How Software Will Eat Strategy. It’s a Little Like Lava

September 18, 2015

In the 1990s, before founding, Mark Benioff (below), explained to me, “Software is like a slow-moving bed of lava. You can’t stop it. Eventually, it eats everything in its path.” Nothing, he boasted, is safe from clever programmers and their tools.Benioff - Lava

Never mind that Benioff soon made his famous declaration, “Software is Dead.” This was a play to disrupt legacy ERP vendors and to create a pathway to cloud computing.

Twenty years later, information of all types is being consumed by the digital lava flow.  Credit big data, the Internet and maybe Benioff.  A feature of this modern renaissance is our new-found ability to access information cheaply and to coax meaning from data that previously didn’t talk.  What we first called Business Intelligence has been supplanted by analytics, the modern shucking knife of information oysters.

There are myriad terms and tussles to characterize the analytics land grab.  For my money, we’re headed toward the ultimate source of insight — strategy.  But first some background:

As the graphic below illustrates, message monitoring, sentiment scoring and influence ranking were early innovations in the leapfroging race to measure the amorphous. Klout was among the first to do this at scale.  Text and sentiment analytics have been more enduring and fundamental drivers.  Just ask Alta Plana’s Seth Grimes.  Cognitive systems is a new and thoughtful catch-all, an idea of IDC’s David Schubmehl.  Predictive analytics is also lurking, a heady niche preferred by the intelligence and financial trading communities and well-served by insurgent providers like Palantir.  Meanwhile, incumbents like Adobe, Facebook, Google, IBM, LinkedIn, Microsoft, Oracle, Salesforce and SAP are romping through the analytics greenfield with automated big data-enabled offerings in marketing, sales, threat detection, fraud analysis and pattern recognition.  While they do, many are smitten with the tracking and trapping of neural impulses and something slightly more obvious, emotion.  And, finally, there’s discourse analytics, an incubating bat cave of bleeding edge entrepreneurs, among them MIT Media Lab’s Ali Hashmi.

Slide1What each and every provider is promising in analytics is, of course, insight.  They’ll tell you what you need to know.  And if they’re really good, they’ll tell you what to do about it.  Call it actionable insight.  Therein lies the rub because insight is a fuzzy thing and taking action on it is tricky by virtue of the variables that engulf every unique customer in every unique market.

If insight and action are the ultimate payoffs, we’ll be talking more about two other descriptors in the analytics sphere: Intent and motive.  Intent is knowing what your customer is expecting to do.  Will they buy the car today or wait?  Will they wait for a better data plan or buy the bundle now?  Motive takes us to an even higher plane by giving clues to a target’s methods and mindset. The what, as expressed by the intention folks, is important.  But the how is even more exciting.  If only we can know a target’s motive…

Decision System Composite SlideThis is where Playmaker comes in.  We are the author and architect of a patented decision system (shown right) that reveals the existence of 24 discrete strategies of influence (aka, plays) and the factors and processes by which influence professionals manage them.  At the heart of the system is what can be called the first periodic table of influence, though it’s more.  It’s a three-dimensional framework of the most basic strategies that underlie every form of persuasion — from tweets to ads to speeches t0 blogs like this.  As well, it offers more than 1,000 options, tips and best practices for taking action on or against an observed play or player.

MIND READER  Strategy has many definitions, but a constant among them all is the idea of plans and planning, which is a close cousin of motive.  After all, to know a player’s strategy (i.e, their plan) is to know not only their motive but their policies and positions — expressed or unspoken — and their state of mind.  Know the play, in other words, and you’re reading the mind of other players and how to manage their moves in a marketplace.

Consider an energy company that exhibits a tendency to dodge tough questions on, say, the topic of fracking.  By their evasions, they can be understood to be running one of two plays in the diverting subclass of The Standard Table of Influence, a Deflect or Red Herring.  If it’s decided to be a Deflect, for example, there are a variety of options for both supporters and detractors to consider — actionable insights for or against the focal player, the energy company.  (Below is a screen image of the online info card for the Deflect and standard guidance for countering it.)


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Apart from our 24 observed and detailed plays, what makes the identification of strategy possible is slavish adherence to ontologies, articulated structures of things that, in this case, organize and reduce the practice of influence into its most basic elemental strategies.  Without it, there would be no buckets into which we can throw our findings of strategy and motive.

Consider the sales person who is forced to agree with a competitor’s claim.  Oh yes, it’s a good idea; our product has that too.  That very concept — to embrace a competitor — is the defining characteristic of a counter-intuitive play called the Bear Hug.  Like the Deflect, described above, the Bear Hug is supported by a variety of recommended counter-plays that a target can use to nullify or co-opt the hugger.  Other tabs list the purposes, pros, cons, methods for decoding and plays for collaborating — all to give advertisers, marketers, sales people, PR pros, politicos and intelligence experts the fullest understanding of the power and potential of our 24 elements of influence.  (Below is a screen image of the online info card for the Bear Hug and its listed benefits.)


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Ontologies don’t simply house the things they explain; they also explain a body of work through its organization.  That the Bear Hug, by example, is assigned to the freezing subclass of five plays, which in turn flow upward to the overarching class of conditioning plays, helps practitioners know that this strategy of overt support is used to stall the progress of other players and mold the perceptions that surround them.

Analytics is here to stay.  And as foreseen by Benioff, it’s only a question of what gets absorbed next into the digital lava flow.  Watch us as we put the Playmaker system and its ontology of influence strategies squarely in the path.


Post by Alan Kelly

Graphics courtesy of Playmaker Systems, LLC. Benioff photo credit: Wikipedia