Customer Success Manager, one would need to (to mention a few):
Manage customer’s intent (e.g. churn) to improve customer satisfaction
Identifying issues wherever they rise is the first step to resolving them
How do I help customers reach the right representative in a shorter space of time, i.e how to reduce call duration periods, repeat calls or call abandonment rates by unhappy callers
How to determine the right time and number to call to reach potential prospects
As product manager, one would try to create a platform/product to answer (to mention a few):
Customer 360 Degree View
Analyze and Understand Customer Pains
Predict the optimal approach to answers calls for higher customer satisfaction rate
Understand which factor which has the highest impact for increased call duration
The business problem, based on the need and understanding, need to be converted into an analytics problem, primarily, can be classified into three parts:
Descriptive analytics and
Predictive analytics
Prescriptive Analytics
Univariate analysis – Only one variable can be taken into consideration for analysis which includes both summary statistics and visualization.
Bivariate analysis – Two variables can be taken into consideration for analysis which includes both summary statistics and visualization.
Multivariate analysis: Multiple variables can be taken for correlation analysis which includes both summary statistics and visualization.
The predictive analysis is subdivided into two parts: Model building and Prediction
Model building: Dataset is to be imported first from the cloud and then respective algorithm has to be chosen based on the type of the response variable (whether it is continuous or categorical).
Algorithms used: Algorithms which have been used to build the models are – Random forest, Generalized linear modelling, XGBoost and Gradient boost. One may need to choose respective distribution family for the corresponding algorithm. Binomial, Multinomial and Gaussian are the families to be chosen. First two are applicable for GLM only. One can choose Gaussian for Random forest, Gradient boost, XGBoost and GLM as well.
Validation metrics and charts: R squared can be considered as the model metric and fit chart can be implemented for visualization when the response variable is a continuous numeric variable. AUC or area under the curve can be taken as validation metric along with corresponding confusion matrix (Heatmap) when the response is a categorical binary variable. Similarly for multinomial response, confusion matrix can be represented along with true positive rate as accuracy measure. After the model is built, the platform provides an option to save the model into cloud so that the model can be reused in the prediction.
Prediction: Datasets and the corresponding models have to be imported first from the cloud, which are already built during the model building. Now one can apply the saved model onto a completely new dataset. In this way, the model can be generalized. It also provides an opportunity to save the prediction result as well. All the saved data, the model name, name of the graphs etc,- everything can be found inside the ‘Workspace’ as separate transactions. This is a very useful feature to keep track the number operations that have been performed inside the platform for a particular user. Trend, Seasonality, Frequency and Anomaly features can also be implemented in the predictive analytics as well.
