A two-stage business analytics approach to perform behavioural and geographic customer segmentation using e-commerce delivery data

ABSTRACT

Customer segmentation is considered the cornerstone for personalisation, target advertising, and promotion assisting both researchers and practitioners to enhance customers’ buying behaviour understanding. Pertinent literature mainly exploits one distinct segmentation type such as behavioural to segment customers solely under one lens. We develop a two-stage business analytics approach that introduces a combination of geographic and behavioural customer segmentation. Our approach is based on data mining and machine learning techniques. We evaluate the suggested approach using e-commerce home delivery data. First, we segment customers based on the products ordered to identify behavioural customer segments with similar product preferences. Then, we perform geographic segmentation. By applying the approach developed we also identify challenges that affect the segmentation process and results. The suggested approach can serve as a guide to business analysts to understand which are the steps that they should perform when analysing similar datasets. Whereas its results may assist third-party logistics (3PL) companies, retailers, and brands in supporting decision making.

KEYWORDS:

• Customer segmentation
• geographic segmentation
• e-commerce
• data mining
• business analytics
• home delivery

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data not available due to legal restrictions

Additional information

Funding

This work was partially funded by by the Horizon 2020 [project Transforming Transport, Grant agreement ID: 731932]; Science Foundation Ireland [Science Foundation Ireland grant 13/RC/2094_2] and Action 2: Support for Postdoctoral Researchers – Year 2017 -2018 – AUEB.