-Using K-Means and R Language.

-Selected dataset from kaggle link:

https://www.kaggle.com/imakash3011/customer-personality-analysis

-K-Means algorithm applied the elbow method to get the optimal k, which is equal to 7.

-To know what the error function used in the model, here we see “cluster means”:

• Plotting a graph of the clustered data:

• Justification of the clustered result :

Customer personality analysis helps a business to modify its product based on its target

customers from different types of customer segments. For example, instead of spending

money to market a new product to every customer in the company’s database, a

company can analyse which customer segment is most likely to buy the product and then

market the product only on that particular segment. So, these clusters appearing here

they perform clustering to summarize customer segments. So now we know who are more into buying products.

1. New & Old Customers with average Income and fairly average amount spent should be focused on

more, better advertising and deals should be provided to them.

2. If a new Non discounted expensive item will be up for sale ads should be targeted better to

customers with high spending nature & income.

3. Customers with low spending natures and low incomes should be targeted with flash deals and

discounts on essentials to keep them connected with the company.

4. Customers tend to spend & purchase more can be worked upon (Like more deals, better more

variable products etc.) to benefit the company.

5. Senior Customers should be connected with company in some or other ways.