How to ace cold calling with Machine Learning

However, with Machine Learning and enough data it might be possible to understand the factors behind successful calls which can then be used to better qualify prospects and tailor the content of the calls.In this article we are going to build a machine learning model for predicting the success of car insurance cold calls using AuDaS, the Automated Data Science team in a box..AuDaS will also use OPTaaS, Mind Foundry’s proprietary Bayesian Optimiser to efficiently navigate the space of possible data science pipelines.AuDaS provides the full transparency of the best found Data Science pipeline (models, parameter values,…) as well as score metrics.Once the runs are complete, we can view the performance of the model on the 10% hold out and we are reassured to see that the classification accuracy is pretty good (72.4%) and that the model health is good!AuDaS Model Health DiagnosticInterpreting the modelThe feature relevance of our model indicates that the yearly average balance of the customer’s bank account seems to have the strongest impact on them purchasing the car insurance or not, followed by their age and the time of the cold call.AuDaS feature relevanceIn other words, if Luke was 40, with a high balance on his account there would be a strong chance of him taking the cold callers offer!This model can then be put into production automatically by AuDaS through a RESTful API.This means that the cold callers could use the machine learning model trained by AuDaS to prioritise the customers they should call which will help them increase the success of the marketing campaign..Please don’t hesitate to reach out by email or LinkedIn should you wish to try AuDaS.AuDaSAuDaS is an Automated Data Science platform developed by Mind Foundry that provides a robust framework for building end-to-end Machine Learning solutions (Classification, Regression, Clustering and soon Time Series) .. More details

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