Insurance Claims Data Analysis and Analytics

This Insurance Claims Data Analysis Dashboard includes Motor Insurance Claims Data based in the UK. The application runs on Qlik Sense Associative Engine which allows users to perform in depth analysis of the claims payments across a wide range of factors including time, location and claim type. In the application you can see how poor data quality and significant outliers can have a direct impact on the performance results of the company.

The Insurance Claims Analytics video below shows how you can use business intelligence to analyze insurance claims data to identify claims fraud, unusual transactions and data quality issues. You can try the Insurance Claims Data Analysis Dashboard yourself here in the demo page.

One of the issues insurance companies face is fraud. Fraud attempts have seen a drastic increase in recent years with the increase in online businesses thus making fraud detection more important than ever. Despite efforts on the part of the affected institutions, hundreds of millions of dollars are lost to fraud each year and quite likely to increase as well. Just like a needle in a haystack relatively few cases show fraud in a large population. Finding these is not just tricky but sometimes impossible too.

A key weapon for insurers in identifying these fraud perpetrators is the analysis of data. In a classical data analysis scenario, insurers need to be able to search for associations in data between similar types of claims, in similar locations, including something unique like a mobile phone number. These associations between the data can lead to a significant increase in identifying the groups of people that commit these types of fraud. This is exactly where a data visualization solution like Qlik Sense can play an important role in this activity.  Qlik Sense can help Insurance Fraud Analysts identify trends, patterns and examples of fraudulent Whiplash claims.

One step further can be predicting which claims are fraud cases using predictive analytics. Predictive analytics do not require insurers to go through the relationships in their data manually and try to find out the cases where fraud probability is high. This task can be tedious if there are many parameters in the claims data but can easily be handled by a predictive model.

For example below, you can see how an automated machine learning tool (Enhencer in the below case) can help to identify fraud cases.

What is Automated Machine Learning (AutoML)?

Machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) from data, without being explicitly programmed. This trained machine learning model can later be used to predict the probability of a future event within an acceptable reliability.

For example, suppose that you want to predict the customer churn for a customer. You can use your historic customer data with customers you kept and lost to train a model using machine learning techniques. Later, you can feed a new customer info (or a set of new customer data) to the model and predict their probability to be lost.

This is a two step process. First you train a model and then you use this trained model to predict (or score in machine learning terms) new data.

Machine Learning training and scoring

Although it looks straightforward, traditional machine learning process requires some intermediary steps.  A typical machine learning application, practitioners must apply the appropriate data pre-processing, feature engineering, feature extraction, and feature selection methods that make the dataset amenable for machine learning. Following those preprocessing steps, practitioners must then perform algorithm selection and hyperparameter optimization to maximize the predictive performance of their final machine learning model.

Traditional machine learning process.

Many of these steps are often beyond the abilities of non-experts and automated machine learning (AutoML) addresses this problem. It is the process of automating the end-to-end process of applying machine learning to real-world problems.

As Janakiram MSV writes in Forbes (Why AutoML Is Set To Become The Future Of Artificial Intelligence):

“AutoML focuses on two aspects – Data acquisition and prediction. All the steps that take place in between these two phases will be abstracted by the AutoML platform. Essentially, users bring their own dataset, identify the labels, and push a button to generate a thoroughly trained and optimized model that's ready to predict.”

Automated Machine Learning

For example, take a look at the predictive fraud analytics for insurance sector example below. The AutoML application used here is cloud based Enhencer Predictive Story Teller. After integrating claims data to the predictive system, predictive model creation is done with a few clicks without expertise in data science and statistics (you can watch a more detailed introduction and demo here : Predictive Analytics and Machine Learning Introduction - Customer Propensity Example)

Predictive Analytics and Machine Learning Webinar - Customer Propensity Example

Beyond business intelligence, where you can slice and dice your historic data to understand what happened and why, predictive analytics will tell you what will happen and make powerful predictions about the future. This will replace intuition based decisions with data driven decision making process. You will also see how powerful machine learning algorithms are open to business users now thanks to self service advanced data analytics tools like Enhencer.

 Enhencer enables business users to use advanced analytics and machine learning on their data without data science knowledge. This allows them to create predictive models on certain metrics within minutes and then predict individual records based on the model (model can be exported to SQL or your favorite BI solution like Microsoft Power BI, Tableau or Qlik).


 For more information please visit https://www.enhencer.com/

Predictive Fraud Analysis - Enhencer (self service data analytics)

Fraud attempts have seen a drastic increase in recent years with the increase in online businesses thus making fraud detection more important than ever. Despite efforts on the part of the affected institutions, hundreds of millions of dollars are lost to fraud each year and quite likely to increase as well. Just like a needle in a haystack relatively few cases show fraud in a large population. Finding these is not just tricky but sometimes impossible too.

Predictive analytics can make fraud detection very easy and help you to seek out the needle in the haystack in no time.

• Using the dynamic segmentation, you can find the customer segments with the highest fraud rates.
• Predict the fraud activity
• You can learn the features of the customer segment likely to become a fraud and take actions to reduce the frauds.

Fast & Powerful Data Analysis

You don't need expensive and complex data science resources to benefit from advanced data analytics. Enhencer enables you to import, analyze and predict in less than few minutes requiring no coding or statistical expertise at all. Enhencer puts the data analysis in a nutshell with 4 steps.

1 - Connect/Upload Data
Upload data of many formats or Import data from various sources like Typeform, Survey Monkey, Google Form and many more in a matter of seconds.

2 - Instant Actionable Insights
Bring down the analysis time from days to minutes as Enhencer empowers the data analysis with decision tree algorithm in an accessible manner for everyone.

3 - Powerful Segmentation
Enhencer is designed with machine learning algorithms that can explore and provide the best & reliable segments automatically no matter the complexity of the data.

4 - Predictive Models
Enhencer builds the perfect predictive model & digs out the variable that truly effects your target automatically leaving you the task of just clicking the predict button.

Advanced data analytics segmentation

Dynamic Data Analytics Features

Enhencer brings all these features together to present the most complete data analysis package featuring from simple & stunning visuals to the most advanced data mining & predictive modeling algorithms. Enhencer also brings machine learning algorithms to the table making the data insights and predictions more accurate and reliable than ever.

Machine Learning Algorithms
Enhencer takes an approach for improving performance and reliability by using machine learning algorithms.

Powerful Segmentation

Enhencer provides powerful & reliable segments automatically from the data using machine learning algorithms.

Stunning Visual Stories

Using intuitive and interactive interface of Enhencer acquire stunning visual stories for all your data types.

Robust Prediction

Predict the unknown using powerful predictive models such as decision tree, random forest, XGBoost & neural network.

Likert & Promoter Score

Survey friendly features to spot the business growth opportunities that no other data analysis packages provide.

Flexible Integrations

Connect and integrate the different data sources from online data collection platforms and database platforms.

For more information, demo-request or free trial, please visit www.enhencer.com

Predictive Insurance Data Analytics and Segmentation - Enhencer

Fast & Powerful Data Analysis

You don't need expensive and complex data science resources to benefit from advanced data analytics. Enhencer enables you to import, analyze and predict in less than few minutes requiring no coding or statistical expertise at all. Enhencer puts the data analysis in a nutshell with 4 steps.

1 - Connect/Upload Data
Upload data of many formats or Import data from various sources like Typeform, Survey Monkey, Google Form and many more in a matter of seconds.

2 - Instant Actionable Insights
Bring down the analysis time from days to minutes as Enhencer empowers the data analysis with decision tree algorithm in an accessible manner for everyone.

3 - Powerful Segmentation
Enhencer is designed with machine learning algorithms that can explore and provide the best & reliable segments automatically no matter the complexity of the data.

4 - Predictive Models
Enhencer builds the perfect predictive model & digs out the variable that truly effects your target automatically leaving you the task of just clicking the predict button.

Advanced data analytics segmentation

Dynamic Data Analytics Features

Enhencer brings all these features together to present the most complete data analysis package featuring from simple & stunning visuals to the most advanced data mining & predictive modeling algorithms. Enhencer also brings machine learning algorithms to the table making the data insights and predictions more accurate and reliable than ever.

Machine Learning Algorithms
Enhencer takes an approach for improving performance and reliability by using machine learning algorithms.

Powerful Segmentation

Enhencer provides powerful & reliable segments automatically from the data using machine learning algorithms.

Stunning Visual Stories

Using intuitive and interactive interface of Enhencer acquire stunning visual stories for all your data types.

Robust Prediction

Predict the unknown using powerful predictive models such as decision tree, random forest, XGBoost & neural network.

Likert & Promoter Score

Survey friendly features to spot the business growth opportunities that no other data analysis packages provide.

Flexible Integrations

Connect and integrate the different data sources from online data collection platforms and database platforms.

For more information, demo-request or free trial, please click.

Self Service Predictive Analytics and Segmentation - Enhencer

Fast & Powerful Data Analysis

You don't need expensive and complex data science resources to benefit from advanced data analytics. Enhencer enables you to import, analyze and predict in less than few minutes requiring no coding or statistical expertise at all. Enhencer puts the data analysis in a nutshell with 4 steps.

1 - Connect/Upload Data
Upload data of many formats or Import data from various sources like Typeform, Survey Monkey, Google Form and many more in a matter of seconds.

2 - Instant Actionable Insights
Bring down the analysis time from days to minutes as Enhencer empowers the data analysis with decision tree algorithm in an accessible manner for everyone.

3 - Powerful Segmentation
Enhencer is designed with machine learning algorithms that can explore and provide the best & reliable segments automatically no matter the complexity of the data.

4 - Predictive Models
Enhencer builds the perfect predictive model & digs out the variable that truly effects your target automatically leaving you the task of just clicking the predict button.

Advanced data analytics segmentation

Dynamic Data Analytics Features

Enhencer brings all these features together to present the most complete data analysis package featuring from simple & stunning visuals to the most advanced data mining & predictive modeling algorithms. Enhencer also brings machine learning algorithms to the table making the data insights and predictions more accurate and reliable than ever.

Machine Learning Algorithms
Enhencer takes an approach for improving performance and reliability by using machine learning algorithms.

Powerful Segmentation

Enhencer provides powerful & reliable segments automatically from the data using machine learning algorithms.

Stunning Visual Stories

Using intuitive and interactive interface of Enhencer acquire stunning visual stories for all your data types.

Robust Prediction

Predict the unknown using powerful predictive models such as decision tree, random forest, XGBoost & neural network.

Likert & Promoter Score

Survey friendly features to spot the business growth opportunities that no other data analysis packages provide.

Flexible Integrations

Connect and integrate the different data sources from online data collection platforms and database platforms.

sssss

For more information, demo-request or free trial, please click.

Enhencer Predictive Storyteller - Data Analytics Live Demo

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.

Improve customer retention and reduce customer churn by self service data analytics

To boom in today’s competitive world, a business must know its customers inside out. To understand your customers is to understand their motivations, their desires, and their fears.

Your customer data can make you discover a lot about your customers if you can use proper customer data analysis tools. With such a tool, you can know why do people buy your products and services and what motivates them and what drives them away.

Customer Retention & Churn

The most precious assets for a business are it’s customers. However, the most important customers are not the ones that a business gains, but the ones who stay longer. It is one thing to count how many people have signed up or walked into store for the first time and quite another to understand what makes them coming back or leave.

Customer retention refers to the ability of a company or product to retain its customers over some specified period. High customer retention means customers of the product or business tend to return to, continue to buy or in some other way not defect to another product or business, or to non-use entirely. Selling organizations generally attempt to reduce customer defections.

Enhencer Self-Service, Visual Data Analytics

Enhencer self-service visual predictive platform selects and runs the best ready-made machine learning algorithm as users click on the visual dashboard. This helps organizations ;

•     to reduce the data analytics time to minutes from days,
•     to reduce dependency on expensive data scientist resources and statistical tools
•     increases the use of data analytic so value extracted from data analytics is increased.

With Enhencer, you can take the power of Customer Retention & churn analysis at your own hand :

•     Learn Customer lifetime value for each customer using the most powerful Machine Learning Algorithms.
•     Predict the lifetime of your customers using the Predictive Power of Enhencer.
•     Obtain precise segments of customers regarding Retention & Churn.

Customer Retention & Churn Analysis Example

You can integrate your customer database to Enhencer and perform customer retention & churn analysis within a few clicks.

Customer Segmentation

In this example, the customer loss is 45.8%. 

You can select the customer properties to let Enhencer automatically create customer segments where loss is minimum and maximum.

Enhencer will select the best ready-to-use machine learning algorithms to create customer segments for you. The top segments are were  customers are more loyal and it goes to bottom, the customers become increasingly less loyal. For example in Segment one, retained customer rate is 75% higher than the average and there are 96 customers in this segment.

In this segment, you can see the customer details. For example in Segment 1, online usage is greater than 14.9 hours per week, primary social media accounts are Youtube, Qzone, Twitter, Instagram or  Vkonta, 3 years cummulative complaint number is less than 3, etc ... Churn in this segment is as low as 5%.

You can extract the specific customers in each segment to use outside Enhencer.

Predictive Intelligence

Enhencer also helps business users to do predictive analytics. In the predictive panel, you can select a customer group with specified properties and see the probability of them to leave. This enables you to decide which customer groups to focus i you want to minimize customer churn.

For example below, customer with primary Facebook and Instagram accounts, obtained by 30 days free trial and aged between 18 - 26 has a probability of 52% of being a lost customer.

You can find out the Customer data analysis capabilities of Enhencer here. You can also request for a demo.

Self-service predictive visual data analytics

You are invited to Enhencer Predictive Visual Storytelling webinar on May 4th 2018 at 10:00 AM GMT +8:00.

In this free event, you will discover how self-service visual data analytics can help you to derive actionable insight from data with no data science experience. Enhencer self-service visual predictive platform achieves this by selecting and running the best ready-made machine learning algorithm as you explore your data by click on the visual dashboard. This helps companies to;

• to free business users from data scientists and perform advanced data analytics
• to reduce the data analytics time to minutes from days
• to reduce dependency on expensive data scientist resources and statistical tool
• increases the use of data analytic so value extracted from data analytics is increased

You will also have a chance to see a demonstrations of Enhencer. Please click register to join.

Event Information

Date :May 4th 2018, 

Time

10:00 – 10:45 AM GMT (Singapore / Malaysia / Hong Kong)

09:00 – 09:45 AM GMT (Indonesia / Thailand)

(Can’t make it or missed it? Click here to request for a demo)

Turkish Technic Selects ICRON's MRO Planning Software

Turkish Technic has selected ICRON’s MRO Planning software to optimize routine maintenance packages for aircraft. Using the software’s Maintenance Cards Interval functionality, Turkish Technic will be able to optimize balancing the distribution of task cards for planned maintenance. According to ICRON, this will increase productivity by minimizing early task card application and increase aircraft flight time.

“One of the most challenging problems for airlines and MRO companies in [the] aviation industry is to apply several maintenance cards that have different application intervals by keeping the duration of aircraft on the ground at a minimum,” says Ahmet Karaman, Turkish Technic’s general manager. “After a detailed research process, we have chosen ICRON for the optimization of our maintenance cards.”

The software, which is part of ICRON’s graphical scheduling and modeling system (GSAMS) platform, has functionality to coordinate schedules for workforce, spare parts, equipment and more. ICRON says it allows for real-time visibility of all day-to-day operations while reducing turnaround times and cost. The software’s user interface supports mobile devices and can run on the cloud, although the company says most customers prefer local server architecture for security reasons.

Turkish Technic says the goal with implementing the new software is to keep aircraft in the air as much as possible by decreasing maintenance turnaround time and minimizing maintenance occurrences at the macro level. According to a spokesperson for the company, ICRON’s software will support these goals by determining the maintenance check concept in a very short time for a new fleet and analyzing the plausibility of check intervals for the existing sub-fleet types.

Maarten Baltussen, ICRON’s chief revenue officer, says the new contract with Turkish Technic is a milestone in the company’s 25-year history. “After having SAESL, the MRO division of Singapore Airlines (Singapore Airlines Engineering Company), as a customer for several years, getting Turkish Technic on board means the recognition of ICRON as a market leader in airline MRO solutions,” he says.

According to ICRON, SAESL’s implementation of its MRO software has dramatically reduced repair turnaround time by automatically updating and optimizing the company’s schedule six times a day in less than half an hour.

Turkish Technic aims to have writing of the software’s code completed at the end of April and it will be designing user interfaces in the meantime. The company’s goal is to go live with the new optimization tool by the end of May.

Only 50% of companies can measure the cost of their data, ICRON survey shows

50% of companies are not able to measure the cost of their data, according to the results of a survey conducted by ICRON, a leading provider of Optimized Decision Making and Supply Chain Optimization software solutions. The survey, which was completed by 165 executives at the IoT EurAsia 2018 event held in Istanbul last week, revealed that approximately half of companies – and only 35% in the manufacturing sector and 43% in the services sector – can accurately gauge their data costs, which may include costs for data collection, data cleaning, data updating, and data storage and access.

The survey also showed that only 68% of the data collected by companies is considered to be reliable and of good quality, while 32% of the data is thought to be unreliable and of poor quality. Only 12% of the respondents said that all the data that their companies collect is reliable and accurate. Furthermore, the group of executives who admitted that they don’t know the cost of their companies’ data also said that only 62% of their data is reliable and of good quality – which means that around 38% of their data costs are related to data that they deem to be unreliable and of substandard quality. 

Alfred den Besten, ICRON’s Chief Marketing Officer, commented: “The results of our survey are fascinating, as such a high percentage of respondents stated that they don’t know what the true cost of their data is and that they think their data is not reliable and of subpar quality. This means that many companies are burning money on collecting and maintaining useless data, and that unreliable data is being used to make important business decisions – and this poses a major risk. To be successful, companies must be able to assess the cost and improve the accuracy of their data and use their data to make the best possible decisions.” ICRON’s President A. Tamer Ünal – who gave a presentation at the IoT EurAsia event on the topic of how to “Instantly transform your data into optimized decisions” – remarked: “Companies today have access to a tremendous amount of data from various sources including IoT devices and back-office systems, and data is the key driver of their business decisions.

The fact that so many executives don’t have a handle on their data costs and don’t trust the reliability of their data demonstrates that there is a real need for systems and solutions that enhance data utilization. ICRON’s groundbreaking optimized decision making platform does exactly that, enabling companies to use their data to automatically generate optimal plans and projections and make optimized decisions that minimize costs and maximize productivity and performance.”

 ICRON will be releasing the full results of the survey in the coming weeks.

Predictive visual storytelling - Data analytics for business users without data science experience

Today, many companies collect huge amount of data which can add tremendous value if data analytics is applied on it. Unfortunately, predictive modeling and machine learning algorithms requires data science expertise and/or specialized statistical tools. These traditional resource are expensive. Many companies cannot afford to keep such employees. Even if they can, the process is rigid. A business users needs new data analytics requirement, he / she needs to ask it from data scientist and wait for the scientist to come up with the new model.

Enhencer Predictive Visual Storyteller is a self-service visual data analytics for business users to derive actionable insight from their data in a few clicks and with no data science expertise and coding.  Enhancer's visual dashboard is easy to use with clicks while best ready-made machine learning algorithms are run in the background in each click.

Enhencer offers segmentation using ready-made machine learning algorithms. Enhencer explores and provides best segments for your target objective no matter the complexity of the data. Enhencer offers instant actionable insights. Users can simply upload their data to Enhencer and within a few clicks and minutes can perform data analysis which requires days to do with traditional data analytics tools.

Example

Suppose that you have integrated your CRM to Enhencer. You want to analyze the customer churn & retention. Suppose that you have a field in your data named customer status which can be "lost" or "existing". Suppose that for a selected group of customers, 48% are lost.

Your CRM holds customer data with many information like age, gender, income, how long / frequent customer uses a service, customer source, customer location, etc. By segmentation, you can find out what segments of customers are retained most and what segments of customers are lost most.

With dozens or even hundreds of properties in your data, it is difficult to intuitively segment the customers. You don't know which categories are significant in customer retention and which are not. Is age a factor? Customer source? Gender?

Below video shows the segments generated by Enhencer. You simply select the value (here customer loyalty) to analyze and Enhencer automatically segments them. Top segments mean which categories of customers are most loyal and bottom segments show the least loyal ones. In the example below, first segment is 110% gain on average which means they are more than twice loyal compared to average loyalty. This segment is :

Salary Amount > 10,397
Age > 29
Gender = Male
Nationality = Japan or UK

The least loyal ones are :
Salary <4,031.
Customer Class = Silver
Nationality = US, Japan, France, UK and Italy.

Locating these significant customer properties among dozens and even hundreds of categories you have in your CRM is difficult if you do not have data analytics. With this info data analytics provides, you can have an action plan (i.e. target high income bucket more or investigate why Silver customers in certain countries are least loyal).

You can also perform predictive analytics in Enhencer on fly. For example, you can select several properties of customers (age range, gender, location, class, nationality, etc ...) and ask Enhencer to predict the probability of this customer to stay loyal to your company.

The platform is available on cloud by monthly subscription or if required can be installed in-house. You can request a demo here.