Customer Segmentation
What is customer segmentation?
The practice of dividing a company’s customers into groups of
individuals that are similar in specific ways relevant to marketing such as
age, gender, interests, and spending habits is nothing but Customer
Segmentation.
Customer segmentation is basically identifying key
differentiators that divide customers into groups that can be targeted. The
main goal of segmenting customers is to decide how to relate to customers in
each segment to maximize the value of each customer to the business.
Nowadays Companies want to know their customers better so that
they can provide useful customers with some goodies and also attract customers
to purchase or make some kind of business with the company. When Companies know
about customers and segment it into groups, it becomes easy for companies to
send customers special offers meant to encourage them to buy more products.
The goals of customer
segmentation are customer acquisition, customer retention, increasing customer
profitability, customer satisfaction, resource allocation by designing
marketing measures or programs and improving target marketing measures .
Customer segmentation also improves customer service and assists
in customer loyalty and retention.
Customer segmentation can also help to save waste of money on
marketing campaigns. As we will be knowing the customers we need to target.
About Machine Learning Customer Segmentation
Earlier Customer segmentation was a challenging and time-consuming
task. This is because for segmenting customers we need to perform hours of
manual poring on different tables and querying the data in hopes of finding
ways to group customers together. So to overcome this task, machine learning is
used to segment customers.
K means clustering is one of the most popular clustering
algorithms and usually the first thing practitioners apply when solving
clustering tasks to get an idea of the structure of the dataset. The goal of K
means is to group data points into distinct non-overlapping subgroups. One of
the major application of K means clustering is segmentation of customers to get
a better understanding of them which in turn could be used to increase the
revenue of the company.
The practice of dividing a company’s customers into groups of
individuals that are similar in specific ways relevant to marketing such as
age, gender, interests, and spending habits is nothing but Customer
Segmentation.
Customer segmentation is basically identifying key
differentiators that divide customers into groups that can be targeted. The
main goal of segmenting customers is to decide how to relate to customers in
each segment to maximize the value of each customer to the business.
Nowadays Companies want to know their customers better so that
they can provide useful customers with some goodies and also attract customers
to purchase or make some kind of business with the company. When Companies know
about customers and segment it into groups, it becomes easy for companies to
send customers special offers meant to encourage them to buy more products.
The goals of customer segmentation are customer acquisition, customer retention, increasing customer profitability, customer satisfaction, resource allocation by designing marketing measures or programs and improving target marketing measures .
Customer segmentation also improves customer service and assists
in customer loyalty and retention.
Customer segmentation can also help to save waste of money on
marketing campaigns. As we will be knowing the customers we need to target.
About Machine Learning Customer Segmentation
Earlier Customer segmentation was a challenging and time-consuming task. This is because for segmenting customers we need to perform hours of manual poring on different tables and querying the data in hopes of finding ways to group customers together. So to overcome this task, machine learning is used to segment customers.
K means clustering is one of the most popular clustering
algorithms and usually the first thing practitioners apply when solving
clustering tasks to get an idea of the structure of the dataset. The goal of K
means is to group data points into distinct non-overlapping subgroups. One of
the major application of K means clustering is segmentation of customers to get
a better understanding of them which in turn could be used to increase the
revenue of the company.
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