What is easier to sell? – Quora

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How Do I Create a Bigger Market for a Product? – Quora

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What is Seasonality?

Seasonality is the predictable fluctuation of sales over a year. 




Summer is the best season to sell ice cream from the back of a truck.



Why is seasonality important?

Because it tells planners when a target will be receptive to a brand’s message, which in turns helps their companies to correct and to optimise their supply chains.



Do Ad Blockers Threaten Publishers and Creators? – LinkedIn

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How much do 1,000,000 Facebook likes cost? – Quora

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What is Customer Satisfaction Research?

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The Future of Media & Advertising – on ADPRO LinkedIn Group

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We’ve Always Done it This Way! – CMO Group on LinkedIn

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What is a Marketing Budget?

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What are Purchase Triggers?   Recently updated !

Listen to this Post Purchase triggers are pieces of information that compel your target customers to act on the benefits your brand offers.    Purchase triggers can be used along the buying decision process, based […]

Will This Entrepreneur Find Advisers for Their New Venture?

Listen to this Post With our product being developed, we are looking for a good team of advisors – we need input, challenges and connections, how can I find them? – Clarity user   Please […]



Automated Medical Treatments

Listen to this Post I want to create a new wearable/portable healthcare device. What area of medicine should I focus on? I am seasoned in writing software and product design. I know healthcare is a […]

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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