Insights

Fill in the Analytics Blanks - Which X Is Most Likely to Y?

The use of analytics that include statistics is a skill that is gaining mainstream value due to the increasingly thinner margin for decision error. There is a requirement for organizations to gain insights, foresight and inferences from the treasure chest of raw transactional data (both internal and external) that many organizations now store, and will continue to store, in a digital format.

A problem, however, is that organizations are drowning in raw data but starving for information to make decisions with.

An experienced analyst is like a caddy for a professional golfer. The best ones do not limit their advice to the pro to factors such as distance, slope, and the weather but also strongly suggest which golf club to use.

There is a continuum of business analytics. The sequence is descriptive, diagnostic, predictive, and at its zenith prescriptive analytics – optimization. Few organizations have attained prescriptive analytics. It can involve linear programming. But be patient. Its time is coming. This blog discusses the third level – predictive business analytics.

Predictive business analytics – seeing the future

Predictive business analytics allows organizations to make decisions and take actions they could not do (or do well) without analytics capabilities. Consider three examples:

  1. Increased employee retention. Which of our employees will be the next most likely to resign and take a job with another company? By examining the traits and characteristics of employees who have voluntarily left (e.g., age, time period between salary raises, percent wage raise, years with the organization, number of job positions held, etc.), predictive business analytics can layer these patterns on the existing work force. The result is a rank order listing of employees most likely to leave and the reasons why. This allows managements’ selective intervention to retain the employee.
  1. Increased customer profitability. Which customer will generate the most profit from our least effort? By understanding various types of customers with segmentation analysis based on data about them (ideally using activity-based costing principles as a foundational analysis), predictive business analytics can answer how much can be optimally spent retaining, growing, winning back and acquiring the relatively more attractive micro-segment types of customers that are desired.
  1. Increased product shelf opportunity. Which product in a retail store chain can generate the most profit without carrying excess inventory but also not having time periods of stock outs? By integrating sales forecasts with actual near real time point-of-sale checkout register data, predictive business analytics can optimize distribution cost economics with dynamic pricing to optimize product availability with accelerated sales throughput to maximize profit margins.

These three examples are “fill in the blanks” questions. Which “X” is most likely to “Y”? One can think of hundreds of others where the goal is to maximize or optimize actions or decisions.

Predictive versus prescriptive analytics – clarification

I was a bit loose by referencing “optimizing” as part of predictive business analytics. To clarify, with predictive analytics what-if scenario analysis can be performed. Keep incrementally changing an independent input variable (e.g., customer order forecasts) for sensitivity analysis of the model’s dependent output (e.g. sales volume, mix and profits). It is a brute force trial-and-error approach to seek the “best” answer.

Prescriptive business analytics says, “Get out of the way. Let the computer calculate the best answer.” A few software vendors are now emerging in the marketplace to do this.

With predictive business analytics, the best and correct decisions can be made and organizational performance can be tightly monitored and continuously improved. Without predictive business analytics, an organization operates on gut feel and intuition; and optimization cannot even be in that organization’s vocabulary.

Article written by Gary Cokins
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