Blog #1 discussed the history of predictive analytics going back to 1689 with Lloyds of London, insurance company. Predictive approaches, irrespective of the computing science and machine learning underpinnings, have been here for over 337 years. Blog #2 will define predictive analytics methods in more detail and in particular, I will explore the relevance and history of artificial intelligence.

Predictive analytics can be defined as ”the practice of extracting information from existing data sets in order to determine patterns and predict future outcomes and trends.” Predictive analytics does not tell you what will happen in the future. Rather, predictive analytics serve as guide posts on the odds or risks, given past patterns of statistical relevance.

As we look forward, it is important to appreciate humans have been analyzing patterns for centuries – just not as fast as we can now. What is vastly different is the speed for pattern detection and the human element although still involved – will increasingly become more marginalized as the rise of machine intelligence continues to grow.


Predictive analytics comes from diverse disciplines that are combined or blended to predict outcomes reliably. The disciplines come from: artificial intelligence, business intelligence, computing sciences, data mining, mathematics, modelling, physics, statistics, text mining, and of course business which is key the problem or use case being solved. Without a question or problem to analyze, predictive analytics won’t yield the insights to advance a target outcome. At the same time, monitoring outliers – edge signals that cluster over time, are strong indicators of new growth patterns.

REFLECTION POINTS – I recall when I was a GM and Senior Director at Xerox, that the company was ignoring the declining margins of copier equipment sales revenue and was slowly shifting into the Professional Services growth areas. Having Data Scientists in Sales that have teeth doing scenario planning can be a powerful force in helping companies compete and evolve more rapidly.

What I have learned as the founder of SalesChoice, a predictive sales analytics company,  is that these diverse inter-disciplinary knowledge and skills are critical to attract to solve the problem we are striving to crack which is: Why do 30% to 60% of middle tier sales professionals not achieve their sales targets and 20% of the highest performing reps help compensate to meet the overall target quotas?

Our thesis is B2B sales professionals are under siege. Less than 31% of a sales rep’s time is spent selling to customers, a drop from 41% in 2011 (Accenture, Age of Distraction Research, 2016). Current methods for engagement no longer work, as productivity continues to plummet. Guiding sales professionals to  focus on the best sales opportunities with the best signals to ensure ensure that the highest odds of win rates is critical to grow top-line revenue.

Underlying sourcing the best signals from diverse sources will require Sales Professionals to embrace the Science of Selling and start to learn and appreciate how Artificial Intelligence methods will unlock growth potential in unprecedented ways.


The history of artificial intelligence (AI) began with myths and stories of artificial beings endowed with intelligence by master craftsmen with stories of “ancient wishes to forge the Gods.” 

The roots of modern AI were planted by classical philosophers who attempted to describe the process of human thinking as the mechanical manipulation of symbols. AI is based on the assumption that human thought can be mechanized. The field of Mechanical—or “Formal Reasoning” has a long history. Philosophers from: Greece, China and India developed structured methods of formal deduction in early BC. Greek Philosophers like: Aristotle (780-850 AD) gave a formal analysis of the syllogism, and Euclid’s, (a pupil of Socrates in the late 5th Century BC), research on elements was a model of formal reasoning. Muslim mathematician al-Khwārizmī (750-850 AD) developed algebra and defined the word “algorithm.”

In the 17th Century, Gottfried Leibniz (1646-1716) speculated that human reason could be reduced to mechanical calculation. Leibniz envisioned a universal language of reasoning that would reduce argumentation to calculation.  He is the father of differential and integral calculus. His research was further developed by: Thomas Hobbes (1588-1679), a English Philosopher, and French philosopher, Rene Descartes (1596 – 1650), as they postulated that all rational thought could be made as systematic as algebra or geometry. These philosophers had the pioneering vision to see the future that physical symbol systems that would become the underlying foundation for the evolution of AI.

Breakthroughs happened in the 20th century in the field of Mathematics (Boole’s The Laws of Thought, Russell and Whitehead’s, Hilbert (1920), Godel (1930), Turing (1940) answered this question that: “all of mathematical reasoning can be formalized in “calculations.

This inspirational work resulted in the first programmable digital computer being innovated in the 1940s, a machine based on the abstract essence of mathematical reasoning. This device and the ideas behind it inspired a handful of scientists to begin seriously discussing the possibility of creating an electronic brain.

British mathematician Alan Turing posed the innovation question “Can machines think?, at a 1956 conference. Predictive analytics came into higher profile in the 1940’s when governments started using computational models. Alan Turing was a pioneering English computer scientist, mathematician, logician, crypt-analyst and theoretical biologist. He was highly influential in the development of theoretical computer science, providing a formalization of the concepts of algorithm and computation with the Turing machine, which can be considered a model of a general purpose computer.

Turing is widely considered to be the father of theoretical computer science and artificial intelligence. His incredible story was made more popular in the recent movie,The Imitation Game, profiles his courage in 1939, when he worked for the newly created British intelligence agency MI6. The movie unveils the story of his recruitment from Cambridge mathematics to crack Nazi codes, including Enigma — which crypt-analysts had thought unbreakable. This breakthrough moment in history helped spur investments into the field of predictive analytics and artificial intelligence, a core foundational science. The movie also shed light on the challenges of living an alternative life-style and the political and societal implications of being a homo-sexual.

Additional AI researchers like: Allen Newell, J. C. Shaw, and Herbert A. Simon created the first Academic Field of Artificial Intelligence with the Logic Theory Machine (1956), and the General Problem Solver (1957). The MIT AI Lab was created in 1958 and the first programming language, LISP, was developed by John McCarthy.


As noted in Blog#1, Predictive Analytics will infect every business process world-wide and eventually all Smart AI Algorithms will be connected into deep learning networks to guide the human race to make more informed decisions, acting like powerful data synthesis guides or agents. We predict they will soon become’s man’s constant companion, never leaving one’s side, never turned off, never tired, acting like a trusted beacon to guide us 24X7. This is why we are calling SalesChoice the First Global Sales GPS tracking system. As more data is analyzed with deeper learning ALGOs, B2B sales will forever be integrated with advance signals.

Deep learning (also known as deep structured learning, hierarchical learning or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using a deep graph with multiple processing layers, composed of multiple linear and non-linear transformations.

Recently deep learning approaches into CRM practices are creating an innovation surge, as actions over time are connected along a customer’s life-time value chain (bread crumbs or data gems mined). This analytics mining dynamic threading has a natural interpretation in value metrics such as: customer lifetime value (CLV or often CLTV), lifetime customer value (LCV), or life-time value (LTV) which is a prediction of the net profit attributed to the entire future relationship with a customer.

SalesChoice has developed a predictive analytics AI engine that can monitor and track all the sales rep and sales management interactions inside the CRM and outside the CRM to predict sales forecasts, best deal pursuits, best pricing ranges, and customer interaction /relationship health.

As all customer interaction communication signals are monitored across all pathways, the predictive models will truly become Big Data predictive models with varying levels of sophistication and accuracy, ranging from a crude heuristic to the use of complex predictive analytics techniques.

With SalesForce‘s new release on EINSTEIN, SalesForce has the right vision as Deep Learning Sciences will embed all CRM processes (Pre-Sales to Post-Service) and fundamentally alter the future of CRM, as we know it. SalesChoice has already extended our AI solution to take advantage of SalesForce’s Wave Analytic capabilities, and you can see a Demo here of our two products interacting harmoniously. We are looking forward to learning more about EINSTEIN at DreamForce 2016, like many SalesForce Ecosystem partners, we will all need to find mutual value pathways to evolve our Predictive World: Our New Normal. Always On – Always Thinking –  Always Learning – as The Science of Selling continues to change the world of B2B sales and transform and constantly challenge our views of reality.

This is an exciting time for each of us as our Predictive Generation continues to emerge rapidly. Are you ready?

Most CEO’s are not, and most Board Directors are not, and most CRO’s and CFOS are not. Time to innovate or be crushed as this WAVE is a BIG Tsunami.


Follow us on Twitter @SalesChoice_Inc or visit our Predictive World Resource Center. Want a Demo of Predictive Analytics to prioritize your sales deals, predict more accurately your sales forecasts or predict your pricing sweet spots, to See More to Win More, book here.


Dr. Cindy Gordon, CEO SalesChoice Inc.

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