Perform Customer Segmentation studies with Data Science
Building a customer classification model to improve bank decision-making process through customer prioritization for mutual fund subscription potential.
Data Science techniques sequentially applied to customer data harvested from the Lloyds Bank’s direct marketing campaign to improve customer identification & classification (KYC) and provide accuracy to segment-specific marketing efforts.
Recommendation analytics applied on the financial institute’s data by applying sophisticated algorithmic techniques to mine patterns and insights from the raw data on financial transactions, demographic factors, and scheme level features, to develop responsive cross-sell and up-sell strategies.
Clustering analysis applied on the entire data universe carried out to find natural groupings within the data and homogenize raw investor clouds into predictable groupings.
Unsupervised learning techniques, such as associations rule learning and clustering algorithms that make no assumptions about target data applied to allow the data mining algorithm to find associations & clusters in the data independent of any pre-defined objective.
Apply Data Science techniques to Customer Segmentation data to accurately and intuitively match the local demand for banking services to corresponding segments of the population determined by customer data classification characteristics.
Leverage big data analytics platforms for the analysis of areas such as growth in the net assets under management and customer acquisition & retention to improve prediction and optimization strategies and develop investor-specific recommendations
Promote Customer Centricity through Machine Learning