Enhencer is a visual data analytics and predictive intelligence platform focuses on the business intelligence and data visualization concepts after the emergence and popularity of the machine learning methods. With Enhencer You no longer need out-of-fashion summary tables and plots, prediction algorithms those are not always accurate, and bulky software to manage prediction processes. Enhencer generates predictive functions that you can use in your action plans by clearly showing the causalities in the data. Enhencer is also an analytical presentation tool which provides you with an elegant and interactive dashboard to display reports.
There is a live demo of Enhencer online you can try on your own. If you have any question or a request for demo, you can fill
the request for a demo form here or
email Enhencer. For more info, you can also refer to Enhencer Predictive Intelligence
Online Help.
# Open Enhencer's
Customer Satisfaction Data Analytics live demo.
Enhencer screen is divided into 3 panels.
Left Panel in the analyze screen contains all the variables from the data. The green highlighted variable indicated the particular variable is selected as “Target Variable” for the analyzes. You can simply change the target variable by simply clicking a different variable.
Currently, Customer Status variable is selected. This variable is categorical and has two possible values : Retention and Churn. As you can see, overall customer retention is 54.2% and churn is 45.9%.
In order to obtain deeper insights from the data, users need to use the “
Enhance” task. Behind the curtain Enhencer used Machine Learning Algorithms to fetch the deeper insights from the data.
Enhance task can be performed by clicking the “Enhance” button. Enhance lets you to use the machine learning algorithm to dig out the dapper insights from the data.
# Click
Enhance button.
Upon clicking the “Enhance” button a new window will pop up named “
Select”. You need to select the relevant variables to observe the effects of the selected variables on the target variable.
Blue highlighted variables are the ones that are currently selected for the Enhance task. The deselected variables are represented with the non-highlighted ones.
#Click
ALL in Select window to select all variables. Click
OK at the bottom of Select Window.
Right side of the analyze screen is reserved for hierarchy view. When “Enhance” is active the tree view is presented to the users. Here Relations and effects of other variables with the target variable can be observed.
Leaf represents a segment and the features above the leaf represents the path to that segment.
Tree View branches from the most important variable effecting customer status to least. Currently, overall is selected which is 4,000 data point size. The first leaf shows the online usage and the split is 14.9895. As you can see, 26.1% of the customers have less than 14.9 hours online usage and their churn is the highest at 84.9%.
# Select Retention from Target Choice to let Enhencer perform segmentation.
Enhencer will create the segments with the highest lift at the top.
Segments is a powerful tool that shows segments of the target variable and their features. When “Enhance” is active segments can be accessed by simply clicking the “Segments” button. If target variable is categorical like this example then in order to access the segments the user needs to choose one of the target choices form the “Target Choice” drop down menu. For any other variable type, it can be accessed directly.
Right side of the analyze screen in this case shows all the segments available for the target variable. Segments are sorted from highest gain to the lowest gain.
# Click Segment 1 to see its properties. As you can see below, this segment has the highest lift.
The proportion of the target choice in the selected segment compared to the overall population is represented by “
Gain & Lift” measures. For this instance, in segment number 1 among 271 customers 90.8% of the customers are retained which is 1.68 times (67.6%) higher than the overall population with 54.2% retention rate.
In Segment 1, you can see the highest retention segment. These are people with 14.9 hours or more online usage, primary social network Facebook, and so on.
You can see which variables effect the target variable most using Effects View.
# Click
Effects button.
Effects is a powerful tool that shows the variables that effects the target variable the most. This means among hundreds of different factors from the data these are the ones to affect the target variable the most.
When “Enhance” is active effects can be accessed by simply clicking the “Effects” button. The right side of the screen shows the effects of other variables. The variables are sorted from “High Dependency” to the “Low Dependency”.
When “Effects” is active users can also take benefit of prediction task. In Enhencer you can either predict by simulation or by uploading a new dataset.
In order to take benefit of the “Predict” task user must have [A] “Enhance” active and be on the [B] “Effects” tab.
Effects is a powerful tool that shows the variables that effects the target variable the most. This means among many different factors from the data these are the ones to affect the target variable the most. Moreover, these are the variables that the user can use to predict new cases either by simulating a case from the variable list in the right or uploading a completely new data.
If the target variable is categorical as in this case, Target Choice should be selected. In our example, Retention is already selected.
Prediction can be of two types. One is the classical test data method, where prediction scores are observed for a new dataset after the model is trained. Another is to simulate a very specific case just to observe how the data scores might change or how specific variables contributes to the prediction scores. This example is the latter one, simulating a certain case.
# Select Online Usage above 20 hours and Facebook as the only primary social media account. Click Predict.
Enhencer predicts that you will have 75% chance to retain this type of users.
