Need to know the best course of action for your company? You need prescriptive analytics, an approach to analysis that mines data for actionable insights and helps you improve your company’s reach and ROI. Prescriptive analytics is more than predictive modeling; it gives valuable conditional outcomes and cause-and-effect insight. In this workshop, we’ll explore the logic of prescriptive analytics and use Excel and R to conduct simple “what-if” simulations, cross-lagged designs, optimization models, and robust quasi-experimental analyses. You’ll leave with a collection of tools that will help you intelligently choose the best path to find new value for your company.

1. Barton Poulson @ MADS 2018.
ANALYTICS.
PRESCRIPTIVE

2. Barton Poulson @ MADS 2018.
A hands-on introduction
to getting actionable
insight from your data.

3. @bartonpoulson
linkedin.com/in/bartonpoulson 71

4. 3
LITTLE
A
HISTORY.

5. 44WILLIAM JAMES.

6. 5WILLIAM JAMES.
My thinking is first
and last and always for
the sake of my doing.

7. 6WILLIAM JAMES.
In other words…
thinking is for doing.

8. 7BARTON POULSON.
IS FOR
DATA
DOING.

9. 8
VS. PREDICTIVE
PRESCRIPTIVE
ANALYTICS.

10. 99PREDICTIVE ANALYTICS.

11. 10PREDICTIVE ANALYTICS.
GOING TO
WHAT’S
HAPPEN?

12. 12PREDICTIVE ANALYTICS.
Models that use historical data
to estimate the likelihood of a
desired outcome… or maybe a
current or past outcome.

13. 13PREDICTIVE ANALYTICS.
Predictions can be very precise
& accurate if data is sufficiently
large & diverse.

14. 1414PREDICTIVE ANALYTICS.

15. 1515PRESCRIPTIVE ANALYTICS.

16. 16PRESCRIPTIVE ANALYTICS.
SHOULD
WHAT
YOU DO?

17. 17PRESCRIPTIVE ANALYTICS.
Models that use historical or
novel data to estimate the
causal relationship between an
event and a desired outcome.

18. 18NOT JUST “IF THIS, THEN THAT.”

19. 19
CAUSALITY.

20. First.
2020
Cause & effect correlated.
Second. Cause occurs before effect.
Third. No alternative explanations.
CAUSAL INFERENCE.

21. Construct
validity.
Measure
what you
mean to
measure.
Internal
validity.
Accurate
causal
inference.
2121
External
validity.
Generalize
to other
methods &
groups.
FORMS OF RESEARCH VALIDITY.

22. 33THREATS TO INTERNAL VALIDITY.
History, maturation, testing,
instrumentation, mortality,
regression, selection, diffusion,
compensatory equalization,
rivalry, & demoralization.

23. 34
ARE
THERE
SOLUTIONS.

24. 35THEORETICAL SOLUTION.
RCT.
THE

25. 36THEORETICAL SOLUTION.
CONTROLLED
RANDOMIZED
TRIAL.

26. 37THEORETICAL SOLUTION.
Recruit many participants,
randomly assign to carefully
manipulated conditions.
Repeat many times with
different methods & groups.

27. 38THEORETICAL SOLUTION.
RCTs aren’t always hard to do.
A/B testing for websites is an
easy, automated version of a
randomized experiment.

28. 3939THEORETICAL SOLUTION.

29. What-if
simulations.
Optimization
models.
4040
Cross-lagged
correlations.
Quasi-
experiments.
PRACTICAL SOLUTIONS.

30. 41PRACTICAL SOLUTIONS.
SIMULATIONS.
WHAT-IF

31. 4242WHAT-IF SIMULATIONS.

32. 43WHAT-IF SIMULATIONS.
Model an outcome and then
manipulate the input values to
see the effect on the outcome.

33. Scenarios.
Sets of
possible
values &
outcome.
Data tables.
Manipulate
values on
1 or 2
dimensions.
4444
Goal seek.
Iterate one
value to
reach a
set goal.
WHAT-IF SIMULATIONS.

34. 45WHAT-IF SIMULATIONS.
Great for heuristic values;
take the results as suggestions.
Easy to do in Excel.

35. 46PRACTICAL SOLUTIONS.
MODELS.
OPTIMIZATION

36. 4747OPTIMIZATION MODELS.

37. 48OPTIMIZATION MODELS.
Models that find the
distribution of time or
resources among several
alternatives that maximizes an
outcome given constraints.

38. One variable.
Setting price to
maximize
revenue.
Many variables.
Allocating
resources to
several projects
in different
markets.
4949OPTIMIZATION MODELS.

39. 50OPTIMIZATION MODELS.
Can do one-variable by hand,
multiple variable models in
Excel using the Solver add-in,
and complex models in R or
Python.

40. 51PRACTICAL SOLUTIONS.
LAGGED
CROSS-
CORRELATIONS.

41. 5252CROSS-LAGGED CORRELATIONS.

42. .5
60CROSS-LAGGED CORRELATIONS.
Analytics
in 2015
Revenue
in 2015
Analytics
in 2018
Revenue
in 2018
.2 .3

43. .3
61CROSS-LAGGED CORRELATIONS.
Analytics
in 2015
Revenue
in 2015
Analytics
in 2018
Revenue
in 2018
.2 .3

44. 62CROSS-LAGGED CORRELATIONS.
Can do simple models in Excel
and more sophisticated models
in R or Python using
specialized packages.

45. 63PRACTICAL SOLUTIONS.
EXPERIMENTS.
QUASI-

46. 6464QUASI-EXPERIMENTS.

47. 65QUASI-EXPERIMENTS.
Estimating causal effects
without random assignment
to conditions.

48. Data can come from:
~ Observational studies.
~ Historical sources.
~ Nonrandomized experiments.
66QUASI-EXPERIMENTS.

49. 67QUASI-EXPERIMENTS.
Regression discontinuity,
interrupted time-series,
propensity score matching,
& block entry regression.

50. 68QUASI-EXPERIMENTS.
Possible to do these in Excel,
but much easier using
specialized packages in R or
Python.

51. 69
IN EXCEL
TRY THEM
& R.

52. 70
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