Thursday, November 10, 2016

Getting Un-Stuck

Getting Un-Stuck by TR Dorfman PhD,

Can’t seem to get things going?  Nothing necessarily wrong, but also nothing positive in the way of change.  Try this.

  • 1.       Acquire and store important quantitative data, especially by date.  This includes sales, marketing, pricing, commodity prices,  and competitors price.  Don’t be restricted by this list-save the key data you think is important.
  • 2.       Decide specifically the issues you would like to solve.  Make a written statement describing the problem.
  • 3.       Have this data analyzed. 
  • 4.       A skilled data professional can help determine the important components of your data in relation to your problem.  Additionally, accurate forecasting is quite possible in terms of predictive analysis of the data.

Thursday, November 3, 2016


Companies of all sizes that utilize predictive analytics:

2x more likely to be in the upper quartile in their financial performance

5x more likely to make decisions faster and more timely than their competition

3x more likely to execute decisions as intended

2x more likely to use their data more frequently when making important decisions.

There you have it.  Increased profitability from faster, better decisions.

TR Dorfman PhD,

Tuesday, November 1, 2016


In corporation Alpha, there’s been a slowdown in orders from major customers for Product A, but not for a similar Product B.  Market intelligence shows an increase in sales for a similar Product A at a competitor.  In the last year Alpha raised the prices twice for both products, but kept the prices in line with the competition.  However Product A sales declined following both price hikes, but not     Product B.

Identify the Problem: 

Why are sales declining for Product A but stable for Product B?

The Solution:

Review of the pricing decision showed management anticipated a rise in the price of a key commodity for both products.  Although these increased costs did not occur, their imminent price rise continued to weight on managements pricing decisions of their products.

Management meeting about the slowdown in Product A revealed data concerning technical support calls for both products.  Product A showed a sharper slowdown in support calls than Product B.

A contract was issued to a predictive analytic company to help figure out the dilemma.  Within a week a forecast time series analysis confirmed the coming price increase in the important component commodity.  Additionally a statistical comparison between the support calls for both products with sales data revealed a sweet spot for the number of support calls that correlated with increased sales.  Also, support calls calculated to account for 26% of repeat sales.

Management maintained their price improvements and increased their targeted support calls for Product A.  Within the quarter the sales declines ended and started their rebound.

Tuesday, October 25, 2016

Predictive analytics- an example

The analytic solution:

You want to determine which components of a recent sales campaign contributed to revenue changes in your Product A.  20 sales personnel are dedicated to this product.

You have data for these the important variables:

 1) Advertising dollars for the campaign.
 2) Hours of product training for each sales personnel.
3) Years of sales experience for sales personnel rated by revenue production.
4) Number of sales presentations documented by each sales personnel.

Predictive analytics can take this data and determine the contribution of each component to the revenue for Product A.  Let's say that the statistical modeling found that the number of sales presentations contributed 64% to revenue increases while advertising dollars contributed 17%.

Would this be valuable information for an executive to know?

Tuesday, October 18, 2016

Dealing with a gifted but difficult manager

As the best business intelligence stems from the internal resources of a company, we count our executives and managers as the leaders of company decision making.  In this group we may have talented but difficult managers.  Talented in the sense these people may have gifted corporate perceptual skills and the organizational cleverness to put those abilities to use.  Difficult in the sense they engender resentment and conflict within the important supervisory and expert corps of the company.

These people usually fall into  two categories.  The first represents the manager with an unusual need for control, and the second represents the manager with a deep sense of envy about others in the group.  Both types of individuals possess an over abundance of entitlement and interpersonal competition.  They differ in their outward characteristics.  The control manager will display more tension and hostility while the other will be highly manipulative.

As it is impossible to find perfection, we cherish our talented managers even with their shortcomings.  The key is finding the right fit.  For the over controlling manager, what could be the proper place?  Likely in areas that require compliance and adherence to important, uniform processes and regulations.  For the jealous manager? Probably in the supervision of older experienced professionals whose most competitive years are behind them.

Careful observation of these difficult but talented people is part of utilizing corporate resources.  In the right slot, they can help a corporation perform to its highest profitability. In the wrong position, the can detract from the ever important group cohesion.  The careful and constructive placement of talented managerial staff represents an ongoing executive responsibility under the umbrella of business intelligence.

Monday, October 17, 2016

Business Intelligence- a beginning overview

For some the concept of business intelligence conjures up images of sleuths and spies secretly operating throughout the world of corporations.  In reality it is much more sober and pragmatic. Business intelligence reaches into the collective resources of the company and produces profitable solutions to business challenges.  The sum of executive/managerial experiences sharpened towards corporate intuition is an example of business intelligence.

In today's world of Big Data, company decision makers combine their corporate intuition with the abundant quantitative information produced and saved by the activities of the organization. Quantitative data, analyzed correctly, can project future answers to problems with clearly defined probabilities of outcome.

In future articles we will explore how this data yields accurate forecasts about company pricing, marketing, valuations and other topics related to business profitability.