Clientrator

A comprehensive customer analytics tool that provides a predictive analytics solution at customer level.


Request a Demo
...

Clientrator has been made to meet your expectations


Lead Analytics

Acts as a report card for your sales and marketing efforts, by tracking lead performance

Market Basket Analysis

Data mining technique used to increase sales by better understanding customer purchasing patterns

...

USE CASE & BENEFITS

Cross-sell & Up-sell

It helps you to determine the group of customers who can be targeted for cross-sell and up-sell based on their past history and purchasing patterns.

Retention Analytics

The process of analyzing user metrics to understand how and why customers churn. Retention analysis is key to gain insights on how to maintain a profitable customer base by improving retention and new user acquisition rates.

Customer Profitability Analytics

It allows businesses and lenders to determine the profitability of each customer or segments of customers, by attributing profits and costs to each customer separately.

CLIENTRATOR MODULES

Summarizes the data uploaded by displaying summary statistics like mean, median, mode as well as the number of outliers and missing values in every feature in the data.

A section where you can create graphs of any kind using any feature in the data and visualize the relationships between the features. This helps in doing some exploratory data analysis before building a model.

In this section, the user can find out which input features are highly correlated to the output feature. It also shows the correlation between all the features in the data. This section helps the user in deciding what features should the model be built on.

In this section, the user can select and build the model of his choice from scratch by entering values of parameters or by applying other methods that improve the accuracy of the model. This section also helps the user in decidng what model to select by comparing the results of the models with each other.

In this section, the user can upload data and run the model created in the build model section to predict the output feature for that data. This section also analyzes the relationship between the predicted feature and features in the data uploaded as well as the accuracy of the predictions.

Time to give it a try

Time to give it a try


As we told you, there’s no substitute for hands-on experience. Try Clientratorfor free




Request a Demo Try it on your own