\n
\n <\/a>
\n How to identify best customers using Data Science ?
\n<\/h3>\nLet us learn how businesses find their top customers.<\/em><\/p>\n![\"Business](\"https:\/\/res.cloudinary.com\/practicaldev\/image\/fetch\/s--j_aeSY9M--\/c_limit%2Cf_auto%2Cfl_progressive%2Cq_auto%2Cw_880\/https:\/\/cdn-images-1.medium.com\/max\/2400\/1%2AusmDe8fIhaAC6ndM2QJcfQ.jpeg\")
<\/a><\/p>\nBusiness Analytics is most important part of many businesses around the world. E-commerce giants, Ride-hailing companies, Product or Service based companies are extensively using Analytics to generate KPIs for their business. Allocation of resources, forecasting of supply\/demand, A\/B Testing of features etc, everything is data-driven.<\/p>\n
Customer Analytics is also an important part of Business Analytics. When dealing with Customer Analytics, Segmentation is important part of this. Companies are using this to identify best, most-paying, loyal and engaging customers. Such customers are definitely an asset for businesses and their promoters too. That\u2019s why businesses want to serve them better.<\/p>\n
![\"Customer](\"https:\/\/res.cloudinary.com\/practicaldev\/image\/fetch\/s--9Fukvv8l--\/c_limit%2Cf_auto%2Cfl_progressive%2Cq_auto%2Cw_880\/https:\/\/cdn-images-1.medium.com\/max\/2000\/1%2ALqG9ESNbBC0NU6LltHd8rg.jpeg\")
<\/a><\/p>\nCustomer Segmentation is one of the common use-cases of Data Science. Businesses use it to generate KPIs concerning customers. <\/p>\n
Let us dive into it.<\/p>\n
So, by definition Customer Segmentation<\/strong> is :<\/p>\n\u2018The process of dividing customers into appropriate classes or segments which may be used to provide better services to them ultimately meeting the business goals of an organization\u2019.<\/em><\/p>\n\n
\n <\/a>
\n Who are best customers ?
\n<\/h2>\nTo understand this better, let us investigate who can be the best customers for a business. <\/p>\n
![\"Main](\"https:\/\/res.cloudinary.com\/practicaldev\/image\/fetch\/s--t_BoZtzu--\/c_limit%2Cf_auto%2Cfl_progressive%2Cq_auto%2Cw_880\/https:\/\/cdn-images-1.medium.com\/max\/2960\/1%2Ay9_bmDX9UBHJoQT4UfKrdg.jpeg\")
<\/a><\/p>\nFirst thing which comes to our mind when dealing with businesses is money. So, the customers which pay a business more are more \u2018dear\u2019 to them. They may want to keep their interests in mind when making certain policies, giving promotions or discounts etc. <\/p>\n
Second type of customers is that which are frequent visitors of that business. A business will also need to align itself with those type of customers.<\/p>\n
Third one can be customers which have recently started interacting with a business. May be they are new to have exposure to that kind of business so, it will be beneficial to convert them into a loyal customer for a business. It might be likely that they have come from another competitor , not satisfied with their services. So, it may be a plus point for this business to win the heart of that customer and ultimately win their loyalty too.<\/p>\n
Also, those customers which are more likely to recommend a business are also important for them. <\/p>\n
But, there\u2019s a point to notice about in the above discussion.<\/p>\n
What\u2019s that point?<\/em><\/strong><\/p>\nPoint is that if you create \u2018value\u2019 for your customer then, you can automatically gain all above kind of gestures from your customer. If your customer gets value by utilizing your services or using your product, then they are definitely going to spend more on your services or product(s). They will be more frequent visitors. Also they will refer more people to you and ultimately your business will get more recent visits by new customers too.<\/p>\n
\n
\n <\/a>
\n Use of Data Science :
\n<\/h2>\n![\"Data](\"https:\/\/res.cloudinary.com\/practicaldev\/image\/fetch\/s--GVlGPals--\/c_limit%2Cf_auto%2Cfl_progressive%2Cq_auto%2Cw_880\/https:\/\/cdn-images-1.medium.com\/max\/2000\/1%2ApRKSWEz0KBagCbi-VJXIAQ.png\")
<\/a><\/p>\nThere are many methods to find out best customers in Data Science. Let us discuss most popular out of them :<\/p>\n
\n
\n <\/a>
\n i ) K-Means Clustering :
\n<\/h2>\nBy K-Means Clustering, we cluster customers into different segments. It depends on different features which are present in a dataset. You can segment customers on the basis of those features from most loyal to less loyal.<\/p>\n
\n
\n <\/a>
\n ii ) RFM Matrix Method :
\n<\/h2>\nRFM Method is used for analyzing customer value. In this method, we focus on Recency, Frequency and Monetary value. Recency means which customers have joined recently, while Frequency means which customers are more frequent visitors. Monetary value refers to those customers who are spending more on a given business\u2019 services or products.<\/p>\n
\n
\n <\/a>
\n iii ) CLV Score :
\n<\/h2>\nCustomer Lifetime Value is the metric used to determine how much a customer is going to benefit you in monetary terms throughout his tenure with a business or service. Simple formula for this is :<\/p>\n
CLV = Lifetime Customer Revenue – Lifetime Customer Costs<\/em><\/p>\n\n
\n <\/a>
\n iv ) NPS Score :
\n<\/h2>\nNPS Score measures customer experience and predicts business growth. It\u2019s full form is Net Promoter Score. Actually it\u2019s an index ranging from -100 to +100 that measures the willingness of a customer to recommend a company\u2019s services or products to others.<\/p>\n
\n
\n <\/a>
\n v ) Other methods :
\n<\/h2>\nThere are some other methods as well in the industry which are practiced commonly. These include Geographic , Demographic, Psychographic and Situational Segmentation. Geographic Segmentation includes segmentation based on geographical location. Demographic Segmentation focuses on particular age groups, races or people with certain backgrounds. Psychographic segmentation segments those customers who are like innovation, and different behaviors depicting their commitment to the brand or service. Situational segmentation focuses on segmenting customers based on different situations. For example, In monsoon season in a particular area, An E-commerce company can show rain jackets, umbrellas and other related items to people living there.<\/p>\n
\n
\n <\/a>
\n Conclusion :
\n<\/h2>\nIn this article, I discussed some short intro of how Business Analytics works at companies. In next articles, I will be discussing implementation of all above discussed methods in Python.<\/p>\n
Business Analytics is most important part of many businesses around the world. E-commerce giants, Ride-hailing companies, Product or Service based companies are extensively using Analytics to generate KPIs for their business. Allocation of resources, forecasting of supply\/demand, A\/B Testing of features etc, everything is data-driven.<\/p>\n Customer Analytics is also an important part of Business Analytics. When dealing with Customer Analytics, Segmentation is important part of this. Companies are using this to identify best, most-paying, loyal and engaging customers. Such customers are definitely an asset for businesses and their promoters too. That\u2019s why businesses want to serve them better.<\/p>\n Customer Segmentation is one of the common use-cases of Data Science. Businesses use it to generate KPIs concerning customers. <\/p>\n Let us dive into it.<\/p>\n So, by definition Customer Segmentation<\/strong> is :<\/p>\n \u2018The process of dividing customers into appropriate classes or segments which may be used to provide better services to them ultimately meeting the business goals of an organization\u2019.<\/em><\/p>\n To understand this better, let us investigate who can be the best customers for a business. <\/p>\n First thing which comes to our mind when dealing with businesses is money. So, the customers which pay a business more are more \u2018dear\u2019 to them. They may want to keep their interests in mind when making certain policies, giving promotions or discounts etc. <\/p>\n Second type of customers is that which are frequent visitors of that business. A business will also need to align itself with those type of customers.<\/p>\n Third one can be customers which have recently started interacting with a business. May be they are new to have exposure to that kind of business so, it will be beneficial to convert them into a loyal customer for a business. It might be likely that they have come from another competitor , not satisfied with their services. So, it may be a plus point for this business to win the heart of that customer and ultimately win their loyalty too.<\/p>\n Also, those customers which are more likely to recommend a business are also important for them. <\/p>\n But, there\u2019s a point to notice about in the above discussion.<\/p>\n What\u2019s that point?<\/em><\/strong><\/p>\n Point is that if you create \u2018value\u2019 for your customer then, you can automatically gain all above kind of gestures from your customer. If your customer gets value by utilizing your services or using your product, then they are definitely going to spend more on your services or product(s). They will be more frequent visitors. Also they will refer more people to you and ultimately your business will get more recent visits by new customers too.<\/p>\n There are many methods to find out best customers in Data Science. Let us discuss most popular out of them :<\/p>\n By K-Means Clustering, we cluster customers into different segments. It depends on different features which are present in a dataset. You can segment customers on the basis of those features from most loyal to less loyal.<\/p>\n RFM Method is used for analyzing customer value. In this method, we focus on Recency, Frequency and Monetary value. Recency means which customers have joined recently, while Frequency means which customers are more frequent visitors. Monetary value refers to those customers who are spending more on a given business\u2019 services or products.<\/p>\n Customer Lifetime Value is the metric used to determine how much a customer is going to benefit you in monetary terms throughout his tenure with a business or service. Simple formula for this is :<\/p>\n CLV = Lifetime Customer Revenue – Lifetime Customer Costs<\/em><\/p>\n NPS Score measures customer experience and predicts business growth. It\u2019s full form is Net Promoter Score. Actually it\u2019s an index ranging from -100 to +100 that measures the willingness of a customer to recommend a company\u2019s services or products to others.<\/p>\n There are some other methods as well in the industry which are practiced commonly. These include Geographic , Demographic, Psychographic and Situational Segmentation. Geographic Segmentation includes segmentation based on geographical location. Demographic Segmentation focuses on particular age groups, races or people with certain backgrounds. Psychographic segmentation segments those customers who are like innovation, and different behaviors depicting their commitment to the brand or service. Situational segmentation focuses on segmenting customers based on different situations. For example, In monsoon season in a particular area, An E-commerce company can show rain jackets, umbrellas and other related items to people living there.<\/p>\n In this article, I discussed some short intro of how Business Analytics works at companies. In next articles, I will be discussing implementation of all above discussed methods in Python.<\/p>\n<\/a><\/p>\n<\/a><\/p>\n\n
\n <\/a>
\n Who are best customers ?
\n<\/h2>\n<\/a><\/p>\n\n
\n <\/a>
\n Use of Data Science :
\n<\/h2>\n<\/a><\/p>\n\n
\n <\/a>
\n i ) K-Means Clustering :
\n<\/h2>\n\n
\n <\/a>
\n ii ) RFM Matrix Method :
\n<\/h2>\n\n
\n <\/a>
\n iii ) CLV Score :
\n<\/h2>\n\n
\n <\/a>
\n iv ) NPS Score :
\n<\/h2>\n\n
\n <\/a>
\n v ) Other methods :
\n<\/h2>\n\n
\n <\/a>
\n Conclusion :
\n<\/h2>\n