The Salesphase Conjecture

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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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