Predictive Analysis

1. Predective Analysis

2. Hello!
I am Vikrant Narayan
Data Analytics enthusiast
You can find me at @vikrant.m.narayan@gmail.com
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3. “ The Holy Grail of marketing is to
proactively pounce upon every
individual customer opportunity by
predicting before hand who will
respond and pre-emptively
intervene each customer loss by
predicting who will defect
- Dr.Eric Siegel
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4. What does it mean?
● It means there is a way to predict the future
using data from the past.
● It is normally done with a lot of past data, a
little statistical wizardry, and some
important assumptions.
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5. Challenges
● Lack of good data is the most common
barrier to organizations seeking to employ
predictive analytics.
● It is really hard to create an accurate
database with unique IDs.
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7. The Statistics
● Regression analysis in its various forms is
the primary tool that organizations use for
predictive analytics.
● The analyst performs a regression analysis
to see just how correlated each attribute is
with every other attribute.
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8. The Statistics
● Using that regression equation, the analyst can then
use the regression coefficients to create a score
predicting the likelihood of an event occurring.
● It’s quite likely that the high scoring events will
happen.
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10. The Assumptions
● Every predictive model has its own
assumptions.
● The big assumption in predictive analytics is
that the future will continue to be like the
past.
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11. The Assumptions
● It is based on the notion that people establish strong
patterns of behaviour that they usually keep up over
time.
● However, the most common reason for invalidation
of assumptions is time.
● The assumptions made have to be relevant to the
contemporary situation.
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13. Relevance to managers
● Managers will feel more comfortable working with
and communicating with others in your organization
about the results and recommendations from
predictive analytics.
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Thank You!