Predictive analytics uses past data to predict future outcomes. It analyzes historical data using statistical techniques like linear regression to identify patterns and correlations that can be used to develop models for predicting things like future sales, customer behavior, and product preferences. Effective predictive analytics requires clean, representative data and assumptions that still hold true over time as factors may change. Managers should understand the data, assumptions, and limitations of predictive analytics models to determine their relevance for decision making.

1. A PREDICTIVE
ANALYTICS PRIMER
- DATA SCIENCE IS FUN-TASTIC

2. What is predictive analytics?
• Predictive the future with the help of past data.
• Predictive analytics is not magic.! It is done with a lot of past
data.
• Statistical Wizardry.

3. This is what happens when predictive analytics is not used:

4. Best examples for the
application of the
Predictive analytics
technique:

5. Predicting when to provide
the next best offer

6. Predicting which product
will earn us the highest
revenue

7. 5%
10%
15%
22%
30%
28%
22%
32%
45%
60%
65%
55%
28%
38%
40%
48%
65% 65%
78%
85% 86% 85%
90%
85%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Sales Revenue
Forecast of next quarter
sales

8. What are the barrier for this prediction?
• Poor historical data
• Assumptions should hold good through out the prediction
• Data Collection through multiple channels when not streamlined will
end up being of no use
• It is extremely impossible to create a single warehouse for housing
all the data.

9. Uses of Statistics for prediction:
• Linear Regression
• Non-linear Regression
• Logistic Regression
• Generating Hypothesis
• Correlation

11. Predicting Models:
• Predictive models are extremely helpful in finding
out the likelihood of the happening.
• Assumptions made should hold good throughout
the prediction
• All the models are tool based formulas on which
the newly added data can be appended for easy
prediction
• Models come in handy when very huge amount of
data is being contemplated

13. Relevance for India Managers:
• Managers should employ predictability
techniques to foresee the future.
• Managers should be aware of the
assumptions being made while using
Predictive analytics techniques.
• Both managers and analysts should
continually monitor the world to see if
key factors involved in assumptions
might have changed over time.

15. Fundamental Questions Managers should keep in
mind while questioning the relevance:
• Can you tell me something about the source of data you used
in your analysis?
• Are you sure the sample data are representative of the
population?
• Are there any outliers in your data distribution? How did they
affect the results?
• What assumptions are behind your analysis?
• Are there any conditions that would make your assumptions
invalid?