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Marketing Analytics Insights Using Machine Learning
Using data science to dive into marketing analytics through customer segmentation and machine learning techniques
Author(s): Saniya Parveez, Roberto Iriondo
Many industry-leading companies are already using data science to address better decision-making and to improve their marketing analytics. With the expanded industry data, greater availability of resources, lower storage, and processing costs, an organization can now process large volumes of frequent, and granular data with the help of several data science techniques and obtain the leverage needed to create composite models, deliver crucial decision-making, and obtain essential consumer acumen with higher accuracy than ever before.
Using data science principles in marketing analytics is a determined, cost-effective, practical way for many companies to observe a customer’s behavior, journey and contribute toward a more customized experience in their decision-making processes.
In this article, we will be using machine learning to segment customers' data, specifically data clustering, PCA, and data standardization for large-scale analytics to dive into specific marketing insights with real-life data.
Segmentation of Customer Data
The segmentation of customer data is the process of ordering (segmenting) target customers into different groups based on demographic or behavioral data so that marketing plans can be tailored more precisely to each group. It is also a vital part of earmarking marketing sources properly because, by targeting particular customer groups, a higher return of investment (ROI) on our marketing efforts can be achieved.
