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

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.


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.


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 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, 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. 



How Bullhorn Pulse Steps into CRM’s Future

I’m impressed. I just finished readingbullhorn pulse logo U.S. Patent 9,189,770 Automatic tracking of contact interactions. This was recently granted to Bullhorn, Inc. for it’s “Pulse” sales acceleration technology. The patent and the product point to the same future of CRM I am evangelizing here at SALESPHASE.  We differ in that Bullhorn Pulse is a magnificent step, versus SALESPHASE’s one-order-of-magnitude greater leap, into CRM’s future Bullhorn Pulse provides an automated data analytic method of analyzing actual customer interactions to get a clearer picture of which deals are winning and which are losing.  It uses its own patented remote access to contact tracking technology to automatically add a copy of any detected email message to the activity records of the sender and contact within the Bullhorn CRM system (or other CRM if I read the technology correctly).

I think Bullhorn Pulse advances sales pipeline technology in three important ways:

  1. Automated, which for me means reduces wasted time, unnecessary effort and the potential for mistakes;
  2. Analytic, which for me means it relies on measurement and not manager or sales rep guesses;
  3. Qualitative, which for me means that it can interpret its measurements to determine which deals are simply “a bunch of activities” and which are “customer engagement.”

I agree completely with the co-founder and CEO of Bullhorn: “Sales activity reporting is dead,” Art Papas has stated, “Lots of activity and no engagement equals no results – we’ve learned this from listening to our customers over 16 years.”  Art’s insights resonate deeply with my past experience as a sales rep, account executive and business development manager. It is the same basic reasoning that lead me to found SALESPHASE.

Now that I’ve read the patent, I will start thinking about interesting ways to integrate Bullhorn’s Pulse technology with the advanced never-before-seen methods SALESPHASE will use to extract intelligence out of far-from-perfect information. Bullhorn Pulse analyzes specific customer interactions which it detects as relevant, such as an email to or from a customer.  SALESPHASE is what I call a deal characterization technology.  It provides a higher level of intelligence and feedback about deals without relying on guesses or any particular communications with customers: it doesn’t look at any specific mode of customer interaction and doesn’t care what the deal is actually about or what the buyer and seller’s internal processes look like, etc.  It is independent of any specific context and therefore it can stand on its own or play well with any established CRM system, whatever suits the user.  You might think, how is that possible? Follow me on Twitter @salesphase and keep an eye on for the beta test signup.  –John Clark


Bad Sales Explanations

sales rep excuses

I was poking around the Internet today looking for examples of “sales rep excuses.” My purpose? To analyze these excuses in light of the more general phenomenon of bad explanations.  I hope you will stick with me on this (about 2,200 words, or <gulp> 146 average tweets).  To gain insight, we need to delve deeper into the topic of sales excuses than a typical blog article might have patience for.  But, (stretching the metaphor a little further), we are more likely to find undiscovered treasure if we make the effort to dive deeper into darker waters.

The properties of good and bad explanations are explored by physicist David Deutsch (Twitter @DavidDeutschOxf) in his 20011  book, The Beginning of Infinity; Explanations That Transform The World.  In a nutshell, progress happens when we form new explanations and drop bad ones.  That’s a solidly intuitive idea; so far so good.  But how do we identify good explanations from bad ones?  Easier than you might think:  good explanations are hard to vary without contradicting existing facts and well-established understanding.  Start fiddling with a good explanation in order to avoid identified inconsistencies and contradictions and the explanation starts falling apart. Furthermore, according to Deutsch, good explanations extend the reach of existing knowledge. I believe I’ve encountered this before under the name of universality. Universal explanations work everywhere every time.  In essence, their reach is infinite.  I’ve struggled to avoid using the term “universality” to explain the purpose underlying Salesphase. I think I like reach better. The goal of Salesphase is to complete the development of a methodology and an associated algorithm with infinite reach devoid of sales mythology and lore.  That’s a tall order, and a topic I will be exploring in this blog over the coming months.

What about bad explanations?  Deutsch, in so many words, tells us that bad explanations are unverifiable and easy to reformulate in the face of contradictory facts.  Hmmmm, that’s already starting to sound applicable to common excuses used by sales reps. Of course Deutsch, being a physicist, is primarily talking about scientific theories that survive criticism and testing; ones that also make new predictions or explain previous mysterious phenomena.  That’s okay, my argument is that sales, as a discipline, does not hold any special place in the universe.  Sales is constrained by the same laws as every other aspect of the observable universe.  So why shouldn’t we take advantage of scientific thinking?  We rarely, if ever, investigate the intersection of theoretical physics and sales, but that is exactly what Salesphase is doing: drawing on current scientific thinking to inform and modify the ancient profession of selling.  (As a side note, I do think that the statistical discipline of Six Sigma made some valiant attempts at using science to increase the “quality” of the sales process,  but, despite its great success transforming manufacturing quality, I never experienced a transformative Six Sigma implementation in the sales department.)

Okay, enough of the general science lesson, it’s time to extend the concept of bad explanations to bad sales explanations. To me, bad sales explanations are sales-related statements that are either unverifiable or easily reformulated when faced with conflicting data. Sales excuses might be testable by a boss and found to be false, but more often than not, there is some reason that a boss can’t or won’t directly test sales rep excuses.  What I’ve found in my experience is that most bosses don’t care for direct confrontation, don’t possess sufficient contradictory evidence, or don’t have the time and resources needed for validating or falsifying sales rep excuses.  In the typical real-world sales environment, sales reps can get away with excuses and live to explain themselves another day.

Now, to get into my analysis of bad sales explanations, we need a good source of sales rep excuses.  A quick Google search turns up 8 Worst Lies Sales Reps Tell The Boss by Geoffrey James (Twitter @Sales_Source).  Whether these actually are the eight worst lies, doesn’t matter.  I spent many years in sales and these examples will do very nicely.  I won’t go through all eight as I’m already pushing 700 words and I am just warming up.  So let me select four representative “sales lies” and let you read about all eight in Mr. James’ article if you like, (

EXCUSE 2. “I have a great memory, so I don’t need to write down what I’ve learned about a customer.”

The bane of a sales manager’s existence is the rep who won’t document his or her activities.  But, finding the right balance between documenting sales and doing sales can be difficult.  I have yet to encounter a major company CRM implementations that strikes the right balance.  The above excuse is a great example of a bad sales explanation as it explains nothing.  It neither explains good sales results nor poor ones.   It might be true or it might be false and either way sales results remain unchanged.  Here’s what I’ve found in my experience: when a veteran sales rep is kicking butt and closing sales at or above quota, no one is too worried about the quality of documentation.  When sales results are poor, the sales manager turns to enforcement of various sales policies such as CRM documentation as a solution.  The thinking is that the problem can’t be solved without knowing who the rep’s customers are and what’s going on with each deal.  Let me tell you though, from the perspective of a sales rep, your customer knowledge is your job security, and documentation can be the enemy.  If you are a sales magician, you don’t want to let too many secrets get out of your bag.  On the other hand, if you’re a sales slacker, you don’t want to make it that much easier for management to replace you.  In both cases you try to avoid detailed documentation.  Besides, filling in blanks and typing up sales calls is time consuming, boring and there are no commissions paid for the work.  I often wondered if anyone ever read or cared about my sales documentation…especially considering I mostly received phone calls from interested managers asking questions I had answered the night before at 11 pm when I was updating SAP CRM instead of sleeping.

As I consequence I’ve been developing the ideas behind SalesPhase ever since.  SalesPhase can be implemented in a way that does not make reps feel replaceable while also interactively gathering critical customer information that is usually hidden in the noise of each selling organization’s proudly semi-unique selling process.  No late-night typing required.  The faster a new technology can help gather and analyze accurate customer information without burdensome documentation of redundant and useless detail, the faster sales reps will adopt that technology, (and the faster such information can translate into booked sales).  Salesphase is developing a full set of good universal sales explanations, ones which can be verified and if proved wrong by emerging information, corrected.  Bad sales explanations will be irrelevant.  Sales reps can continue making excuses all they want, but  in so doing they will be naked and useless (the bad explanations, that is!)

EXCUSE 5. “I’ll make quota; my deals will close at end of quarter.”

This is a classic delay technique to get a pesky boss off one’s back without causing too much alarm. The lives of sales reps are very often tied to quarterly performance. A deal that closes at the beginning of a quarter is just as good as a deal that closes at the end of the quarter when it comes to calculating quarterly performance and commissions.  For a sales rep, a perfect quarter might be playing golf and lying on the beach for 2.9 months, then ringing the cash register in the final days of the quarter. (Never mind that the company does better when sales close at a steady pace thus keeping production output and cash inflow at some optimum rate.)  Sales manager performance and bonuses also tend to be tied to quarterly performance, and so nasty grams (in whatever electronic form) aimed at poorly performing sales reps might start going out after the first monthly report reveals a potential sales problem.  A quick reply that “my deals will close before the end of the quarter” will generally buy time. Unfortunately, this excuse is a text book bad sales explanation because it isn’t testable before the end of the quarter AND if the rep’s prediction turns out to be wrong, the excuse can be quickly reformulated into one of the other oft-used sales rep excuses for why deals didn’t close.  News flash:  if this excuse buys time it’s because a faulty sales pipeline or other faulty sales metric fails to predict the true likelihood of specific deals actually closing this quarter.  If a company had access to the Salesphase algorithm in the form of a CRM add-on or stand alone application, such a statement could be quickly justified or nullified.  If deals are going to close, the algorithm will back up the statement.

EXCUSE 6. “We lost that deal because our price is too high.”

It’s not too often that a customer will walk away from a done-deal thinking they paid too little for your product or service.  I tend to start from the perspective that customers will claim that any price over $0.00 is “too high”.  So, the price-too-high excuse is a solid go-to excuse with tremendous reach because it’s easy to defend in a wide variety of situations and it’s often difficult for a manager to test because the customer will almost always agree that price was a key problem. (It’s one of the easiest and most used excuses that buyers use for not buying).  Ideally, from my perspective, a customer buys because it needs a solution to a problem and my product has (rightly or wrongly) been identified within the time and resource constraints of the customer to be “the best alternative.”  Under this definition, it’s possible that a deal is lost due to high price.

Sales management, however, almost always blames the rep:  “you didn’t sell value!”  I’ve heard this many times.  I worked for years for 3M Company, which is rarely the cheapest alternative in any selling situation.  So, I know value selling inside out, and I’m sorry, but sometimes the price is just too damn high.  But, ironically, I don’t ever recall losing a deal I was negotiating based upon price.  If price (as one factor in the value equation) was going to be a problem, I made sure that deal never got to the negotiating stage. It was eliminated early on as a non-viable solution for that customer’s problem.  Too often sales reps stuff their territory pipeline with deals that are unlikely to close and then fall back on bad sales explanations such as, “The price twernt right.”

EXCUSE 7. “I haven’t called that customer but I have it scheduled for later today.”

Yes, I’ve had to pull this bad boy excuse out, or some variant, in a time of need (typically during a call from my boss or a product marketer).  Is there any sales rep that is on top of every customer call on their list?  This is more of a face-saving game played between manager and rep.  Of course, if the boss calls at 5pm it’s easy enough to reformulate this one as, “I left a voice mail and will call again tomorrow.”  Or, I’ve got an appointment tentatively scheduled with this customer on Thursday.” When I would fall back on this classic excuse, that particular customer call would graduate to the head of my high priority list so that I could get back to the requester as quickly as possible with the results of the call. This particular excuse isn’t really significant as a bad sales explanation, but it does point to an interaction with the boss that is awful deep in the details. If a boss is calling to ask such a question, my guess is the wheels are already loose and about to come off the bus. The only open questions is, who will get thrown under the bus?  The author of “The 8 Worst Sales Lies” correctly points to this as a problem of a rep not being on top of his or her stuff, so to speak. Anytime that is the case, expect a whole host of bad sales explanations to come streaming out of interactions. Salesphase doesn’t rely on interactions about schedules, sales calls, customers, etc. The data that will emerge from the application will make obvious which reps are on top of their game and which are not.

In general, I believe that a complex set of factors interacting over a long evolution of “sales methodologies” has left plenty of room for bad sales explanations to continue thriving. If they didn’t work, they would not be so universal as to be the subject of a top eight list in an article. The fact is, bad sales explanations often have greater reach than the underlying company’s selling system.   Why? Precisely because they are untestable, and easily reformulated for use in varying situations.  Universal template excuses can be easily adapted to exploit weakness in the company selling system no matter how many times management rolls out a new set of tools or best practices.  Salesphase would take a different approach based on a set of universal good sales explanations (sales laws) that tap into hidden customer information.  Combine this with new customer data, as it is collected, and a much clearer picture of the quality of the sales pipeline emerges, including the likelihood of closing each and every deal regardless of its location in the sales pipeline.

To comment or discuss in more detail or privately, tweet or follow John Clark, @salesphase.