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What is a Sensitivity Analysis?

As far as marketers are concerned, sensitivity analyses help us to predict the financial effects of changes in the customer experience.

Video courtesy of financewithexcel

Pairs well with: Uncertainty Analysis



Marketing Applications of Sensitivity Analyses.

  • Effective and efficient allocation of resources
    • Improve data management
      • Identify critical drivers 
      • Compare alternative models

Main Flaws of Sensitivity Analyses.

  1. Analyses are based on historic (old) data
  2. Accuracy depends on computing power
  3. Expectations are subjective data interpretations
  4. Correlations are not always evident
  5. Unknown synergistic interactions



Maximise your computing power and reduce inaccuracies.

1. Design the sample.

2. Survey the sample at a number of points in its input space

2. Variable Selection or Screening.

Identify which input variables are contributing significantly to the output. Screening is often used to weed out low-relevance variables before the main analyses begin.


4. Select an emulator.

Emulators are mathematical functions that approximate the behaviour of a data model, using less computer power than the model itself.

Educational videos:



  • What will the effect be if these variables deviate from expectations.
  • Which variables would cause the largest deviations?


  • Future rates
    • Taxes
    • Interest
    • Unemployment
    • Operating expenses
    • Accidents


5. Train the emulator with data from # 2

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