a unique processing system

Modern business intelligence systems are rising to meet this challenge by encompassing more explanatory variables into their algorithms.  But even so there is still a problem.  These models are still confined to looking backwards, to past events, to formulate guidance about what to do in the present and future.  Sales & marketing decision makers need to complement their insights from historical data with an approach that can work in the constantly changing environment of their markets in real time.  That approach has to draw on the processing capabilities of a system uniquely suited to the ambiguities and constant flux of dynamic markets: the human brain.

Analysis on the run

Humans and machines don’t “think” in the same way.  Consider the following scenario.  A sales representative for a seller of food products has a particular customer with whom she has a track record of selling condiments like ketchup, mustard and soy sauce.  The customer is a fairly frequent buyer and so there is an extensive transaction history going back over a number of years.  The sales rep’s company uses a quantitative analysis and optimization tool to predict likely demand, providing its sales reps with guidance for pricing and other marketing decisions.

The sales rep is doing some prep work ahead of a meeting later this week with the customer, including reviewing the price recommendations the optimization system has provided.  As she glances through the morning paper, the sales rep notices that one of her company’s major competitors has launched an intensive three-week promotional campaign for customers in the local area.  Immediately she knows that her customer also buys from the competitor, and no doubt will be fully aware of the competitor’s promotional campaign.  She leans back in her chair, looks again at the price recommendations in the display screen of her PDA, and takes a deep breath.  What should she do?

Gut instinct versus science

The traditional answer would be: go with your gut instinct.  After all, this is what good salespeople do – call up their years of experience to figure out how to play nuanced competitive angles and make the sale.  It is likely that within seconds of reading that newspaper announcement about her competitor’s promotional campaign, the sales rep had already processed in her mind the  information, evaluated its importance and formulated the outlines of a plan.  As the sales call approaches she will refine her thinking even further, and may in fact defer the final decision about how to make the offer until minutes before the meeting, depending on whatever factors she believes to be in play at that time.  It would seem, then, that the prudent thing to do would be to override the system’s price recommendations.  After all, the computer’s algorithms are not privy to any of the knowledge and insights that are firing up the neurons in the sales rep’s brain.

Overriding the system is not an optimal solution, however.  In opting for human insight, the sales rep is losing the benefit of the scientific insights culled from a highly sophisticated analysis of patterns in past activity.  That analysis is highly granular – it applies to the unique characteristics of this particular customer and the set of products that are featured in the sales call.  Surely there is enough value in these insights to give our sales rep pause before she hits the override button?

Quantitative intuition

Fortunately, she won’t have to make this choice between human instinct and scientific analysis.  One of the most important recent innovations in predictive technology platforms is the means to provide real time input from the field – even in the moments right before a live sales opportunity – into the system.  We call this making the system “market aware”.

Market aware systems can confer distinctive advantages.  The key is how to essentially “code” qualitative input from decision makers into the algorithms that run the scientific analysis.  For example, our sales representative needs to convey to the system that the sale will likely be under more competitive terms than usual.  In other words, she needs to take a qualitative statement along the lines of “this is going to be a tougher sale because of our competitor’s aggressive promotion” and translate it into a digitized language the computer will understand.  There are different ways this can be done – from a relatively simple algorithm numerically ranking competitive intensity to more complex machine learning analytics.  Machine learning is a fast-developing field helping to make commercial technology solutions increasingly adaptable to constantly changing environments.

What happens from a practical standpoint is that our sales rep codes the information into a feedback loop that interacts with the system’s algorithms – effectively “informing” them about something happening in real time that they need to take into account.  The models are then able to recalibrate their analysis and provide new recommendations to the salesperson.  Now she has the benefit of two important – and distinct – types of analysis: the quantitative knowledge of historical demand patterns with this customer and the basket of goods she is trying to sell; and the qualitative judgment around an ambiguous situation that requires brainpower more than computational power.  Combined, these two analytical approaches provide what we call quantitative intuition – a means to combine deep granular insights from your company’s transactional data records with the ability to adapt to dynamic market developments in real time.

It’s hard enough for enterprises to locate those rare possessors of this sales magic and retain their services, but harder still to deal with the fact that in today’s choice-rich, multifaceted demand environments even those talents alone are not sufficient to achieve sales excellence.  We live in a world, after all, where there are purportedly more SKUs (stock-keeping units) on the planet than there are species of living organisms (see for example Eric Beinhocker’s excellent book “The Origin of Wealth”).  A sales representative working for a company with over 100,000 SKUs, which is the norm for large companies in fast-moving goods industries, has to deal with a dimension to the art of the deal that unfortunately has little to do with charm, wits or good grooming: he or she has to figure out on a daily basis which subset of five or six products, out of that universe of tens of thousands, to offer to customers at whatever combination of price points might stand the greatest probability of winning the business.  The computational dimensions of that notion are staggering – quite simply, they are beyond the realm of the feasible when contemplated by the unaided human brain.

Enter technology and the computational powers of quantitative methods.  That which overwhelms the human mind amounts to a few split microseconds of run time for robust data management platforms.  Revenue optimization models can sift through billions of customer-product combinations to recommend pricing configurations with relatively high probabilities of success.  Perhaps these quantitative models could replace those hard-to-find sales skills – after all, if these models can really crunch all that data and recommend prices with the highest likelihood of success, then anyone holding a BlackBerry can access the information and make the sale, right?  Not so fast.  The world may have changed a great deal from 1964, when Mayer and Greenberg produced their article, but intuition is still intuition, and it is no less a necessary ingredient for sales success today than in years past.  For all that computers can achieve, intuition and empathy are simply not things they do.

But is it possible to teach intuition?  At first blush that would seem to be a stretch.  In the minds of many the concept of quantitative methods is intertwined with that of an opaque, algorithm-powered monolith that spits out Delphic recommendations based on historical data crunched through a process unknowable and unviewable by mere mortals – what is commonly (though not always accurately) referred to as a “black box.”  The problem is that in dynamic environments like consumer goods demand markets, decision makers have to negotiate offers based on a kaleidoscope of real-time inputs that require intuitive judgment.  For example, say that you are a distributor in the food services industry and you see a news item that a national wholesaler has opened a discount distribution center in your sales territory.  How would a salesperson process and assign a value to this information?  As human beings, we are uniquely able to compose propositions out of discrete units of information and then embed those propositions within other propositions and so on, creating a hierarchical tree of a limitless number of propositions.

For example, upon reading the headline “National Wholesaler Opens Discount Distribution Center” a sales rep might begin to formulate a succession of hierarchical propositions in rapid sequence:

• wholesaler opens discount distribution center
• wholesaler who is our competitor opens discount distribution center
• wholesaler who is our competitor opens discount distribution center right down the street from our biggest client
• wholesaler who is our competitor and offers everyday low prices opens discount distribution center right down the street from our biggest client
• wholesaler who is our competitor and offers everyday low prices opens discount distribution center right down the street from our biggest client who was a tough price negotiator in our last sale

Our empathetic, capable sales rep will immediately assign a value of high importance to this information and use it to gauge the tone, tenor and negotiating position of the upcoming sales call with this client.  What if the sales rep could also “inform” the quantitative revenue optimization system about this development and have it factored into the ensuing price recommendations ahead of the sales call?

In fact that is possible in today’s environment.  Market awareness models are able to take qualitative human insights, like our sales rep’s awareness of the real-time implications of the competitive threat, and translate them into quantitative factors the models can employ, in conjunction with all the other relevant variables, to produce improved decision support recommendations.  Of course this is not a brainlessly simple exercise: we still face the challenge of translating the sales rep’s instinctual thought process into a language the machine will understand and recognize.  Nonetheless, market awareness models are one of the leading areas of development in the application of quantitative methods to marketing and sales problems.  It all comes back to that basic question posed by Mayer and Greenberg more than 40 years ago: what makes a great salesperson, and how can we best capture and deploy those skills throughout our organization?