It’s kinda like overhearing me ask someone for the time at a cocktail party, shoving your wrist under my nose, and yelling “WANNA BUY A WATCH?” But I digress.)

I clicked the link, because I am a curious sort, to see what this company’s angle was. First of all, the “brand” that I mentioned was actually “Chicago,” as I recently spent a few days there at SOBCon. I started with the negative mentions, and it became clear fairly quickly that the actual sentiment analysis was not to be trusted, since there was no delineation between tweets that were negative about Chicago, tweets describing something negative that happened while in Chicago, and tweets where “Chicago” was basically a non sequitur to an unrelated negative sentiment. Readers of this blog have heard this story before.

It was the positive mentions, however, that really sparked an epiphany. There were over five times as many positive mentions of Chicago as negative mentions, which on the surface seems to have some significance, right? Except, as I poked around through these positive mentions, here’s what I found:

RT @justinbieber: I need to work on my curveball. Hey Im Canadian we play hockey. Haha. I love Chicago – http://mlb.mlb.com/video/play.j …

If you know anything about The Bieber, you know I didn’t just find a few of these, either. There were thousands of retweets about Justin Bieber throwing out the first pitch at a recent Chicago White Sox game. If you just look at the original tweet, it is clearly positive about Chicago, and no one would quibble with any monitoring platform on that score. What about the retweet, however? Was Bieber’s original message retweeted by thousands because they also love Chicago? Or because they love hockey? Or Canada? Or…The Bieber?

Any and all of the above, surely, which neatly highlights the distinction Liz Strauss often makes between social media monitoring and social media listening. Setting the sentiment angle aside, however, the really interesting thing about this retweet is what it suggests about the brand mention as a base metric. If you use a monitoring platform to at least count brand mentions, you may be tempted to track the rise and fall of this number, associate it with some external events, and make some assumptions. They do this on CNBC every day, when they tell you with a straight face that a particular stock went up or down “on news of higher unemployment” or “on the basis of its recent earnings announcement.”

The truth is, the anchors on CNBC don’t really know why a stock went up or down in the short term, because in the short term the stock market is a random walk. The human mind hates chaos, and naturally wants to impose patterns and order on what it perceives–even where no pattern exists. If you were employed by Chicago Tourism, and you were tracking “brand mentions,” you might think you had a pretty good month. But Chicago’s insertion into Bieber’s tweet–and the concomitant repetition of that tweet – were random occurrences as far as Chicago is concerned. Yes, they certainly got a few more impressions of the brand, but when those impressions fall back down to pre-Bieber levels next month, what did that metric really tell you?

The retweet, then, is truly a confounding variable in social media monitoring for brand mentions. Just as you truly cannot gauge sentiment without knowing motive, you also can’t really process a retweeted brand mention (again, except as a gross “impression”) without fully grokking what part of the original message actually sparked the retweet. With retweets as confounding variables, the “metric” of mentions truly becomes a random walk, which means ascribing a rise or fall in said mentions to external events or internal efforts is potentially ruinous.

I’m not arguing against social media monitoring here – not by a long shot! I use and recommend several platforms. Blaming social media monitoring platforms for recording this metric is akin to blaming Microsoft if you give a crappy PowerPoint presentation. ‘Tis a poor potter who blames the clay. What I am suggesting, however, is that you distance yourself a bit from the numbers, and use these tools not as a means to an end – the tabulation of mentions – but as an entry point to a conversation. Listening, not monitoring. It may seem slightly ironic to have a “numbers guy” advising you to take a metric with a grain of salt, but I am not a “numbers guy”; my job is to tell stories with numbers. The random walk of brand mentions almost never tells a story in isolation.

And now, your moment of Zen.

The Confounding Variable Of The Retweet is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!