Nowadays, the abundance of data and the advances in machine learning and big data applications reduce the need for top-down segmentation of customers. However, smart customer segmentation based on commonalities help companies better address customer needs to provide the right experience and divide resources efficiently.
Market segmentation enables businesses to reach the consumers that would most benefit from or enjoy the company’s products or services. Companies can either concentrate their marketing on the most effective channels or reach out to dissatisfied former customers.
A good model should use data collected from customer registration, transaction information, and previously completed qualitative or quantitative studies. In particular, customer registration and past transactions data should be inspected through the lens of a company’s specific product. For example, age and gender is very important data for a clothing company, but life stage less so.
Since segmentation is an issue of demographics, census data and public records are valuable sources of data. However, private surveys and questionnaires also contain important information.
Additional external data includes cookies and web analytics tools. Academic studies on demographics and behavioral also help immensely.
The main challenges of customer segmentation are the dynamic nature of some segmentation characteristics (e.g. place of residence, life stage, etc.) and the difficulties of managing the large amount of data the model uses. An additional difficulty lies in assigning segments; many data providers offer pre-made segments but these may not suit your customer base.
Finally, even after useful customer segments have been assigned, selecting the best marketing campaign for them remains an issue.
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