Month: September 2017

The Salesphase Conjecture

Salesphase logo

Current sales technologies, including advanced data mining and statistical analysis, only begin to probe the limits of what we could know about successfully closing complex high value long sales cycle opportunities.  Simple low value short cycle sales are more adequately dealt with by existing and emerging sales technologies. Salesphase applies far more advanced mathematical techniques to answer questions that sales reps and their sales and executive management are asking about complex deals.  These concepts have aided me in closing major aerospace contracts with customers…customers that drove me to develop a new approach to complex sales.  Until proven theoretically, or practically on a wider scale, I refer to the underlying concepts for this new approach as, The Salesphase Conjecture. 

The Salesphase Conjecture: A customer organization can be described mathematically by a customer state. Customer and selling organizations are systems of agents.  As the number of customer and sales agents involved in a deal increase, the complications in closing such deals increases exponentially.  Regardless of the number of people involved, an overall customer state can be constructed.  The customer state can be mathematically represented as a superposition (e.g. a linear combination expressed as a weighted sum) of basis states. One is free, in principal, to choose the set of basis states, so long as they span the entire space of possible answers to a given question (that is, 100% of possible outcomes are represented within the space of states).  We might refer to the total space as “Deal Space,” Opportunity Space” or “Sales Space”. Salesphase chooses a set of basis states that are invariant across industries, customers, and technologies as its primary set of basis functions.  These invariant states are a symmetry that makes developing a general sales algorithm possible.   One may then represent a customer’s state with respect to closing any particular deal as a complex wave function in a generalized Sales Space.  The square of the wave function generates a probability density for predicting the answer to key sales questions one might ask about a customer.

Prob of closing

In Dirac Notation:  The probability, P, of closing a sale, c, at time equals the square of a complex-valued customer wave function, Ψ, projected onto the “closing phase” eigenfunction basis, pc

By choosing the eigenfunctions of a different operator as the set of basis functions, one can arrive at a number of different useful and important mathematical representations of the same customer state. If one picks the eigenfunctions of the opportunity momentum operator as a set of basis functions, the resulting wave function provides information on the timing and velocity of a deal as it approaches closure.  Other basis states may relate to the total necessary resources required to optimize the probability of closing a deal.

The customer state, represented as Ψ, contains, in theory, perfect information about the customer with respect to any question one might ask about the customer.  However, the limits of our knowledge, computational power and resources, prevent us from ever modeling or extracting perfect information about complex deals.  To improve the quality of extractable information, Salesphase applies existing advanced mathematical techniques, selects appropriate basis states and takes proper measurements so that we can vastly improve approximations of a customer’s state with respect to a particular deal.

More generally, Salesphase asks questions about how well a customer system is currently configured to buy a high value product or service when there are selling or buying teams involved in a deal.  The better a selling organization is at extracting this type of information from a customer organization, the better the strategic information obtained, and the more accurate the predictions are (which result from analyzing probabilities derived from the eigenvalues obtained from repeatedly measuring complex -valued functions in Sales Space).

John Clark, john @ salesphase . com; Twitter @salesphase

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The Trouble with Sales Science

Forecast 2017

Forecast 2017, the 3rd annual conference defining the future of “sales science” is now history.  I attended this conference, hosted by Base CRM, all day today, September 18, 2017.  I attended it last year as well.  I think it is fantastic and I will attend again next year.  They’re on the right track to something, but I’m not so sure that sales science is that track.

If you’re a sales operations or management executive, and you want to understand the current trajectory of sales technology, then this is a must-attend conference.  If you are wondering about the intersection of data mining, statistics and sales, yes, please attend next year.  That said, this is not, to my mind, a science conference.   Presentation of falsifiable scientific theories of closing sales? No. Peer reviewed papers? No.  Crazy new conjectures on what makes sales “tick”?  No.  New sales applications for cutting edge science, mathematics or computational complexity invented or discovered for other purposes?  No.

Forecast, the annual sales conference, is closer to an 8-hour infomercial for Base CRM, but I don’t see that as a negative.  This is genius marketing, and I applaud Base CRM.  They put on a great event and it’s worth the time and money to attend.  But after decades in sales (and having designed and implemented CRM systems going back to Act 1.0), I can say that Base CRM is missing an incredible opportunity to found a new branch of science.

Georg_Cantor3

David Hilbert inventor of Hilbert space

I can only speak personally about my scientific effort at Salesphase, which is focused on high-value, long sales cycle complex B2B deals.  But I have to believe there exists a potential to bring together the best geek minds on the science of sales.  Maybe it could be an off-shoot or sub-conference of Forecast.  I hope I’m not alone in this thinking, but take Salesphase as an example: It posits that only a fraction of the information important to closing complex deals is ever captured by a CRM program or sales automation tool.  Not only that, but the data it fails to capture is far more important to closing deals than what it does capture.  But to fully flush out the Salespahse conjecture, and its value to sales software applications, requires an understanding of complex Hilbert space, vector calculus and complexity theory.

Base CRM is a company uniquely situated to take advantage of incredible computational techniques that have never before been applied in the realm of sales.  Techniques that (metaphorically speaking) replace the current beads on an abacus and notches on a stick with quantum field theory.  I’m not exaggerating either:  The mathematics behind CRM and sales automation tools is how old?  At best 330 years old, as in Newton’s Principia Mathematica published in 1687.

NewtonsPrincipia

Newton, co-inventor of the Calculus

Babylonian math

Babylonian √2

At worst we’re talking 3,617+ Babylonian lunisolar years old.  The clay tablet pictured at right is a Babylonian calculation of the square root of two. At least they were thinking about irrational numbers, which is on the scale of the floating point mathematics used in CRM data analytics.   But what about imaginary numbers?  They can represent valid non-deterministic states of a real system–states which cannot be directly measured.  This has interesting applications to the possible states of customer systems.

 

If a sales application took advantage of the power of Hilbert Space to model complex sales, that would move us up to the first decade of the 20th century.  Throw in Topology and Braid Theory and we could even push sales science into the Cold War era!  Salesphase is topological in some aspects, that is, it can be represented as a geometrization of a new sales calculus.  At least it uses mathematical concepts from the 1950’s.  I’m confident intelligent young people could get sales into the 21st century of mathematical concepts.

Here’s the key problem: most of the data collected in CRM systems has little to do with successfully closing a big or complex B2B deal.  Does logging a voice message left for a buyer count as “data”? Yes.  Does it count as data relevant to closing a deal?  No.  Does a data point representing a sales presentation at a customer’s office accurately tell us about the health, complexity or probability of closing a big sale?  Maybe.  But, I’m certain there is a wide standard of deviation on the usefulness of this data.  For better or worse, sales reps, in my experience, tend to be either over optimistic or too pessimistic.  Either overstating a chance of closing, or sandbagging a sure thing as a “maybe.”

CRM “simple math” drove me crazy as a senior Sales Executive at 3M Company.  At that time, for a hobby, I was writing science book reviews for a literary website, and I thought, “Why aren’t we using these amazing current mathematical/scientific advances to improve sales performance?”  I personally used just a little of what I’d learned to crack the enigma that was Boeing Commercial Airplane’s Wichita Division (now Spirit Aerosystems).  Ten years later though, I’m still asking the same question: why are sales organizations using the most advanced mathematical and scientific techniques?  My answer so far is, “the language barrier.” The vocabulary that scientists and mathematicians use has no meaning to sales software developers.  I would enjoy being a translator in this case, I think a lot could be accomplished.

Unfortunately, the “sales science” behind the latest software tools is more limited to statistical data analysis of information gathered by CRM programs.  Despite what I am saying, these efforts WILL be successful.  That’s because doing a better job analyzing mounds of information is guaranteed to improve sales performance.  But is it the optimum method in all cases?  No.  Anyone with a computer science degree knows that for big problems, search-sort is less efficient than similar sized decision problems.  Salesphase is more like a set of decision problems, it does not require mounds of often stale contact data and sales history to predict the next best move, or the probability of closure, or the “state” of a customer with respect to signing a contract for your product.

I have always maintained that much of CRM data is either to “explain what you’ve been up to” to a manager, or to funnel information to marketers.  Of course, that’s not always the case.  I once solved a major customer nightmare (involving the manufacture of specialty optical fiber) using Siebel CRM 7.5.2. It worked because there were so many people involved over such a long period of time that I could not rely on my memory or email.  I probably could have used an Excel spreadsheet, but I was part of the Siebel implementation team for my small corporate business unit, and I wanted to demonstrate to my boss what CRM could accomplish.  3M Optical Components won a supplier award from Honeywell Defense & Space as a result.  So, don’t get me wrong.  I’m pro-CRM.

During Forecast 2017, the closest thing that came to science was the Gong.io presentation by CEO/Founder, Amit Bendov.  Why was it the best? Because it wasn’t anecdotal.  It wasn’t the perspectives of a VP of Sales, a Chief Revenue Officer, or an executive panel talking about inspiring sales teams.  Amit presented the outcomes of data generated by a what appeared to be proper study of 3 million minutes of recorded sales meetings/calls.  It revealed important correlations with “success,”  where success is defined as “moving a deal forward.”  My ears perked up for this.  The average length of a successful sales presentation is 46 minutes; the longest sales rep “monologue” in a successful sales meeting is 76 seconds; in the second half of a demo, the amount of times dialogue switches between sales rep and customer increases 36%; finally price isn’t discussed in detail until 38-46 minutes into an average successful presentation.  Clearly, the number 6 plays some mystical role in sales success!  I loved this.  It’s a bunch of useful benchmarks.  How might successful sales science theories be measured?  By comparing results of sales outcomes using a new theory with the benchmarks that represent a spectrum of traditional sales techniques (as represented by Gong’s sales meeting data set).

I see great things for the growth of the Forecast annual conference.  In a sales technology industry dominated by Salesforce.com, Inc., this conference is a welcome respite.  But, please, if there’s anyone else interested in building a community for rigorous sales science, contact me.  –John Clark, john @ salesphase . com.