Cloud costs comprise about 20% of IT spending, and no one wants a shockingly high bill.
As managing cloud expenses grows, companies increasingly emphasize optimizing their cloud expenditure. According to Global Industry Analysts spending on public cloud services is expected to reach around $800 billion by 2027. However, managing costs is tricky because assets are scattered across different clouds, and most solutions only offer reactive monitoring. It’s time to bridge the gap between cost control and business success. Having a robust cloud strategy is of the utmost importance, and fintech organizations like Indium Software can assist you in managing and developing the right strategy for your cloud needs. Read how Indium can assist.

FinOps for Cloud Cost Optimization & Multi-Cloud Cost Monitoring

You have undoubtedly gotten that CALL from the CFO, Finance team, and the money inspectors from the management inquiring about your monthly cloud spending if you’ve managed a cloud data platform. And it usually comes like this:

1. Seriously, what on earth is happening with our cloud usage? Did someone leave a money faucet open?
2. Are we just flushing money down the drain with this cloud service? Did a bunch of spendthrifts infiltrate our company overnight?
3. Why is our cloud costs out of control? Do we have predictive analytics tools or solutions available to help forecast our future cloud costs?

And trailed by too many French and Latin words!

Challenges in one infographic!

FinOps is like having a financial guru for your cloud expenses. It helps you keep track of all your cloud assets and manage them from one place. You can monitor your cloud usage and see where your money is going. Predictive data analysis by co-relating cost, revenue, and business metrics assists customers in achieving unit economics and understanding how specific units and/or customers impact cloud metrics, including cost, utilization, and performance! Using FinOps, businesses can save a ton of money, around 40% to 50%, 99.95% Uptime, and 100% accurate cost center mapping, according to Gathr, while making better plans and budgets.

1. Inform: Get real-time visibility of your cloud costs and understand what’s driving them. See a detailed breakdown of your spending patterns, allocations, budgets, forecasts, and analytics.

2. Optimize: Use the insights from the “inform” phase to make intelligent adjustments. Fine-tune your configurations, find and eliminate underutilized services, uncover potential discounts, and compare costs across different workloads.

3. Operate: Put your goals into action. Share spend data with stakeholders, optimize instance sizes, establish cloud governance, and automate processes. Make cloud operations integral to your design and development, ensuring a robust and cost-effective system. Implement governance mechanisms to monitor your cloud infrastructure, estimate costs, and conduct continuous audits.

Multi-Cloud Cost Monitoring allows organizations to effectively track and manage their cloud expenses across multiple cloud platforms such as AWS, Azure, GCP, and Oracle Cloud. With Predictive Analytics for Cloud Cost Optimization!

Source: Gathr

What else do you need apart from this dashboard? Predictive Analytics > Model Building > Predictive Model > Real-Time Prediction! Read about data visualization here.

Just transform your cloud spending landscape! Our out-of-the-box cloud cost optimization solution provides enterprises with the following:

1. Unified multi-cloud cost visibility: Monitor and compare costs across AWS, Azure, Google Cloud, and Oracle Cloud in one place.

2. Granular visibility into cloud costs: Analyze costs by regions, instances, top services, resources, and operations.

3. Tag compliance: Monitor and improve cost attribution with easy visibility into tagged and untagged resources.

4. Advanced alerts & recommendations: Receive alerts for budget exhaustion, costly instances, and anomalies, with customizable thresholds and integration with enterprise tools.

5. Improved cloud operations and ROI: Optimize containerized workloads, monitor Infra as Code pipelines, and streamline Kubernetes applications for increased automation and observability.

Predictive analysis can be useful for banks to predict customer behavior and preferences. This can help decide what products can be sold to which category of customers and help improve customer experience. It can also predict market fluctuations which help organizations address issues at the right time to get the best outcome. Predictive analysis can also help prevent fraudulent transactions by blocking suspicious access to a customer’s account. Based on customer credit scores, it can predict which customers are likely to miss payments and whom to lend money to, and it plays a significant role in gaining a competitive advantage and helps in better decision-making. Read here

Where to go from here?

All of the above are crucial for effective cost control, regardless of whether you utilize third-party tools, your CDP’s features, a customized set of services, or a combination. It’s rare to find a single solution that solves every problem. I highly recommend forming your team to put the right resources in place to monitor, surface, and optimize cloud costs and usage.

The next time you have a conversation with the CFO, they might say, “Fantastic job! Look at the money we saved this month!”

The post Scaling Up or Down: How Predictive Analytics and FinOps Can Optimize Your Cloud Spending appeared first on Indium.

The integration of AI and NLP in data warehousing has opened new horizons for organizations, enabling them to unlock the hidden patterns, trends, and correlations within their data that were previously inaccessible. AI, with its cognitive computing capabilities, empowers data warehousing systems to learn from vast datasets, recognize complex patterns, and make predictions and recommendations with unprecedented accuracy. NLP, on the other hand, enables data warehousing systems to understand, analyze, and respond to human language, making it possible to derive insights from non-formatted data sources such as social media posts, customer reviews, and textual data.

The importance of AI and NLP in data warehousing cannot be overstated. These technologies are transforming the landscape of data warehousing in profound ways, offering organizations unparalleled opportunities to drive innovation, optimize operations, and gain a competitive edge in today’s data-driven business landscape.

Challenges Faced by C-Level Executives

Despite the immense potential of AI and NLP in data warehousing, C-level executives face unique challenges when it comes to implementing and leveraging these technologies. Some of the key challenges include:

• Data Complexity: The sheer volume, variety, and velocity of data generated by organizations pose a significant challenge in terms of data complexity. AI and NLP technologies need to be able to handle diverse data types, formats, and sources, and transform them into actionable insights.

• Data Quality and Accuracy: The accuracy and quality of data are critical to the success of AI and NLP in data warehousing. Ensuring data accuracy, consistency, and integrity across different data sources can be a daunting task, requiring robust data governance practices.

• Talent and Skills Gap: Organizations face a shortage of skilled professionals who possess the expertise in AI and NLP, making it challenging to implement and manage these technologies effectively. C-level executives need to invest in building a skilled workforce to leverage the full potential of AI and NLP in data warehousing.

• Ethical and Legal Considerations: The ethical and legal implications of using AI and NLP in data warehousing cannot be ignored. Organizations need to adhere to data privacy regulations, ensure transparency, and establish ethical guidelines for the use of AI and NLP to avoid potential risks and liabilities.

Also check out our Success Story on Product Categorization Using Machine Learning To Boost Conversion Rates.

The Current State of Data Warehousing

• Increasing Data Complexity: In today’s data-driven world, organizations are grappling with vast amounts of data coming from various sources such as social media, IoT devices, and customer interactions. This has led to data warehousing becoming more complex and challenging to manage.

• Manual Data Processing: Traditional data warehousing involves manual data processing, which is labor-intensive and time-consuming. Data analysts spend hours sifting through data, which can result in delays and increased chances of human error.

• Limited Insights: Conventional data warehousing provides limited insights, as it relies on predefined queries and reports, making it difficult to discover hidden patterns and insights buried in the data.

• Language Barriers: Data warehousing often faces language barriers, as data is generated in various languages, making it challenging to process and analyze non-English data.

The Future of Data Warehousing

• Augmented Data Management: AI and NLP are transforming data warehousing with augmented data management capabilities, including automated data integration, data profiling, data quality assessment, and data governance.
• Automation with AI & NLP: The future of data warehousing lies in leveraging the power of AI and NLP to automate data processing tasks. AI-powered algorithms can analyze data at scale, identify patterns, and provide real-time insights, reducing manual efforts and improving efficiency.
• Enhanced Data Insights: With AI and NLP, organizations can gain deeper insights from their data. These technologies can analyze unstructured data, such as social media posts or customer reviews, to uncover valuable insights and hidden patterns that can inform decision-making.
• Advanced Language Processing: NLP can overcome language barriers in data warehousing. It can process and analyze data in multiple languages, allowing organizations to tap into global markets and gain insights from multilingual data.
• Predictive Analytics: AI and NLP can enable predictive analytics in data warehousing, helping organizations forecast future trends, identify potential risks, and make data-driven decisions proactively. Example: By using predictive analytics through AI and NLP, a retail organization can forecast the demand for a particular product during a particular time and adjust their inventory levels accordingly, reducing the risk of stock outs and improving customer satisfaction.

Conclusion

In conclusion, AI and NLP are reshaping the landscape of data warehousing, enabling automation, enhancing data insights, overcoming language barriers, and facilitating predictive analytics. Organizations that embrace these technologies will be better positioned to leverage their data for competitive advantage in the digital era. At Indium Software, we are committed to harnessing the power of AI and NLP to unlock new possibilities in data warehousing and help businesses thrive in the data-driven world.

The post Revolutionizing Data Warehousing: The Role of AI & NLP appeared first on Indium.

The democratization of data facilitates quicker decision-making, operational effectiveness, financial success, and improved customer experiences.

Data Challenges

We recently spoke with a customer about their challenges, and the most frequent ones were:

• Don’t have access to data.
• No integrated views of the data
• No trust in the data
• Not sure how to use the tools.
• Data SME availability

Why Data Democratization

Data Democratization will enable access to data for different departments or roles. Also ensures users can understand and visualize data in the context of their specific use cases. Some of the following users heavily rely on the data to do their regular activities.

• Sales
• Product and Engineering
• Marketing
• Customer Service
• HR
• Executives

Self-service is essential so users from organizations can explore, visualize and search for information without relying on IT-Teams who are busy and expensive resources.

Also read: Certainty in streaming real-time ETL

Data Governance and Security

Data democratization allows many users from the organization to access the data, and this leads to a potential risk of data security. So, we need to maintain control of the data platform to ensure high security and data governance. Every organization has their own unique method of security and access policy. The policy needs to be updated and kept up to date to have better data governance. Some of the key processes for better governance are:

• Create a guideline for who can access data and the type of data.
• Set the role-based access.
• Perform data discovery and profiling and create the policy based on the profiling reports.
• Set the policy for PII, HIPA and GPDR data.
• Enhance the base policy with encryption, masking, and tokenization across each data service accessed.
• Monitor the data access.
• ·Audit the data access.

Democratizing Data

Here are some of the steps that can be considered for implementing data democratization.

• Study the existing data landscape:
  • Is data on-premises, in the cloud or both?
  • Tools and technologies used to build the data platform.
  • Tools and technologies used to analyze data.
  • Volume of data managed.
  • Number of source systems
  • Level of data maturity

• Understand the stakeholders’ needs, business goals or objectives.
• Involve the business and end users in various implementing phases to achieve their decision making process
• Enable self-service analytics tools where IT involvement is very minimal and makes data governance easy
• Assess the data solutions.
• Address the following in data analytics solution.
• Descriptive Analytics — What Happened:
  • Uses historical data to measure performance. For example, tracking sales revenue.
  • Diagnostic Analytics —What Caused This?
  • Incorporates pattern recognition to help users diagnose why something is happening.
  • Predictive Analytics —The following events will take place:
  • Uses historical data, current trends, and modeling techniques to predict future performance.
  • Prescriptive Analytics —What Can We Improve?
  • Prescriptive analytics suggests future steps you can take to influence the outcomes predicted.

• Provide training to users for newly implemented democratization toolset.
• Make data accessible.

Benefits of Data Democratization

• Deliver trusted data to all employees and help on improved decision making.
• Data team can focus on strategic solutions rather than spending internal processing, which saves time and effort.
• High level data quality makes greater trust in the data and decisions
• Everyone in the organization understands the data and data becomes a second language in the organization.

The post Unlocking the Power of Data Democratization: Empowering Your Entire Organization with Access to Data appeared first on Indium.

Data analytics has proved to be an invaluable tool for improving security, preventing fraud, and increasing operational efficiency in the BFSI sector by analyzing raw data to uncover trends and insights.

We will examine the top 6 data analytics use cases in this article that are propelling the BFSI sector’s digital transformation.

1. Fraud Detection and Prevention Using Data Analytics in BFSI

Fraud is a constant threat in the quick-paced world of financial services and can cost banks, insurance companies, and other financial institutions a lot of money. It should come as no surprise that cybercriminals frequently target the BFSI sector, searching for vulnerability to exploit, given the amount of money at risk.

BFSI institutions can, however, turn the tables on these fraudsters thanks to the power of data analytics. Financial institutions can identify and stop fraud before it even starts by utilizing advanced analytics techniques like predictive modelling, machine learning, and data mining.

The secret to success is searching through the massive amounts of data produced by BFSI institutions for patterns and behaviors that could point to fraudulent activity. Financial institutions can identify potentially fraudulent activities and act before they cause significant financial harm by developing predictive models based on historical data.

Data analytics is a way to stay ahead of the competition as well as a tool for preventing fraud. BFSI institutions can spot new opportunities and maintain a competitive edge by utilizing the insights gained from data analytics.

BFSI institutions can protect their customers’ funds and open up new doors for growth and success by using the right analytics techniques and a commitment to constant vigilance.

Few Examples

To improve fraud detection and prevention, the BFSI sector can use data analytics in a number of ways. These strategies include, as some examples:

Money laundering

Fraudulent activity may involve moving money through multiple accounts to conceal the source of money that was obtained illegally. Using data analytics tools to identify anomalous patterns in transactional data, which can then be reported to the bank, it is possible to identify potential money laundering activities. While an investigation is being conducted, the bank may take the necessary action, such as alerting the appropriate parties or freezing the affected accounts.

Insurance Fraud

Making a false claim for financial gain constitutes filing a fraudulent insurance claim. Data analytics tools can be used to examine claims data and look for patterns and discrepancies with legitimate claims to find these fraudulent activities. Insurance companies are able to identify and stop the payment of fraudulent claims in this way.

False Credit Card Transactions

Data analytics solutions can identify possible fraudulent activities by examining credit card transaction data, including purchase history, transaction amounts, and location information. This enables banks to recognize such transactions and stop them from being approved, ultimately preventing fraud.

2. Personalized Customer Experience Through Data Analytics in BFSI

Ingenious business models that personalize customer journeys and advance financial inclusion have been developed by BFSI organizations thanks to the advanced capabilities of data analytics technologies like machine learning and Artificial Intelligence (AI). With the help of data analytics, BFSI institutions can use bots to communicate with customers in a variety of languages and dialects, offering individualized and practical branch-like services.

Furthermore, based on customer activity, big data and AI-driven data analytics can analyze customer profiles, behaviors, and needs, enabling institutions to suggest suitable financial services and products. Data analytics solutions have sophisticated natural language processing and machine learning capabilities that allow for accurate understanding of customer intent, facilitating contextual engagement and raising customer satisfaction.

Customer data analytics, for instance, can enable chatbots and voice assistants to give customers wise investment and savings advice. AI-enabled voice assistants can also assess a customer’s loan eligibility, facilitate disbursement, and keep track of equated monthly installments thanks to data analytics (EMIs).

Also Read: Testing a bank application: A Success Story

3. Risk Management Through Data Analytics in BFSI

The BFSI industry is exposed to a variety of risks, including credit, operational, regulatory, liquidity, and market risks, all of which have the potential to endanger their operations. BFSI institutions use data analytics tools to effectively identify and manage these risks to reduce their impact.

Businesses in the financial services industry (BFSI) can learn more about various facets of their operations and spot potential risks by analyzing data. These insights can be used to evaluate risks and create individual mitigation plans for each one. Data analytics, for instance, can be used to analyze customer behavior, spot fraud, keep an eye on market trends, and assess the creditworthiness of customers. Due to their ability to manage risks in real time and make informed decisions, BFSI companies can avert potential problems before they become serious.

4. Predictive Analytics for Investment Decisions in BFSI

Predictive analytics is an essential tool for BFSI companies to use when making informed investment decisions. BFSI companies are constantly looking for investment prospects. These businesses can analyze historical data and statistical models to gain insights into upcoming market trends by utilizing predictive analytics, allowing them to recognize and seize potential investment opportunities.

Here are a few ways that BFSI uses predictive analytics to make investment decisions.

Portfolio management and assessment

Using historical data, predictive analytics can assess the returns & risks related to a specific investment. The predictive analytics model can assist BFSI firms in identifying trends and patterns that may indicate an investment’s likelihood of success or failure, enabling them to decide whether to pursue the investment opportunity or not.

Financial advisor assessment

Firms can analyze the performance of an advisor (internal and independent) by using data analytic techniques. It is possible to predict which advisor is expected to bring in higher revenues by assessing their past performance. Firms can in turn keep these set of advisors highly motivated thus enabling them to beat their past performance and generate higher revenue.

Customer Segmentation

To classify customers based on their investment preferences and behavior, BFSI companies use predictive analytics. Predictive analytics models can identify patterns and trends in customer data through customer analysis, allowing BFSI companies to tailor their investment products to the specific requirements of various customer segments.

5. Regulatory Compliance Through Data Analytics in BFSI

BFSI institutions operate in an environment that is highly regulated, and failure to comply with regulatory requirements can result in costly fines, negative legal consequences, and a damaged reputation.

As a result, these companies must devise creative strategies to guarantee that all legal requirements are met. One such solution that can assist BFSI companies in complying with regulations is data analytics.

Here are some strategies for using data analytics to help BFSI firms comply with regulations:

Reporting

BFSI organizations, as previously mentioned, heavily rely on data. However, it can be difficult to manually generate reports that show compliance with regulatory requirements. In situations like these, data analytics is essential. BFSI companies can use data analytics tools to analyze all data pertaining to compliance activities and produce reports that show regulatory bodies that the company complies with its compliance obligations.

Monitoring Compliance

By examining vast amounts of data related to compliance, BFSI companies can use data analytics to track their adherence to regulatory requirements. This makes it possible for them to spot potential compliance problems and take appropriate action to stop them from developing into serious issues.

Audit Management

By giving auditors the knowledge, they need to assess compliance and pinpoint areas for improvement, BFSI companies can use data analytics to support the auditing process. This reduces the possibility of regulatory fines and helps organizations avoid costly compliance mistakes. Data analytics can offer insights that help auditors in their evaluation process by analyzing data pertaining to compliance activities, such as identifying potential risks and areas of non-compliance. BFSI companies can avoid compliance problems and guarantee that they are successfully adhering to regulatory requirements by doing this.

Read our Success Story on : Real-time collaborative Fraud Analytic solution to combat Identity Theft

6. Cybersecurity Using Data Analytics in BFSI

Cyberattacks and fraud are very common in this sector. An isolated security lapse can result in sizable monetary losses and harm to a company’s reputation. Because of this, data analytics are essential to identifying and preventing cyber threats. Cybersecurity is therefore of the utmost importance in this industry. The BFSI industry can promote cybersecurity through data analytics in the following ways.

Threat Detection

Data analytics can identify potential cyber threats by examining trends and patterns in network traffic or other data sources within BFSI systems. The BFSI company can take appropriate action to eliminate the threat as soon as an abnormal activity is discovered, preventing further damage.

Incident Response

Data analytics helps incident response by supplying real-time data and statistics on cyber threats and security incidents. This allows BFSI organizations to quickly respond to potential security incidents in order to stop them from escalating.

Risk Assessment

Data analytics can be used by BFSI companies to assess the risks of cyberattacks. They can identify areas of risk and create risk mitigation strategies to protect their data from unauthorized access by analyzing data on potential vulnerabilities and cyber threats.

Compliance Management

To make sure that BFSI companies adhere to the various cybersecurity standards and regulations governing their operations, data analytics tools can be used. With the aid of these tools, the company can identify compliance gaps in cybersecurity-related activities and take the necessary corrective action to be following legal requirements and industry best practices.

Wrapping Up

The BFSI sector has always relied heavily on data, but data analytics is pushing that dependence to new heights. BFSI companies can use data analytics to drive digital transformation and open new opportunities for growth by leveraging their data.

BFSI companies can reduce fraudulent activity, personalize customer experiences, increase operational effectiveness, and guarantee regulatory compliance by using data analytics. Furthermore, data analytics can aid in the detection and prevention of cyberthreats, protecting sensitive data from unauthorized access.

BFSI businesses must embrace digital transformation and use data analytics tools in order to stay ahead of the competition. They can accomplish operational excellence by doing this, giving them a competitive advantage in the market.

Our team is here to support BFSI organizations integrate data analytics into their processes as they lead the way in digital transformation. Get in touch with us right away to find out more about how we can support your digital transformation efforts by assisting you in maximizing the power of data analytics. Click here for more details…

The post How Data Analytics Is Transforming the BFSI Sector appeared first on Indium.

How are healthcare organizations leveraging predictive analytics to derive actionable insights from their ever-growing datasets? We find out here.

Staying ahead of Patient Health Deterioration

It is the most essential application of predictive analytics in healthcare.

It helps caregivers react quickly to any change in a patient’s vitals and gather foresight into possible deterioration before symptoms are evident.

A 2017 study demonstrates this: at the University of Pennsylvania, a predictive analytics tool using machine learning and EHR data helped identify patients vulnerable to severe sepsis or septic shock a full 12 hours before the onset of the illness.

Predictive insights are particularly valuable in the intensive care unit (ICU), where timely intervention can help save someone’s life and prevent patient health deterioration.

The increased adoption of wearable biosensors offers manifold benefits, too, for care providers. They enable remote health monitoring and help detect early symptoms of health deterioration.

Preventing Patient self-harm

Early identification of individuals likely to self-harm will help provide the essential mental healthcare to avoid potentially serious or fatal events.

According to the World Health Organization, almost 800,000 people die of suicide each year, which is one person every 40 seconds.

Studies have showed that predictive analytics, using electronic health record (EHR) data and depression questionnaire, helps identify individuals at higher risk of committing suicides or other forms of self-harm.

In a study led by Kaiser Permanente (a leading American healthcare provider) and conducted together with Mental Health Research Network, EHR data combined with a depression questionnaire helped accurately detect those with a higher risk of attempting suicide.

Another study, featured on the American Journal of Psychiatry, aimed to build and validate predictive models with the help of electronic health records to predict suicide attempts and suicide deaths after an outpatient visit.

Based on predictors such as prior suicide attempts, mental health substance diagnoses, mental health and more, it was found that within 90 days of a mental health visit, suicide attempts and suicide deaths among individuals in the upper one percent of predicted risk were 200 times more common than those in the bottom half of the predicted risk scale.

Predicting patterns in patient utilization

Predictive analytics helps healthcare organizations ensure adequate staffing levels for busier clinic hours, minimize wait times and improve patient satisfaction.

With the help of big data visualization tools and analytics strategies to model patient flow patterns, healthcare centers can ensure the inpatient department has adequate beds available for patient admission, that the outpatient and physician offices have enough resources to reduce patient wait times and manage workflow and scheduling adjustments accordingly.

Scheduling changes help nurses and doctors cope with the increased patient flow while reducing the burden on them, thus ensuring they provide timely care and improve patient satisfaction.

Data Security

Predictive analytics and artificial intelligence (AI) play a key role in boosting cybersecurity, with the sophistication of cyberattacks (involving malware, phishing and more) rapidly on the rise.

Confidential patient information worth big money, a vast network of connected medical devices, outdated technology, among other factors, make the healthcare industry a constant target of cyberattacks.

Predictive analytics tools and machine learning help calculate real-time risk scores for different transactions and requests, making the system respond differently based on how the event is scored.

David McNeely from the Institute for Critical Infrastructure Technology says: “Once the risk score has been determined in real-time, the system can use this during a login event to either grant the access for a low-risk event or to challenge for Multi Factor Authentication [MFA] or possibly block the access for high-risk events.”

Create risk scores for chronic diseases

Early identification of individuals with a higher risk of developing chronic illnesses is essential for two reasons. It gives care providers and patients the best chance of preventing long-term health issues. It also helps mitigate the potential cost and complexities of the treatment.

By creating a risk score—from examining patients with identical characteristics, gathering lifestyle and clinical data and using algorithms to understand how various factors effect patient outcomes—healthcare providers gain insight into the type of therapy and wellness activities which can benefit their patients.  

Summary

As far as health management is concerned, prediction is the foundation for prevention and treatment. Predictive analytics helps healthcare providers in different ways. In addition to those mentioned above, the technology helps identify individuals likely to miss a clinical appointment and send timely reminders, manage supply chain to enhance efficiency and cut down on unnecessary costs, develop effective therapies and new medication, improve patient engagement and more.

Given its manifold benefits, it’s no wonder that, according to a 2017 study by the society of actuaries, 89 percent of healthcare providers were then either already using predictive analytics in their organizations or planned to in the next five years.

The post Top 5 use cases of Predictive Analytics in Healthcare appeared first on Indium.

Businesses spend tons of money on marketing to acquire new customers and increase customer base. However, businesses can save a lot by just retaining existing customers. Yes, according to customer retention stats , 5% boost in customer retention increases the businesses profit to 25% to 95%

Some of the industries that are affected by churn rate are insurance companies, online streaming services, ecommerce, subscription services (Gyms) etc. In this blog, we are going to consider the example of Insurance companies. This blog will showcase the struggle insurance companies face to retain their customers and how analytics can help address the issue.

Who is a lapsed customer?

A lapsed customer is who has not come back to buy in an expected amount of time. In this case a customer who does not renew his/her insurance policy. This happens due to many reasons. The customer might not be happy with the service, they might get a better service for the same price or they do not like a particular product.

No matter what the reason is, it is your responsibility to bring them back, otherwise, you are leaving money in the table.

Luckily, with advanced analytics, you can target certain customers and create specific marketing campaigns to encourage them to buy from you again.

Analytics Case for Lapsers

How to methodically attack the lapsers problem?

As an Insurance company, you will collect a massive amount of data from your customers. Not just customer data, but also the transaction data with multiple product lines of general insurance, life insurance, health insurance & medical insurance etc.

The problem here is that the data sits in the computer and most businesses are not capitalizing on the benefits it can bring to the business.

Lapsed customer is one of the big problems the insurance industry faces. With the help of advanced analytics, insurance companies can not only solve the lapsers problem but also target the right products to the right customers, thus bringing more revenue.

To demonstrate, let us divide the customer base into 3 segments and Strategize accordingly

1. Lapsers – Customers who have moved out of the system. We have to strategize a plan to win back the lapsers
2. Likely to Lapse – Customers who are most likely to move. We have to retain them.
3. Loyal Customers – Customers who have a low likelihood to move. Cross-sell other products to these customers

Lapsers – Win-back Strategy

The best strategy to convert a lapsed customer into a loyal customer is by reaching out to them and persuade them with similar products. You must figure out what will be the “next best product” to sell to the lapsers.

Fortunately, analytics can help in finding the right products for lapsed customers.

Find the next best product with the help of a collaborative filtering recommendation technique. This technique will help you by filtering out items that a customer like based on reactions by similar customers.

Keep in mind, use a limited set of 3-4 recommended products rather than all products to win back the customer. Suffocating your customers with all your products will not help you achieve your goal.

Likely to Lapse – Retention Strategy

The next step would be to identify the customers who are likely to lapse and make sure they are not leaving you. To identify that,

• Build a lapse model to find the likely lapsers.
• Take the top 30-40% high probability lapsers and try retaining
• Retain using the same product or Sell another product

Loyal Customers – Cross-sell Strategy

Loyal customers are the ones who are most likely to stay with you. These customers like your products and will be ambassadors for your brand. The best strategy for them is to cross-sell your products.

What product to cross-sell to loyal customers?

Use association rules to increase cross-sell conversion. The Association rules method will be apt for uncovering the relationship between variables in large databases. This rule-based machine learning method will help in the cross-selling strategy.

Once you have the data and plan, all you have to do is make sure that cross-selling campaign is run successfully.

Customer who buys ‘a’ GI and ‘b’ TI also buys ‘c’ Insurance

In a Nutshell

This blog explained the use case of Insurance industry alone. However, analytics can be used across multiple industries. In the future, more and more businesses will use predictive analytics to forecast events and gain actionable insights that will help them in making the business better.

By using analytics, you are not only getting a competitive advantage but also save time, resource and money in the long run. After all, data is only a strategic asset when you can put it to work. Analytics will only help in identifying whom to target with what, it is the organization’s responsibility to make sure the best products and services are delivered to their customers.

The post How Insurance Industry can benefit from Advanced analytics? appeared first on Indium.

According to a KDNuggets analysis, there are over 2 billion gamers (players) globally. This offers a huge potential to generate revenues from your game. But this advantage is counterbalanced by the millions of games vying for gamer attention. This means that gamers have a choice and will flit from one game to another unless something compels them to stick on and return. The high numbers mean nothing unless they translate to customers for the game developer.

What sells, what lures the gamers, and what retains their attention – these are questions they need answers for. A clear understanding of the following 5W1H that can help in improving the quality of the current games and introduce features that will make the game far and wide, both in popularity and revenue generation:

1. Who are the players
2. What types of features are popular
3. Where are the revenues coming from
4. Which marketing channels to focus on
5. What is the difficulty level that gamers are willing to play at
6. How to devise strategies to create new markets

The Power of Insights from Game Analytics

Gaming is no child’s play. It may be entertainment or thrill for gamers, but for the game developer, it is all about data-driven strategy formulation for business growth. They need to understand their audience, build a loyal customer base who return to their games often, and influence others to play.

The information that the game developer is seeking is easily available in the modern data-centric times we live in. Access to real-time data is a reality that makes it possible for developers to capture trends and gain insights that can help them improve their game design, content, and flow as well as introduce new features and levels that have a higher chance of success. Gaming analytics and data science allow producers to understand how gamers engage with their games and decide how the game should evolve to continue to delight customers.

Some of the key ways in which game analytics can facilitate this is through:

• Predictive Analytics for analyzing current engagement patterns to predict future trends and plan game development strategy accordingly. This requires skills in data science.
• Churn Analytics to help understand what makes players leave, the difficulty level at which the gamers exit, analyze the root cause, and the like. This is descriptive analytics and happens at three levels:
  – Assessing when players prefer to play  – whether they play every day or during weekends
  – Whether the free version or the paid one draws more traffic
  – This determines where the revenue generation is higher and to focus more on the more profitable aspects of the business
  – Up to which difficulty level gamers play and the reasons they exit
  
• Customer Experience Analytics is another area that helps assess the popular features of the game

The insights thus gained help with:

• Enhancing the gaming experience
• Improving the revenue generation possibilities
• Creating the right marketing mix to leverage different channels
• Identifying strengths and building on them
• Correcting weaknesses

Indium – The Gaming and Analytics Expert

To derive meaningful insights, it is important to have an understanding not only of gaming and the game but also data, how to extract it, and leverage it. A solution provider like Indium Software, a diversified Big Data, Analytics, Digital, and Gaming company, combines experience with expertise to help you tap into data and translate it into success. Not only does Indium have a strong analytics capability but also has a center of excellence for gaming, iXie, that is well equipped with different devices and a strong team representing interest across genres.

The Indium team works with a range of tools for data extraction, transformation, AI, and visualization to create interactive dashboards that are customizable and available to different functions based on their needs. These include:

• Python, a programming language
• Random forest, an algorithm
• Tableau, PowerBI and Qliksense visualization tools
• Apache nifi, DBT, data extraction and transformation tools 
• AI & ML algorithms such as Regression Algorithms, Tree Algorithms, Boosting  techniques, PCA, clustering

With seven years of experience working with more than 50 gaming companies and 150 gaming titles across genres, the Indium team ensures you get the inputs you need to improve the monetization of your game.

For features testing, the team can play games on different devices using players from different genres to assess the game’s functionalities, performance and identify any glitches or deterrents to proceeding further.

If you would like to make your game a real game-changer and benefit from the Indium gaming heritage and Big Data and analytical expertise, contact us here: https://www.indiumsoftware.com/inquiry-now/

The post A Peek Into Indium`s Expertise In Game Analytics appeared first on Indium.

Some of the key drivers will be the increasing disposable income that has led to an improving purchasing power of people. The convenience of ordering products online on e-commerce platforms and retail stores will further stimulate market growth.

To meet this growing demand and understand its customers better, e-commerce businesses are increasingly investing in advanced business intelligence and analysis tools. This can provide insights into which products are moving fast, in which markets and how to improve their operations to service the customers better, maximize profits and gain a competitive edge.

3 Focus Areas

E-commerce analytics falls into three main areas:

• Data Visualization and Descriptive Analytics: Dashboards created using historical data of customer behaviour and sales records provide snapshots of all key metrics for improved decision making
• Predictive Analytics: Using churn prediction, market-basket analysis and the like, e-commerce marketplaces can predict the demand for products and design promotions to cross-sell and upsell for improving sales and customer engagement
• Cognitive Analytics: Video and images are analysed for product classification based on predefined parameters to quickly upload new products and avoid errors and time delays associated with manual intervention

Challenges and Benefits

For e-commerce platforms and online stores of retail outlets, an understanding of which products are moving, where their customers are coming from and what their customers are saying are very important.

When a product is performing well, they can boost it further by creating suitable marketing collaterals and also pair it with likely related products to increase the overall sales and growth.

A product which is not performing well will need equal efforts to promote and special offers and discounts to increase its visibility can be designed to improve its sales.

Based on geographies, retail businesses can also plan their campaigns for their stores in those locations and step up promotions for those geographies where they have a presence but not as many footfalls.

Machine learning and artificial intelligence can be used for cross-selling and upselling of related products. For example, when someone is purchasing a mobile, relevant accessories can be displayed to encourage customers to purchase a mobile case or headphones, and so on. When a customer purchases a particular model, they can be tempted with a higher model with better and more features.

Analytics can also be used to understand conversion rates from footfall to sales and the insights used to improve the conversions. Reviews, both positive and negative, are a storehouse of information on what works and what doesn’t.

Negative Review Analytics helps to build the product line with quality to meet customer expectations. Sentiment analysis allows the e-commerce players to build on their strengths, rectify their weaknesses and retain the unsatisfied customer.

For instance, in an e-commerce site, a particular bag was very popular but soon, negative feedback started pouring in. On analysis, it was discovered that the bag was still good but a flap that was added as a design element was made of a different material that did not last long as expected. This is valuable input for the e-commerce marketplace as well as the manufacturers to improve.

Competitor analysis can also be used to devise marketing and, more importantly, pricing strategies to improve the edge over business rivals. Marketplaces and FMCG can especially benefit from this.

Use Cases

Indium used sentiment analysis for a sports retailer where the reviews were analysed to understand customer perception and feedback of the products. Indium’s proprietary data extraction tool, Tex.Ai enabled extracting key phrases to gain insights on customer views. This helped the sports retailer improve on its design and customer service.

For an e-commerce aggregator, Indium used teX.Ai to automate product classification.

Chats with customers, either on chatbot or by a customer executive over the phone can be another rich source of insight into customer satisfaction levels. Using data extraction, the discussion can be analysed for what the customer needs, how it was responded to and if it had been concluded to satisfactorily. This is crucial in building customer loyalty and training the executives and the chatbots to ensure there is a closure.

Analytics can also be used for resource optimisation to reduce the waiting time of customers trying to reach a representative.

Indium Advantage

Indium Software, in its more than two decades of existence, has been providing holistic solutions on cutting edge technologies. It has carefully built a team that is a judicious mix of domain and technology experts.

Our e-commerce team can set up and run a marketplace from the ground up using the latest technologies including in-build analytics. It can also build solutions for analytics on existing platforms using machine learning and artificial intelligence. Strong solution architects, subject matter experts and expertise in analytics make Indium an ideal partner for e-commerce platforms and retail brands seeking to leverage the World Wide Web.

The post Analytics in E-commerce and Indium’s Expertise appeared first on Indium.

It is a differential interest business when we compare the lending rate of the bank to the customer and the borrowing rate of the bank from the Federal Reserve.

In the case of tightrope business, it becomes cardinal to tighten any leakages of revenue via delay in interest payment and capital erosion by default.

Just like any other industry, where the payment is to be performed after the product purchase, there are bound to be defaulters and late payees. In financial services, it is cardinal to track every customer based on his behaviour.

Besides the initial checks for his loan paying ability by checking the credibility score and demographical variables, there is a behaviour pattern that gives rich insights on the customer’s payment behaviour.

And when the transaction behaviour is combined with demographics and the product characteristics which in this case can be the interest rates, loan period, installment amount and others, it throws up light on what the customer is bound to do – whether he is going to delay, pay on time.

This type of modelling is called Propensity Modelling. It is used in a variety of cases such as propensity to buy, default, churn.

The Defaulters’ case

A financial services company was already monitoring the customers by a factor – that is if he has delayed his payment.

Once a customer delays he gets into the blacklist, on the other hand, the customers who are prompt are always in the whitelist.

Is there more to this logic we can build? We have important variables on hand – the mode of payment, the days between payment and the due date.

Then there are loan characteristics like interest rate, time period, installment amount and others.

Using these, we can build a statistical model to tighten the logic. The objective of the model is prediction of the default. To refine it further can we classify the customers as defaulters and non-defaulters.

While the classification of customers as defaulters and non-defaulters sound more clear and exciting, in the models we don’t get labels but a numeric score, in this case, a probability of default based on the combination of characteristics.

We can utilize this probability to define a threshold for defaulters or non-defaulters. Often the business comes up with these definitions of the customers, in this case, it was decided to have three types – Least Risky, Slightly risky, Risky, just like a modified 3 rating Likert Scale.

There are many classification models in use – decision trees, logistic regression, XG Boost models, and Neural Networks.

Exploratory Analysis

Before touching the modelling tasks, it is fundamental to understand the data and fix up issues.

A preliminary exploratory data analysis (EDA) on the distribution of variables, find the missing values, correlation between the variables. It gives answers to these questions.

• How to do missing value imputations?
• Are there variables we can dismiss by empirical methods?
• Can we get some important insights from EDA?

Correlation

For example, when performing correlation test some variable combinations such as gross loan- net loan, balance amount- Loan status might show a high correlation.

One of these variables has to be removed to increase the explaining ability of the model. Also, it decreases the computation complexity with fewer variables.

Box Plots

Some plots that will help us know about the distribution of variables are box plots. They give the distribution of the variables.

For instance, when the installment amount was plotted for 3 types of customers (Least risky to Slightly to Highly Risky), the distribution of highly risky was lower than the least risky customers.

De-facto, our assumption might have been as the installment amount increases the risk increases, whereas this plot threw that assumption upside down.

With the increase in installment amount, customers were paying better. A plausible explanation could be the customers are lethargic when the amount is low. Possibly!

Bar Plots

Cross-tabulations of some important variables gives a relationship between the variables. At the bare minimum, the risk category and variables like tenure, installment amount shows up good insights.

To quote the case of tenure tabulated with the risk type, as the tenure increases the risk of default increases.

A reasonable explanation could be, customers become lethargic when the commitment period is long, so much common for the business and life!

Looking into other variables like the vehicle make in case of auto loans, the house type purchased in case of home loans can give important insights.

Certain vehicle makes or house types can be more prone to default, the significance of the relationships can be tested using Chi-square tests.

Modelling

An XG Boost model was fit on the data to find the probability of risk of default.

The training to test ratio can be set at a standard size of more than 60: 40. To give more allowance for training and at the same time not ignoring the size of the testing set, we kept the ratio at 70:30.

A variable importance test is one which ranks the variables that explains the explanation power of independent variables to dependent variables.

A random forest model was run to find the ranks of variables. Some important loan characteristics like interest rate, installment amount, loan amount were the usual suspects.

XG Boost algorithm develops on the decision tree model by voting the best classifying decision trees.

The subsequent model trained develops on the errors of the previous model, hence it has a starting point from the previous model.

We fine-tuned parameters for the model to improve the accuracy. For example, the number of trees, as there were less than a million records we fixed this as 40.

The max depth was kept at 8 as we have reduced the number of significant variables to be input in the model to 15. The learning rate was experimented with values of 0.1 and on both sides.

The confusion matrix was generated to find the accuracy, prediction and recall.

Just to explain the accuracy, it is how accurately the model predicts the positives and negatives.

The accuracy turned to be consistent at about 70% when cross-validation was done by random cutting to generate 10 runs of the model.

The classification model was scored on the real-time customer database. It shows up three probabilities for every customer, one each for Least risky, slightly risky and highly risky. For some customers,

• Case a – it can be a clear demarcation with one of the probability tending to 1
• Case b – for others the probabilities can be evenly split.

LIME outputting

LIME is the abbreviation for Local Interpretable Model Agnostic explanations. Many of the times, business requires simple explanations in short time, they don’t have time to wrap their head around the measures like variance, significance, entropy etc.

and how they combine to explain the classification of labels. When a customer is presented to be with high risk for default, how can we explain that to business in simple terms?

LIME does that for us, it explains how each variable is powering the classification. Although it cannot be precise, it is an approximate explanation of why the model is tending to classify the customer as such.

The image below shows an example of different variables at interplay to predict the customer’s risk type.

Putting everything together to use

We have a set of insights coming from the EDA, the model is throwing up the risk metric and the LIME outputs are interpreting the model results. How to get the acts together with the three components?

The main advantage of doing an EDA is it gives heads up insights. At a very early stage, the business can post red flags for certain customer types.

As seen earlier, we will be able to predict a defaulter, even before the person defaults once by taking into consideration the variables combinations like instalment amount, period of the loan, interest rate.

The set of insights are automated and can be run every quarter or six months to generate the red flags.

The Classification model being the main component, predicts the default risk. The probability of the customer to default can be used in many ways by business.

• The operations team can take up the top decile of the risky customers, monitor them closely and frequently.
• The sales team’s incentives can be tuned as per the default risk.
• The marketing team can focus on campaigning for targeting certain vehicle makes or house types, certain geographies as they know which are more prone to default.

To judge fairly a machine output, we have to give allowances to some really tricky and wacky predictions from machine learning.

It completely runs by past data and hence some predictions can be completely wrong. 

LIME function helps in digging deep into those cases and understand the logic and rules employed by the model.

It will be able to give the exact reason as to why a person is classified as such, maybe a new line of thinking to the business.

The post Loan Defaulters Prediction appeared first on Indium.

Being a marketer, one would recognize the immense power of data. Never before have we had access to data like we do today.

For many organizations difficulties arise in collecting, integrating and storing the data. However, making use of this data to drive better business decisions gives organizations a competitive advantage.

And I sure am not talking about reporting here.

Of course it’s intriguing to know what happened in the past and those monthly excel sheets might even get read once, but the organizations that use this historical data to focus on the future and predict future outcomes are the organizations that are surging ahead by leaps and bounds and are discovering enormous value.

When you look at the world of data science today, there is a lot of sophisticated work happening in the field that may be beyond your scope of understanding.

But, Predictive Analytics is something that is within reach for just about anyone and is waiting for it’s advantages to be exploited.

To put it simply, predictive analytics is making use of historical data to predict the likelihood of future outcomes.

The major case in point is increasing your measure of success because you can optimize anything that can be measured or defined.

Predictive models are very different from descriptive models – which can tell you what happened in the past, and diagnostic models – models that can explain or provide rationale as to why something happened.

Now that you know what Predictive Analytics is about, you should be intrigued about it’s applications.

We’re going to see 5 applications that will get you thinking about how you are going to make use of data to boost performance across various verticals in your organization.

Conversions – Yes, we’re all chasing conversions. At the same time it is critical to know who is converting and this is exactly where understanding and targeting the right prospects comes in to play. With the wealth of customer data already in your possession, predictive analytics can help you with quite a few things.

• Customer Loyalty : Predictive models will help you understand what segments and behaviors point towards the tendency to keep on consuming your products and services. Predictive models also help you understand the behaviors and attributes that are likely to cause a switch to another brand.
• Lifetime Value : As you’re scouting for new prospects and evaluating the existing customer base, you can make use of your data to forecast the net profit that will be accredited to the entire future relationship. How this helps is you can target your outreach, marketing campaigns, bonus/loyalty programs etc. more accordingly.
• Churn : Losing customers is never good for business. However, predicting the risk of a customer abandoning your brand can help you drive more targeted and personalized retention programs.
• Market Basket : The checkout basket can be turned into an advantage with the use of predictive analytics. You can understand which products are purchased together and which are likely to be purchased one after the other. This helps you identify your buyer’s purchasing behaviors.

Marketing budgets are better allocated when predictive analytics is used. The newest tools in the market, the best techniques when combined with the bundle of data being generated via every click and impression is a huge opportunity to make sure every marketing dollar is well spent.

• Marketing/Media Mix : There are lots of channels, up and down the funnel where you are likely to spend money. Being able to credit each touchpoint with value in the purchase path and predicting the budget allocation can help you attain more performance out of less spend.
• Audience Targeting : The “spray and pray” targeting tactic has become old school as today, we are gaining more and more data about who may become a customer and where we can find them. Predicting the probability of someone in the audience converting to a customer and the value that they bring can help the targeting become more precise and lessen the marketing dollars being spent.
• Purchase Intent : Usage of customer data/behavioral data to predict the intent of purchase for any lead/prospect can be immensely valuable to an organization. This can also be modeled to predict digital’s role in driving offline sales.

If you are investing in digital assets like websites and mobile apps, it only make sense that you’ll want to make sure that you’re getting the most from them.

Predictive Analytics can help you understand what factors will result in the best content, what areas can be customized to particular users and which areas of the digital experience are ideal for optimization.

• Content optimization : Time and resources are spent on creation, development and maintenance of content and it we have a lot of data about how the content is performing. From this data, pulling out factors that have been successful will help guide your content strategy in a way where you will produce pages and experiences with a high likelihood of achieving the set goals.
• Personalization : The combination of digital experiences and customer data results in you starting to segment and predict which group of users is likely or not likely to respond to your messages, offers etc. Today, the personalization tools give you the power to achieve user level customizations to give people what you know they are likely to want.
• Testing Strategy : A/B and multivariate testing is not a new phenomenon but the difficult part of testing is figuring out what to test. Predictive analytics can help you understand which grey areas of the experience need maximum improvement and it also helps define a hypothesis. Apart from providing a better experience for the users, the results can also feed the model for improved accuracy.

Risk is a very broad category. In reality though, all organizations try to mitigate risk with every action of theirs.

Data is used to pin point the factors that tend to create risk and then predict unwanted scenarios that are likely to occur in order for you to come to terms with the unknown and mitigate consequences.

• Fraud : This one is for the e commerce space where a lot of work has gone in. Organizations can use their own data in order to evaluate factors that are likely to be associated with fraudulent activities and in addition they can address these issues by improving security by adding more steps for checkout, selective payment options etc.
• Collection & Recovery : The accounts receivable has a direct impact on your cash flows and making sure you have a handle on accounts receivable is imperative as it also affects the organization’s ability to operate. Predictive analytics can help identify at risk accounts and will help formulate strategies that mitigate collections risk and have high success rates.
• Pricing : Pushing a product out in the market is influenced by price. With a price too high, there is the risk of acceptance and volumes ; with the price too low, profitability becomes an issue. Prediction of price elasticity, pricing gaps, thresholds and profitability targets can be done with the help of existing products and competitive data. This will help you arrive at an optimal price point.

Marketing and customers are extremely important, yes. However, at the end of the day the products and services have to be delivered with maximum operational efficiency.

Demand prediction to Supply chain management – Predictive analytics can prove to be an integral part of the planning and execution stages of operations.

• Forecasting : Be it planning of production cycles, demand predicition for new products and services or estimating financial performance, historical data can be used to model plausible scenarios or outcomes. Those models can be manipulated to understand what should be done now to impact the results you are most likely to see in the future.
• Network Optimization : Networks can mean many things, this may include supply chains, processes and just about anything that has inputs, outputs and dependencies. Using the data to work around the factors that influence the efficiency of each node within the process will help find the optimal paths through them.

These are just a few areas in which organizations can leverage the power of predictive analytics to make informed decisions about future states.

The tools and technology available today make these analyses accessible to almost every organization.

What’s left to do? Identify a business challenge, evaluate the data you have to work with and finally come up with a modeling solution that will help you see the future and make decisions driven by insight.

The post 5 Ways how Predictive Analytics can help you appeared first on Indium.