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Successfully developed a clustering model to deduce two distinct customer segments amongst the entire population of customers based on their distinct demographic details and behavioral characteristics.

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SayamAlt/Online-Retail-Customer-Segmentation

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Online-Retail-Customer-Segmentation

Context

Customer Segmentation

Customer segmentation is the process by which you divide your customers up based on common characteristics – such as demographics or behaviors, so you can market to those customers more effectively. These customer segmentation groups can also be used to begin discussions of building a marketing persona. This is because customer segmentation is typically used to inform a brand’s messaging, positioning and to improve how a business sells – so marketing personas need to be closely aligned to those customer segments in order to be effective.

Customer Segmentation

Customer segmentation is popular because it helps you market and sell more effectively. This is because you can develop a better understanding of your customers’ needs and desires. The business impact of doing this is even more important, and effective customer segmentation will help you to increase customer lifetime value. This means they will stay longer, and spend more. By better understanding the customer, and therefore being able to target them more effectively, you can drive greater loyalty.

Customer Segmentation

Data Set Information

This is a transnational data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail. The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers.

Attribute Information

Feature Description
InvoiceNo Invoice number. Nominal, a 6-digit integral number uniquely assigned to each transaction. If this code starts with letter 'c', it indicates a cancellation.
StockCode Product (item) code. Nominal, a 5-digit integral number uniquely assigned to each distinct product.
Description Product (item) name. Nominal.
Quantity The quantities of each product (item) per transaction. Numeric.
InvoiceDate Invoice Date and time. Numeric, the day and time when each transaction was generated.
UnitPrice Unit price. Numeric, Product price per unit in sterling.
CustomerID Customer number. Nominal, a 5-digit integral number uniquely assigned to each customer.
Country Country name. Nominal, the name of the country where each customer resides.

Relevant Papers

  • The evolution of direct, data and digital marketing, Richard Webber, Journal of Direct, Data and Digital Marketing Practice (2013) 14, 291–309.
  • Clustering Experiments on Big Transaction Data for Market Segmentation, Ashishkumar Singh, Grace Rumantir, Annie South, Blair Bethwaite, Proceedings of the 2014 International Conference on Big Data Science and Computing.
  • A decision-making framework for precision marketing, Zhen You, Yain-Whar Si, Defu Zhang, XiangXiang Zeng, Stephen C.H. Leung c, Tao Li, Expert Systems with Applications, 42 (2015) 3357–3367.

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Successfully developed a clustering model to deduce two distinct customer segments amongst the entire population of customers based on their distinct demographic details and behavioral characteristics.

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