This document discusses predictive analytics and its importance for managers. It explains that predictive analysis uses customer data collected through digital marketing to predict future customer wants and needs. This allows companies to determine customer lifetime value, recommend the best products, and accurately forecast demand. The document outlines that while data scientists build predictive models, managers must communicate the meaning and implications of the analysis. It stresses that both making assumptions and checking their validity are essential parts of creating an effective predictive model.

1. Predict the future!
A Predictive Analytics
Primer
Thomas H. Davenport

2. Be a “Bhavisyavani” (The future predictor)
 What customers see or shop, all these data are collected and
analysed to predict what they will want in future from you.

3. Learning 1: Customer lifetime value (CLTV)
 How much customer will buy from the company over a time
 Do you have the next best thing?
 Do your website recommends the best product offering?
This questions are answered
by using Predictive Analysis.

4. Learning 2: Digital Marketing is at heart
Majority of the data are collected through
online customer touchpoints.
The manager needs to decide where will
be the maximum customers attracted and
data can be easily gathered.
Digital marketing strategies are important
in very useful for current as well as future
market developments.

5. Learning 3: Forecasting
Data gathered through
digital marketing is also
useful for production
planning.
Production department can
forecast demand with more
accuracy and provides
opportunity to fulfil
customer’s wish.

6. Manager Vs Data Scientist
A manager’s work is to
communicate data and
make meaningful
interpretation of analysis
presented by data science
team
To form hypothesis, gather
data and maintain properly,
a data scientist is working
around the area of statistics
and predictive analysis
modelling.

7. Statistics isn’t
wizardry!
 The myth in managers
that they don’t have to
crunch number or data
will speak for themselves.
 Managers have to
communicate right
meaning of right data at
right time.

8. Approach
to the
future
A Good Manager
Statistics
Assumptions
Data

9. The Data
What
customers
are
buying?
Most common barrier
for good predictive
analyse.

10. The Statistics Regression
Modelling
Analysis
Check
Validity
Set
hypothesis
Know which models to
use. Set a hypothesis.
Create a predictive
model.

11. The
Assumptions
Assumptions
in model
Check
validity
Modify
Re-run
modelling
Applicability
of model
A model without stated
assumptions is no
model at all!
The one clause that
makes model dynamic
with time

12. When any one of them fails, you fail!
Everyone assumed housing
price will go up
No one assumed
that it could come
down

13. When all succeeds, you succeed!

14. The Ultimate Checklist
Source of data
Data size and
characteristics
OutliersAssumptions
Conditions violation
assumptions

15. Data analysis is a spiral.
More you analyse, more
you drawn deep to the
core.
Abhishek Rana
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