Use Case Library
The use case library below contains customer stories that illustrate how businesses leverage the power of NEMESIS to solve the problems at credit union, banking, healthcare, manufacturing and more.
Use our library of use cases to explore real-world applications in your industry vertical, or look for applications in your specific field of interest.
Financial services are facing challenges in increasing their share of wallets due to fierce competition from other institutions providing better offers or more competitive rates. Furthermore, smaller institutions lack the same range of products and services as larger banks, making it difficult for them to meet their members’ needs to retain them.
Detecting member profitability is also difficult in these organizations due to the poor data quality and limited resources available to analyze data.
NEMESIS’s Value
Gain insight into your most profitable members' behaviors and characteristics. Make predictions about what products and services will resonate with them. Implement targeted marketing strategies and retention programs. Improve customer profitability and share of wallet.
NEMESIS enables your team to quickly deploy action-oriented analytical solutions. NEMESIS offers easy-to-use but powerful actionable insights, data modeling, customized dashboards, and case management capabilities, enabling users to take action immediately.
Data
This case requires the use of transaction data, including geo-location information, time parameters, member identity profile, credit report, etc.
Financial services are facing challenges in increasing their share of wallets due to fierce competition from other institutions providing better offers or more competitive rates. Furthermore, smaller institutions lack the same range of products and services as larger banks, making it difficult for them to meet their members’ needs to retain them.
Detecting member profitability is also difficult in these organizations due to the poor data quality and limited resources available to analyze data.
NEMESIS’s Value
Gain insight into your most profitable members' behaviors and characteristics. Make predictions about what products and services will resonate with them. Implement targeted marketing strategies and retention programs. Improve customer profitability and share of wallet.
NEMESIS enables your team to quickly deploy action-oriented analytical solutions. NEMESIS offers easy-to-use but powerful actionable insights, data modeling, customized dashboards, and case management capabilities, enabling users to take action immediately.
Data
This case requires the use of transaction data, including geo-location information, time parameters, member identity profile, credit report, etc.
Financial services are facing challenges in increasing their share of wallets due to fierce competition from other institutions providing better offers or more competitive rates. Furthermore, smaller institutions lack the same range of products and services as larger banks, making it difficult for them to meet their members’ needs to retain them.
Detecting member profitability is also difficult in these organizations due to the poor data quality and limited resources available to analyze data.
NEMESIS’s Value
Gain insight into your most profitable members' behaviors and characteristics. Make predictions about what products and services will resonate with them. Implement targeted marketing strategies and retention programs. Improve customer profitability and share of wallet.
NEMESIS enables your team to quickly deploy action-oriented analytical solutions. NEMESIS offers easy-to-use but powerful actionable insights, data modeling, customized dashboards, and case management capabilities, enabling users to take action immediately.
Data
This case requires the use of transaction data, including geo-location information, time parameters, member identity profile, credit report, etc.
According to Sumsub's report published on BusinessWire, in 2022, fraud has dramatically increased in five countries, with 5.1% in the USA alone.
The report stated that fraud in the banking sector has increased nearly 100%. Account takeovers and chargeback fraud are popular schemes used by fraudsters. Stolen bank cards are used by fraudsters targeting the financial services, e-commerce, and gambling industries. Payment fraud grew 40% from 2021 to 2022—a dramatic increase that led to a rise in share globally.
NEMESIS’s Value
NEMESIS can assist you in detecting abnormal or suspicious activities before the damage happens.
With NEMESIS, fraud analysts can identify patterns or anomalies in member behavior that may indicate fraudulent activity. No coding is required in the process. The all-in-one platform offers data cleansing, data modeling, and customizable dashboards. It also provides an in-built Case Management System where analysts can take immediate action without leaving the platform to prevent losses.
Data
This case requires the use of transaction data, including geo-location information, time parameters, member identity profile, credit report, etc.
According to Sumsub's report published on BusinessWire, in 2022, fraud has dramatically increased in five countries, with 5.1% in the USA alone.
The report stated that fraud in the banking sector has increased nearly 100%. Account takeovers and chargeback fraud are popular schemes used by fraudsters. Stolen bank cards are used by fraudsters targeting the financial services, e-commerce, and gambling industries. Payment fraud grew 40% from 2021 to 2022—a dramatic increase that led to a rise in share globally.
NEMESIS’s Value
NEMESIS can assist you in detecting abnormal or suspicious activities before the damage happens.
With NEMESIS, fraud analysts can identify patterns or anomalies in member behavior that may indicate fraudulent activity. No coding is required in the process. The all-in-one platform offers data cleansing, data modeling, and customizable dashboards. It also provides an in-built Case Management System where analysts can take immediate action without leaving the platform to prevent losses.
Data
This case requires the use of transaction data, including geo-location information, time parameters, member identity profile, credit report, etc.
According to Sumsub's report published on BusinessWire, in 2022, fraud has dramatically increased in five countries, with 5.1% in the USA alone.
The report stated that fraud in the banking sector has increased nearly 100%. Account takeovers and chargeback fraud are popular schemes used by fraudsters. Stolen bank cards are used by fraudsters targeting the financial services, e-commerce, and gambling industries. Payment fraud grew 40% from 2021 to 2022—a dramatic increase that led to a rise in share globally.
NEMESIS’s Value
NEMESIS can assist you in detecting abnormal or suspicious activities before the damage happens.
With NEMESIS, fraud analysts can identify patterns or anomalies in member behavior that may indicate fraudulent activity. No coding is required in the process. The all-in-one platform offers data cleansing, data modeling, and customizable dashboards. It also provides an in-built Case Management System where analysts can take immediate action without leaving the platform to prevent losses.
Data
This case requires the use of transaction data, including geo-location information, time parameters, member identity profile, credit report, etc.
For insurers, inaccurate claim reserves can lead to staggering loss ratios and insolvency concerns. With funds being allocated to these reserves before there is any report of a potential loss, the margin for error can be catastrophic. According to McKinsey, there was approximately a $30 billion increase in loss costs in 2021.
The traditional approach to estimating these reserves is based on life tables and survival analysis–using data accumulated over the years among various insurers. Using an A.I. predictive analytic and machine learning approach, survival analysis can be incorporated and improved over time–using automated data collection and an intelligent rule base to outperform traditional approaches with a significant margin.
NEMESIS’s Value
Claim managers are empowered by NEMESIS to analyze the optimal claims reserve for the customers. With the drag-and-drop feature of NEMESIS, no coding is required in the process. Claim adjusters can easily segment the customers and set the claim reserve based on customer data. With NEMESIS Insight and Case Management System, the managers are able to overview the effectiveness of the current setting, assigning claim adjusters to take immediate action without leaving the platform.
Data
This case requires the use of claim historical data and customer data, including demographic data and behavioral data, such as credit history, claim type, insurer description, policy information, insured person description, claim amount, etc.
For insurers, inaccurate claim reserves can lead to staggering loss ratios and insolvency concerns. With funds being allocated to these reserves before there is any report of a potential loss, the margin for error can be catastrophic. According to McKinsey, there was approximately a $30 billion increase in loss costs in 2021.
The traditional approach to estimating these reserves is based on life tables and survival analysis–using data accumulated over the years among various insurers. Using an A.I. predictive analytic and machine learning approach, survival analysis can be incorporated and improved over time–using automated data collection and an intelligent rule base to outperform traditional approaches with a significant margin.
NEMESIS’s Value
Claim managers are empowered by NEMESIS to analyze the optimal claims reserve for the customers. With the drag-and-drop feature of NEMESIS, no coding is required in the process. Claim adjusters can easily segment the customers and set the claim reserve based on customer data. With NEMESIS Insight and Case Management System, the managers are able to overview the effectiveness of the current setting, assigning claim adjusters to take immediate action without leaving the platform.
Data
This case requires the use of claim historical data and customer data, including demographic data and behavioral data, such as credit history, claim type, insurer description, policy information, insured person description, claim amount, etc.
For insurers, inaccurate claim reserves can lead to staggering loss ratios and insolvency concerns. With funds being allocated to these reserves before there is any report of a potential loss, the margin for error can be catastrophic. According to McKinsey, there was approximately a $30 billion increase in loss costs in 2021.
The traditional approach to estimating these reserves is based on life tables and survival analysis–using data accumulated over the years among various insurers. Using an A.I. predictive analytic and machine learning approach, survival analysis can be incorporated and improved over time–using automated data collection and an intelligent rule base to outperform traditional approaches with a significant margin.
NEMESIS’s Value
Claim managers are empowered by NEMESIS to analyze the optimal claims reserve for the customers. With the drag-and-drop feature of NEMESIS, no coding is required in the process. Claim adjusters can easily segment the customers and set the claim reserve based on customer data. With NEMESIS Insight and Case Management System, the managers are able to overview the effectiveness of the current setting, assigning claim adjusters to take immediate action without leaving the platform.
Data
This case requires the use of claim historical data and customer data, including demographic data and behavioral data, such as credit history, claim type, insurer description, policy information, insured person description, claim amount, etc.
Quantifying potential risks in future customers, clients, policies, and premiums can oftentimes feel like a gamble for financial service and insurance companies. In 2021 the insurance industry experienced a $3.8 billion net underwriting loss in property and casualty claims alone, and an 11% increase in loss adjustment expenses.
With financial risks on the rise, many enterprises are turning to the power of artificial intelligence for their risk management needs. A.I. predictive analytic platforms analyze these risks–considering things like credit history, claims history, auto and homeowner records, demographic and geographic, income growth, market conditions, etc–and automate the data collection process. Identify risks before you’re upside down with predictive analytics.
NEMESIS’s Value
Risk analysts can easily analyze customer behavior with NEMESIS drag-and-drop features. No coding is required in the process. The all-in-one platform offers data cleansing, data modeling, and customizable dashboards. Risk analysts don’t need to go to other platforms to complete the analysis.
What’s more, NEMESIS provides an in-built Case Management System for Risk analysts to take immediate action without leaving the platform.
Data
This case requires the use of historical customer data, including demographic data and behavioral data, such as credit history, claim type, insurer description, policy information, insured person description, claim amount, etc.
Quantifying potential risks in future customers, clients, policies, and premiums can oftentimes feel like a gamble for financial service and insurance companies. In 2021 the insurance industry experienced a $3.8 billion net underwriting loss in property and casualty claims alone, and an 11% increase in loss adjustment expenses.
With financial risks on the rise, many enterprises are turning to the power of artificial intelligence for their risk management needs. A.I. predictive analytic platforms analyze these risks–considering things like credit history, claims history, auto and homeowner records, demographic and geographic, income growth, market conditions, etc–and automate the data collection process. Identify risks before you’re upside down with predictive analytics.
NEMESIS’s Value
Risk analysts can easily analyze customer behavior with NEMESIS drag-and-drop features. No coding is required in the process. The all-in-one platform offers data cleansing, data modeling, and customizable dashboards. Risk analysts don’t need to go to other platforms to complete the analysis.
What’s more, NEMESIS provides an in-built Case Management System for Risk analysts to take immediate action without leaving the platform.
Data
This case requires the use of historical customer data, including demographic data and behavioral data, such as credit history, claim type, insurer description, policy information, insured person description, claim amount, etc.
Quantifying potential risks in future customers, clients, policies, and premiums can oftentimes feel like a gamble for financial service and insurance companies. In 2021 the insurance industry experienced a $3.8 billion net underwriting loss in property and casualty claims alone, and an 11% increase in loss adjustment expenses.
With financial risks on the rise, many enterprises are turning to the power of artificial intelligence for their risk management needs. A.I. predictive analytic platforms analyze these risks–considering things like credit history, claims history, auto and homeowner records, demographic and geographic, income growth, market conditions, etc–and automate the data collection process. Identify risks before you’re upside down with predictive analytics.
NEMESIS’s Value
Risk analysts can easily analyze customer behavior with NEMESIS drag-and-drop features. No coding is required in the process. The all-in-one platform offers data cleansing, data modeling, and customizable dashboards. Risk analysts don’t need to go to other platforms to complete the analysis.
What’s more, NEMESIS provides an in-built Case Management System for Risk analysts to take immediate action without leaving the platform.
Data
This case requires the use of historical customer data, including demographic data and behavioral data, such as credit history, claim type, insurer description, policy information, insured person description, claim amount, etc.
In the financial industry, it is very common for customers to fail to pay on time, either on an occasional basis or on a regular basis. Payment delays can result in both collection and interest losses for financial institutions.
Financial services can mitigate risks with predictive analytics by identifying potential risks and taking appropriate measures. Analyze customer behavior to discover potential instances of default and take proactive steps to prevent them. Improve late payments and defaults to reduce overall payment risk.
NEMESIS’s Value
Risk analysts can easily analyze the member behavior with NEMESIS drag and drop features-zero coding. The all-in-one platform offers data cleansing, data modeling, and customizable dashboards. It doesn't require analysts to access other platforms for analysis. NEMESIS provides a built-in Case Management System for the analysts to take immediate action without leaving the platform.
Data
This case requires the use of member historical data, including demographic data and behavioral data, such as the data include the member's average account balance, vintage, member geographic data, member occupation, purchased credit product details, etc.
In the financial industry, it is very common for customers to fail to pay on time, either on an occasional basis or on a regular basis. Payment delays can result in both collection and interest losses for financial institutions.
Financial services can mitigate risks with predictive analytics by identifying potential risks and taking appropriate measures. Analyze customer behavior to discover potential instances of default and take proactive steps to prevent them. Improve late payments and defaults to reduce overall payment risk.
NEMESIS’s Value
Risk analysts can easily analyze the member behavior with NEMESIS drag and drop features-zero coding. The all-in-one platform offers data cleansing, data modeling, and customizable dashboards. It doesn't require analysts to access other platforms for analysis. NEMESIS provides a built-in Case Management System for the analysts to take immediate action without leaving the platform.
Data
This case requires the use of member historical data, including demographic data and behavioral data, such as the data include the member's average account balance, vintage, member geographic data, member occupation, purchased credit product details, etc.
In the financial industry, it is very common for customers to fail to pay on time, either on an occasional basis or on a regular basis. Payment delays can result in both collection and interest losses for financial institutions.
Financial services can mitigate risks with predictive analytics by identifying potential risks and taking appropriate measures. Analyze customer behavior to discover potential instances of default and take proactive steps to prevent them. Improve late payments and defaults to reduce overall payment risk.
NEMESIS’s Value
Risk analysts can easily analyze the member behavior with NEMESIS drag and drop features-zero coding. The all-in-one platform offers data cleansing, data modeling, and customizable dashboards. It doesn't require analysts to access other platforms for analysis. NEMESIS provides a built-in Case Management System for the analysts to take immediate action without leaving the platform.
Data
This case requires the use of member historical data, including demographic data and behavioral data, such as the data include the member's average account balance, vintage, member geographic data, member occupation, purchased credit product details, etc.
Financial institutions must follow critical guidelines to avoid risk. This may be true, but credit unions and banks may limit their growth potential for businesses or even prevent their members from taking full advantage of their products and services.
Discover patterns that would otherwise be impossible to detect using predictive analytics while maintaining those guidelines. Identify ways to avoid giving high limits to members that could result in loss or fraud. Avoid churning customers by providing low limits.
NEMESIS’s Value
With NEMESIS, business teams can rapidly deploy analytical solutions that are focused on action. The all-in-one platform offers easy yet powerful actionable insights, data modeling, customizable dashboards, and case management capabilities that allow users to take action immediately. Maximize revenue and minimize risk by setting the right limits.
Data
This case requires the use of transaction data and customer account data. Statistically derive limits for each customer segment based on historical limit denials and increases.
Financial institutions must follow critical guidelines to avoid risk. This may be true, but credit unions and banks may limit their growth potential for businesses or even prevent their members from taking full advantage of their products and services.
Discover patterns that would otherwise be impossible to detect using predictive analytics while maintaining those guidelines. Identify ways to avoid giving high limits to members that could result in loss or fraud. Avoid churning customers by providing low limits.
NEMESIS’s Value
With NEMESIS, business teams can rapidly deploy analytical solutions that are focused on action. The all-in-one platform offers easy yet powerful actionable insights, data modeling, customizable dashboards, and case management capabilities that allow users to take action immediately. Maximize revenue and minimize risk by setting the right limits.
Data
This case requires the use of transaction data and customer account data. Statistically derive limits for each customer segment based on historical limit denials and increases.
Financial institutions must follow critical guidelines to avoid risk. This may be true, but credit unions and banks may limit their growth potential for businesses or even prevent their members from taking full advantage of their products and services.
Discover patterns that would otherwise be impossible to detect using predictive analytics while maintaining those guidelines. Identify ways to avoid giving high limits to members that could result in loss or fraud. Avoid churning customers by providing low limits.
NEMESIS’s Value
With NEMESIS, business teams can rapidly deploy analytical solutions that are focused on action. The all-in-one platform offers easy yet powerful actionable insights, data modeling, customizable dashboards, and case management capabilities that allow users to take action immediately. Maximize revenue and minimize risk by setting the right limits.
Data
This case requires the use of transaction data and customer account data. Statistically derive limits for each customer segment based on historical limit denials and increases.
In July of 2022, the Department of Justice announced criminal charges related to "ghost patients" for more than $1.2 billion in alleged fraudulent telemedicine, cardiovascular and cancer genetic testing, and durable medical equipment schemes. Additionally, the Centers for Medicare & Medicaid Services, and Center for Program Integrity announced it took administrative actions against providers involved in similar schemes.
These schemes account for more than $1 billion of the total alleged intended losses associated with today’s enforcement action. Prescription fraud comes at an astronomical cost to physicians, hospitals, insurers, and taxpayers. Proper analysis can provide these insights and discoveries, however often times too late. With A.I. predictive analytics these schemes can be curbed before they are detrimental.
NEMESIS’s Value
Using NEMESIS, analysts can see claim details with trends and scores to identify whether this claim has a high probability of being a medical ghost patient fraudulent case. Using the historical data of medical insurance claims, analysts can easily build a predictive model to foresee the next move of fraudsters.
Data
This case requires the use of historical claim data, including geo-location information, time parameters, policy information, claim type, claim amount, insured person description, etc.
In July of 2022, the Department of Justice announced criminal charges related to "ghost patients" for more than $1.2 billion in alleged fraudulent telemedicine, cardiovascular and cancer genetic testing, and durable medical equipment schemes. Additionally, the Centers for Medicare & Medicaid Services, and Center for Program Integrity announced it took administrative actions against providers involved in similar schemes.
These schemes account for more than $1 billion of the total alleged intended losses associated with today’s enforcement action. Prescription fraud comes at an astronomical cost to physicians, hospitals, insurers, and taxpayers. Proper analysis can provide these insights and discoveries, however often times too late. With A.I. predictive analytics these schemes can be curbed before they are detrimental.
NEMESIS’s Value
Using NEMESIS, analysts can see claim details with trends and scores to identify whether this claim has a high probability of being a medical ghost patient fraudulent case. Using the historical data of medical insurance claims, analysts can easily build a predictive model to foresee the next move of fraudsters.
Data
This case requires the use of historical claim data, including geo-location information, time parameters, policy information, claim type, claim amount, insured person description, etc.
In July of 2022, the Department of Justice announced criminal charges related to "ghost patients" for more than $1.2 billion in alleged fraudulent telemedicine, cardiovascular and cancer genetic testing, and durable medical equipment schemes. Additionally, the Centers for Medicare & Medicaid Services, and Center for Program Integrity announced it took administrative actions against providers involved in similar schemes.
These schemes account for more than $1 billion of the total alleged intended losses associated with today’s enforcement action. Prescription fraud comes at an astronomical cost to physicians, hospitals, insurers, and taxpayers. Proper analysis can provide these insights and discoveries, however often times too late. With A.I. predictive analytics these schemes can be curbed before they are detrimental.
NEMESIS’s Value
Using NEMESIS, analysts can see claim details with trends and scores to identify whether this claim has a high probability of being a medical ghost patient fraudulent case. Using the historical data of medical insurance claims, analysts can easily build a predictive model to foresee the next move of fraudsters.
Data
This case requires the use of historical claim data, including geo-location information, time parameters, policy information, claim type, claim amount, insured person description, etc.
7-10% of patients are misidentified when their EMPI and EHR records are being researched. The American Health Information Management Association reports that 8-12% of EHR records are duplicates. Preventable medical errors are the third leading cause of death in the United States, causing an estimated 440,000 deaths per year.
Cleansing inaccurate patient records can be expensive for hospitals. The average cost to resolve a single duplicate medical record is $1,000. If 8% of a hospital’s records are duplicates, the costs to clear their EMPI database and EHR records increase significantly. Predictive analytics can identify and prevent costly and potentially life-threatening medical record errors.
NEMESIS’s Value
Using NEMESIS, the specialists can quickly capture the duplicate records and missing values through NEMESIS data profiling feature without any coding. Removing or merging the duplicated records can be done by simple drag-and-drop feature. After saving the data cleansing pipeline in the system, the specialists do not need to repeat the cleansing process. They can simply schedule a batch processing to ask NEMESIS to run the same pipeline with the new records.
Data
This case is about the data cleansing of EMPI and EHR data.
7-10% of patients are misidentified when their EMPI and EHR records are being researched. The American Health Information Management Association reports that 8-12% of EHR records are duplicates. Preventable medical errors are the third leading cause of death in the United States, causing an estimated 440,000 deaths per year.
Cleansing inaccurate patient records can be expensive for hospitals. The average cost to resolve a single duplicate medical record is $1,000. If 8% of a hospital’s records are duplicates, the costs to clear their EMPI database and EHR records increase significantly. Predictive analytics can identify and prevent costly and potentially life-threatening medical record errors.
NEMESIS’s Value
Using NEMESIS, the specialists can quickly capture the duplicate records and missing values through NEMESIS data profiling feature without any coding. Removing or merging the duplicated records can be done by simple drag-and-drop feature. After saving the data cleansing pipeline in the system, the specialists do not need to repeat the cleansing process. They can simply schedule a batch processing to ask NEMESIS to run the same pipeline with the new records.
Data
This case is about the data cleansing of EMPI and EHR data.
7-10% of patients are misidentified when their EMPI and EHR records are being researched. The American Health Information Management Association reports that 8-12% of EHR records are duplicates. Preventable medical errors are the third leading cause of death in the United States, causing an estimated 440,000 deaths per year.
Cleansing inaccurate patient records can be expensive for hospitals. The average cost to resolve a single duplicate medical record is $1,000. If 8% of a hospital’s records are duplicates, the costs to clear their EMPI database and EHR records increase significantly. Predictive analytics can identify and prevent costly and potentially life-threatening medical record errors.
NEMESIS’s Value
Using NEMESIS, the specialists can quickly capture the duplicate records and missing values through NEMESIS data profiling feature without any coding. Removing or merging the duplicated records can be done by simple drag-and-drop feature. After saving the data cleansing pipeline in the system, the specialists do not need to repeat the cleansing process. They can simply schedule a batch processing to ask NEMESIS to run the same pipeline with the new records.
Data
This case is about the data cleansing of EMPI and EHR data.
Banks and credit unions are in a constant battle to maintain the loyalty of their members. Member attrition is discovered only after the customer has switched institutions.
According to a study, repeat business contributes to over one-third of revenue for more than half of the businesses surveyed. In addition, 90 percent of the former customers said they could be persuaded to stay. Customer retention is therefore crucial.
NEMESIS’s Value
With NEMESIS, business teams can rapidly deploy analytical solutions that are focused on action. The all-in-one platform offers easy yet powerful actionable insights, data modeling, customizable dashboards, and case management capabilities that allow users to take action immediately.
Acquiring new customers is more expensive than keeping existing ones. Predict to identify which customers have the highest churn risk and access data that predicts their intent for future purchases, retaining the most profitable customers. Increase customer loyalty, improve retention and boost sales and revenue.
Data
This case requires the use of member historical data, including demographic data and behavioral data, such as the data include member average account balance, vintage, member geographic data, member occupation, purchased credit product details, etc.
Banks and credit unions are in a constant battle to maintain the loyalty of their members. Member attrition is discovered only after the customer has switched institutions.
According to a study, repeat business contributes to over one-third of revenue for more than half of the businesses surveyed. In addition, 90 percent of the former customers said they could be persuaded to stay. Customer retention is therefore crucial.
NEMESIS’s Value
With NEMESIS, business teams can rapidly deploy analytical solutions that are focused on action. The all-in-one platform offers easy yet powerful actionable insights, data modeling, customizable dashboards, and case management capabilities that allow users to take action immediately.
Acquiring new customers is more expensive than keeping existing ones. Predict to identify which customers have the highest churn risk and access data that predicts their intent for future purchases, retaining the most profitable customers. Increase customer loyalty, improve retention and boost sales and revenue.
Data
This case requires the use of member historical data, including demographic data and behavioral data, such as the data include member average account balance, vintage, member geographic data, member occupation, purchased credit product details, etc.
Banks and credit unions are in a constant battle to maintain the loyalty of their members. Member attrition is discovered only after the customer has switched institutions.
According to a study, repeat business contributes to over one-third of revenue for more than half of the businesses surveyed. In addition, 90 percent of the former customers said they could be persuaded to stay. Customer retention is therefore crucial.
NEMESIS’s Value
With NEMESIS, business teams can rapidly deploy analytical solutions that are focused on action. The all-in-one platform offers easy yet powerful actionable insights, data modeling, customizable dashboards, and case management capabilities that allow users to take action immediately.
Acquiring new customers is more expensive than keeping existing ones. Predict to identify which customers have the highest churn risk and access data that predicts their intent for future purchases, retaining the most profitable customers. Increase customer loyalty, improve retention and boost sales and revenue.
Data
This case requires the use of member historical data, including demographic data and behavioral data, such as the data include member average account balance, vintage, member geographic data, member occupation, purchased credit product details, etc.
Segmenting members and understanding their behavior is crucial to increase sales and revenue. Marketing campaigns need to target important member segments which have different behavioral characteristics and needs. Some credit unions and banking companies that neglect targeting important segments may find themselves falling behind their competitors, struggling to increase revenue.
Predictive Analytics enables marketers to understand the behavior patterns of their members and identify opportunities to present the right types of offers or messages that will result in increased sales or member loyalty.
NEMESIS’s Value
The power of NEMESIS in marketing campaigns is not simply about a customer segmentation model, but about the campaign performance feedback and tracking through the NEMESIS in-build Case Management System (CMS). With NEMESIS CMS, marketers can review the feedback and results from the member service department in near real-time. All these data will be automatically saved in NEMESIS and ready to use for campaign performance review.
The result data can be fed back to the customer segmentation model, generating more accurate model results. With the machine learning technology of NEMESIS, marketers can make better targeting to offer the cross-sell products.
Data
This case requires the use of member historical data, including demographic data and behavioral data, such as the data include member average account balance, vintage, member geographic data, member occupation, purchased credit product details, etc.
Segmenting members and understanding their behavior is crucial to increase sales and revenue. Marketing campaigns need to target important member segments which have different behavioral characteristics and needs. Some credit unions and banking companies that neglect targeting important segments may find themselves falling behind their competitors, struggling to increase revenue.
Predictive Analytics enables marketers to understand the behavior patterns of their members and identify opportunities to present the right types of offers or messages that will result in increased sales or member loyalty.
NEMESIS’s Value
The power of NEMESIS in marketing campaigns is not simply about a customer segmentation model, but about the campaign performance feedback and tracking through the NEMESIS in-build Case Management System (CMS). With NEMESIS CMS, marketers can review the feedback and results from the member service department in near real-time. All these data will be automatically saved in NEMESIS and ready to use for campaign performance review.
The result data can be fed back to the customer segmentation model, generating more accurate model results. With the machine learning technology of NEMESIS, marketers can make better targeting to offer the cross-sell products.
Data
This case requires the use of member historical data, including demographic data and behavioral data, such as the data include member average account balance, vintage, member geographic data, member occupation, purchased credit product details, etc.
Segmenting members and understanding their behavior is crucial to increase sales and revenue. Marketing campaigns need to target important member segments which have different behavioral characteristics and needs. Some credit unions and banking companies that neglect targeting important segments may find themselves falling behind their competitors, struggling to increase revenue.
Predictive Analytics enables marketers to understand the behavior patterns of their members and identify opportunities to present the right types of offers or messages that will result in increased sales or member loyalty.
NEMESIS’s Value
The power of NEMESIS in marketing campaigns is not simply about a customer segmentation model, but about the campaign performance feedback and tracking through the NEMESIS in-build Case Management System (CMS). With NEMESIS CMS, marketers can review the feedback and results from the member service department in near real-time. All these data will be automatically saved in NEMESIS and ready to use for campaign performance review.
The result data can be fed back to the customer segmentation model, generating more accurate model results. With the machine learning technology of NEMESIS, marketers can make better targeting to offer the cross-sell products.
Data
This case requires the use of member historical data, including demographic data and behavioral data, such as the data include member average account balance, vintage, member geographic data, member occupation, purchased credit product details, etc.