Almost every organization analyses their huge amount of data
and gains insights to predict future events. They utilize various analytical
techniques like Predictive analytics to improve customer relationships and enhance business growth. It
is a statistical technique used to predict future outcomes by transforming past
data with the help of machine learning.
There are predictive analytics tools available that can help corporations use past data and
current data to predict future trends accurately. Take a look at a few
industries to understand how this could help them.
- Retail industries use this predictive analytics tool to
ascertain the right inventory and shipping schedule. It is also used to plan
the store layout.
- Airlines use it to fix prices based on the day & time
of travel,
the type of occasion, etc.
- Hospitality industries predict the occupancy on any given
day using past data. This also helps in setting the right pricing and maximizing
sales.
- Another interesting avenue for using this could be in law
enforcement agencies. Agencies could predict criminal behavior and can, in a
way, reduce the damage.
- Banks & Financial institutions use this to minimize
banking frauds, cyber-attacks, etc.
The applications are endless, and that is the reason why
this field is a hot topic at present. The basis for any predictive analytics
tool is the models. It is the foundation that encompasses the past and current
data to generate actionable insights. Some of the models which are typically
used are:
1. Customer lifetime value: The customer lifetime value model predicts the likely event of customers spending more on any
product/service in their lifetime
2. Customer segmentation model: Here, the model judges the
similarities of purchases and groups them together as segments. This could help
in better communication to the intended segment.
3. Predictive maintenance model: In a manufacturing plant,
this model can help the organization predict the exact maintenance schedule to
minimize breakdown.
4. Quality assurance model: In a typical manufacturing
setup, it becomes essential to maintain product quality. This model helps in
maintaining the quality of the product.
There are many techniques that the user could apply in the
model. Many of these functionalities are available in most of the predictive
analytics tools available in the market.
I. Decision tree: The decision tree is a statistical
technique that allows the users to take a better-informed decision. This uses a
schematic flow that looks tree-shaped and has the respective statistical
probability of each decision. The branches let the user know all the courses of
action and how one action leads to another.
II. Regression: Regression analysis is the most commonly used technique. Many industries use this. The model helps ascertain the relationship between a variable (dependent variable) with other variables (independent variable). This could be used to predict the asset values of stocks or commodities. Governments could also use this to measure the impact of education on society or the impact of access to drinking water on the economic growth of a community. The uses are endless, and one can get accurate results through this.
III. Neural networks: As the name suggests, these are
algorithms that help ascertain relationships within datasets by mimicking how
the animal/human brain works. The use of machine learning is significant here.
These are just glimpses for users to understand
the entire gamut of solutions that predictive analytics tools can provide.
Start with trusting the insights from the data and building the entire
decision-making machinery of the organization with that help. It is by no means
simple. It is best if the organization does a pilot at a certain aspect of the
business operation and then takes it across the organization.
