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A repository on running an A/B test to identify the impact on brand awareness created by running a personalized advertisement by SmartAd as opposed to a generic advertisement

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WanjohiWanjohi/A-Btesting_SmartAd

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A-Btesting_SmartAd

For the company SmartAd , the major source of revenue is from charging clients based on the user engagement each served automated advertisement receives.

Data & Hypothesis

"If you want to do good science, you should determine your hypotheses before looking at the data, you should clean your data without the hypotheses in mind, and you should keep in mind that p-values are not substitutes for common sense." -Joel Grus (Data Science from Scratch)

The experimental feature for this dataset is -being exposed to an ad from SmartAd- .

This divides the population into two groups:

  1. Those exposed to an ad by SmartAd(exposed)
  2. Those shown a dummy ad (control)

Our null hyoothesis (Ho) = the change in the advertisement made for the exposed group would result in no change in the conversion rate

Method

Evaluation Metric

Limitations and Assumptions

Insights

Conclusion

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A repository on running an A/B test to identify the impact on brand awareness created by running a personalized advertisement by SmartAd as opposed to a generic advertisement

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