In this video, explore a case study with the goal of assessing the efficacy of new training models.
- [Darnell] Hi, my name is Darnell,…and I am the Chief Talent Officer at WearOne.…One of the hallmarks of our growth strategy…has been tremendous investments in a vast library…of training modules for our sales staff.…We are contemplating rolling out…new virtual training software designed to help implement…virtual reality training for our staff.…The idea is that through VR, we can simulate…real-life customer situations for our sales force.…However, the training modules are expensive,…as they require significant up-front cost…for equipment and tailored design.…
So I have been asked to assess the potential effectiveness…of these new training modules.…- We are often asked to forecast how a change in a policy,…or implementation of a new program,…will affect our bottom line.…An entire field of economics, called program evaluation,…has been developed to try and provide guidance…in just such a situation.…In this circumstance, we're going to demonstrate one way…our CTO can gauge the potential effectiveness…of this new, but more expensive, training option.…
Author
Released
1/29/2018- Qualitative vs. quantitative data
- Data analytics success stories
- Making predictions
- Asking the right questions
- Collecting data
- Understanding averages
- Sampling: pros and cons
- Forecasting
- Cause and effect
Skill Level Intermediate
Duration
Views
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Introduction
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Welcome1m 19s
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1. Data Analytics in the Business World
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Introduction to Wear One1m 17s
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Types of data1m 50s
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2. Predictive and Prescriptive Analytics
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Predictive analytics2m 14s
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Solution: Make predictions2m 19s
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Prescriptive analytics1m 57s
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3. Asking the Right Question
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Role of business acumen1m 39s
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4. Unlocking the Data Within
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Data collection issues2m 22s
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Case study 3: Explanation2m 57s
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5. Understanding Averages
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Case study 4: Explanation2m 58s
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Context is everything1m 12s
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6. Sampling
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Pros and cons2m 33s
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7. Cherry Picking
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What is cherry picking?2m 53s
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Case study 6: Revenue1m 8s
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Case study 6: Explanation2m 45s
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8. Forecasting
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Hurricane Matthew2m 33s
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Case study 7: Explanation2m 44s
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Issues to consider1m 5s
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9. Correlation versus Causation
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Cause and effect1m 52s
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Case study 8: Explanation3m 20s
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Causal questions37s
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Conclusion
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Next steps49s
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Video: Case study 7: Forecasting customer complaints