Many of the leading social media monitoring suites come with some form of sentiment analysis technology, and this technology is used for a number of applications to track buzz, or measure crisis management, or the gauge the efficacy of a campaign. I’ve hinted here in the past that I remain unsure about what to do with sentiment analysis, which has of course prompted a number of folks in the space to drop by and comment, for which I am grateful. I do, however, want to elaborate on my “discomfort” about automated sentiment analysis here, because it is something to which I’ve given a fair amount of thought, and it deserves an equally fair shake here.

One issue, of course, is the open question of what you actually do with sentiment data. I am sure there is some relationship between social media sentiment and other key business metrics, but the onus is on the sentiment analysis folks to show that. When social media sentiment goes up or down, what if anything does that translate to? For example, when the “Motrin Moms” controversy raged across the Twittersphere, social media sentiment for Motrin likely plummeted. But did sales go down? A survey found that actual opinion of average moms was markedly less negative (in fact, most knew nothing about it) so what would you do with this information if you were Motrin? The right answer is not “nothing,” I’ll grant you, but without some way to square the disconnect between social media analysis and other business measures, what do you then do with sentiment analysis? I’m not saying the sentiment analysis got this wrong–I’m merely saying that it got this different. Without knowing the cause of the delta or the extent of the correlation, I honestly don’t know what to do with the data!

Let me state this up front – sentiment analysis is getting better. As processing power and algorithms grow increasingly more powerful and sophisticated, this will naturally happen. Still, every recent comparison I’ve seen in print between man and machine for determining sentiment has machine losing by enough of a gap that I don’t feel I could ever look at the results of an automated sentiment analysis and not feel like I have to go back and check it again–which defeats the purpose, I suppose.

Here’s what it’s good at: let’s say I post this on Twitter:

“I love my Toyota.”

I suspect that’s a no-brainer for any of the leading sentiment analysis tools. Where they have been getting better lately is with natural language processing and learning how consumers actually talk about specific categories and brands. So if I also post:

“Toyota FTW!”

…many of the leading tools will get this one right, as well. The next frontier for sentiment analysis is doing better with complex phrases that are comparative or conditional, like these:

“If my Toyota would stop when I pressed the brake, I’d LOVE it!”

or

“I love Toyota, but it’s pretty tough to beat my Yugo on looks alone!”

At this point, I expect some of the sentiment analysis folks to chime in and comment that yes, their tool can handle these as well. I’m not a computer scientist, so I won’t dispute any of that. But consider this: I actually do own a Toyota hybrid, and I am assuming that the accelerating problem and the braking problem will cancel out somehow and make my “average” ride safe. I don’t personally believe any of the examples I’ve just listed. In fact, I believe the opposite. If you’ve made it this far, you now know my sentiment about the brand in question, but how would a computer handle this particular blog post in an automated sentiment analysis? Am I expressing a sentiment about the car in question, or not–and if so, what?

The answer for many systems would be to take the logical step of avoiding sins of commission (labeling this as positive or negative) and instead risking the sin of omission–not categorizing it at all. In fact, that’s exactly what Ignite Social Media’s Brian Friedlander found when he examined a Radian6 sentiment analysis and found that 77% of the brand mentions he looked at were tagged as “neutral;” in other words, the algorithm didn’t make the wrong choice (labeling a positive as negative), rather in close or complex cases, it defaulted to neutral. From a computer science perspective, that’s probably the right choice. Again, I’m no computer scientist, and I am absolutely not picking on Radian6 here.

What I am, however, is a survey research guy, and it is when I put on my sampling methodology hat that I see the hidden bias inherent in this approach–the non-response bias. What Brian’s analysis also uncovered makes a lot of sense–while only 28% of the brand mentions tested came from microblogging (like Twitter), 61% of the posts marked with a positive or negative sentiment came from microblogs. This makes total sense–it is much easier for a computer to make the right call on 140 isolated characters of “Toyota FTW!” than it would in this blog post, which will absolutely show up on Toyota’s social media monitoring radar by dint of the number of times I’ve mentioned the brand. A computer scientist would rather have the machine make no choice than make the wrong choice–and that’s fair. But consider then what your sentiment profile sample looks like.

If 60% of your identified sentiment comes from 28% of brand mentions, and those mentions are weighted towards Twitter and other microblogging solutions because it’s easier to be accurate, than the majority of your sentiment profile is being determined by a tiny universe of unrepresentative consumers (the small percentage of online users with Twitter profiles) and not by the significantly larger sample of consumers on Facebook, leaving comments on blogs or posting to message boards. Now, you can weight the responses derived from Twitter down in the mix, which would mitigate the impact of microblogging on your overall sentiment profile, but determining those weights is tricky, and even then you are left with the non-response bias of all the untagged/”neutral” mentions in other platforms, making comparisons difficult. I believe I have seen Conversition talk about weighting their data by source, so clearly I am not the only one thinking along these lines, but know that this expertise comes from a human, not a machine. And weighting by source to account for differential response rates is not the same thing as weighting to account for a differential in sentiment identification rates.

All of this is one man’s roundabout way of saying that I don’t have the computer science expertise to challenge the accuracy of sentiment analysis, so I won’t. But when you look at your sentiment analysis data, also consider the sources of that analysis. The iceberg analogy probably works best here–some small percentage of your sentiment is “visible” (i.e., easily categorizable) by a machine, while the rest lies submerged under the ocean. But allowing your sentiment profile to be disproportionately weighted by microbloggers, who are the few, and not adequately represented by other social media users, who are the many, may lead you to draw conclusions about the iceberg from the tip that aren’t, in fact, accurate. Sample is everything.

Your take? Believe me when I say this–I want to be proven wrong. But I want to be proven wrong by something that doesn’t involve a proprietary, black box solution, because that will only be the exception that proves the rule. What say you?

The Hidden Bias Of Social Media Sentiment Analysis is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

Last week’s post on six degrees of social media monitoring inspired a lot of great comments, especially from some of the folks at the sharp end of the stick who are working for companies like Radian6, Conversition, Trackur and others. I’m pleased that you’ve all connected with this post, because I think you all have a role to play in a much larger endgame. Radian6’s Amber Naslund posted today about connecting the dots between social media engagement and other functions/departments within companies. I think part of the disconnect she addresses–a small part, but a part–is the over-emphasis on the tactical aspects of social media (brand mentions, customer complaints, even sentiment) and not enough on what mining the social web could become. As much as I love “buzz tracking” and “trending topics”, if that is all that the tools are used for, those are the conversations you’ll be part of. Mining what Katie Morse and her colleagues have at Radian6, or what Annie Pettit has at Conversition, Andy Beal at Trackur, or Larry Levy has at Jodange, etc. to show the deeper levels of insight into future products and services possible from truly analyzing the social web will get these conversations started at higher levels within the company.

Amber talks about creating an attitudinal shift within the enterprise, but the leaders in the social media monitoring space have a bigger stake and role to play in making those shifts happen. If you all can show the strategic value of your data–not just in providing a record of the past, but by actually providing insight into the products and services customers might want to buy in the future, you’ll have the CEO herself monitoring her Twitter dashboard every day. The social web, and the newly empowered consumer it has created, will become elevated from marketing channel to part of the very theory of the firm.

Take our biggest research project, for example–my company is the sole provider of exit polling data for the major news networks during U.S. Elections and Primaries. In the short term, our data provides our clients with content–who voted and what issues were important in the decision. In the long run, however, trending all of that data and mining it over time allows us to capture and predict much more profound migrations in the electorate. You all have a similar power–and a similar charge. The trend is your friend–and really mining the migration of the character of social media discussions over time to show the tectonic shift in customer expectations will be the real key to showing everyone in the enterprise that human business has changed, and corporate attitudes have to change just to keep up. So, by all means, keep tracking the Oscars, or SXSW or a thousand other interesting, buzzworthy items (we do!) but also show us what you can do at the 50,000 foot level, where the folks who really need to hear these conversations reside. Those are conversations I’d love to have. So let’s start them here!

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

Just a thought.

As I write this, we are days away from the trend-making interactive, music and film fiesta-val that is South by Southwest (SXSW). I’ve never been (I seem to always have a conflict) but like you I’ll be able to follow along the “backchannel” by monitoring Twitter. I fully expect that come this weekend, “SXSW” will be a trending topic on Twitter, and will likely stay there for days if not weeks.

If you agree with that (and if you don’t, by all means say so in the comments), you then have to ask yourself why that will likely be so. Is it because SXSW is so globally important? Or because Twitter’s reach far exceeds its grasp? I think you know the answer to that question.

I certainly don’t want to come off as a curmudgeon here–I use Twitter daily–but consider this: very soon, as the darling-of-SXSW mantle is passed from Twitter to Foursquare/Gowalla and Plancast, your Twitter stream is going to be polluted with people checking in as “the mayor of Austin Convention Center” or getting swarm badges at Stubbs. If that is in fact what you see, it’s a sure sign that you need to broaden your horizons a bit. Twitter is what you make of it, and it is a poor potter that blames the clay. So when you get sick of SXSW tweets, remember that the Twitter selection bias is easily preventable.

On SXSW And Twitter is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

If you connect with a company via social media, it’s likely to be someone in Marketing, or Customer Service. It might be an evangelist, or a community manager, or even the CEO. But when you then reach out to that company to explore some kind of business relationship–i.e., you want to spend money with them–you then don’t speak with that person. You speak with someone in sales–and all too often they don’t have the context, the connection or the relationship. These interactions are rarely as satisfying. This, it seems to me, highlights two important issues:

1. Social Media engagement shouldn’t be constrained to any one departmental silo.

and

2. Your company is probably not ready for #1.

An Observation About Sales and Social Media is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

A few days ago I posted my thoughts on social media monitoring for market research.One thing that has become apparent to me over the past few months is that there are a lot of folks using social media monitoring tools to listen for brand mentions, but truly they are capable of so much more with a little forethought and planning. Jason Falls recently posted a survey to determine exactly how people are using these tools (take it!) while others seem to be questioning how the tools are used or constructed. One thing is for sure, however–social media monitoring for brand mentions is not a substitute for research. The mere fact that people are talking about your brand means little unless you can really be sure of the context of those conversations, and equally importantly, who isn’t talking about your brand and why.

Listening to the social web for brand mentions is really just the first “degree” of social media monitoring– a tactical tool for a tactical purpose. To really get the most out of your social media tools, you need to go a few levels deeper–and not all of it can be automated. With that in mind, here are my six degrees of social media monitoring from the consumer insights perspective:

1. Brand Mentions. This is really the lowest common denominator of social media monitoring, but even without going any deeper, monitoring brand mentions allows your customer service reps to go out and solve problems, meet customers and understand their requirements. On a deeper level, monitoring brand mentions over time allows a company to measure their conversational “velocity” and even take a bankshot reading of the effectiveness of a social media awareness campaign by simply measuring the increase (or decrease) in  brand mentions over time. Monitoring for brand mentions also includes monitoring the specifics around those mentions–is someone having a problem with your product? Is someone having a great experience? All of those tactical interactions really fall under the orbit of this first “degree” of social media monitoring.
2. Sentiment. Actually, I’m not a huge believer in sentiment analysis–yet–for two reasons: it isn’t yet as accurate as an intern would be, and even if it were–I’m not even sure what you do with it other than track it over time. There is certainly no correlation I am aware of between brand mentions and sentiment, or even “social media” sentiment and actual sentiment. Taking snapshots of sentiment is a lot like day trading–anecdotal events will “spike” sentiment one way or the other over the short term, and while you should never ignore a crisis, I don’t think you need sentiment analysis to tell you if you’re in trouble. Sentiment analysis over the medium and long term, however, may be a useful metric to track the effectiveness of your social media campaigns over time.
3. The competition. Now we are just starting to scratch the surface of social media monitoring for strategic research purposes. Tracking your own brand mentions is only really interesting if you have other benchmarks with which to compare your data. If your mentions are going down while people are talking more about your competition, then you may have a social media awareness problem. But if mentions in your category overall are declining, you have a very different set of issues. Conversations about problems with competitive products are also extraordinarily useful to track, because you make money by solving those problems–whether they are with your products or someone else’s.
4. Direct Consumer Needs. Surrounding all of those conversations about your brand and your competition are specific consumer needs and desires–the context that wraps around the mention. Brand mentions about a car, for instance, are generally wrapped around the features and benefits of those cars. These can be parsed out and coded (by humans, folks–is your goal to automate or to understand?) by category to come up with conversational segments. Once you’ve mastered the first degree of monitoring and you’ve reached out to individuals having problems with your seat belts or Von Flavin valves or whatnot, you are ready to take a more strategic look at all of these issues. One of the dangers of a focus group, for instance, is allowing one or two articulate and persuasive people to run roughshod over the other respondents, leaving the undisciplined observer with a false impression of what the groups were about, or what the mainstream opinions really were. In our first “degree,” above, you solve the problems of the squeaky wheels, but in this stage, you determine whether or not you really have a problem. I talked a lot more about this the other day, but once you have a segmented, coded set of “buckets” that your brand conversations fit into, you can take a more macro- view of the immediate issues and concerns people really have with your product or service beyond the simple fire-fighting stage. This step is work, but well worth it.
5. Indirect Consumer Needs. Once you’ve fought the fires in the first step, and revised your offerings to social media participants based upon the last step, you are ready to steer the ship out into bluer water. Lexus did this when they launched with an extensive market research initiative that went beyond just asking prospective customers how many airbags they wanted. They listened beyond the “car”-focused conversations and really heard what their customers wanted to experience while driving. For the harried, affluent-but-time-starved prospective Lexus customer, that experience had nothing to do with anti-lock brakes, or acceleration or even lower price. Lexus launched their brand on the strength of one word: quiet. By going beyond the four degrees listed above and diving deeper into what the lives of their customers were really like, they determined that the potential Lexus buyer was looking for an oasis from their hectic work and home lives, and what they sold those customers was a quiet, peaceful place to park their tookus while they drove off to the rat race. Advanced social media monitoring is a great way to assess those indirect consumer needs–what else are the people talking about your category talking about? How can you provide the truest definition of value–to delight the customer profitably–by skating to where the puck is going?
6. Social Needs. This is the stuff that really excites me–the macro-level trends of a population that transcend even the previous discussions and look at the disaggregation and re-aggregation of populations around new and ever-changing societal norms. Sociologists know all about Dunbar’s Number, for instance, but what happens when an entire lifegroup or psychographic cluster of people changes the denominator by shifting the definition of “friend?” When generations of children who met locally on the kickball field now meet virtually on the Arathi Basin, what are their social needs? What do their parents need to know? Why are there so many “Tweetups?” Why does Tony Robbins have almost two million followers on Twitter, but the Dalai Lama has ten percent of that? Why does over 1% of the world’s population play Farmville? The seeds of those answers can be found deep within the unstructured data of the social web. And those are the answers that will power the global economy for years to come.

So get listening, already.

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

I am honored to have been asked to moderate an upcoming panel called “Building Brands Online/Building Online Brands,” at this year’s Radio And Internet Summit at the NAB in Las Vegas. If you are at all involved in streaming media or online music of any kind, this is the place to be, trust me. I’ll have more details on the panel soon, but expect to get some good, practical advice about what works–and what doesn’t–for building media brands online from some of the current leaders in the streaming audio space. And, since I’ll be moderating, it’ll be another good opportunity to take my own advice on five tips for moderating a panel.

See you there?

Building Brands Online is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

There’s a fantastic interview on O’Reilly Radar today with Dennis Crowley, co-founder of Foursquare. Of course, the topic of revenue models came up, and one of Crowley’s ideas was to create “scrappy promotions” for local businesses–check in five times at the same coffee shop and get a free cup, for instance. The “loyalty scheme” is a pretty promising angle for location-based apps like Foursquare and Gowalla (and exciting, hyperlocal variants like North Carolina’s own TriOut.) Certainly they provide the “scrappy” promotional angle that Crowley speaks of, but they also provide local businesses with a loyalty scheme that makes sense and doesn’t requires consumers to carry around a rolodex full of punch/stamp cards or other pocket detritus.

As consumers, we should be rooting for the success of these ventures, and for this revenue model in particular. Not only can local businesses profit from location-based apps and services, but their bigger-box brethren might potentially profit from incorporating similar check-in loyalty schemes. The “loyalty card” system, as practiced by national chains like Borders or Barnes and Noble, or by national grocers like Albertsons or Kroger, has two purposes. The first, to incentivize frequent purchases, is certainly well-understood by consumers. The second, information-gathering purpose is not so well-understood, and slightly more sinister. In effect, when you use your VIP card at your local Stop N Shop, you are providing the company a detailed record of purchases tied to your account, so that they can research the buying habits of consumers. Though the data isn’t tied to you personally, it still represents more of your personal data spilling around the Interwebs, out of your control and owned by corporate entities. For most Americans, the trade-off seems innocuous enough–after all, if your frequent customer card saves you 10 bucks a trip at your local supermarket, who cares that they know you bought 6 cases of Rolling Rock and a case of diapers?

Still, a little part of me cringes every time I am asked for my card at the grocery store, or while buying books. Maybe I don’t want my buying habits analyzed. What the Foursquares and TriOuts of the world enable is the promise of a world where I get to choose when–and where–I release my purchase data. “Checking in” at my local coffee shop (the outstanding Jessee’s Coffee, in Carrboro) may not provide my barista with purchase data tied to a customer, but it does provide them a way to incentivize loyalty without asking for my personal data (or requiring space in my crowded wallet), and gets them a little social proof/free advertising to boot in the process through my public advocacy of their coffee. They win, and I win by keeping my personal purchase habits private. Of course, I am still tweeting my location everywhere, but I am not going to do this unless I am comfortable doing so at the business in question, so the check-in doesn’t just instill loyalty, it reflects it.

This leads to another potentially fascinating angle for location-based services to pursue–a true market basket analysis of consumer habits. Current loyalty programs enable businesses to conduct what we market researchers call market basket analyses of purchase data–by determining what items frequently get purchased together or at least by the same types of consumer, companies can optimize promotions (the coupons you get printed out with your receipts) and even optimize the physical layout of their stores. There is a reason why Godiva chocolates are sold near the jewelry counters, and why (to use my example above) beer and diapers are often near each other in convenience stores.

What location-app data could enable is something far more profound–a kind of meta market basket analysis. Individual companies would not get the individual purchase data specific to each visit, but the Foursquares of the world would be throwing off heaps of data about what types of businesses are typically frequented on the same day, or even in succession. The obvious conclusions are the types of restaurants people go to after the game, or the clubs people visit in succession, but consider this–if Home Depot customers were also often checking in at a car parts store on the same day, or visitors to a certain salon also frequented a certain cafe shortly after, the cross-promotional and business optimization opportunities are truly endless. The more complex that data is, the richer the insights gained. At the national level, this data would yield incredible insights, but at the local level, that’s scrappy promotion gold. Listening, TriOut?

Foursquare, Loyalty Cards And Market Baskets is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

So, you’ve taken the big first step in social media for your business: listening. You’ve set up Google Alerts for your brand, done some Twitter searches, and maybe even signed up for some heavy duty monitoring services from the likes of Radian6, Trackur or Tweetfeel. Sentiment analysis is far from perfect, but on a pretty good day, it will give you a pretty good idea of the consumer zeitgeist out there for your products and services. So, let’s stipulate that you are listening, you can measure the conversations, and you have a pretty good idea whether or not those conversations are trending positively or negatively towards your brand.

Now what?

Certainly part of social media monitoring operates under the aegis of customer service–if people are tweeting problems, your customer service representatives should be out there actively listening to and solving those problems. Those are important interactions, but they are tactical interactions. How do you incorporate that information into more strategic initiatives?

Before you can tackle that, you first have to  make sure that your ears are as wide open as they can be. Listening to Twitter is fine, but Twitter conversations represent a small fraction of the overall universe of social media conversations, and are subject to biases unimaginable (though I’ll have some exciting data on that score soon…). If you want to elevate social media monitoring from the tactical to the strategic, you owe it to yourself to take in those inputs from as many sources as possible, to insure the stability of your data and improve the representativeness of your sample. So–Twitter, Facebook, blogs or message boards? The answer is yes.

Once you have ensured that your input stream is as robust as possible, then you are ready to go beyond simple sentiment analysis and start to segment those conversations. In market research, we do this all the time with open-ended responses to quantitative surveys. In most national survey work we do, we leave openings for respondents to provide open-ended responses to questions–the “whys” behind the “whats.” In order to make sense of those responses, we code them–we have trained survey teams go through these verbatim answers one-by-one and assign them categories. There may be, for instance, 1000 different responses to a question like “why are you dining less often at J.P. McBeers?” but chances are they fit into one of a handful of buckets. Developing those buckets is an iterative process, but after going through a hundred or so responses, y0u’ll probably be 99% there, and the rest will go fairly quickly. Are there computer algorithms to do this? Yes, but they aren’t as good as people, and once you have a good code list it’s fast work anyway–that’s what you have interns for. Many of the top social media monitoring platforms have tools to help, so feel free to use them if you have them.

So, let’s say that you’ve combed through 10,000 brand mentions for J.P. McBeers, and you’ve segmented them into buckets–declining food quality, appearance of other casual dining alternatives, change in economic circumstance, etc. The next step is to go back out into the wonderful world of social media and probe around some of these segments by engaging with commenters. The wonderful thing about social media for customer service reps and community managers–the infinitely variable interactions between company and customer–turn out to be the bane of your market research team. We like to control for variables–it helps us provide better answers to your questions. In terms of this exercise, that means doing something that might be counter-intuitive to your community managers, but really just gives them a framework to do their jobs more effectively. For each of the conversation segments you have identified, come up with a few questions you’d like to ask those people, and standardize them. Have your social media teams go back to the folks who made the original comment, tweet or post, and ask them a specific follow-up question–just one or two, and make them the same every time (for each specific bucket/issue.) Asking one or two really great questions the same way every time beats asking 1000 different questions six ways to Sunday, and gives you more confidence in extrapolating quantitative data from social media–and insight from that quantitative data.

In the case of our example, let’s say that one of the big buckets of conversation topics revolving around J.P. McBeers are conversations revolving around people spending less going out to eat. A crappy consumer insights team would take that data and deduce that J.P. McBeers needs to cut its prices. You can do better. Here’s what I would like to ask the folks who made these comments: have your economic circumstances changed for the worse? If not, what are you spending more money on lately? Knowing the answer to that opens up a whole new world of consumer insights for not just the brand in question, but the evolving needs of  your target consumers and how you can best serve those needs.

If you can standardize the question(s), you can standardize the responses and make apples-to-apples comparisons. Is this the last word on reliable consumer insights? Hardly. Will it let you make some intelligent hypotheses that you can test in situ or with split-tested offers? Definitely! To me, this represents the real next generation of market research for brands–reengagement after the question, turning market research into relationships, and relationships into market research. The key is to do a little thinking beforehand, standardize on a few really good questions, and empower your engagement team to go out and get the answers.

Social Media Monitoring 201: The Market Research Perspective is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

There are lots of ways to measure the effectiveness of social media campaign, but only one right way to start. Before you start a Facebook Fan Page, or open a corporate Twitter account, the one mandatory step you need to take to be sure any of it is worth your time is to identify the key performance indicators (KPIs) of your business, and measure them first. You might use the data, you might not–but if at any point down the road you need to measure exactly how much social media engagement has added to your bottom line, you’ll want to know exactly where you were when you started. A small additional expense, perhaps, but you’ll thank me later, trust me.

Take this recent survey for a Houston-based bakery/cafe chain called Dessert Gallery covered recently by Harvard Business Review. The company wanted to measure the business impact of maintaining a Facebook Fan page, so they first surveyed their email database to collect baseline information, and then began their engagement. After three months of constantly updating their Fan Page with promotions, reviews and other content, they resurveyed their list to measure a variety of key metrics, including Net Promoter Score, average price per ticket, and average visits per month–three of their KPIs.

Almost all of the results reported in this survey can be tossed into the “correlation does not imply causation” bin. The chartjunk-y graphic on the page highlights differences in the Net Promoter scores between Facebook Fans and others, but this can easily be explained by the fact that becoming a restaurant’s Facebook Fan probably meant that you were already a Real Life Fan–in other words, whether or not you are a Facebook Fan is little more than a trailing variable, and recording that datapoint merely provides another way to segment out the most passionate members of your existing database.

Similarly, Facebook Fans reported a greater level of emotional attachment to Dessert Gallery than did others, and were more likely to choose it over other establishments whenever possible. Again, none of this proves much besides the fact that people who sign up for your company’s fan page are probably already fans. And, if all they had done was survey customers at the end of the process, that’s what they’d be left with–an expensive way to segment an existing user base.

Except…there is one other data point in this survey, one that should have been the focal point of the accompanying graphic, and one that is impossible to dismiss as easily. By surveying customers before the campaign, and after the campaign, they had measurements from customers who had completed both survey instruments (easily trackable through cookies or email analytics). Those customers who had completed both surveys (and I do wish the article’s authors had included the sample size there) and became fans reported an increase in store visits per month. That’s apples-to-apples, friends–and and the clearest indicator in the whole article that the social media engagement may have actually moved the needle on a KPI, and not merely been associated with a needle-move.

Are there still possible explanations for this behavior change that don’t rely on Facebook as a causal variable? Sure–there is the Heisenberg effect of simply being surveyed, which heightens your awareness of being an important customer, especially if those customers were made aware after Survey 1 that there would be a Survey 2. That’s just one of several possible confounding variables. But in this study, you do have a very clear sense of a way forward in measuring social media ROI. If you can measure a population pre- and post-engagement, and show changes in the same sample based upon the variable of social media engagement, you’ve come a long way in proving or disproving the case for continued expenditure of resources.

And none of it would have been possible without the initial survey. Is it an additional commitment of your talent and treasure to measure before and after your social media initiatives? You betcha. Do you think Dessert Gallery questions that expense? Or questions the time and effort they put into their Facebook Fan page now? Not a chance.

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

I caught myself doing something today that is ultimately counterproductive. Maybe you’ve done the same thing.

The post I wrote yesterday on the great Forrester Analyst Blog Kerfuffle seemed to hit a chord with some people, and I was very fortunate to have the post picked up on Social Media Today. This led to a lot of people I’ve yet to meet retweeting this post, and I dutifully added them all to my “following” list. I’m pleased that the post got some traction, and thrilled that there were some Twitter users out there that shared my opinion.

In research, one of the primary things we deal with is the concept of selection bias. In a convenience sample, the researcher is selecting the respondents, usually with some kind of quota of mind. In a typical online survey, there is a self-selection bias–you are only getting the opinions of the folks who chose to go out of their way to respond, which often leaves the sample stacked with people who feel very strongly one way or another about the topic of the survey. What is missing from both of these examples are the opinions of the people you didn’t select–either left out by the researcher, or not sufficiently motivated to respond to an online survey. We call this “non-response bias,” and it is something we have to model all the time (we do this in the Exit Polls pretty rigorously, as you might imagine.)

When you follow people on Twitter because they replied to you, or retweeted your post, you are stocking your follower list in much the same way. If you aren’t too careful, you’ll end up with a group of followers that can tilt anywhere from generally positive/favorable to your tweets to downright sycophantic. This might be great for your ego, but it’s misleading and ultimately bad for the strength of your ideas.

Of course, you should follow people interested in your ideas, and definitely strike up conversations with those who choose to engage them. That’s being a solid social citizen. But as a reality check, it’s also helpful to do a Twitter search on the topics you write and tweet about. Actively seek out people who may or may not have engaged with you, but who definitely would sit on the other side of a given issue or argument with you. Follow them, too.

It’s all too easy to “curate” your twitter stream to what seems comfortable–to stock it, albeit unconsciously, with attaboys and kudos, retweets and replies. But you are in charge of your Twitter experience, and that means going outside your comfort zone a bit to ensure that you are seeing all sides of the issues that you care about. Maybe you’ll engage, maybe you won’t–but your ideas will be stronger in the end by mitigating for “Twitter selection bias.” And, speaking from experience, you won’t get such a swelled head, either.

The Twitter Selection Bias is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

Information wants to be free. Forrester wants to charge you for it. Even before the recent decision by Forrester to forbid its analysts from blogging about their coverage areas on personal blogs, these two facts were headed for a collision.

First, let’s be fair: Forrester isn’t stopping employees from blogging about things outside of their coverage areas on their personal sites, and they aren’t stopping them from blogging about their coverage areas period–they are simply requiring that content to be hosted at Forrester.

Still, there has been quite a kerfuffle over this move on the blogosphere. GigaOm takes the populist stance, declaring that like media, research needs to be social, too. Probably won’t get too many arguments from the chattering classes on Twitter there. Most of the comments against Forrester’s decision list a bunch of “personal branding” reasons, but a company has every right to insist on an employee’s full attention, just as it is every employee’s right to move on when their desire for a personal brand grows stronger than their desire to build someone else’s brand. Critics of Forrester’s move point to their loss of Charlene Li and Jeremiah Owyang, but those anecdotes are not representative of the analyst industry as a whole.

I’ve read a lot of posts excoriating Forrester for their decision, but I doubt few (if any) have been written by actual paying clients of Forrester. I’ve been one, so that has informed what will probably be a pretty unpopular post here on BrandSavant

I’ve been on both sides of the analyst paywall in my career. In a past life, I managed analyst relations for a company, and was in the position of writing those checks to Forrester, Gartner, et al. I can assure you that those checks were plenty big. I have enormous respect for Josh Bernoff and some of his colleagues at Forrester, but I wouldn’t have written checks of that size to “Josh Bernoff, LLC.” I wrote them to Forrester. I wrote them because Forrester granted me access to data–to information I could not get elsewhere–and the acquisition of that data comes at a high cost. Bernoff and other analysts add tremendous value to that data by helping their clients navigate through the interpretations and implications. But the competitive intelligence and primary research data collected by Forrester is at the heart of anything of value that they write.

In other words, the value of their analysis is inextricably tied to the value of the data they are analyzing, and that data is not only owned by Forrester, its very existence is dependent on the fact that Forrester can collect sensitive data from companies, sanitize it and synthesize to provide insight to its clients without exposing them. Take away that data, and the opinions of Forrester’s analysts are just that. And you know what they say about opinions–they’re like…uh…everyone has one.

Every time a Forrester analyst puts pen to paper on a given coverage area, they are doing so with the benefit of access to that information. Hosting those conversations at the Forrester blog is a tradeoff, but one that preserves the value of Forrester’s intellectual property (IP.) Forrester clients don’t pay Forrester to know stuff. They pay Forrester to know stuff before anyone else, or know stuff other people don’t know. They pay for a competitive advantage, and they pay a lot. If Forrester’s IP were spread all over the interwebs, the value of their analysis would naturally go down. This would make them more popular in social media circles, but less so in the writing-five-and-six-figure-checks-for-valuable-information circles, who would naturally start to question why they are paying Forrester when they can just aggregate the personal blogs of Forrester analysts.

As a personal example, I’d love to blog here about the work we do for the networks on the National Election Exit Polls, but anything I would write about would be dependent on access to that data, and that data is owned lock, stock and barrel by our clients, and they are the ones who will actually be sending little BrandSavant Jr to college. Can not do it. Similarly, analysts don’t own the information they are analyzing, so it becomes difficult to extricate Forrester IP from the blogger’s IP. Forrester has every right to dictate how its IP is used, when the scarcity of that IP is an existential question for the company.

Finally, much has been written about how this potentially tramples on the rights of the Forrester bloggers. Again, we’ll see if this really prompts a mass exodus of analysts, or a renaissance in Forrester-hosted blogging–which would actually be good for clients. But consider what can happen to the reputation of Forrester (and its clients) when coverage areas are discussed on personal blogs. Now THAT’S a real kerfuffle. A human mistake, yes–it was owned up to, and all involved have moved on (and it’s a testament to his character that these blog pages are still on his site.) But this could have (and maybe did) hurt Forrester right in the wallet. It’s not my intent to rehash that particular incident, but let’s all agree it was a significant black eye for the company and indeed the analyst industry as a whole. Forrester can afford to lose an analyst here and there–but they can’t afford incidents like this.

And, as always, your comments are welcome!

Why Forrester Made The Right Call About Employee Blogs is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

I gave this presentation last week on behalf of the Association for Downloadable Media, and it didn’t turn out too bad! If you have an hour to spare to see the latest research on podcast sponsorship, advertising and consumer behaviors, here it is in all its glory

The Edison/ADM Consumer Attitudes To Podcast Advertising Study from Tom Webster on Vimeo.

Consumer Attitudes About Podcast Advertising is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

This is really just a brief rant, but why is it so hard to find a commercial presentation software package that does brilliant data graphs? PowerPoint is just…awful, and while Keynote makes purty slides, the graphs are more designed to be works of art, and not to clearly communicate a dense amount of information. Newcomers like Sliderocket show promise, but the range of charting tools isn’t quite there yet. Maybe I’ll succumb to Tufte’s advice, skip the slides and go with handouts–there is a fine line between data-rich graphs that are unreadable and data-poor graphs that are low-resolution chart junk.

What do you use?

Data Visualization For Presentations is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

There has been a lot of recent discussion on metrics for social media, and I think it’s a healthy discussion to have. Amber Naslund has a great series on some of the metrics that matter, and K.D. Paine’s PR Measurement blog is also a really smart place to get an education on the subject. Often, the issues that people are having with social media metrics have nothing to do with the social media part–it’s a basic understanding of metrics, period.

Most of the work on this topic centers around two dimensions: the stuff you did, and the results you got. Easy peasy. But, there is another dimension to social media measurement that often gets overlooked. It gets overlooked, because it’s often a painful admission–opportunity cost. Any time you spend marketing has two costs–what it cost you to do the thing, and what you could have made doing some other thing. It’s the first thing you learn in economics, but it’s also the first thing you forget in business, because in business, as in poker and the stock market, we tend to forget the “bad beats” and artificially inflate the wins.

I’ll give you an example. My company, Edison Research, is a small, highly effective unit. We pulled off the largest single-day research study in history–the 2008 Election Exit Polls–with a full-time staff of about 25 people. We don’t really have a sales or marketing arm, per se–most of the “marketing” I do is squeezed in around client projects, so my time is definitely a finite resource. Yours is, too, so you’ll understand this story all too well.

I recently wrapped up a marketing/thought leadership initiative for the company that had a social media component. If I measured the stuff I did against the returns I got, it did OK. A bunch of people tweeted about it, a bunch more visited our site, and a bunch of trade publications covered the effort. Measured. But–and it is a very big but–I spent about 30 hours of my time, and another 40 hours or so of our staff’s time on this. Thousands of dollars, yes, and also time that could have been spent on other initiatives. I did a little post mortem on the project this week, and decided that it would have been cheaper for me to just mail everyone on our prospective client list a check for a hundred bucks and hope for the best. For the handful of prospects I really hoped to land from the effort, it would have been cheaper to hand-deliver those checks.

I’m being facetious, but only a little. The amount of time I spent on this initiative has a definite dollar cost, and those dollars could have been spent on a full-page ad in a relevant trade magazine, or an event sponsorship, or banner ads. You need to measure all of these things–because you need to know not only what the ROI of a social media initiative is, you need to know if the “I” could have been spent better, elsewhere.

Many of you engage in social media because it’s cost effective. Maybe that’s true, and maybe it isn’t–your mileage can and will vary with each initiative. But it is important that as your capital permits, you hold social media initiatives to the same standards you would hold any other marketing, PR or sales initiative. To do otherwise is to sell yourself short by failing to adequately value your time. The choices are easy when all you can “afford” to do is social media. But as you grow, you have to be able to look at social media with a clear eye, and a realistic sense of social media’s opportunity cost. Often–and I am convinced of this–it is well worth that cost. But never forget the bad beats.

This blog post took some of my time. If I consider my value–both to my company and my family–I can combine my actual salary with an aspirational, potentially inflated sense of self-worth and ballpark the cost of this post at $100. Should I have paid someone else $50 to write it instead? Those are the types of questions you should challenge yourself with, every day.

I’ve just had a bad beat, but I had the systems in place to measure that honestly, adapt my approach, and regroup for the next round. Here’s what I know:

Fail fast.

Spin lots of plates, but start them one at a time.

Measure your time, honestly.

Measure the value of your time, fairly but aspirationally.

The past is a sunk cost.

Do better. Move on.

The Most Painful Social Media Metric is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

People who know me, know that I’m one of the biggest Apple fan boys ever, so it might surprise them to learn that I’m going to take a pass on Apple’s new iPad. Your mileage may vary, but I have two particular use cases for such a device: First, I’m a road warrior–an inveterate frequent-flier–and I’m on the road for at least half the year. So, I’m looking for something lightweight, powerful and travel-friendly that I can work on. The second use-case I have is for something I can work on in coffee shops/restaurants or anywhere else I can snag Wi-Fi, since I don’t work in a traditional office setting. For those scenarios, again what I need is something I can type on that has significant battery life, in case I need to be away from a power strip for a prolonged period of time.

Yes, the iPad is thin and light, and yes, it does purport to have up to 10 hours of battery life (which, using the MacBook Air Battery Distortion Calculator I’ll take to mean 6 hours.) But–and this is a very big but–did you watch Steve Jobs try to type on that keyboard during his live demonstration? Did that look comfortable to you? I haven’t seen a device so tailor-made to produce an ergonomic injury since Steve Martin invented the “OptiGrab” in The Jerk. You already know how the touchscreen keyboard is going to feel if you have an iPhone, and just because your typing surface is larger doesn’t mean it’s going to feel natural or comfortable. Without the haptic feedback of a physical key, the “give” that prevents fatigue from settling in as I type, there is just no way I am going to want to write on that thing for more than 10 minutes (go try it on your window for 5 minutes and tell me how it felt.) The promo video for the iPad trumpets its ability to adapt to how you want to work, but that is only a meaningful distinction if your greatest usability concern is whether to work in portrait or landscape.

If you are a knowledge worker, you already know you can’t work on this thing for long. If you are a traveling knowledge worker like me, that means that you’d have to pack the iPad AND a laptop (and an iPhone). In other words, a third device. Jobs claims that the iPad is in fact a third category between the iPhone and the MacBook, but as a consumer I didn’t ask for a third device to carry. Apple’s marketing team would tell me that it isn’t meant to replace the laptop, but if it can’t at least stand in for one, it’s too big to cart around in addition. The iPad implementation of iWork is pretty, but “I Work” with a keyboard. Even a stylus and some Newton-era handwriting recognition would have been a welcome addition.

This leaves the iPad as media player, and surely it is a beautiful one. Blows my Kindle away for eBooks (though the screen glare might prove fatiguing), and presents a superior experience for movies. Again, however, for my personal needs I am looking for things that travel light, and the single greatest feature of the iPhone is that it gives me the iPad experience in my shirt pocket. Had Apple started with the iPad and then come out with the iPhone a few years later, I might view the iPad differently. Instead, they made the iPhone bigger, which–again–I didn’t ask for.

For my criteria as a hard-working, well-traveled knowledge worker, this is not a transformative device like the iPhone was. And yet it was built up to be just that–Apple fostered the hype prior to the event, and then pitched it like it was Moses’ third tablet, not a computer. The icing on the cake was Jobs’s positioning of the iPad’s price point as some kind of boon to humanity. Maybe if it dispensed clean water, or cured TB he’d be doing the world so great a favor. But in the end, it’s a big iPhone. Too big for my front pocket, too poorly suited as a writing tool, it is relegated to a coffee table curiosity, something cool to have laying around when your Windows-using friends drop by for drinks. My coffee table books are rarely read.

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

Pollster.com’s Mark Blumenthal posted an article on the National Journal about two new ventures bringing “professional” polling to the masses. Essentially, these new ventures (Pulse Opinion Research and Precision Polling) allow any one with a credit card to inexpensively commission and field a telephone survey (either a robo-call or a recording of the purchaser’s own voice) on the topic of their choosing.

The fact that these ventures are being run by “professional pollsters” is irrelevant: once you plop down your fee, you are the pollster, and there won’t be any professional guidance–or oversight–on how your questions are formulated or how these polls are used. Want to run a push-poll? Go for it. Circumvent the “do not call” rules with a thinly-veiled attempt to “gauge interest” in a new product or service? Be our guest. Blumenthal wisely notes that should we become flooded by a plethora of these self-serve robo polls, three changes will need to take place in order to safeguard the integrity of our industry: better measures of quality, better disclosure, and safeguards against abuse. Yes, please.

I’ll add a fourth: better outreach by AAPOR, CASRO and enlightened advocates in the press to educate journalists and/or bloggers on how to read data, and the questions they should be asking. I’ve commented here numerous times on the sad state of survey reporting, and the fact that the vast majority of tech journalists merely pass on the results of study after study without once questioning the sample composition, the claims of representation, who paid for the survey or even what the exact questions were. I am sure there will be good studies done under the aegis of these two enterprises; I am equally sure there will be poor ones as well. The fourth estate has a responsibility to do more than “pass along” the results indiscriminately. As data reporting quickly gets shortened to 140 characters, online reporting has an even greater responsibility to separate the wheat from the chaff.

Often, the survey itself does a fine job disclosing its limitations, but the press (and the tech press, especially, which is woeful in this regard) confounds “respondents” with “all Americans” or “Households.” With thousands of non-representative convenience samples on various topics, journalists will inevitably be confronted with surveys that offer differing and sometimes contradictory results. The responsibility of the journalist should be to first ask and answer why those surveys might differ before blindly passing them along to the public. In survey reporting, it’s often the sins of omission, not commission, that do the most harm to readers.

(N.B.: this was originally posted on Edison’s blog, but I thought it was relevant in this space as well.)

When Everyone Is A Pollster, What Happens To The Polls? is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

Jason Calacanis is angry at comScore and would like us all to boycott their measurement service. The gist of his ire is that comScore is now augmenting their panel-based measurement service with a server-based measure which requires sites to install a small pixel-tracker across their pages/widgets/etc. Since comScore is charging $5000 to have this tracker installed, Calacanis claims that comScore is blackmailing sites–pay up if you want the “accurate” numbers. He goes on to berate comScore for being wildly inaccurate, especially compared to server-based measurement platforms like Quantcast or Google.

I don’t have a dog in the comScore fight, but I do know that raw server-based measurement data is also wildly inaccurate, but because it inflates (rather than deflates) unique user counts, no one complains. This reminds me of the Clinton/Dole race of 1996, where so many of the pre-election polls called for a Clinton win, but by a significantly larger margin than his actual margin of victory. Since a win is a win, the polls largely got a pass from the American people–but the polling was pretty off, regardless.

My sense is that most people wouldn’t complain if their server-based measures overstated unique users by a couple hundred percent or so–which some experts believe cookie deletion alone could cause–so comScore naturally gets a little more scrutiny when their panel-based measures appear to understate users by comparison. But it really doesn’t matter what site owners or publishers want–it’s what advertisers want. Generally, they want accuracy, and they want qualitative / demographic / psychographic / geographic data as well. The only source for the latter, of course, is survey research, which comScore access via its panel.

Survey vs. Server as false choice continues to be one of my big themes here at BrandSavant, and the answer should always be both. Server-based measures give you perfect measures of meaningless numbers (“Traffic”) while survey-based measures give you imperfect measures of meaningful numbers. Both are estimates.

So, you could look at what comScore is doing the same way Calacanis is–holding us ransom for “the real numbers”–or you could see this as comScore trying to make a better product, and that product is going to cost more money. Server-based measurement companies are quick to say “I told you so” to comScore’s move to mimic their cookie-tracked data, but the real story here is that comScore’s hybrid approach signals an improved approach, and the cookie-trackers of the world would be wise to incorporate survey/panel data into their own products, even at the expense of millions of phantom “users.”

I believe in the hybrid approach in theory. I can’t comment on comScore’s actual product, so I won’t. I do think the price to play is quite high, and will be potentially onerous for some startups and small businesses. I assume the market will take care of that. So, at least in that respect, if you think comScore’s price is too high then by all means “boycott.” It’ll probably come down if they can’t sell it. But claiming that they are holding back the “more accurate numbers” assumes facts not in evidence. Panel-based measures are not less accurate than server-based measures, merely different. The ideal solution would be to focus on continuous improvement of server-based measures by calibrating them with survey-based measures and vice-versa. Pitting them against each other, as Calacanis and many of the commenters to his article do, is drawing a line in the sand where none should exist.

Two Cents on Jason Calacanis’s comScore Imbroglio is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

Seth Godin has some smart things to say on his blog almost every day, but today he said something that really stuck in my craw. His post “Too much data leads to not enough belief” has all the hallmarks of a real crowd pleaser–it’s been Tweeted hundreds of times already–with his conclusion that “data crowds out faith…without faith, it’s hard to believe in the data enough to make a leap.” Seth concludes his post by asserting that “relying too much on proof distracts you from the real mission–which is emotional connection.”

Who is going to argue with that? Of course emotional connection and vision are prerequisites to taking a leap. I’ve taken the leap myself in three startups (started two of them), presented data to scores of entrepreneurs and conducted research on hundreds of new products and services over the years. Without faith, new ventures would never be born. So pushing the false choice of “too much data” over emotional connection is an intellectual shortcut, at best. There’s no such thing as too much data. There’s only “enough” data, and not enough data, and the definition of “enough” is a personal one. But framing the argument as “too much” data vs. vision and faith casts “data” as a straw man. I’d rather have “too much” data AND faith.

See, it’s never the data that “kills” a project, or stalls a venture. Never. It’s the people. There are some people who are so risk-averse that they will keep looking for data until they find the nugget they can cling to that says “Stop.” Others look for that one data point that says “GO!” But it’s more about the reticular activating system of the person in question than the quality–or quantity–of data available. Some people need data–lots of it–and won’t go forward without it. Now, I realize that Seth would also acknowledge that it really comes down to the people–he notes that “The skeptic will always find a reason, even if it’s one the rest of us don’t think is a good one.” But it’s wrong to say “data crowds out faith.” Data is a crutch for the faithless.

Business lore gives us plenty of romantic stories about visionaries who pushed forward against the odds only to prevail in the face of “data.” There is, however, a survivor bias inherent in relying on those anecdotes. For every Jeff Bezos who doggedly pursued his vision to victory, there are thousands of entrepreneurs who fail. Few of them failed because they got the “vision” part wrong. We don’t see those stories, but they are out there. Very few of them would tell you that they wished they knew less.

When you search for or commission primary or secondary research data for a new venture, the data is bound to highlight the negative–the dangers of a new approach, the potential pitfalls, the risk. You can count on that. It might also show you a safer path. In the studies I’ve presented to clients over the years, I’ve seen four different kinds of reactions to what is essentially the same data:

• One reaction is to fear the risk, and terminate the venture (or at least the approach). Sometimes that is warranted, sometimes it is not.
• A second reaction is to cling to the “safe” course highlighted in the data as a means to significantly reduce or eliminate risk. The results of this approach typically create lackluster, “me-too” products that fail to inspire.
• A third approach is what I call “ostrich syndrome”: to stick your head in the sand and ignore the data. Damn the torpedos–I have vision! Sometimes that’s enough. But think of all the brands out there that have achieved cult status–the brands for which customers will not only advocate, but even put up with inconvenience to use! Iconic brands like Apple, Nike, BMW, etc. Are their leaders visionary? You betcha. But I can assure you that they are also absolutely swimming in data.
• Their secret? The fourth approach. Data, especially market research data, can highlight risky areas and safe areas. The unimaginative gravitate to the safe; the uninformed blindly move to risk. The truly visionary leader uses data like a lighthouse–pointing out the rocks in the shoals that might sink the ship before it even ventures into deeper water. If the venture is worth the risk, the ship sails on–but the more you know about the rocks on the bottom, the better the chance you’ll make it to the new world.

It’s the latter approach–and my clients who embrace it–that has yielded the most satisfying engagements of my career, and the most successful products and services. Research and data are nothing without vision and emotional connection. But claiming, as Seth does, that “relying too much on proof distracts you from the real mission–which is emotional connection” is kinda like saying that the gas gauge distracts you from the thrill of winning the race. It’s a poor driver that would quit the race because the gas gauge is running low. But it’s a stupid driver who wouldn’t install one in the first place.

{/rant}

What say you? Is there such a thing as “too much” data? Unlike Seth’s blog, my comments are on and open, so let’s hear it!

Data Doesn’t Kill Ideas – People Do. is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

I checked out an article on CNNMoney.com today on fitness gadgets, and it was the gazillionth article I’ve read that split a simple list post into multiple pages (one for each list item) in a desperate attempt to increase page impressions. This was the straw that broke the camel’s back, at least for this camel. There are two ways to monetize what is essentially a list post: embed relevant contextual ads throughout a one-page article, or embed relevant contextual ads on each of “n” pages, as this particular CNN article does. I only pick on this article because it’s the most recent example I’ve seen of this unfortunately widespread practice.

I don’t know about you, but this style of content presentation pisses me off enormously, and when I actually do read these, I click “next” as fast as possible to finish the article. Sure, I could hit the “print” link. I get that. But I can tell you what I am not doing–even looking at the ads. In other words, they may register as impressions, but I am clicking through the screens so fast there is no chance I am taking an action. The IAB should fund a study to determine the efficacy of ads on a one-screen article vs. a franken-clicker article. Impressions aren’t the endgame, and the sooner advertisers push back the better.

A Research Topic the IAB Desperately Needs To Pursue is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!

…with this headline from TechCrunch: Study: Internet radio reaching 32% of households. Seems harmless, except the actual survey, from L.E.K., was an online survey that made no claims to national representation (Pew estimates that 26% of American adults aren’t even online) and correctly refers to its sample as “respondents” throughout the survey. At least 95% of the time, there is nothing wrong with the surveys that journalists cover, it’s the reporting that comes up short.

The Woeful State Of Survey Reporting Continues… is a post from: BrandSavant. Copyright 2010, Tom Webster. Thanks for reading!