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. 



Cracking the Sales Enigma

Russland, Verschlüsselungsgerät EnigmaThe process of selling is a process of decryption. Customers are rarely transparent when it comes to their internal decision process for purchasing products and services.  The standard method of selling is to build relationships, gather as much information as possible, and then make what really amounts to intuitive guesses about the next best move to close.  Seasoned sales reps and deal negotiators excel at picking up “customer signals,” interpreting their meaning and then taking appropriate actions towards winning the deal.  Of course, such people are also just as good at knowing when to give up on a sale that is a “waste of time.”  The problem is, the average rep is mediocre at decrypting what I call the Sales Enigma.

enigma logo


Poor decryption introduces inefficiencies and waste into the sales process and calls into question the value of sales reps in the age of Big Data and sophisticated data gathering and analysis algorithms.  Does spending a significant sum of money on Salesforce or similar CRM sales software solutions improve the odds of cracking the Sales Enigma?  In my experience, no.  CRM just makes a bad problem worse by adding more cost, time and complication to it.  I’ve been thinking a lot about this lately while reading Andrew Hodges’s biography,  Alan Turing: The Enigma

Without Benedict Cumberbatch’s portrayal of the “father of computer science”in the recent film, “The Imitation Game,” most people would not have heard of Alan Turing.  However, Turing’s work on computable numbers in 1936 was inspirational to me in 2008 and lead me to the mathematical ideas underlying Salesphase. But, what I’ve been thinking about lately is Turing’s actual approach to  deciphering WWII German navel communications encrypted by the Enigma machine. June 7th is the anniversary of both D-Day (1944) and Turing’s death (1954), so I thought I’d write about Turing’s influence on the underlying mathematical principals of Salesphase.


History currently holds that Alan Turing died from self-inflicted cyanide poisoning two years after he was convicted of “Gross Indecency” as a homosexual.  He had been put on probation and forced to undergo “chemical castration.”  I think reasonable questions remain open about the cause of death, which was not thoroughly investigated.  Certainly
at the onset of the Cold War, a homosexual with a background in military cryptography and knowledge of atomic secrets, may have been considered a target for enemy blackmailing.  The idea that Alan Turing would betray his country to keep his homosexuality hidden is ludicrous in hindsight,  but certainly it was a common fear within the intelligence agencies of the United States and Britain that may have caused them to prefer a dead Turing to an alive one.  But, such conspiracy theories will remain for future historians to sort out.

Many people think of Alan Turing as having “broken” the German’s Enigma encryption code, but that is not exactly what happened.  The story is a long and fascinating one involving many people that I won’t cover here.  I do highly recommend the 2013 lecture by James Grimes at the Perimeter Institute, where he not only explains the history, but demonstrates code breaking with an actual German Enigma machine.


small image enigma


Turning’s genius was in realizing that it was not necessary to to search through more than a thousand trillion possibilities looking for the daily German encryption settings that would turn intercepted gibberish morse code signals into understandable German military
intelligence.  He realized, based on the mathematics of group theory and logic, that if one guessed at a word that was probably contained in the encrypted message, AND one knew the internal construction and wiring of the Enigma machine (which he did thanks to a Polish mathematician), then it was possible to build an electro-mechanical machine to help quickly find the most likely initial settings used on the Enigma machine THAT DAY.




In less than 20 minutes, the “British Bombe” as it was called, could rule out millions upon billions of possibilities as being impossible candidates for the daily German Enigma machine code setting.  What was left was a list of possible codes that could be checked by human analysis to find the right one.

Is this difficult to understand?  Not so much if you think about it another way:  You lose your car keys, but you know you had them yesterday.  Where could they be today?  You know they’re around somewhere because your car is in the garage.  That means looking anywhere you were the day before leads to a contradiction–if the keys were at your friend’s house, your car would be there, not here.  So, no reason to call you friend and ask if she’s seen them.  As you look around the house you might rule out other possibilities:  the keys can’t POSSIBLY be in that pair of pants because I got a stain on them last week and meant to drop them at the cleaners. Etc. etc.  By ruling out contradictions you continually narrow down the places to search to the most probable locations.  If you’re the type of person that searches randomly when you loose something, well, you might want to read up on the life and mathematics of Alan Turing.

Alan Turing’s life and work is much more interesting than Salesphase, but I do want to finally explain the connection:  Salesphase operates like the British Bombe to help sales reps decipher and interpret customer signals about particular deals. Salesphase mathematically quantifies the hidden information and complexity of each deal and then uses key information to eliminate great swaths of uncertainty and complexity that too often are considered “information.” In the Salesphase algorithm email communications, phone call notes, sales meetings agendas, etc. are noise.  CRM applications, like Salesforce, encourages the collection of “noise”  which it then attempts to organize and visualize using statistical approaches. Salesphase operates in a completely different way.  It uses answers to a finite number of straightforward conversational non-technical questions to eliminate noise and identify the most probable next steps to closing a deal. Instead of complicated output, it generates a multidimensional shape that gives reps a visually intuitive understanding of the status of any opportunity regardless of customer, technology, deal size, complexity, etc.   It gives an average sales rep access to the genius of Alan Turing.

Salesphase logo



Georg Cantor, Disruptive Thinker 100 Years Ahead of His Time

Happy Birthday Georg Cantor, legendary German mathematician (and philosopher).  He would have been 161, today, March 3, 2016.  Without Cantor, the Buzz Lightyear Toy Story mantra, “To infinity and beyond” would be nonsense.  (Ah, did you laugh at that because it was nonsense?)  Well Cantor wouldn’t be laughing based upon  his invention of transfinite numbers.

Inventor of Set Theory and Transfinite Numbers

German mathematician, Georg Cantor (b.1845 d.1918)

It is well-established in set theory (which Cantor also invented) that there are an infinity of infinities, each more infinite than the one prior.  If you’re interested in learning about this concept, try the fast-paced entertaining book or audio performance that includes Cantor’s set of infinities, Zero: The Biography of a Dangerous Idea.

Sadly Cantor was too far ahead of his time.  He started suffering bouts of depression later in life, which historians often link with the harsh criticism he faced in his own time.  His former professor, Leopold Kronecker, himself an elder statesman of German mathematicians and number theory, called Cantor a “corrupter of youth” for teaching his crazy number ideas at university.  The journal Acta Methematica withheld publishing important work by Cantor.  The editor wrote him that his paper was “…about one hundred years too soon.”  I won’t go any further into Cantor’s history as it is well-covered across the Internet.

SALESPHASE owes a debt of gratitude to Cantor for his invention of set theory, one of the fundamental underpinnings of mathematics.  His mingling of mathematics and philosophy were personally inspirational to me during the early formation of SALESPHASE and its new calculus for measuring, modeling, evaluating and interpreting imperfect information about complex strategic “deals” that can involve commercial, social, research or political opportunities.

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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


Overthrowing Newtonian Sales Thinking

Newtonian Sales CRM is Old School

Without knowing it, the sales component of typical Customer Relationship Management systems–even the most advanced applications available–remain sadly and securely rooted in Newtonian thinking.  SALESPHASE is changing that by throwing out the fundamental axioms of sales acceleration technologies and replacing them with new thinking, new math and new algorithms hidden completely behind an ultra-simple user interface and experience.  But that’s getting ahead of the thought train I’m trying to conduct here, so first let’s go back a few hundred years to Newton.

Newtonian thinking includes in its most fundamental principals the following logic: Given perfect information at any point in time (initial state) we can predict what will happen at any future time (final state).  Of course this is theoretical, because we rarely have access to perfect information.  But my experience with CRM and sales acceleration applications over the past 15 years has been witness to a continuous striving towards “perfect information”.  Technological progress with respect to the Internet, smartphones, data collection and analytics feeds right into the Newtonian paradigm of seeking the illusive fountain of perfect information.  If we can know exactly what our customers are thinking, planning and doing we could (in theory) custom design a marketing and sales strategy for each customer.  This was impossible a decade ago, but technology today allows even huge companies (with the requisite resources) to nurture a sense of one-to-one relationship with customers.  This is why I’m invested in Marketo for its digital marketing technology designed to provide real-time engagement with customers whether by email, video, social media, etc.  But digital marketing is not always the answer, especially for high-value long sales cycle products and technologies. SALESPHASE is aimed squarely at complex sales where the cost of getting to the deal phase with a customer is substantial.

I do not dispute that the better our information is, the better our actions based on that information will be.  But there is a marginal cost associated with moving to the next level of “better information” in your business, whatever that might be.  And as information increases, gathering, storing, analyzing  and finally using it gets more complicated and costly.

The Cost of Perfect Information is Infinity Dollars

I think we can agree intuitively, without any formal proof, that the marginal cost of better information approaches infinity as we approach the limit of perfect information.  This implies that perfect information is both functionally and theoretically impossible to obtain.  This is no breakthrough in sales thinking or information science, but acknowledging this problem allows us to ask if there is some other fundamental principal or principals which could take sales metrics from an 18th century basis to a 21st century basis?  Well, we don’t even need to go that far.  We can stop in the early 20th century to find an appropriate new axiom:  between 1905 and 1915, Einstein abolished our scientific pursuit of perfect information in fixed space and time, and replaced it with the philosophically challenging but wonderful uncertain universe of “the quantum” (at small scales) and the flexibility of space and time at large scales and high momentum.  What could this possibly mean for sales acceleration applicatons?

Background Independent Selling

While Newtonian sales is about pursuing perfect information, Background Independent Selling is about extracting actionable knowledge out of far from perfect information.  I call it Background Independent because it can be implemented regardless of ANY differences between sellers, buyers, products, technologies, industries, etc., (all of such things I refer to collectively as “the background”).  For more context on what I mean by background independent, go here to read how the background environment is a source of sales friction.

So MUCH of the data that is pumped into sales CRM applications is highly specific information, but so what?

  • Is it accurate?
  • Is it useful?
  • Is it cost effective?

I have designed, implemented and used sales CRM applications across multiple companies over a 15-year period and, for the most part, I’d say the data is spotty, full of mistakes, and rather useless.  Marketing people would still call my mobile phone looking for customer information and insights, and my manager just looked at sales numbers and bugged me for answers if I weren’t hitting targets.  Sale reps who repeatedly hit targets were let off the hook on their sloppy CRM data, and poor sales reps were lashed and then tied to their laptops to spend hours logging data to prove they were working.

After implementation, I found CRM systems would take on a life of their own…they had to be FED and became the master instead of the servant.  CRM including sales-related components is implemented from the top down as a tool of management, not a tool that makes sales people better at their job or one that eases their job so that they can spend more time with family (or in the field).   I can’t think of any company that has EVER implemented a CRM process that its sales force found to be invaluable.

Failure to Launch

Recently one of my colleagues was attending a conference of business aircraft owners.  She was seated for dinner with a CEO who remarked that he’d spent a fortune implementing a major enterprise CRM system for his sales force only to s#^%-can it months later.  One can often attribute these failures to lack of planning, substandard technical implementations, insufficient training, or poor promotion of the new system.  But in this case, the primary issue was a complete lack of buy-in from the sales force.  In my opinion, the CEO’s complaints were unrelated to whether it was SAP, Oracle/Siebel or Salesforce; so I’m not going to name names here.  But I’ve worked with all three and know that the problem was platform-independent.

Three Reasons Sales People Despise CRM

No need to reinvent the wheel: I agree with John Holland’s May 2015 blog article, Three Reasons Sales People Despise CRM. (Read. Learn. Sell.)

  1. Salespeople ask or wonder WIIFM (What’s in if for me?);
  2. One size does not fit all transactions; and
  3. Entering CRM data is time consuming and doesn’t match deal flows.

I think these reasons require no further explanation, but certainly, please follow the link to the article if you wish to dive deeper, or read my earlier blog article on The Trouble with High Tech Sales Tools.


The main impetus for starting SALESPHASE has been to resolve the three reasons sales people despise CRM.  SALESPHASE is not a standalone CRM.  It does operate as a standalone sales acceleration tool,  but I see it as a complementary addition to existing CRM applications.  SALESPHASE objectives are as follows:

  1. Sales people happily exchange what they are currently doing for SALESPHASE because the benefits will show up as soon as the app is started and without any training.  If the rep knows how to use his or her phone, that all the training required;
  2. One size actually DOES fit all, it’s background independent;
  3. It vastly reduces data entry because most of the data collected before was useless anyway;
  4. It doesn’t lag or even match deal flows, it gets ahead of deal flow by extracting subtle key information that is usually lost in the noise; and,
  5. It generates measurements that allow comparison of sales rep performance, deal risk and probability of close regardless of the size of the deal.  More importantly, it exposes the probability that a deal is accurately reflected at the right position in the sellers pipeline.
  6. It distinguishes very early on which deals will be simple and fast from those that will be longer and more complex.  This allows proactive sales resource allocation and reveals potential hidden problems before it’s too late to solve them.

I will discuss the details under nondisclosure, but please bring someone from your team who possess a PhD in mathematics, as well as a solid mathematical programmer—not a coder.  Email me: john at sales phase dot com, or tweet me @salesphase.

Is Gauge Variance Messing With Your Sales Team?

Discrete Scale Invariance

Discrete Scale Invariance

Let’s tap into the abstract concept of gauge variance to distinguish a particular type of common sales team dysfunction. First, bare with me as I explain what this gauge variance thing is all about. I will start with the exact opposite property, gauge invariance because the most fundamental aspects of the universe possesses this property–more often called gauge symmetry.  This extremely abstract property is difficult to adequately describe here, but its effects are very important to us. For example, we literally see things because electromagnetism (light) is a necessary consequence of the gauge invariance of the electron field.  Because the dynamics of electrons are constrained by gauge invariance, it necessarily follows (thanks to some mind numbing graduate level mathematics), that photons of light MUST exist to transmit energy (some would say “information”) between electrons. I for one am very happy Mother Nature requires gauge invariance because, among other things, it enables me to see Facebook status updates alerting me to stomach ailments of “friends” I haven’t spoken to in 25 years.

Setting aside Facebook and electromagnetism, think of gauge invariance like this: if I measure the value of a sales deal in dollars and my customer measures the value in euros, we do NOT get different results in terms of value. There is always a publicly available market-driven currency conversion factor which we can use to verify that we both measured the same value for a deal regardless of our choice of currency (our choice of gauge). Now contrast this with measuring something like the coastline of California.

If I measure the beautifully rugged California coastline with a yardstick and my counterpart measures it with a ten-foot pole, will we get the same result?  No!  The concept of “length” in this case fails gauge invariance–it is gauge variant.  It turns out that the length of any randomly uneven surface we measure can grow to infinity as the choice of measuring stick gets shorter.  As our choice of ruler gets smaller, our measurement picks up ever smaller variations in what makes up the “edge of California.”  As we would expect, this is related to resolution.  As you zoom in on the coast, you continually see more detail of the actual coastline.  In essence, measuring a coastline is scale dependent.  Ask me how long the coastline of California is and I will ask you how big is your ruler?

Now, my proposition with respect to sales is that sales teams often measure deals (informally or formally) in ways that are scale dependent. To say the same thing in different words, sales analysis tends to be gauge variant.  It is the differences in choice of gauge which lead to misunderstanding, disagreement and wasted effort.  For example, I found during my 15+ years in sales that sales management and marketing tend to work at the macro scale to set pricing, make national sales predictions, etc.  Sales reps conversely tend to work at the micro scale of individual customer agents, budgets and local competitive conditions.  It doesn’t take a lesson in physics to intuitively understand that this leads to mismatched results and disagreements about what is happening or what is possible.  Nevertheless,  I prefer the rigor of math and science to stimulate my thinking and analysis of age-0ld sales problems.

Gauge variance whether in physics or sales is a constraint that a theory must have to remain consistent (only sensible answers and no contradictions).  In the sales world, this can be intelligently worked around or swept under a carpet (preferably up in the marketing department where no one will look), but how often does that currently happen?  How often did I see, or participate in, ground-up sales estimates that started with local competitive and customer conditions and then rolled up logically to the larger scale of national sales budgets and pricing decisions?   How about never. It might get talked about, but it never actually happened.  Budgets, pricing, quotas, etc. were all top down starting from CEO/CFO expectations for the division, and from division leaders to business unit directors.  Business unit directors would then work feverishly with sales and marketing management to come up with plans that would be acceptable “upstairs”.  Finally, at the annual division sales meeting that kicked off each new year, I’d get a territory expectations report that made me want to shout, “You want WHAT when?”  This is only one example of where the problem of gauge variance starts to rear its ugly head in sales departments.  At each point in the process, from the CEO down to the sales rep, there are dozens of conscious and unconscious choices of gauge that fail gauge invariance and lead to unexpected results, disagreements, confusion and general sales friction.  Tackling this issue might be too grand a vision to start.  What about within sales departments?  Why not acknowledge problems of gauge choice upfront?

Typically a sales manager will inquire how a rep is coming along in terms of hitting sales targets.  If the rep is at or above quota and feels confident, then the response will be “No problem!”  But if there is a problem, the rep typically responds with a stream of detailed customer “issues” that are creating barriers to success.  The manager’s choice of scale is likely “percent of quota” or “percent of budgeted revenue dollars.”  The reps response is based on some completely different measure such as customer specs, customer budgets, etc.  The question is who is right and who is wrong?

It’s hard to tell wrong from right when the choice of gauge is not explicit.  The manager can be right that the sales rep is under-performing budget, while the sales rep is also right that according to his choice of gauge, he is presently achieving super-human progress against insurmountable odds. What would be better is to have ways to measure performance at specified scales and then a way to connect those results across differing scales.  Great sales managers are intuitively good at changes of scale and how to connect between them.  Once a sales manager sees a problem at one scale, she changes her perspective and engages with the rep at the scale where a problem is occurring.  Conversely, a great sales rep who is under performing plan will FIRST acknowledge that according to the choice of sales management gauge, his numbers are “in trouble.”  Then he openly leads his manager into a discussion based on a different scale and asks for guidance and help AT THAT SCALE, which when rolled up to another scale will make a positive difference.

From the beginning of inception I have developed SalesPhase to avoid scale dependent problems that occur in sales measurements.  How?  By taking advantage of something known mathematically as discrete scale invariance, which in this discussion might be better termed, discrete gauge invariance.  By carefully choosing a gauge that works at multiple (but fixed) scales important to corporate management, sales management and sales reps, a significant amount of sales friction can be avoided.  When management and reps share a language of measurement that is discrete gauge invariant, there is far less disagreement and far greater focus on solving the problems at hand.

If  you are interested in discussing the mathematics and science underlying the SalesPhase approach to sales management, tracking and communications, follow me on Twitter or tweet me privately, John Clark @SalesPhase.



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.



The Trouble with High Tech Sales Tools

I’ve been involved in the sales efforts of leading edge technologies for more than 20 years: ultrafast LCDs, liquid crystal holographic optics, virtual reality, digital micro displays, aerospace composites, etc. It doesn’t matter whether the company is a Silicon Valley startup or a Fortune 100 conglomerate–I see the same old types of approaches to selling. While product technologies being sold move ahead at light speed, “sales process technology” ambles forward at a more earthly pace.

You might disagree and rightly point out products from Salesforce, InsideView, comScore, and so on. However, I think of these as high-tech sales tools allowing selling organizations to do more with less. These are fantastic products, don’t get me wrong, but do they reach their full potential when the underlying sales processes have not changed all that much? I loved using Business Objects and had to beg permission from sales management to slice & dice my own data searching for hidden patterns left undetected by the canned sales reports.

I was working for 3M at the time and it was as if they’d purchased Ferraris for the sales force but detuned them into Fiestas before letting us drive into our territories. Even then it was still difficult for the sales (and marketing) people to understand and effectively use the sales technologies being adopted at the highest levels of the organization. I took part in a Six Sigma project to roll out a SAP CRM implementation. Time after time I was stymied when trying to take advantage of the power and flexibility of theses new technologies. Instead we essentially digitized the same old process that had been used in the prior system in a round-robin going all the way back to pencil and paper.

New systems were rolled out ostensibly for the benefit of the sales teams. How many times did I hear the pitch, “You guys are going to LOVE this!”? What would inevitable follow amongst my sales brethren was eye-rolling, groans, and the implicit understanding that we were soon in for a problematic implementation with a steep learning curve ending with more hours spent documenting at home for no additional pay. All this so that business unit management could roll up the information to make better predictions to senior management whose butts were on the line to the CEO whose butt was on the line to the Board of Directors and the shareholders. Did I ever hear a salesperson say, “This technology is awesome! I’m killing my quotas, getting home early for dinner with the kids, and sleeping like a baby all because of that new software management gifted us from heaven!”? No, I didn’t.

The better a sales person was, the more he or she could ignore any new system and keep selling the old fashioned way. One of the best and most senior of my sales colleagues would mumble, “Here we go again.” He’d survived 27 years of management shakeups, strategic re-orgs and new software rollouts, without being seduced by any of the changes. Ultimately, we all knew, it was “beating numbers” that made one successful, not early adoption of new technology.

That’s not to say it wasn’t possible: I used the power of SAP to organize several disorganized seller/buyer engineering teams. Derailed multimillion dollar projects needed to get back on track before going to corporate fisticuffs, e.g. expensive litigation and soured corporate relations. One project involved positioning systems for nuclear subs carrying Trident missiles, and another for anti-rotation systems for satellites needing to see terrorist nose hairs from outer space (well, more like near-earth orbit).

These were important projects that deserved the time and effort to master new software tools. But is that the typical case if you, for example, need to close a $50,000 abrasives purchase order with an aircraft repair station? Not as much. You have to close a LOT of those POs to make your number and you’re not going to do it at your desk (or in your car on a tablet).

My vision is to upgrade the sales process itself to match the software technology now available to selling organizations. I’ve spent the last ten years formulating my ideas. Now it’s time to build a team and raise some money.

Consider SalesPhase to be a catalyst that can supercharge the interaction between sellers, buyers and cool new CRM, data, and analytic tools. But first we need to go beyond the standard sales pipeline…far beyond.

Follow me on Twitter, John Clark, @SalesPhase to comment or discuss privately.

Sales Friction & Broken Symmetry

friction“SALES FRICTION” is wasted energy in the form of wasted time and resources during the process of a buyer and seller coming together to close a proposed deal. During 15 years managing complex high value long sales cycle B2B transactions, I found great advantage in finding ways to reduce sales friction. Simply stated, aligning sales efforts with the customer’s buying phases was the key to reducing friction (as happens in any physical system which gains coherence). But, how to do this consistently when starting with imperfect or zero knowledge of the customer’s buying agents and internal processes?

Due to superior knowledge of their sales process, sellers naturally select their internal sales funnel as a “sales gauge.” Unfortunately this is a poor choice of gauge if one is trying to measure the likelihood of a SPECIFIC customer making a SPECIFIC (large or complex) purchase. There are statistical correlations between a vendor’s sales stage and probability of closure, but can we do better than these correlations? Yes, we can.

Salesphase is a methodology that is independent of any particular buying or selling process. The scale and scope of the deal makes no difference. Sounds “universal?” Let’s check:  to claim a “universal solution” to any set of problems (including sales friction), it is well established historically & mathematically that such solution MUST be based on one or more underlying physical or abstract symmetries. “Symmetries” can be difficult to define, and as an aspect of Salesphase is kept securely tucked “under the hood.”  But in the context of the underlying algorithm, symmetries are groups of transformations which leave some property of a system invariant (fixed, unchanged, indistinguishable).

Finding hidden symmetries can be difficult. Customer and vendor organizations and processes often appear specialized, unique and yes, asymmetrical, when compared to one another. Why? Because symmetries can be “broken” by external forces, e.g. culture, terminology, system noise, etc. Broken symmetries, when not purposefully designed into a system, necessarily imply at least two things:

  1. There exists hidden “embedded” useful information; and
  2. Some property or characteristic of the system remains invariant despite observed “local differences.”

Salesphase exploits invariant properties of the selling/buying process to unlock hidden information critical to reducing sales friction and other sales dysfunctions. Salesphase realistically assumes a world of IMPERFECT/INCOMPLETE customer information which can be enhanced as information is learned or modified. Salesphase is a methodology leading naturally to an algorithm, and is not competitive to CRM or data analytics. Instead it is a framework for extending the value of existing tools while reducing total sales effort and increasing the accuracy of sales predictions.

Tweet or follow John Clark, @Salesphase, to discuss.