Business and marketing professionals are in a constant hunt for the potential prospects and they invest time, money and effort in filtering to arrive at the potential prospects with high chances of conversion. This reduces cost of acquisition of customers and increases profitability.
Look-alike modeling is an effective targeting system that reduces your investments required to acquire new customers and helps your business to expand with a stable business model.
In this case study, we present a machine learning based lookalike model for a bank to target prospects for investing with the bank. This was done based on previous campaign data. We gave details of sequence of steps required for building the model like analysis of various data imbalance handling techniques, modelling methodology, validation and interpretation of results. The final model pipeline has performed well over the existing targeting system in use giving a 450% improvement.
Our Work
Industry
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Industry
Function
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Function
Optimizing Marketing Spend with Marketing Mix Modeling
Using Statistical Modeling and Empirical Methods
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Principal, Benjamin Realty Advisors
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CFO at Pearl Hospitality
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Principal at Nomenclature Inc.
