Seeking Beta Testers for a Web-based Sales Opportunity Portfolio Analysis Tool

Incite! Decision Technologies has recently developed a simple yet sophisticated web-based sales opportunity portfolio analysis tool that is ready for beta testing. Now we're seeking parties that would be interested in participating at no cost and no obligation.

Specifically, we are looking for progressive sales managers in firms whose sales team pursues high value, low-frequency sales. Examples of target firms might be...

• Engineering, architecture & construction firms
• Professional service firms
• Capital equipment manufacturers
• Start-ups

The purpose of the tool is to provide

• Improved accuracy of revenue realization and timing forecasts;
• Guidance on how to allocate resources to maximize the likelihood of deal closure;
• Guidance on opportunity selection and prioritization.

Ultimately, you will be able to determine if the sales opportunities you are pursuing are worth the time, effort, and resources.

If you are interested in learning more or know someone who might be, please, contact me via LinkedIn message or send me an email from our web form.

The Power of Negative Thinking: “How do we know this opportunity is worth the time and effort?”

The sales process is an inherently risky business. It’s difficult to know if and when a deal will close, what clients really want regardless of what they have stated (i.e., the client may have failed to frame their own needs properly), and what competitors offer in price and quality of deliverables.

Compounding the external uncertainty, we often get in our own way by importing certain kinds of biases into our assessment of the value of the sales opportunities at hand. These biases can include…

• Unwarranted optimism or wishful thinking – personal enthusiasm or a natural disposition to believe that desired outcomes will most likely occur; or, inflating initial estimates of desired outcomes to appear more effective than is warranted;
• Sand-bagging – under reporting potential outcomes to appear heroic when better than anticipated outcomes materialize;
• False precision – reporting anticipated outcomes with an unjustified level of certainty, usually as a single-point estimate rather than a range;
• Availability – recalling values that are memorable, easily accessible, recent, or extreme;
• Anchoring – using the first “best guess” as a starting point for subsequent estimating;
• Expert over-confidence – failure of creativity or hubris (e.g., “I know this information and can’t be wrong because I’m the expert.”);
• Incentives – the SME experiences some benefit or cost in relationship to the outcome of the term being measured, adjusting his estimate in the direction of the preferred outcome;
• Entitlement – the SME provides an estimate that reinforces his sense of personal value.

Without bias-free assessments in our decisions to actively pursue sales opportunities, it's nearly impossible to know how to allocate sales and support resources effectively to maximize the likelihood of capturing sales in a profitable and efficient manner. In short, when given the opportunity to pursue multiple opportunities with limited resources, it’s often difficult to know if any given opportunity is worth the time.

As odd as it may sound in a culture that seems to demand almost endless optimism, the Power of Negative Thinking actually helps us to overcome our biases as well as inform us how to obtain better information about the external uncertainties we face. By “negative thinking” we do not mean cynicism or toxic nay-saying. Rather, we refer to a process that asks us to consider critically the opposite of what we too easily assume (or wish) to be true. While Negative Thinking could lead us to consider the effects of unfortunate outcomes or conditions (the opposite of desired outcomes) on sales opportunities...

The best laid schemes o’ Mice an’ Salesmen, Gang aft agley

...it could also lead us to consider the possibility of desirable outcomes or conditions (the opposite of the unfortunate) for situations that we often easily dismiss.

No, no, boy, that's no way to make a plane. That'll, I say, that'll never...fly!

But the Power of Negative Thinking goes beyond our merely considering what can happen. We must also consider the “why” and “to what degree” those things could happen. We can account for the “what,” “why,” and “to what degree” in a process called probabilistic reasoning. But that's the second step. The Power of Negative Thinking begins with accurately framing an opportunity, which requires that a sales team answer the following questions:

• What is the real opportunity? 
• What are our goals and objectives?
• What are the client's goals and objectives?
• What are the decision boundaries and open decisions?
• What are the sources of uncertainty? 

Answering these questions helps the team know that it has the right reasons in mind to pursue an opportunity and what constraints in their current level of knowledge limit their ability to make unambiguous decisions about what opportunities to pursue and how to go about pursuing them.

Probabilistic reasoning helps a sales team then answer these questions:

• What is the likely range of outcomes for the uncertainties? 
• What are the effects of uncertainties on sales goals, revenues, and profit? 
• How much risk do we face with each opportunity; i.e., how much could we lose by pursuing one opportunity over another?
• What insights can we create for contingency plans or options?
• How do we prioritize our set of current opportunities?

The effect of taking these two steps in a structured way reveals the Power of Negative Thinking so that the sales team can recognize when an opportunity is worth pursuing…or not. Ultimately, not only does the Power of Negative Thinking give the sales team a more accurate assessment of the current state and possibilities they face, they can also develop more effective contingency plans to increase the likelihood of achieving results their organization—and their clients—desire.

An Interesting Christmas Gift

Over the holidays, the New York Times delivered an unusual juxtaposition of headlines and content, and apparent lack of self-awareness, to illicit such a hearty chuckle from its readers as to make the cheerful Old Saint jealous.


[image originally provided by @ddmeyer on Twitter]

To those imbued with the skill of basic high school Algebra 1, the information in the article about Sony’s revenues for the first four days of release of “The Interview” were enough to solve a unit value problem. If we let R = the number of rentals, and S = the number of sales; then,

• R + S = 2 million 
• $6*R + $15*S = $15 million 

With a little quick symbolic manipulation, we see that S = 1/3 million in sales and R = 5/3 million in rentals. That exercise provided just enough mental stimulation and smug self-righteousness to prepare for the day’s sudoku and crossword puzzles. #smug #math

However, not too far into the sudoku puzzle we might realize that a deeper, more instructive problem exists here, a problem that actually permeates all of our daily lives. That problem is related to the precision of the information we have to deal with in planning exercises or, say, garnering market intelligence, etc. A second reading of the article reveals that the sales values, both the total transactions and the total value of them, were reported as approximations. In other words, if the sources at Sony followed some basic rules of rounding, the total number of transactions could range from 1.5 million to 2.4 million, and the total value might range from $14.5 million to $15.4 million. This might not seem like a problem at first consideration. After all, 2 million is in the middleish of its rounding range as is $15 million. Certainly the actual values determined by the simple algebra above point to a good enough approximate answer. Right? Right?

To see if this true, let’s reassign the formulas above in the following way.

• R + S = T 
• $6*R + $15*S = V 

where T = total transactions, and V = total value. Again, with some quick symbolic manipulation, we can get the exactly precise answers for R and T across a range of values for T and V.

• S = 1/9 * V - 2/3 * T 
• R = T - S 

Doing this we now notice something quite at odds with our intuition - the range of variation between the sales and rentals can be quite large as we see in this scatter plot:



[Fig. 1: The distribution of total transaction values for various combinations of rental and direct sales numbers.]

Here we see that the rental numbers could range from about 800 thousand to 2.4 million, while the direct sales could range from nearly 0 to 700 thousand! Maybe more instructive is to consider the range of the ratio of the rentals to direct sales:


[Fig. 2: The distribution of the ratio of rentals to direct sales for various combinations of rental and direct sales numbers.]

If we blithely assume that the reported values of sales were precise enough to support believing that the actual value of rentals and unit sales were close to our initial result, we could be astoundingly wrong. The range of this ratio could run from about 1.11 (for 1.5 million in total transactions; 15.4 million in sales) to 215 (for 2.4 million in total transactions; 14.5 million in sales). If we were trying to glean market intelligence from these numbers on which to base our own operational or marketing activities, we would face quite a conundrum. What’s the best estimate to use?

Fortunately, we can turn to probabilisitic reasoning to help us out. Let’s say we consult a subject matter expert (SME) who gives us a calibrated range and distribution for the sales assumptions such that the range of each distribution stays mostly within the rounding range we specify.

[Fig. 2a, b: The hypothetical distribution of the (a) total sales transactions and (b) total value assessed by our SME.]

Using the sample values underlying these distributions in our last set of formulas, we observe that in all likelihood - an 80th percentile likelihood – the actual ratio of the rentals to sales falls in a much narrower range – the range of 3 to 9, not 1.11 to 215.

[Fig. 3: The 80th percentile prediction interval for the ratio of the rentals to sales falls in the range of 3 to 9.]

Our manager may push back on this by saying that our SME doesn’t really have the credibility to use the distributions assessed above. She asks, "What if we stick with maximal uncertainty within the range?” In other words, what if, instead of assessing a central tendency around the reported values with declining tails on each side, we assume there is a uniform distribution along the range of sales values (i.e., each value is equally probable to all values in the range)?



[Fig. 4a, b: We replace our SME supplied distribution for (a) total sales transactions and (b) total value with one that admits an insufficient reason to suspect that any value in our range is more likely than any other.]

What is the result? Well, we see that even with the assumption of maximal uncertainty, while the most likely range expands by a factor of 2.7 (i.e., the range expanded from 3-9 to 1.7-18), it still remains within a manageable range as the extreme edge cases are ruled out, not as impossible but as fairly unlikely.

[Fig. 5: Replacing our original SME distributions that had peaks with uniform distributions flattens out the distribution of our ratio of rentals to sales, causing the 80th percentile prediction interval to widen. The new range runs from about 1.7 to 18.]

The following graph displays the full range of sales and rental variation that is possible depending on our degrees of belief (as represented by our choice of distribution) about the range of total transactions and total value.

[Fig. 6: A scatter plot that demonstrates the distribution of direct sales and rental combinations as conditioned by our choice of distribution type.]

By focusing on the 80th percentile range of outcomes in the ratio of rentals to sales, we can significantly improve the credible range to estimate the rentals and direct sales from the approximate information we were given.

[Fig. 7: A scatter plot that demonstrates the distribution of direct sales and rental combinations as conditioned by our choice of distribution type, constrained only to those values in the 80th percentile prediction interval.]

Precise? Not within a hair’s breadth, no, but the degree of precision we obtain by employing probabilities (as opposed to relying on just a best guess with no understanding of the implications of the range of the assumptions) into our analysis improves by a factor of 13.1 (assuming maximum uncertainty) to 35.2 (trusting our SME). If our own planning depends on an understanding of this sales ratio, we can exercise more prudence in the effective allocation of the resources required to address it. Now, when our manager asks, “How do you know the actual values aren’t near the edge cases?”, we can respond by saying that we don’t know precisely, but using simple algebra combined with probabilities dictates that the actual values most likely are not.

Incite!Sales: Sales Portfolio and Forecasting System

Sales forecasts are notoriously biased, which leads to misallocation of resources and financial surprises. Our sales portfolio & forecasting system removes bias from forecasts to give you a more accurate view of your sales reality so that you can make more informed decisions about opportunities to pursue. http://incitesales.incitedecisiontech.com

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Sales Portfolio & Forecasting System from Robert Brown