An Analysis of the article
A Predictive Analytics Primer
by Thomas H. Davenport
An important question
first -
Examples of Predictive
Analysis in companies -
●
customer lifetime value (CLTV)
measure
●next best offer” or product
recommendation capability
●forecast of next quarter’s sales
●Use of digital marketing models
to determine what ad to place
on what publisher’s site
A predictive analytics primer
List the two most
(important/interesting/
informative)
insights from this ARTICLE ?
The first insight is -
Predictive Analysis is gaining
popularity and managers
need to know a few basics
along with past data, simple
statistical knowledge and
assumptions to keep up.
A predictive analytics primer
The second important insight
is the knowledge of where and
predictive analysis errs.
Lack of good data, exclusion
of key variables in
assumptions, change in
consumer behavior with time,
use of wrong statistical model
are some of the key reasons
for the same.
Why and How are these
insights relevant to a
manager in India?
Predictive Analyses can help
managers increase sales and
marketing. It'll give them an
idea about customer
preferences and how to
capitalize upon them.
A predictive analytics primer
Predictive Analyses are
already in use in several
multi-national companies.
Creating reliable customer
databases is a challenge.
Managers should come up
with ideas like Shopper card
etc used in Central,
Pantaloons to get an idea
about what their customers
buy.
Use of loyalty cards to build customer
preference databases :
In a diverse market like India,
predictive analyses will go a
long way in maximizing sales
if managers can correctly
survey and create these
models.
.
- PARUL VERMA

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A predictive analytics primer

  • 1. An Analysis of the article A Predictive Analytics Primer by Thomas H. Davenport
  • 3. Examples of Predictive Analysis in companies - ● customer lifetime value (CLTV) measure ●next best offer” or product recommendation capability ●forecast of next quarter’s sales ●Use of digital marketing models to determine what ad to place on what publisher’s site
  • 5. List the two most (important/interesting/ informative) insights from this ARTICLE ?
  • 6. The first insight is - Predictive Analysis is gaining popularity and managers need to know a few basics along with past data, simple statistical knowledge and assumptions to keep up.
  • 8. The second important insight is the knowledge of where and predictive analysis errs. Lack of good data, exclusion of key variables in assumptions, change in consumer behavior with time, use of wrong statistical model are some of the key reasons for the same.
  • 9. Why and How are these insights relevant to a manager in India?
  • 10. Predictive Analyses can help managers increase sales and marketing. It'll give them an idea about customer preferences and how to capitalize upon them.
  • 12. Predictive Analyses are already in use in several multi-national companies. Creating reliable customer databases is a challenge. Managers should come up with ideas like Shopper card etc used in Central, Pantaloons to get an idea about what their customers buy.
  • 13. Use of loyalty cards to build customer preference databases :
  • 14. In a diverse market like India, predictive analyses will go a long way in maximizing sales if managers can correctly survey and create these models.
  • 15. .