A simple concept, simply illustrated. Some small percentage of Internet users create content on the social web – let’s call it 10% for discussion – while the remainder do things like curate, consume, etc. Also, some percentage of your customers represent your core customers – the people who are most passionate and loyal purchasers of your product. Again, for the sake of argument, let’s also call that 10% of your customer base. So, you have a small percentage of content creators, and a small percentage of business drivers. That’s what you know.

Here is what you probably don’t know: where those two types of people intersect, i.e., what percentage of the people talking about your brand on the social web are also your best customers? Ideally, there is some overlap here, as my brilliantly artistic Venn diagram suggests:

The intersection of those two circles – the passionate, core consumers who also talk about your brand online – is gold, right? You want to make these people happy, don’t you? Well, yes and no. You do if and only if you can actually identify them. And you can’t identify them, unless you know what that Venn diagram looks like for your brand.

Not knowing what this diagram looks like can destroy your brand, if you use the social web as an input. Your quickest path to extinction is to make business decisions based upon information mined from social media if you don’t know what your Venn diagram looks like – and if it turns out to look significantly different to the one above.

There are two distinctly different dangers here – one obvious; the other, less so. Here’s the obvious one:

In this instance, the people talking about your brand and the people who actually buy your stuff have very little overlap. Here, mining social media for insight serves not as a proxy for “voice of consumer,” but as a dangerously irrelevant “voice of the noisy.” And if you don’t actually know what this Venn looks like for your business, you run the very real risk of ruining your product, brand or reputation with your actual customers if you derive “actionable” insights from the social web that turn out to be irrelevant banter. In other words, if the folks at Maybach designed their cars based upon mining Twitter data, they’d probably have a jacuzzi in the trunk.

Again, where the values of social content creators align with the values of your core customers, social data is potentially useful, but not always useful. Consider this, very different Venn diagram:

The amateur mind sees this as an optimal situation – after all, your core customers and your social content creators are in near-perfect alignment, right? Yet this Venn could kill your business just as efficiently as the previous example – indeed, it might even happen before you realize it.

This sinister pattern is what I would call the Optimization Trap. Slavishly following clickstream data will get you here quicker than anything. Imagine, if you will, mining your data to discover that Fridays are the best day to send your emails, and farm implements are your most successful topics. So, you send out Friday Farm Implement emails. From there, you mine your Twitter data to discover that most of your audience is athletic, so you tweak your Friday Farm Implement email to talk about combining farming and exercise. Subsequent mining reveals that your Friday Farming Activity emails are most often opened by young men, so you include videos of top models using your farm implements for various activities.

With each optimization, you’ve actually veered further and further away from the dead center of your market. If you over-optimize, you end up continually refocusing on ever-smaller bullseyes, moving closer and closer to irrelevance and away from the needs of the majority of your market. Eventually, you will achieve nearly 100% optimization, making five people really happy. You’ll be like the frog placed in a pot of cold water, slowly heated to boiling. By the time you realize you’re in trouble, you’re being served with garlic butter and a nice green salad. Tastes like chicken!

Two of these Venn diagrams could kill your business – if you make decisions based upon social data and don’t know what your diagram looks like. Luckily for you, it isn’t rocket science to draw your brand’s diagram – if you do the work. This is the kind of work I do for clients every day, but the most important thing you can do to determine these things about your customers is simply to ask them. Only when you calibrate your social data mining with other online or offline research can you know the nature of your two circles. Figuring out the size of these circles – and the extent to which they overlap – is the key to making social media data useful. Not doing the work is negligence at best, and could indeed be fatal.

Sorry about my “art.” I won’t quit my day job.

The Venn Diagram That Could Destroy Your Business is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

I look forward to the yearly release of Edelman’s Trust Barometer – particularly for its global perspective, which helps me provide context for my own international clients. This year’s report is chock full of insights, and some remarkable shifts. Some of the changes from 2011 can be attributed to a bit of a change in the methodology of the research, but (as a researcher myself) I appreciate the transparency in reporting those changes, which I believe make the data richer.

David Armano has a nice piece here on the shift in trust from organizations to individuals which merits a read, but what also struck me was the juxtaposition of two findings in particular:

First, about the only thing that showed an increase in trust was media – and in particular, social media, which showed a rise in trust from 8% to 14%. Again, methodology changes explain some of this, but not all of it, and it is clear from the data that as some of the institutions we have formerly trusted appear to crumble around us, we are, as a society, engaging in a “flight to comfort” by relying more and more on our social networks (online and offline) for our daily inputs.

Second, with the global decline in trust towards most institutions comes an axiomatic rise in skepticism (and, for some, cynicism). Nearly two thirds of those surveyed in the Trust Barometer indicated that they need to hear something at least three times before they believe it – with 28% saying four or five times. Repetition and trust go hand in hand – and, as some other data I have seen corroborates, repetition itself has a hand in creating trust.

These two facts, taken together, illustrate a very powerful concept regarding influence. While the purveyors of online influence measures (Klout, PeerIndex, Kred, et al) focus our attention on top-down measures of influence (the “top 10″ in a given topic is often all you see), we are all influencers, as Tamsen McMahon often says. Identifying “influencers” is merely step one in a more vital process – getting people to do the things you want them to do. Using online influence measures is potentially a useful first step, but it does focus you on “elephant hunting,” and sometimes those elephants net you a little noise, but nothing more.

The Edelman data suggests an alternative approach – the bottom-up approach. Hearing a message from a top-scoring “influencer” might make me read, or retweet a message – but seeing it repeated by five people I actually know, like and/or trust makes it law, regardless of the measured “influence” of those people. And getting the attention of those people, where the noise level is a little lower, is a pretty straightforward process with some time-honored components: sampling, trial, acknowledgement, recognition, reward and testimonial.

I realize I’m not saying anything too earth-shattering here; rather, I am simply suggesting that reframing your thinking about influencer outreach – flipping the funnel, as it were, from top-down elephant hunting to bottom up empowerment – might yield some fresh, new ideas, and get you thinking differently.

Which is my only real goal here at BrandSavant.

Here is the Trust Barometer summary report – well worth your time.

Influence From The Bottom Up is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

I was delighted to be asked recently to contribute a brief video interview to the excellent Social Media Explorer, run by my friend Jason Falls. SME will be hosting a series of five events all over the country under the “Explore” moniker, and he has definitely raised the level of discourse – both by inviting top talent to speak (Aaron Strout, Tim Hayden, DJ Waldow, Zena Weist, Copyblogger’s Brian Clark and many more – all at the top of their game) and by challenging that talent to truly deliver a world-class educational experience. I will also be speaking, and I’ll be giving attendees a four-step process to improve their critical thinking about social media data to become better consumers – and creators – of information.

Jason asked me to talk about the difference between research driven by science, and research driven by the content creation imperative. I also made Jason pee a little. Worth your 12 minutes, I trust.

Oh, the first Explore event is in Dallas on February 17th. Register here today and save a pile.

Exploring Digital Marketing Data and Research With Tom Webster from Jason Falls on Vimeo.

Relying on Data Produced As “Content” – A Video Interview is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

Klout says I am not an expert on headphones. I think I am. But, my social media activity around the topic has not been sufficient to warrant notice by Klout or any other influence service. So here I sit, bereft, betrayed, bewailing and bemoaning — in short, a beloser.

So what’s a poor boy to do? Surely I don’t want to turn my tweets into a withering torrent of headphone-related detritus, in a futile effort to show on the big Klout board. No, that would decrease my influence, if anything, if my social media output turns into a one-note joke. My tweet stream is about me, not an arbitrary list of topics. I’m a person, after all, not a dictionary (unlike my sesquipedalian-word-loving uncle Noah).

This is where I think Pinterest could come in. In a sense, it’s like a combination of Instagram and Squidoo – combining the content curation of the latter with the social aspects and personality of the former. Of course, there is a lot of duplication between Squidoo and Pinterest, but where Squidoo is organized around topics, Pinterest is organized around people. Setting aside the “social shopping” implications for brands, Pinterest is also a great place to curate pretty much anything you consume, including media and, of course, headphones. But for brands, the relative popularity of relevant pages might just be a better way for them to curate people.

If Pinterest takes off, brands combing through their server data might just find that the initial interest in their product early in the clickstream funnel might have come from someone’s Pinterest board. And if the makers of some of the headphone-related products on my “Audiophilia” pinboard happen to see a lot of clicks coming through that particular page, well – guess what? I’m an influencer about headphones, regardless of what an algorithm says, based upon an actual relevant behavior. After all, the best predictive measure of whether or not I might be influential about headphones in the future, is if I have, in fact, influenced people about headphones in the past.

Don’t Like Your Klout Score? Here’s How To Do An End Run is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

Last night, my company conducted the Iowa Caucus Entrance Poll on behalf of the National Election Pool (NBC, CNN, CBS, FOX, ABC, and the Associated Press.) The results, you may already know: Mitt Romney beat Rick Santorum by eight votes. It was clear from the data, and from the actual vote count, that we were in for a long night – it was an insanely close race.

Except, of course, in social media. The “big data” being thrown off by the web told a very different story. Conventional measures of volume showed Ron Paul dominating the social web by as many as five times more mentions than his nearest competitor. A straight-up search for relevant mentions on Topsy shows the race between Romney, Paul and Santorum as…well, not much of a race. If we look at search volume (and even confining it to Iowa, which hardly any of the social measures did) it’s still a one horse race – Paul by a landslide. One sentiment measure I saw quoted calculated the volume of Romney’s positive sentiment as sixth amongst candidates.

To date, I have never seen a repeatable correlation between social media mentions/sentiment and the actual vote. Sure, you could back-test a model that weighted to correct for the Ron Paul phenomenon, but the real test is to apply that exact same scheme the next time and see what you get – after all, most of these social tote-boards also got the relative gap and order wrong for other candidates, as well. I’m not discounting the possibility that it can be done; however, it hasn’t been done and it will be diabolically difficult to do so.

In short, to date I have not been presented with a replicable model which shows, candidate for candidate, that the number of people tweeting about a politician has anything to do with the number of people in Cedar Falls, Iowa who actually got in a car, drove to a high school gymnasium and raised their hand. Can it be done? Maybe. Maybe not. I’m not pessimistic, I’m merely skeptical (in other words, don’t prove me wrong – surprise and delight me.) I do know that raw mention-counting is a long hiding to nothing.

My only point here is that in the case of the Iowa Caucus, there is an enormous gap between what people on social media say, and what people in Iowa actually do.

Now, examine that last sentence. Replace “the Iowa Caucus” with the name of your brand, and replace “people in Iowa” with “your customers.”

Do you know how big that gap is? It is knowable – as I’ve often said in this space, it isn’t a black box mystery, if you do the work. But if you don’t know that gap, you’ll never make sound business decisions from social media data.

What Your Brand Needs To Know About The “Social Media Caucus” is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

Here are my predictions for the coming year:

[...]

I don’t do this sort of thing–never have. The rate of change over the past decade has made the “five-year plan” a punchline, and I’ve never been in the forecasting game. I’m not denigrating it – some of my friends are really pretty good at it. I rely on their judgment.

My business has always been about the reliable discernment of the present. I find this to be rarer than you might think. Expect more of the same here in 2012, at least until Quetzalcoatl engulfs the world in flames, sometime in September. Well, there’s one prediction, anyway.

Happy New Year – and thank you so much for your attention and your valuable contributions and comments here at BrandSavant over the past year. You keep me going.

My Bold Predictions For 2012 is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

Last September, at Social Fresh Charlotte, I had the honor of being asked to speak about social media monitoring, and how to evolve from basic “trolling for mentions” into something that resembles actual decision support for brands and companies. I ended up arriving at the venue seconds before my talk, thanks to a “truck immolation” on I-40, but my passenger (DJ Waldow) kept me calm throughout, and I think it was a great talk. BIG thanks to Jason Keath for allowing me to post this.

The audio is slightly out of sync with the video. Just pretend it’s a foreign film dubbed from the original Websterese. Enjoy.

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

Yesterday, my friend Jay Baer wrote a thoughtful piece entitled “Why Social Media Has Ruined Your Advantage.” The gist of his provocatively-titled piece was that social sites and services like Twitter, Yelp and TripAdvisor have given customers enormous power to loathe or laud companies in public, and the knowledge that these tools are (or should be) monitored in real time by companies has created an immediacy expectation. Social tools have essentially outed your call center and found it wanting.

The comments on Jay’s piece were lively and engaging, as usual. I was particularly struck by the ever-shrewd Mark Schaefer’s reference to the information disparity that used to exist between companies and consumers as “the ether,” and Ike Piggot’s comment that “the delta between what our customers THINK can be done, and what can REALLY be done is growing,” which is a fine distinction to make. The fact is, companies have been profiting from operating in “the ether” for decades – it’s the arbitrage of an inefficient market. We have accepted things like “please allow 48 hours for a response” because we didn’t know any better, and we lacked the power to find out. Now, however, the differential response times between companies are exposed for all to see, and companies like Radian6 are sharing their own benchmarks for response times that are, frankly, unattainable for companies that are not structured around real-time response.

The Theory Of The Firm

These are not tactical issues, and they go beyond a company’s development of a “real-time response strategy.” No, they strike at what I am increasingly fond of calling the theory of the firm: the company’s very raison d’être. You cannot have a real-time response strategy if your responders are not empowered in real-time. And your employees cannot be empowered in real time unless the entire company wheels around on that very pivot point. The difficult question your company must ask, however, is not how your company can make this pivot; rather, it’s should you make that pivot. I’m sure I will get some comments here that the obvious answer is yes, but business is all about constraints, and economics the study of scarcity. Resources applied to customer services do not magically appear because we wish them to; Lavoisier’s principle of mass conservation is as true for corporate resources as it is for chemistry (though, given that Lavoisier was beheaded, I’m a little nervous writing this post.)

In short, as the Marines would say, your business has to pick the hill it wants to die on.

The Hill You Choose To Die On

Treacy and Wiersema’s The Discipline of Market Leaders, now 15 years old, is still the best “pre-social” book on the subject for my money. The central tenet of this book is that companies must choose one of three focal points: Operational Excellence (today, think of Southwest Airlines, or Wal-Mart), Product Leadership/Innovation (Apple) or Customer Intimacy (no finer example here than Ritz-Carlton.) Companies must pick one, and cannot (by definition) “focus” on more than one. Winning companies pick a focus and wheel their operations around to deliver upon that focus. What this necessarily implies is that a company that focuses like a laser on product innovation cannot “also” focus on customer intimacy; yet, what some of the comments to Jay’s piece reveal is that customers are forcing that issue, due in no small part to the gap that Ike suggested between what they think can be done, and what the theory of the firm dictates can actually be done.

We are lousy with business fables about the outliers – the companies that seemingly do it all – but the hard truth is that not every company is equipped to succeed at customer intimacy, and not every company needs to, either. I am typing this on an Apple product (a MacBook Pro) and you might be reading it on one, as well. Apple sucks at customer intimacy. I’m not sure I care. This would matter if they were not superior at their chosen focus, product innovation. There’s hardly any anecdotal evidence that Apple even monitors social media – yet we keep buying iPhones and iPads in droves.

Can You Have It All?

My question, though, is this: has the social web invalidated the Treacy/Wiersema model? With every company’s “call center” on view for all to see, do we instead return to the earlier, bipolar view of the theory of the firm elucidated by Michael Porter, in which companies choose between product differentiation and cost leadership, with customer intimacy serving as table stakes? I don’t know the answer to this question, but it is profoundly important. Social media does not scale very well at the customer service level. Companies that could formerly operate in the Schaeferian “ether” are now exposed; their customer service function revealed to be understaffed, unempowered, or both. If customer intimacy, propelled by the social web, becomes de rigueur for the firm, there are going to be cost implications. It’s all well and good to proclaim in the comments to Jay’s piece, as many did, that slow or inadequate customer service is “unacceptable,” but are those same people willing to pay the price of a universally higher bar, from the visible costs of higher prices, to the more sinister toll exacted by constraints on innovation?

These are not easy answers – and in some respects, for an economy built around change and innovation, these are false choices. When the airlines deregulated, it was in response to a change in consumer demand – a willingness to trade convenience and service for lower prices. New airlines emerged that were built to compete on operational excellence, and the incumbents suffered (this week’s bankruptcy filing by American Airlines is the last rumble of this very long digestive process.) Airlines like Southwest and JetBlue emerged with new business models, and copying those models has so far proven to be fatal for the legacy carriers. Similarly, attempting to mimic the social practices gleaned from over-hyped case studies will surely lead to ruin for companies not engineered around the same theory of the firm as the exemplars they copy.

A Case Study That Matters

This is why the impact of the social web cannot be underestimated. Customer expectations are changing – they have changed. Obviously your business needs to change with them – but if your response to that change is a “social media strategy,” you’re on a long hiding to nothing. The airlines had the enormously refinanceable value of their planes to fall back on as they struggled to recover from the profound impact of deregulation; your business, however, may not be so lucky. If (in your industry) a focus on customer intimacy is no longer a “choice,” a la Treacy/Wiersema, then treating it like one is likely to cause irreparable harm to the other aspects of your business if you do not, in fact, change the theory of the firm to wheel about on this new inflection point.

To me, the best example of this kind of existential change lies not in the case studies of our currently celebrated crop of social media superstars, but in a well-documented (and with good reason) example from the past: the legendary TPS. No, these are not the reports Lundberg keeps asking for, but rather the Toyota Production System. I spent a good deal of time studying TPS when I got my MBA, and whether you are in manufacturing or an affiliate marketer, you’d do well to do the same. The TPS was less a “manufacturing strategy” than a complete change in the theory of the firm. Toyota realized that they couldn’t simply increase their manufacturing quality standards unless their employees were thoroughly empowered to make those changes – in other words, they couldn’t have “just-in-time” inventory unless every employee was empowered to act in real time. So one of the central tenets of TPS was less about “six sigma” or other trailing variables, it was simply this: any employee can stop the assembly line. Car companies that attempted to copy the trailing variables (engineering tolerances) without wheeling about on this new, central theory of the firm rapidly got themselves into trouble.

I believe that the successful models for a new, social business will have a similar approach. As we move from the “just-in-time” era to the real time era, the theory of the firm must change to keep pace, or new upstarts built on better models will upend the incumbents again. If your business is not structured to allow employees to “stop the line,” you might find the line stopping anyway – only not at a time of your choosing. Now, again, maybe if you are Apple, and your dominance in product innovation has created such a strategic moat that this sort of shift is not imperative, well – good for you. But business articles and books that beseech you to “be like Apple” are amongst the crappiest pieces of advice you’ll ever read. You aren’t Apple. You’ve got to figure out another way. In other words, if you are not the market leader in innovation or operational excellence – and don’t have a plan to get there – you’d better be nailing customer intimacy and be structured to do so. And as I write this on the cusp of 2012 (likely the end of the world, anyway), that means a business structured around real-time public feedback and rapid response.

The sign at the top of this post can be found in nearly every dry cleaner and print shop in America. It states (in defiance of Treacy and Wiersema): “Price, Quality, Service: Pick Any Two.” What the great “deregulation” of the social web has done is this: it’s made one of those two choices for you.

Social Business: Be Careful What You Wish For is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

Image: "Station Bullhorn," by Nate Beaty

Where Matt spun me around was with this subtle point – we are consumed with focusing on influencers, but ignoring the influenced (and you should download his excellent presentation here.) As Matt noted, finding people at the moment they are actually being influenced online is really about identifying a future customer at the point of need, and turning them into an *advocate.*

Who Are The ‘Influencers’?

One of my “primary influencers,” Tamsen McMahon (who was also at this session) is fond of noting that “we are all influencers.” We all hold sway over some group of people, about some topic, in some context. Yet, the current crop of automatic influence measures will never do a *great* job at picking up on this subtlety, because they are engineered to look at aggregated data and predict a specific response. In essence, they are built to look at an abstraction – my online “activity” – and extrapolate a predictive measure: how well or poorly I am likely to disseminate a given message, or encourage a given action, based upon my previous behavior.

There is merit to predictive measures, and I’m not going to bash them here – they are getting better. But it strikes me, taking a cue from Matt, that we are looking at abstractions and “hoping” for a specific, when we could just as easily (more easily, in fact) look for specifics and then reward future, similar behaviors. In my day job, we know that one of the best predictors of future voting behavior is whether or not you voted last time. Influence works in a similar fashion – we need only flip the funnel around. Rather than (or in addition to) attempting to predict specific future behavior from a mass of general data, we could also look for specific instances of *actual* influence being wielded in situ, and then reward/encourage future behaviors accordingly. To go back to my voting analogy, the best way to determine whether or not I am an influencer about, say, cars is to observe me actually influencing someone to test drive or consider a given car, and not merely by the fact that I talk about cars and generate retweets.

The ‘Influenced’

I’ll give you a specific example that occurred online last week between Matt Ridings and I (and Matt, to his endless credit, worked it directly into his Blogworld talk. Have I mentioned yet that you should follow him on the Twitter?) Last week, I observed Matt on Twitter asking for a recommendation about comfortable earphones. We are close friends, so he knows that I am a bit of an audiophile (and by “a bit” I mean I have a problem.) I replied to him that he should try equipping his earphones, whatever the model, with foam tips from a company called Comply – I use their tips on all of my in-ear ‘phones, because they are super comfortable and preserve the sound quality.

The attentive folks at Comply were clearly monitoring these conversations, and they helpfully popped in to hook Matt up with some to try. Comply was listening at the point of need, and they acted to provide a sample to a potential customer. As Matt points out, however, there is a more subtle point to be made with this first level of interaction – by listening to Matt at his point of need, and solving his problem (hopefully with a great product), they might have just created something beyond an “influencer,” they might have just kindled the spark of an advocate. Matt may or may not be a topical influencer about audio, but if he has a great experience with Comply, he will certainly be a compelling online advocate for their brand.

Rewarding Matt at the time of need, however, is just the first level of this model of influence. Matt is dead on that focusing on the influenced is an incredibly efficient way of building advocacy. None of the online influence measures would have tagged me as influential about audio – I simply don’t talk about them online that much. But my friends know that I have a wealth of knowledge about audio, and I’ve recommended countless products over the years. My aggregate behavior would never predict this specific interchange; yet, there it is. A few tweets online, and a product is sold. Does Klout think I am influential about headphones? Nope. Am I? Demonstrably.

Finding this specific interactions – the “have you actually voted in the past” of online influence – is an easy and efficient way to identify influencers. This isn’t about predicting whether or not I might wield influence about headphones, it’s observing directly that I was the catalyst for an action that the brand cared about. Comply doesn’t need to look at my aggregate profile and “hope” I’ll talk about their specific product, because now they know that I can and will actually move their product. I’m an advocate.

Cultivating The Advocate

As Tamsen pointed out in our Blogworld exchange, however, rewarding Matt and responding to that exchange was just the surface level. Tamsen (who, besides wielding considerable “clout” with me, is also the VP of Digital Strategy at Allen & Garritsen and a supah smaht cookie) noted that the second level of engagement for Comply would have been to reward me for my advocacy – or at least to have provided some form of incentive to encourage future recommendations. Had Comply simply butted in and pushed their product without my recommendation, it would have been seen by Matt as intrusive. But my advocacy warmed up the prospect, so to speak, so the “intrusion” was welcomed. Finding an actual, brand-specific influencer for Comply should be gold to them, and they should do more to cultivate those specific relationships where they are discovered.

Purists might point out that this kind of “incentivized” advocacy could be seen as my simply being bought and sold asking an affiliate marketer. But, that’s really my problem, isn’t it? After all – in this model – the proof is in the pudding. I either encourage trial or I don’t, right? If I am no longer seen as influential about this brand, it wouldn’t be be because I am compensated, per se. It would be because I stopped being a compelling advocate and encouraging online interactions similar to the one I had with Matt. That would only happen if somehow my friends and online connections questioned my integrity. That’s kind of a “life” problem that I don’t anticipate having.

The Story

Still, I’m not angling for free Comply products. No, my reward is the same as anyone else’s who genuinely and passionately recommends a product to a friend: that they tried the product and loved it, thereby making the recommender (me) feel smart and loved. Right? So the “reward” I really want is for Matt to hop back on the Twitters and tell the world that he tried a product based upon my recommendation, and it was awesome – making me, by the transitive property of awesome, also awesome. This would make me happy – but (and this is the third level of influencer engagement here) it would also make Comply happy. I’ve shared my story with Matt, so the next step is for Comply to help Matt “complete the sentence” by providing an incentive to (publicly) thank me for my awesome recommendation. Products aren’t sold in social media on features – they are sold on stories.

Sharing those stories online brings in the silent audience – the people lurking, following me or Matt – who quietly file this anecdote away until they reach their point of need. It’s a simple matter for a company to add “how did you find out about us” to their order page (and, by the way, to reward customers somehow for sharing that data) or to do some other form of primary market research. Doing so might allow Comply to see that my little online exchange with Matt might have actually led to 25 or 30 sales. Who knows. And encouraging Matt to close the loop on this “story” more than doubles the chance that future, prospective customers will observe this advocacy.

The Shift

Once that “story” has been completed, there is a lasting social record of my ability to recommend a brand in a specific category, and instigate an action that brand cares about (i.e., more than a “retweet.”) This interaction is public, and valuable to Comply. It’s more than that, however, and that is where a shift in perspective is required to access the fourth level of this particular model. Comply operates in a category with partners (earphone manufacturers) and competitors (Monster, for one). If I were the maker of a high-end earphone (say, Sennheiser), I would be doing more than simply monitoring social media for mentions of my brand, or even my competition – I’d be searching specifically for interactions just like the one that Matt had (and setting up that kind of smarter research station is the kind of work I *love* to do.) Again, no aggregate, predictive measure would discern that I am an influencer about headphones, but Sennheiser could easily see from the public record of my dealings with Comply that, actually, I am.

Sennheiser could then do the work to determine whether or not I would be a good potential “influencer” or even advocate for their brand as well. And that is a straight-up influencer campaign. Allow me to sample the product, blow me away (that’s super important) and then cultivate a relationship. If the product is great, the interaction positive and a relationship maintained, Sennheiser stands a decent chance of being in my consideration set the next time someone asks me to recommend some headphones. The only difference here is that they didn’t approach it from the fat end of the funnel by using a predictive tool to find people online who are active and talk about headphones – they used a diagnostic tool to find someone who can get someone else to try a headphone or headphone related product.

Beware False Choices

Again, predictive tools have their place, and I am suggesting a different way, not a replacement way. And the top of the funnel methods (using automated influence lists) are really better suited for awareness, while the approach described in this post is more about trial and conversion. So I’m not setting the two approaches in conflict, and neither should you. And in both cases, you should check your work. If you are interested in a pretty useful, do-it-yourself way to approach measuring your efforts, I wrote a post on a true measure of influence a few weeks ago that I think you’ll find useful.

I’m not in the business of running influencer campaigns (and Matt Ridings has forgotten more about that then I’ll ever know) so I’ll leave the details to you. I am, however, in the business of asking better questions for businesses in order to get better results. Tying research – doing your own work – to these efforts is crucial in the development of a learning organization, and it is the learning organization that succeeds while the others use the same tools and information that everybody else has. Ask better questions, make smarter decisions – but above all, check your work. That is the only way to lift the “influencer” campaign from a mere tactical interaction, to one that actually might just alter how you do business and affect the theory of the firm. Darwin would approve.

Flipping the Funnel:
The Four Levels of Influence is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

During my recently completed Blogworld keynote, I sketched out four steps towards turning data into insight, instead of chartjunk. I’ll post the slides soon, but to recap, my four steps were as as follows:

1. Know What You Don’t Know.
2. Ask Better Questions.
3. Prove Yourself Wrong.
4. Do Your Own Work.

I was pleased to see these themes also raised throughout Blogworld (particularly in Amber Naslund’s fine keynote, and a fantastic talk by kindred soul Jason Falls.) Specifically, the theme of asking better questions really seemed to strike a chord, and it’s been gratifying to see so many of the post-conference tweets and articles return to that point, which was the central focus of my talk.

It’s not difficult to ask better questions – often, it simply a matter of asking the wrong questions first, but remaining curious. If you continue to seek to disconfirm, and not to confirm, you will eventually be led to the right questions – and that is the key to insight.

I’ve picked on the recent spate of data-dredging research enough, so I won’t continue to flog a very dead horse. What makes these sorts of studies (i.e., the “best day/time to tweet,” and their ilk) so pernicious is that they are resolutely incurious. “What is the best time to post a press release” is the wrong question, or at least it’s a question that puts the cart well before the horse.

No, the better question is this: does day/time of posting have any effect upon the success of your messaging? Data dredging will never give you this answer. The only way to determine the “if” (before you can tackle the “then”) is to conduct a series of controlled experiments, eliminating or mitigating other variables, to determine just how much (if anything) day, time, message length, etc. all contribute to the overall variance of the success of your communication. If it’s significant, then and only then can you go about the business of determining which day or time to post is right for you. You know, do your own work.

Saying, by the way, that this sort of data dredgery is a “starting point” is a bit of a cop-out. That implies that while the “optimal” tweet is, say, 130 characters, yours might be different, so you need to sift through your data as well. Sounds reasonable, doesn’t it? But, again, that seemingly benign point makes the assumption that the hypothesis has been “proven.” It hasn’t. The right questions haven’t even been asked. Using it as a starting point does not mean to determine which day, time or length of tweet is right for you. No, it means that your next step should be to try and prove it wrong.

It’s important to realize this, as well: data generated for the purposes of content creation is inherently incurious.

You know what it’s like – you have a blog, and some sort of editorial calendar (or at least an internal voice that tells you “I need a post by 5:00!”) Data for the purposes of content creation can’t afford to keep asking better questions. It’s not evil, per se, but it stops when it gets to the first answer, because the “answer” is the generation of an article, a chart, or an infographic. The goal is to create content, not to keep uncovering rocks on what might end up as a wild-data-goose chase.

Data for the purposes of content generation is incurious because it seeks to prove or show something, and not to learn something. If you seek to confirm something, you probably will – it’s an ancient and sinister bias amongst humans. Finding the real truth is a painstaking process of disconfirmation. You have a hypothesis, and you seek to prove it wrong. Eventually, you will reach a point where you either do, or you cannot. In either case, you have reached a truth. This is what I mean when I cite my constant refrain to do the work.

Finally, friends who know me know that I don’t use the word “incurious” lightly, or academically. To me, it’s a vulgarity – the research equivalent of the F-bomb. Please don’t be incurious. Curiosity is not cynicism, it’s skepticism. Skeptics ask better questions. Do so with regularity, and better answers are assured.

Asking Better Questions is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

Every time I see research like this, which claims that the number of Twitter followers does NOT correspond to influence, another little piece of my soul rots away. Apparently, using “data,” the study’s authors put the lie to the “million follower fallacy” by demonstrating that users with high follower counts don’t spawn a proportionate number of retweets or mentions.

Well, I think all this proves is that social media doesn’t scale very well (and calls to mind this somewhat related piece of contorted data dredging.) But more troubling is the growing acceptance amongst marketers that activity somehow equates to influence. Influence isn’t spawning retweets. Influence is getting people to do what you want. Justin Bieber doesn’t want you to retweet him, he wants you to buy his music and pay to see his concerts. Right?

Similarly, “influence” measures are really activity measures. Else, how could they decline in the short term? It is highly doubtful that my influence – online or offline – waxes and wanes as much as my online activity does, but the most popular influence measures “punish” you for taking a few days off. In short, they treat the absence of evidence (the lack of activity) as evidence of absence – indicative of declining influence. Poppycock.

But, back to the “million follower fallacy:” I have no doubt that Twitter users with greater than one million followers spawn a disproportionately lower amount of activity than those in the middle tiers. Conversations just don’t scale like that, unless you have 20 people operating your account. It’s conversations that spark mentions and retweets, and those with greater than one million followers are necessarily broadcasters – by necessity, if not by choice.

And here is the glaring hole in the reasoning of this study that you could drive a tractor-trailer through: if the disproportionately lower activity level associated with million follower accounts somehow equates to lower “influence,” how in the hell did they get greater than a million followers in the first place?

If you can get a million followers on Twitter, you are influential by definition.

Confusing Activity With Influence is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

I am super excited to be featured as a track keynote speaker at Blogworld Los Angeles in a few weeks. This will be the fifth Blogworld I’ve attended, the third I’ve spoken at, and my first keynote at what has become one of my favorite conferences of the year.

This year, I’m doing something a little bit different. Instead of a research presentation, I’m going to take a step back and look at the withering torrent of information the social web is throwing off, and help attendees cut through the crap and make sense of it all. My session is called, appropriately enough, Drowning in Numbers: Turning Social Media Data into Insight, which long-time readers here will recognize as the principal theme of this blog.

Social media is ushering in a new era of Big Data, but not all of that data is usable, useful or actionable. So, this year, I’m not going to add to the problem by presenting more . Instead, using Captain Crunch, rotting meat, tea and spray-tan, I’m going to talk about how to think about all these data, and what you can do with it to improve your lot in life.

If you are in the business of creating content for your brand, product or service, this is the conference to attend. I hope you’ll make it to my session, which kicks off the whole conference on Thursday morning, November 3rd, at 9:45 AM. Also speaking at the same time are two young upstarts to social media, Liz Strauss and Chris Brogan, so if my session doesn’t tickle your fancy, you’ve got two pretty great alternatives, and that’s just the beginning of three very packed days.

So do come. You can even use this code to save a little dough: BWEVIP20. I’ll be there the whole time, so even if you don’t make my keynote, look me up. I’d love to meet you.

Drowning In Numbers is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

Influence scores, as we know them today, are all based upon algorithms. Algorithms are commonly confused with formulae, but they are surely two different things. The volume of a circle is a formula – it’s math. That x number of retweets has y effect on your influence score, however, is an algorithm. There might be some math in there, but I like to think of algorithms as math plus assumptions.

An influence score makes assumptions about the value of your follower count, how many people click on your links, etc., and then bashes those assumed values together with yet another set of assumptions – their supposed relationship to each other. Yes, there are mathematical functions involved, but just as the “likely voter model” many pollsters use for pre-election polls can never predict whether or not a specific individual will actually vote, the influence score will never be able to predict the impact of an individual on the behavior(s) you are trying to influence.

And that’s really the biggest issue with these scores, isn’t it? All of the algorithms being used by these services are amalgamating the behaviors of the many, and attempting to assign a value to the individual. This kind of inductive reasoning is always problematic. Here’s why:

Measure Three Times, Cut Once

There are, broadly, three kinds of measures: descriptive, diagnostic, and predictive (and these aren’t mutually exclusive – the best measures have elements of two or three of these all rolled into one.) Descriptive measures tell us what happened. Diagnostic measures tell us why it happened. And predictive measures help us make good guesses about what might happen in the future. The modern crop of influence scores (and I’m talking specifically about the single, reductive and non-context-specific number from 1-100 most of these sites spit out) are, I would argue, purely descriptive measures.

What Klout scores (or those from PeerIndex, or TweetLevel) can fairly be said to reflect is this: activity. It’s demonstrably true that increased activity on social networks (particularly Twitter) has a correlation with higher scores. Activity is not “influence,” of course, but it is something, and I’m not prepared to dismiss that something out of hand. So my influence score may in fact reflect some measure of my activity online, and my ability to encourage some form of activity in others. Thus, my score is descriptive of that activity level. It is not diagnostic of that level, however.

The scores, as they are presented, are inscrutable. My Klout score has fluctuated a fair amount in the past 60 days. I’m not sure why. I’m sure there are some very defensible assumptions for that fluctuation built in to Klout’s algorithm, but the point is that the reasons for that variance are entirely opaque to me. In other words, my score, and even the peripherals around it to which I have access, do not tell me why the fluctuation occurred. Thus, influence scores can not be used as diagnostic measures. (My topics, however, are right on the money. Klout is nailing this lately.)

A Cosmetic Problem

Similarly, the scores are predictive of nothing, which actually makes them very difficult to use. For example, I’m fond of comparing my Klout score with Snooki’s Klout score. After several months of concentrated effort, I have finally pulled ahead of Snooki (see, Mom? I told you I’d eventually make you proud.) But if you represented a cosmetics company trying to launch a new brand of sub-premium skin bronzer, who would you target – me, or Snooki? The answer is obvious, of course, but consider this: if my Klout is 68, and Snooki’s is 65, how much worse would I be at pushing bronzer? Would Snooki be twice as effective? Three times? A thousand times? There are two answers to this, of course. One is that as I am just one shade darker than an albino, the right answer is probably one million. The other answer is – you cannot possibly tell, and the scores obfuscate this, if anything.

So we have a purely descriptive measure – the influence score – but we lack the diagnostic and predictive measures that would allow us to do what every organization should be doing: learning, optimizing, and getting better. How can your company or brand take a flawed measure – the influence score – and make it better?

What Are We Really Trying To Measure?

Well, since the various influence measures are based upon a series of assumptions, let’s make a few of our own, here. First of all, most popular influence measures are heavily, if not entirely, based upon Twitter activity. Twitter’s asymmetric nature essentially means it functions as a broadcast platform – the few, reaching the many – so let’s start with something we can sink our analytical teeth into: reach and frequency. When an individual tweets out a link to some kind of content or offer, they do so with two hopes: that their followers will click on the link, and that their followers will retweet or otherwise disseminate the link to their networks, thereby increasing the potential reach of the message. So, when someone solicits, either explicitly or craftily, one of the various social media power users to help disseminate a message, the clear hope is that their message will be spread to as many people as possible using network effects.

While the exact relationship between followers and impressions is nearly impossible to calculate using clickstream measures (you have no way of knowing, after all, how many of your followers actually had the opportunity to see your message, let alone read it), it’s safe to say that more is better; in other words, there is undoubtedly a positive correlation between follower count and the number of people who interact with a given message to those followers. So, let’s assume that the behavior you are measuring for is retweets: tacit endorsements of your message, and increased exposure. Again, this is a pure reach and frequency game, and far easier to measure than “influence,” per se.

Introducing “APM”

Here is a thing you can know: the average number of retweets per follower on Twitter. If you sifted through all that clickstream data from Twitter and examined tweets that contained links (we’ll exclude “conversational” tweets,) you could come up with the number of people who retweeted a given message, and then compare that to the number of followers to the original tweeter. In other words, if I had 5000 followers, and my typical links are retweeted by an average of 20 people, then I have a concrete number to look at: I can generate one retweet for every 250 followers, or 4 for every 1000. This smells suspiciously like a CPM number, doesn’t it? But to be cute, let’s call it “APM,” or actions-per-thousand. If my average link tweet gets retweeted 20 times, and I have 5000 followers, I can generate 4 APM.

With me so far? Now, let’s say that we do this for all Twitter users over a period of time to come up with an “average” APM. It won’t look as linear as the graph below suggests, but roughly let us assume that the average tweeted link is retweeted 10 times for every 1000 followers of the original tweeter. So, as the graph below shows, 20,000 followers would get me 200 retweets, 30,000 would elicit 300, and so on. So, the “Twitter average” APM is 10 (it isn’t, by the way ) .

So now I have a benchmark by which to measure my influencer campaign. Back to my original example, suppose my sub-premium bronzer brand (Ecruage, by CASPER) used Klout Perks to identify people with Klout scores above 65 to target. Now, since neither Snooki nor I have “Cosmetics” as a topic, this requires a bit of a leap of faith on the part of our brand, but not the worst one I’ve seen. So, Snooki and I each get sent a crate of bronzer, and we go to town on the Twitters. Snooki has a lot more followers than I do, of course, but we can both fairly be graded on the APM scale I’ve outlined above.

So I try this crappy bronzer, and I tweet about it. My followers expect me to talk about social media research, consumer behavior, bad music and gin, so my crappy bronzer message comes off as a bit of a non sequitur, as the graph below illustrates:

So while the average Twitter user might generate an APM of 10 (10 actions per 1,000 followers), on this particular message I only got an APM of 4.2. Not so good, CASPER! Snooki, however, gets all serious about this bronzer, and tweets the crap out of it. On an apples-to-apples, retweets-per-follower basis, her graph might look like this (Snooki is the top line):

So, on the topic of crappy bronzer, Snooki might have initiated an APM of 15. There is a clear delta between Snooki’s effectiveness in disseminating this message (the top line) and mine (the bottom line). Two things about this delta: first, it’s endlessly reassuring to me (this is not a contest I’d care to win.) Second – that delta between the expected value (10 APM, or retweets-per-thousand-followers) and Snooki’s (15 APM) can fairly be described by one word:

Influence.

This is influence, folks. Whatever magical power Snooki worked on this crappy bronzer message (a likely mixture of the relevance of her message to her audience, her perceived authority on the topic, and the actual logical content of her tweet) she was simply better at disseminating this message than I was – and not by a little. The variance shown between her APM and the expected APM IS influence – it’s the mojo she worked using the same system as everyone else, measured like-for-like, that made her far more effective at getting people to spread her message. More message dissemination = more awareness = more trial = more usage. The circle of marketing life goes ever on and on.

The APM Index

Now, if you’d really like to wow your CMO, you could convert Snooki’s effectiveness and my (in)effectiveness into indices, which allows you to compare all of the “influencers” whom you targeted relative to the average. Here’s a primer on calculating index scores if you need one, but essentially all you do is divide the average for the category into the number you are comparing it to, and multiply by 100. This means that the average for ANY index is 100 (in essence, if you divide the average into the average, you get 1, which multiplied by 100 = 100.) Snooki’s APM of 15 equates to an index of 150 ((15/10) x 100), while my paltry effort comes out to an index of 42.

So, to close the loop on this, we started with two similar Klout scores:

Snooki: 65
Tom: 68

…and we end up with our own, topic-specific measure of actual, observed influence – as expressed by the differential in message dissemination:

Snooki: 150
Average: 100
Tom: 42

In my example, there is considerable difference between the original descriptive statistic (the Klout score) and this statistic, which moves us much more in the direction of a predictive statistic (at least on the topic of bronzer, and perhaps the category of cosmetics) that the learning organization can use to make the next “influencer” campaign even better. The influence score helped to make the initial cut, perhaps, but the only way for your company or brand to truly gauge influence is to do the work, and determine which individuals outperformed the average, and which underperformed.

Caveats, Carefully Considered

Now, there are a couple of things (at least) that one might take issue with here – both of which could fairly be described as oversimplifications on my part. The first, obviously, is that the mystical force that allowed Snooki to generate an APM of 15 compared to the average of 10 might not wholly be attributable to “influence.” But if it ain’t an answer, I don’t care – it at least serves as a handy heuristic for the nearly unmeasurable constellation of circumstances between the original tweeter and his/her audience that caused the message to mysteriously do better than the average would have predicted. Influence? Yeah, I think so. It’s at least behavioral, relevant, and a lot closer to “influence” than the activity-based scores we currently have – with the bonus of being relevant to your brand.

The other bone you might pick with me here is that my calculation – and reducing the whole model to differential message dissemination – is also overly reductive. I’ve taken what is surely a complex system and turned it into a back-of-the-envelope calculation. You’re right – it is a back-of-the-envelope calculation. That’s why companies might actually do it. You don’t need an analytics whiz on your staff to take this first pass at measuring your influencer campaigns, and until everybody catches up with you, this’ll do. Master this first, then break out the HAL 9000 when it’s time to make finer distinctions. (I also know a really smart social media research company that could help. Just sayin’.)

The bottom line is this – let’s say you actually use influence scores as some kind of crude segmentation – how will you test your work? How will you know, in other words, if your efforts were successful – and more importantly – what you can learn from them to make them better? The answer, I would submit, is to start with the current crop of popular influence measures as a first pass, but remember that they will never be as accurate as your own performance measures, even as crude as the one I’ve suggested here. There is nothing wrong with Klout, PeerIndex or any of these measures. There are only lazy marketers. And if you are reading this far, my friend, at word 2,200, you are surely not that.

A True Measure Of Influence is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

Presented without comment. Happy Friday.

1. Infloo.nz

2. Kredib.ly

3. Persuadr.com

4. Pretentio.us

5. Sway.ed

6. Authorit.ay

7. Dontyouknowwhoiam.com

8. Verisimilidude

9. Cajolr

10. Ginuwine

10 Hot New Influence Measures is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

Edison, in conjunction with Arbitron and Scarborough, recently released a study of in-car media consumption called “The Road Ahead – Media and Entertainment in the Car.” Radio still enjoys tremendous reach, so it was not surprising to see that Radio topped the usage charts for car-based media consumption. The medium certainly has the advantage of a healthy installation base, of course – it’s likely that a higher percentage of cars have an AM/FM radio than do homes, or workplaces.

Snapshots are rarely helpful, however. In our business, we have a mantra: “the trend is your friend.” In-car radio usage has declined, and other entertainment options from satellite radio to iPods have become more and more viable. Indeed, one of the more astounding stats from this survey was the proportion of 18-24 year olds who have plugged Pandora into their car stereo directly from their mobile phones: now one in five.

Still, Radio does maintain a healthy user base of in-car listeners, and from a strict reach perspective, continues to remain “the king of in-car media.” The hallways at the recently concluded NAB Radio Show were full of positive takeaways and comments about the study, which on the one hand is extremely gratifying, but on the other – more than a little troubling.

Take this graph, which shows the percentage of in-car media consumers who indicated that they “love” using various media in their car:

What the industry chose to focus on were the quantity graphs – yes, radio is still the most widely consumed in-car medium. However, the industry ignored the message of the quality graphs, like this one, which highlight what everyday consumers are increasingly telling us in study after study – they just aren’t that passionate about radio. While some of that can be attributed to radio’s reach, look at the top items on this list – they are all digital. Even streaming an AM/FM station over the phone engenders more passion than terrestrial broadcasting.

What’s more, not only are in-car consumers most passionate about digital options, look what else the items at the very top have in common: content. Satellite radio is not genius technology, and lacks the interactivity of other popular digital audio platforms, such as Pandora – but SiriusXM continues to invest in content. It’s content that puts “audiobooks” as the third most “loved” item on this list, especially as our average commute times continue to creep ever upward. Of course, another thing that Pandora, SiriusXM and audiobooks have in common – all are either nearly or completely commercial-free. Radio’s terrestrial bargain – free music, in exchange for attention to commercial messages – may still be intact, but the industry has yet to embrace the fact that expectations are different online; while simply re-purposing a terrestrial stream online might be cost-effective, it’s hardly the most competitive solution.

In short, it’s investment in innovation – either in technology or content – that is driving passion. Radio makes some of those investments, but needs to make more. Instead, too much of radio’s precious capital continues to be spent copying Pandora and Groupon. No one loves copycats. Yes, radio must make up the technology gap with its digital competition. And it can’t allow the “Daily Deal” to damage their local sales advantages. But the real gap – the most sinister gap of all – is the passion gap. Jukeboxes, simulcasts, automation, failure to invest in talent, 16-minutes of spots per hour and half-price races to the bottom will never make people love radio.

Radio can ape Slacker, out-cheap Groupon and continue to push HD all it wants. People don’t talk about things they don’t care about. If your station is not being talked about on social media, you don’t have a social media problem. You have a passion problem – the passion that you are not engendering amongst your listeners. And that is far more sinister than a technology problem.

Radio’s Passion Gap is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

Have you ever found something valuable, just lying on the street? I have a vivid childhood memory of walking to grade school (I would have been about 10) along a shady, tree-lined street where I grew up in northern Maine, and coming across just such a find. I was on a sidewalk adjacent to a grassy median, likely kicking a rock (which explained why I happened to be looking down) when I found it: a twenty dollar bill. Looking back on it, it was a remarkable find – the bill was folded up, and the winds were calm, so it wasn’t blowing around – just caught up on a pine cone, or stuck in the long, rarely-mown grass of my town’s public spaces.

Naturally, I picked it up. For a ten-year-old growing up in the 70′s, this was a fortune. I tucked it in my book bag and didn’t say a word to anyone; nor did I dare to spend it for at least a week. Rest assured, it did indeed burn a hole in my pocket, and was quickly converted to comic books and candy (I would not have made a very good money launderer.) While the candy didn’t last long, my fortuitous find did have a more significant impact – it changed my behavior. To a young boy without a knowledge of statistics, that grassy median was more than just part of the scenic Maine landscape, it was a gold mine.

From that day on, I walked that way to school *every day.” When I would get to the grassy median, I would absolutely scour the grass, convinced that another $20.00 awaited me – or perhaps something even better. Surely, this was my lucky patch.

About two weeks later, I struck paydirt: there, lying in the grass in that same median, was a shiny Cylon action figure. No, this was not the smokin’ hot Caprica Cylon from the modern Battlestar Galactica series, this was a genuine 1970′s toaster-style Cylon. And I was a HUGE Galactica fan. Statistics be damned – you just try and tell THAT 10-year-old boy that there wasn’t something magical going on with that patch of ground.

Today, of course, while I’d like to think my childish sense of wonder is intact, my knowledge of statistics is a bit more robust. The $20.00, and even the Cylon action figure, were surely coincidences. In fact, my radical behavior change – walking with my head down, scouring the same patch of ground every day – might have even had an opportunity cost: who knows – the other side of the street might have had a Viper, or maybe even Muffet. But I happened upon a coincidence, and I didn’t question it. I never found anything on that patch of grass again.

Whether we know it or not, we face the same temptation that 10-year-old Tom faced every day with analytics – blog analytics, social media data, email stats and more. We mine our tweets and retweets, and discover that noon is the best time for us to post. Or we discover that more of our emails are opened on Thursdays. In short, we look at historical data, and we find a $20.00 bill. We watch webinars telling us that we are more likely to find that twenty bucks on a weekend, or after midnight, or on a boat, or with a goat. We accept the easy answer – the “what.” We don’t ask the more difficult question: the “why.”

Finding those data nuggets is not science any more than my finding that twenty bucks was science. THIS, my friends, is science: you mine your social data and discover that, in the past, more of your tweets were retweeted on Wednesdays than other days of the week. You do not have an answer. You have a question: “hmmm – I wonder if time or day has an impact on the success of my social media efforts?” That you observe a particular day of the week as being more or less fertile is merely coincidence, until you prove it isn’t. That means designing a controlled experiment to determine if there even IS a day/time effect on YOUR social media messages, long before you start to speculate on what day or time might be effective.

I’m getting tired of junk science. Of course you will find optimal times to tweet/email/etc. based upon historical data. Random chance assures that. But that doesn’t give you a fact. It gives you a hypothesis – and before you start loading up your email blasts for the weekend, you first have to determine if there even IS a correlation between day, time or any other variable and the success metrics you care about. That is science. Not doing that work is nothing more than walking by that patch of grass, day after day, looking for a $20.

Good luck with that.

Looking For Money In The Grass is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

I’m currently sitting in an event in Boston called GeoM2, an afternoon of panels discussing the future of location-based marketing. It struck me that there are lots of players in this space focusing on a moment in time – the check-in, the conversion, the experience. So many LBS services are in fact focused on the offer – if you are in the neighborhood, come get some nachos.

The offer is valuable, certainly, but from a retail strategy standpoint, what’s the point of the offer? For Groupon, the offer is typically geared towards trial. For Foursquare, it’s loyalty. In all cases, however, the offer might change over time, but the view of the consumer is static.

Consumers aren’t static, however. Though the layers have gotten more complicated, the sales funnel hasn’t gone away. Brands still need to migrate consumers through the great circle of marketing life – Awareness, Trial, Churn, and Satisfaction/Loyalty. All roads lead to one of these four places: as trial becomes loyalty becomes evangelism, that evangelism creates new awareness, and the circle moves ever on and on. Shouldn’t my “offers” reflect my changing place on that great wheel?

If I’ve had one prior check-in, I’ve already had trial, so my offers should naturally migrate to loyalty/repeat business incentives. If I check in five more times within a certain period, I should get that offer – mayor or no. And If I check in a lot, it’s time for that business to start treating me like a brand ambassador, and not as a mere upsell opportunity. For example, I have coffee at the same place in Carrboro, NC, every day I am in town (Jessee’s, btw – best beans in town!) I check in every time. I will never become the mayor. But I have a deep relationship with Jessee’s. Trial-oriented offers aren’t right for me. And they’ve already got my loyalty – the metric tonnage of my prior check-ins can establish that. Now what they should be encouraging is evangelism. My “offers” should revolve around my introducing someone new to Jessee’s, and checking in with them together. That’s a relationship you can’t assume from the first check-in, but it isn’t rocket surgery to get there from the data I am already providing Jessee’s.

This truly consumer-centric model for LBS should also translate to the actual products and services being offered themselves. I check-in to The Franklin Hotel lobby bar from time to time. Their long-running, never changing Groupon offer is for a cheese plate. I don’t want a cheese plate. When I check-in, I want an offer tailored to my previous purchase history (which is, without fail, a Plymouth Martini, up with a twist.)

How would The Franklin know that they should offer me a Plymouth Martini? From modeling my place on the great circle of marketing life, of course. If I have checked in ten times at The Franklin over a few months, chances are I’d be responsive to the 10th check-in just flat out asking me: what’s your drink? After all, collected profile data online is exactly like dating someone. If I’ve gone out with you ten times, chances are I’ll let you get to second base. Just sayin’.

If LBS apps and services expect to hit a home run, they have to do a little more than just buy me dinner.

Location-Based Services And The Customer Lifecycle is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

This headline (from Forbes!!!) makes my eyeballs bleed: Heavy Facebook Users Prone to Drug Use, Study Says. According to the article, “Teens who report using social media are five times likelier to use tobacco, three times likelier to drink alcohol and twice as likely to try marijuana, according Columbia University’s National Center on Addiction and Substance Abuse’s survey.” The article goes on to state that “70 percent of those who said they engaged in these risky behaviors also reported using social media sites like Facebook, Google+ and MySpace daily, while the remaining 30 percent did not.”

I’m not even going to go in to my usual “correlation does not equal causation” spiel here. Too easy. Instead, I offer this helpful and easily corroborated stat: if you look at our data (from Edison’s The Social Habit) or data from other credible sources, such as The Pew Internet And American Life series, you’ll note that over three quarters of teens – and well over 80% of online teens – are using social networks.

Which, if this study is even remotely correct (or being reported correctly) means that 4 out of 5 teens are drunken, whacked out meth-heads. Facebook is the DEVIL!

And THAT’S…The More You Know.

Your Crappy Stat Of The Week is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

Anytime I speak about Klout, what gets retweeted is my criticism of the “scores,” and never anything else – like, for instance, that they are seriously honing in on “topic” and context. When I look at my Klout page, my topics of influence are Market Research, Social Media Measurement and B2B – I’ll take that. The “score” in and of itself is a bit silly as presented, though. My Klout is equal to Snooki’s. There isn’t a party, gathering or room in the world in which I would be equally influential to Snooki. There’s a good thought exercise for you. As long as that number is presented without context, I can’t hold Klout blameless here.

No, my big issue with Klout isn’t with Klout at all – but how lazy marketers use it. Jay Baer notes that some kind of tier or triage system is beneficial to companies – and I couldn’t agree more. I’m Unobtanium level with Hilton Hotels. They can tier me based upon something that absolutely has meaning – purchase behavior. The day they treat me differently because of how I tweet, however, will be a dark day in Hilton history.

Here, though, is the biggest reason why I rail against lazy marketers, and not Klout – when you treat me differentially because of my “influence” score, you are assuming facts not in evidence. I am not aware of any credible studies that demonstrate ANY tie between Klout score and the observation of some desired behavior BEYOND a mere retweet. There is no evidence that a message from an influencer has any impact on trial, usage, churn or customer satisfaction. Could such evidence be produced? Certainly – though it would be idiosyncratic to brand or possibly vertical. But no one *does the work*. So the Klout score is a poor proxy for…something. I doubt it’s influence.

I guess I’m not smart enough to get my head around quantifying the business impact of marketing by Klout score. What I can get my head around is this: Klout can give you a list of people who at least “talk about” the category your brand might be interested in, and you can use follower counts to work into a poor man’s contextual reach and frequency. That has value. What has yet to be proven is the *business* impact of a “high scorer” talking about your product as compared to a “low scorer.” After all, people bash AT&T constantly on the Twitterz, but we keep renewing our contracts. There are just so many facts not in evidence – and so many offline variables – that, again, I’m just not smart enough to figure out what to *do* with the Klout score.

Klout is on to something.They are iterating to something useful, and there is no shortage of smart people working on the problem. It just isn’t influence. To influence me, you must change my state. There is no evidence that a high Klout score correlates to this. Not yet. Color me skeptical, though not cynical.

Note: I left this as a comment on Jay Baer’s outstanding post, Why Critics of Klout Are Missing the Big Picture. It was kinda long – you know, like a blog post. So, reduce, reuse and recycle, citizens of earth!

On Klout-Bashing is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

Yesterday I gave a presentation at Social Fresh in Charlotte, NC, on turning social media monitoring into social media research. They are not the same thing. When I had finished my withering torrent of slides, the first question I took from the audience was this: “What tool would you recommend to get started in social media monitoring?”

I do get this question a lot. I never answer it with a “tool.” Instead, I “reinterpret” the question thusly: “How do I get started in social media monitoring?” This is a slightly different proposition, and one that doesn’t require a tool, per se. The thing about any of the popular tools is this: they give you a geyser of information from any and all sources of online content. The first time you run a search for your product or brand, you might get 3000 mentions, all from Twitter. Or you might get 250 message board posts, plus a few hundred tweets, and some blog comments for good measure. How do you make sense of this unstructured, unfiltered information?

Well, a good first step is to start where your customers already are. Monitoring tools are often overwhelmed with Twitter posts. If Twitter is an important channel to your customer base, then the feed you get from your monitoring tool is likely going to be more relevant to your decision support than if your customers aren’t on Twitter. Both “Dell Hell” and “Motrin Moms” were Twitter-borne kerfuffles. The former incident changed Dell’s culture forever. The latter; a blip on the radar, oft-cited at social media conferences but nearly unknown to the Motrin-buying audience. The key difference? There are a lot of IT pros and Dell customers active in social media, particularly on Twitter. Twitter is a relevant channel. For headache sufferers, however, not so much.

For your brand, Twitter might be enormously important. For others, much of the content, interaction and engagement might be centered on message boards, or a few popular blogs with active commenting communities. You need to know where your customers are, before you can begin to calibrate and therefore make sense of the noisy firehose of social media monitoring. Does this mean that you ignore a channel? Never. It might mean, however, that you weight its importance up or down in the calibration of your social media monitoring metrics, depending on how representative that channel is of your current base of customers and prospects.

“Be where your customers are” is axiomatic, and hopefully ingrained into the minds of every competent marketer today. But this advice is rarely carried over into social media monitoring. If a customer has a problem on Twitter – you respond to that problem. That’s a tactical interaction. If, however, you are really seeking to gain strategic insight from the mass of information you get from Twitter, you need to know just where Twitter posts (or any other form of content) sit in the pecking order. Again, not to ignore – but to calibrate accordingly.

And how do you know what social channels, if any, are important to your customers? If you read this blog regularly, I think you know the answer. You ask them.

And that’s the first step in choosing a tool.

The First Step In Choosing A Social Media Monitoring Tool is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!