Spiral Marketing: The More You Know, The More You Can Know

February 10, 2010

Google Buzz or Google Fizzle?

Ah, Google – you evil bastard! You slipped in another Facebook / Twitter / (insert social network of your choosing) Killer into our gmail accounts while we slept, blissfully unaware of your latest misguided attempt at social network domination. At least the Wave and the Buzz make me think that, hey, I need a vacation; Google Docs? Not so much. But at the end of the day, as much as you’d like to, you will be hard-pressed to transplant the Facebooks and Twitters of the world. You have more money than most to spend on technology – but technology has little to do with the problem you are trying to solve.

Here’s the thing: Facebook, Twitter, and other social networks are marginally interesting technology, but I venture to guess that I could pay some very clever college students all the pizza and Meisterbrau they can consume, and they’ll come up with a pretty good approximation of the Facebook, Twitter or ______________ technology in a matter of weeks. Facebook technology has as much value as Twitter or Google Buzz technology: next to nothing.

Yeah, I said it. All this social media technology by itself is worth about as much as, well, Google Wave. Because the value of Facebook is that some enormous number of people use it every day, and they use it a lot. Same with Twitter. It isn’t the technology of the network that matters: it is the network itself that counts. Facebook could easily go away – it happened to MySpace, and it could also happen to Twitter. But MySpace wasn’t killed by technology or Rupert Murdoch’s continued misunderstanding of all thing interwebz (although it hastened its decline).  MySpace is dying because people aren’t using it the way they used to.

The value of a network is generally (and very loosely) based on the number of people that use it (Metcalfe’s Law). The value of a network is more precisely based on the number of possible subgroups within the network (Reed’s Law). On this count alone, your chances of transplanting a Facebook or Twitter go from “No way in Hell” to “Geez, if I drink enough, I could see it happening.”  Why?  Because you brilliantly gave away a veritable plethora of free email accounts (I think I have, like, 87 of them). This gives you a network with an enormous number of users – a necessary precondition to taking over the social networking world.

But this isn’t a network problem alone – it is a value problem. And while having a huge network (through registered gmail users) is a necessary factor in your dastardly global dominance scheme, it is not sufficient.  And this is where you will fall short again.  Because email is more ubiquitous than the largest social network – everyone’s got an account – but the economic value of a network is based upon the aggregate value of the interactions on the network. And people don’t value email interactions. If they did, email would be synonymous with “printing money.”  Which I suspect is what you are trying to do, at the end of the day. But I don’t want to interact socially through my email – I would rather not do ANYTHING through my email, just as I never write letters anymore. There is already enough garbage in email to negate any value it ever might have had – and hey, don’t get me wrong, I use email; I just use it when I have no other possible means of communicating what I need to communicate. But until you can tell me what in the hell “fwd:fwd:fwd:re:fwd:fwd:re:fwd:re:puppies” means without me having to open the damn email, I’ll pass.

The two necessary and sufficient preconditions for achieving social media world dominance and the economic and intellectual imprisonment of the world are 1) a very large and active community; that 2) delivers high perceived value  in the interactions of the community (and “noise” does not equal “value”).   With Google Wave you took a fair (although off target) shot at delivering more valuable interactions, but you couldn’t drive the numbers. With Google Buzz, you are using your huge gmail user base to capture numbers, but without improving the quality of the interactions in that network.

The good news? You’ve got the problem surrounded. The bad news? I think the Buzz you’re hearing is really the sound of a fizzle.

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June 19, 2009

Twitter Vs. Facebook? That Is NOT The Question

It has become popular occasionally to ask whether one would pick Twitter or Facebook if you had to choose.  The question, hypothetical though it may be, makes no sense – the implied choice is based upon a false analogy.  Taken to the extreme, the question is about the same as asking whether you would prefer a black phone or a black photo album.  But in this rush by the social media “gurus” or hobbyists to “pick what’s best,” I think they are missing the forest for a few trees, some of which – like MySpace – may or may not be around in a year.

I think the important question, and perhaps the question that people are trying to answer when they get is this discussion, is how do we understand best how to use social media.  And how we best understand the use of social media is a lot like understanding how to best build a house (to use an old analogy).  I don’t look in my toolbox, see a hammer, nails, saw, level and square, and suddenly understand how to build a house.  Similarly, I cannot look into my “social media toolbox” and understand how to build or influence a group of people.  I need to first build my blueprint – my end-state vision of what I am trying to accomplish – and then, understanding the goal, I look to my toolbox to determine which tools I will need and how I will use them to accomplish that goal.  And in any social media campaign – which may be as mundane as building your own personal influence, or as business critical as creating a social marketing campaign that seeks to reach an audience that is immune to more traditional marketing techniques – these goals, these “blueprints” will be as different as the people or the businesses they are meant to support.

Those who know me know that I believe very strongly that the world is full of answers – ask someone a question, and you are highly likely to get an answer.  And since you’ve gotten an answer, you assume everything is great, and you act upon that answer.  The only problem is that, in over 20 years of consulting experience, I’ve rarely seen efforts go wrong because a company couldn’t get an answer to the question they posed; they have gone wrong because the company asked the wrong question to begin with.  The implications of this point are that objectively, an answer can be right or wrong given the question you ask, but the “rightness” or “wrongness” of the answer you get doesn’t help you if you started with the wrong question to begin with.  And I have, and have very often seen clients, ask bad questions for which they get the “right” answer, which leads to a totally disastrous result.

Getting back to the subject of this post, I am saying that “Twitter vs. Facebook?” is a badly formed question; it has no chance of getting beyond personal preference to a more unified view of the social landscape, and how you become a part of the conversation taking place there.

So what are the right questions? I think the first question any individual or business has to ask is “what are my goals for a social marketing campaign?”  I can’t give you a cookie cutter that fits every industry, although in a later post I’ll consider some “value chains” that you may seek to influence with social media, and how those relate to questions you must answer to develop your goals and strategy.  The blueprint for the house depends on the house you seek to build.

But once you’ve had the architect come round to help understand the house you need, the house you want to build, how then do you apply social media tools to the building of this house?  One option I am tempted to try is to create a dartboard, with slots for various SM tools, and 3 darts – then use any tool you land a dart in.  This appeals to me only to the extent that a) it’s kind of funny; and b) I always drink when I throw darts.

But for the sake of this discussion, I will let that go, and instead assert that there are certain characteristics of social media represented to a greater or lesser degree in each of the social media tools and techniques.  There are also other tools that amplify the effect of these characteristics, but I’ll save that for later as well.  To support a mental model for evaluating these tools, I personally think these 9 characteristics are highly relevant:

  1. Reach – how many in my “social graph” can I reach with a given message.  Note that reach is relative and I believe closely related to specificity – while I may be able to “reach” a whole lot of people on Twitter without any notion of specificity (or targeting) of messages, I may reach far fewer individuals on LinkedIn, but the people I reach are much more valuable because my message is very specific to them.
  2. Interactivity – does the tool of choice allow for near real time interaction, or is it less interactive (Am I talking on the phone or exchanging letters?)
  3. Speed – how quickly could my message propagate to a given number of people?
  4. Repeatability – how efficiently and effectively can I repeat a message I receive to share it with other users?
  5. Specificity – given a social media tool, how specific or diverse can I expect the body of messages to be?
  6. Depth – How “deep” is the information provided in a given message?  Is it a headline, or an in-depth posting on a specific topic?
  7. Persistence – if you view a conversation as evolving over time, then any given topic within that conversation has a “window of time” where it is the focus of discussion.  How long does a given message persist in the conversation – how long is the window open for someone to receive the message?
  8. Searchability – Given that messages persist within the window of the current social conversation for a limited period of time (a “meme”), how easily can I recall information once it has passed out of that window?  In general, the greater the depth of the information, the more searchable it is.
  9. Measurability – to be honest, this is a placeholder right now in my opinion.  I think we are in the mode in the social media space of “inventing” science, without having gone to the trouble of correlating a specific measure to a specific outcome.  I think many are working toward getting us there, but I don’t believe we yet know what measurable outcomes correlate with true business (and / or reputational) value.  Until we do, measurability has to be taken with a grain of salt . . .

So I think these are important characteristics to assess with any given social media tool, just as I can (and at times have tried) to measurethe “Hammerability” of a hammer, a screwdriver, a pair of pliers, and a random piece of metal.  You may have a different model for how you think of these tools – if so, great, write your own blog post and send it to me.  But using my characteristics, I’m going to apply them to several types of social media tools:

  • Twitter – the fastest growing social network on the interweb (it’s the internet, or the world wide web, folks!), and a micro-blogging platform.  Say anything you want, as long as it is 140 characters or less.
  • General Social Networks – a social network based on interpersonal social relationships; in all likelihood, I could have named this Facebook and no one would have disagreed.
  • Business Networks – a list of contacts and discussions related to business topics, such as LinkedIn, Plaxo, etc.
  • Special Purpose Social Networks – social networks related to a specific group or topic, business or social.  This may be a local area mother’s group, or a collaboration community for a given business, etc.
  • Blogs – dissertations and commentaries on a specific topic (don’t look now, but you’re reading one, by the way).

This is by no means meant to cover every social media tool and variation thereof, and as I said earlier, it specifically excludes tools, such as StumbleUpon, that I would argue are used to amplify one or more of the characteristics above.

What I have done, based on the characteristics and types of social media I’ve outlined above, is assigned (somewhat) subjective values to each characteristic for each social media type and created a Radar map.  All other things being equal (many times they are not, but let’s skip that question for this post), then the goal of a social marketing strategy should be to cover as much area of this graph as possible:

Social Media Radar Map

Social Media Radar Map

Now, it may not be obvious from the above, but if your goal (in general) is to cover as much area of this graph as possible, you have to use multiple social media tools to do so.  To illustrate, I can see from the radar graph below that while Twitter gives me great immediacy and interactivity, it does not give me a great deal of depth of content or persistence; blogs, on the other hand, do provide the depth and persistence, but without the interactivity.  By combining the two, however, I can cover a large area of the graph:

Twitter / Blog Radar Map

Twitter / Blog Radar Map

As you can see, by combining Twitter with Blogging, in a coordinated marketing campaign, I can cover a lot of important social media “territory.”  The other interesting thing to note is that Facebook and Twitter do not cover the same area, but if I combine Facebook and blogging or Twitter and blogging, I cover a great deal more area.  In fact, by combining Twitter and Blogging, I cover virtually ALL of the area that Facebook is covering: should we then be surprised that two top priorities of Facebook are to make it more interactive (like Twitter), and to begin to incorporate blogging elements?

Twitter / Facebook / Blog Radar Map

Twitter / Facebook / Blog Radar Map

A couple of points, for the sake of completeness: in a very specific context, specialized social networks and business networks deliver a great deal of benefit (within that context).  My discussion above is meant to serve to illustrate the general principles of a broad-based social marketing strategy, and therefore I have disregarded to a large extent the advantages of a very targeted campaign using these niche social media tools within a very narrow context.  That doesn’t mean they are more or less important than other social media uses; it only means that the hypothetical “house” I was building did not conform to the constraints within which these more specialized tools may be much more effective than broader based social media.

I also intentionally disregarded Authority & Credibility as characteristics of the social media platforms.  I don’t think it is possible to say that, because you have a friend that you think knows everything there is to know about subject XYZ, that this person can objectively be thought of as authoritative and credible.  Questions of authority and credibility aren’t characteristics of the social media tools and platforms I spoke of here, but result from the response to the content appearing on these platforms.  In this case, all of the members of the conversation will judge the authority and credibility of the information, rather than the medium itself.  @naomimimi and I were speaking yesterday of a “clique-ishness” that still persists on Twitter – the “Twitterati” tend to feed each other’s sense of authority and credibility.  As these platforms become more widely established and egalitarian, I believe that the merit of the ideas and thought leadership will supplant the “reputation” of the individual posting the content, leading to a much richer and more informative landscape of ideas and opinions – a much more highly functioning “hive mind.”

So ultimately, I think there are those that can and will answer the question of Twitter vs. Facebook, or any one social media tool or technique vs. the other – for themselves, given their personal preference.  I believe that a much more interesting question, however, is how do we evaluate and employ a suite of social media techniques to conduct a robust, wide-ranging, energetic, and informative social marketing campaign.  What house do you want to build, and how do you best employ the social media tools available to build that house?

Kev

April 8, 2009

Twitter: What Does “Deeper Analytical Tools” Mean In Twitter Context?

Mark Davidson, a consultant specializing in Micro-Blogging, Social-Web Strategy, and Social Media Marketing (www.twitterstars.com / @markdavidson or www.twitter.com/markdavidson) posed the question – on Twitter – “I think that as more businesses become serious about using Twitter, there is a definite need for deeper analytical tools. Agree or disagree?”

I’ll be posting more on Twitter as I can (stealth start-ups are not conducive to a lot of blogging activity – I may also be the world’s laziest blogger), but for those 17 people in Outer Mongolia that have yet to hear of Twitter, it is a “micro-blogging” site, allowing you to share 140 character thoughts (a “tweet”), forward others’ tweets, and reply directly to people.  You may “follow” people, in which case you see their tweets; others may follow you as well, in which case they see your tweets.

There are really two “vectors” in the physics of Twitter – one is the reach of a given Twitter user – I believe Mark is well over 32,000 followers, and some have a quarter million or more followers.  On a good day, I probably have about 8 (more or less).  As with anything we measure, I suppose, there are those that seek to acquire followers simply for the sake of an audience – more must equal better, even though the content they deliver doesn’t warrant this assumption.  Others – informed and experienced individuals such as Mark, @chrisbrogan, @stejules, etc. have a clear focus on their audience and what they wish to deliver or portray to that audience.  This is the second vector in Twitter-physics: the messages themselves, composed most reliably of words and phrases (although it is possible to link to external content).  For the sake of completeness, it should also be said that there are a broad spectrum of other users – individuals with focii on very targeted subjects, such as the Myelin Repair Foundation; networkers such as @zaibatsu or iJustine, growing broader reach; businesses (and mercifully, we won’t mention Mars’ Skittle brand debacle here); and of course, we pedestrian social butterflies just looking to connect and communicate – with a dangerous and disruptive anarchical substrata typified by people like @Irant (that would be me).

So getting back to Mark’s question: “I think that as more businesses become serious about using Twitter, there is a definite need for deeper analytical tools. Agree or disagree?” – I don’t see how it would be possible to disagree with this statement.  But the question itself begs further analysis: what do we mean by “deeper analytical tools?”  If you buy into my hypothesis that there are two vectors that drive Twitter – the audience reach of a given user, and the content of the messages sent via Twitter – then that would suggest that there are two, frequently interrelated, categories of analysis.

The first: what is the breadth and quality of my reach as a Twitter user?  If I am a company account (or represent a company), how extensive is my reach?  Am I reaching the right people – the target market for the company?  What degree of influence do I exercise over this audience?  If I am in a consumer setting, seeking to influence through PR the perception of a particular brand, then the targeting may be less important than the size of or influence on the audience.  If I am Bugatti, however, then the size of the audience is clearly (at least in my mind) secondary to how well targeted my audience might be, and my influence on that audience; if I am talking to tens of thousands of individuals, of which none can afford a Bugatti, then my impact on the business is far less than reaching a much smaller group of wealthy individuals who can.  So in terms of Audience Analytics, one must build a model that explains and informs the business regarding these two factors: Targeted Reach (rather than absolute audience size) and Influence. Now, this may or may not be a good example for Twitter, but is meant to illustrate an important consideration when evaluating the first Twitter vector – Audience Reach.  As with anything in business, you have to know who you are trying to reach, and why, to be effective.  What it is difficult to argue, however, is that the degree of influence you have on your target audience is never unimportant, which leads to the second Twitter vector: the content itself.

Twitter itself is a vast cocktail party, where all and sundry might be the topic of discussion at any given point in time.  One group may be talking about social media techniques; others about graphic design considerations; others about various companies, brands, products, social causes; and yet others “riffing” on some silly topic or another.  All of these conversations occurring simultaneously.  And all happening in 140 characters or less, which leads to a great deal of “shorthand,” grammatical omissions, typos, and acronyms.  In an unstructured conversation, it is difficult enough to develop analytics meaningful to describe the “essence” of each conversation if, miraculously, everyone spelled every word properly, abbreviated nothing, used no shorthand or jargon.

At this point, one might be tempted to say “well, search engines do it all the time,” to which my response would be to throw the “bullshit” flag.  Search engines substitute speed and volume for meaning – if you don’t believe me, search for a topic – say “U.S. foreign policy in the Middle East” – and see what you find.  I suspect you’ll find first that Google can return millions of results in something less than a few tenths of a second, and you’ll be tempted to say this is good.  Then read through the links, and you will find that those actually dealing with the topic are some subset of the entire set of links returned from your search, and you can only determine which apply by looking at all of them.  Yes, that’s correct – all of them.  Further, even if you wade through thousands of pages of results separating the wheat from the chaffe, you will find conflicting information of varying quality – how do you decide the relative merit of one result contradicted by another?  How do you synthesize this information?

And yet, by listening in on millions of conversations, it is possible to find a wealth of incredibly useful information – much better information than has been available to businesses to date.  That information will span the breadth of customer interaction with a particular company – awareness and preference building, brand perception, pricing, quality, customer service and support, R&D, etc.  But to discover that information, a “semantic web” – a contextual framework for understanding the language relevant to a given business – is necessary.  I’ll give you an example from my past.

The beer industry is a tad bit more complex than Homer Simpson’s preference for Duff Beer in a can would suggest – for example, most people become aware of and develop a preference for their beer not from the liquor store, but from trying it at restaurants and clubs.  Some prefer bottled, some canned, and some draft.  Some prefer a type of beer – a lager, pilsner or stout; others a brand – Coors, Miller, Modelo.

So let’s assume that I am using Twitter to understand the conversation relevant to Coors beer.  It should be as simple as searching for “Coors,” right?  Well, even prior to Coors merging with Molson and then Miller, the answer was “not even close.”  To really understand the conversation, I would have to search for all of the information regarding the company and all of its brands: Coors, Coors Light, Blue Moon, Killian’s, Henry Weinhard’s, etc.  Some of that may have devolved into a common shorthand or synonymous phrases for certain products – “CL” might be Coors Light, as may “Silver Bullet.”  And some may misspell certain words – “Kilian” rather than “Killian’s.”

If that isn’t complex enough, you also need to have some way, from millions of comments, of evaluating the context of a comment – not just that someone is talking about Coors, but also HOW they are talking about Coors.  How do I evaluate on some scale the degree of positivity or negativity regarding the company or brands?  More difficult, how to I discern useful feedback that may help improve the product or extend the brand?  At some point, human beings will have to look at and synthesize these “deeper analytics,” but given the explosive growth of Twitter (and other social networking platforms), the amount of information will rapidly overwhelm the resources available to analyze raw data.

Having said that, let’s make the problem more complicated, by understanding that it is insufficient to simply understand what people are saying about your company and your brand(s).  You also have and should take advantage of the opportunity to understand what the Twitterverse is saying about your competitors – Budweiser, imports, micro-brews, etc.  So not only are you listening for product feedback, you are also listening for competitive intelligence, as a gauge of consumer sentiment as well as a source of R&D innovation.

As an extension of this model, which effectively becomes a learning model, are some measures of its accuracy and sensitivity to changes in the conversation – you have to know how well you are picking up trends, which are NOT captured reliably through keywords, hashtags, or other constructs, and ARE reflected in specific terms and phrases, and their juxtaposition to other specific terms and phrases.  If I see a comment that contains the word “Coors,” what have I learned?  If I see a comment that contain the words “Coors Light” and “Bud Light,” I know a bit more – at least what one Twit perceives as a competitive product to Coors Light.  If I see the juxtaposition of “love” and “Coors Light” (which can only be a complete hypothetical in my mind at least), and “Bud Light” juxtaposed with “sucks” in the same comment – now I’m getting more out of this conversation.

So it would seem a core of any analytical system to evaluate the content of this Twitter conversation is a “learning” semantic web that grows and evolves with feedback from analysis of previous information.  This sort of “fuzzy logic,” artificial intelligence and neural networks are theoretically possible; it seems clear when analyzing a completely unstructured conversation that these techniques will have to be applied to content analysis to provide an analytical basis for the content and tenor of the conversation relevant to your competitive landscape.

Part and parcel of such a system is not only that you can categorize comments, but also some measure of how reliably and accurately your analysis is, which would typically be expressed in terms of miscategorizations and false positives and negatives.  For example, if my analytics determine that a certain comment is a value statement and it is positive – i.e. Coors Light is perceived positively – then how certain can I be of that result?  How often is a comment properly categorized – such as a value statement rather than a feature request – and how certain can I be that a comment perceived as a positive statement is truly positive?  I may evaluate a comment as a positive value statement that may in fact be a feature request: “It would be great if Coors Light came in a self-cooling can.”

To date, “analytics,” which are rapidly proliferating on Twitter, have been based on counts, ratios and value judgments masked as “analysis.”  It may well be true that the ratio of tweets to retweets is a valid measure of something, but I don’t think anyone has done any extensive, longitudinal studies that would provide evidence of this fact – Twitter is simply too new and early in its evolution to have developed true science.  Clearly, if Twitter is to become a line item in a company’s expenses, it will need to be justified in some way: public relations, marketing, product development, whatever.  Reliable analytics to support this expenditures do not exist, and thus, “deeper analytics” must be developed to support the business case for using Twitter.

These analytics will measure the two primary drivers of Twitter’s value: Audience Size and Content Analysis.  The key components of Audience Size will be Targeted Reach (size of audience that corresponds to company’s target market) and Influence (to what degree does the company’s messaging drive quantifiable behavior – awareness, preference, selection, sale, follow on, referrals).  The components of content analysis, requiring a fairly extensive “learning” contextual framework for that analysis, are categorization of content (and reliability factors related to that categorization) and some measure of sentiment along some spectrum from very negative to very positive (again, incorporating some measure of accuracy and reliability).  The categories of comments should foot to some measure of value – awareness/recall, new customers, positive brand associations and preference, conversions, repeat sales, referral sales, new product features or brand extensions, etc.

These “deeper analytics,” however, while necessary, will never be sufficient to truly understand and influence market sentiment.  While these deeper analytics will allow for more effective and efficient analysis of an exploding conversation, they cannot replace trained minds – only help to distill thousands of comments down to dozens, and through proper categorization and measurement, help signal these analysts of emerging trends (not keywords or phrases!) that can be used to influence the success of the business.

The fundamental set of equations that drive every business are not going to change because of Twitter, but Twitter could become a remarkable source of competitive intelligence for companies that understand and invest in making it a key component of their strategic arsenal.  The successful companies that use Twitter to competitive advantage will commit to Twitter, will invest in Twitter and the analysts required to understand this flow of information (people like @danzarrella), and they will utilize “deeper analytics” aligned with strategic goals and objectives to which these analysts’ efforts can be employed.  There is nothing I have seen that comes anywhere near approximating the deep analytical capabilities I have sketched above, but I feel confident that somehow, some way these tools will evolve.  They must: the massive cocktail party that is Twitter holds too many veins of pure gold to be ignored for too long.

KB

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