Category: Data Analytics

Customer analytics in retail: A complete guide with charts and shopping cart.

Customer Analytics in Retail: A Complete Guide

December 1, 2025

How Retailers Use Customer Analytics to Drive Personalization & Growth Customer analytics is now one of the most important growth levers in modern retail. As consumer expectations shift toward frictionless, omnichannel experiences, retailers must understand who their customers are, what they want, and how their behaviors are evolving. For C-suite leaders, strategy executives, and enterprise tech teams, customer analytics is no longer an operational capability—it’s a board-level strategic asset. What you will learn in this guide: Customer analytics turns retail data into actionable insights for personalization and profitability. Retailers analyze behavior using POS, loyalty, digital engagement, and predictive models. Segmentation and CLV analysis help retailers improve retention, target high-value audiences, and reduce marketing waste. Modern retail analytics stacks combine CDPs, cloud data warehouses, BI, and AI. What Is Customer Analytics in Retail? Customer analytics in retail is the process of collecting, integrating, and analyzing customer data to understand behavior, predict future actions, and personalize the shopping experience. In simple terms: Retailers use customer analytics to know what customers want, what they buy, why they buy it, and what will make them return. Core components: Descriptive analytics: “What happened?” Predictive analytics: “What will happen next?” Prescriptive analytics: “What should we do about it?” Outcome:Better personalization, higher loyalty, smarter merchandising, and sustainable revenue growth. Why is customer analytics important for retail? Customer analytics helps retailers make informed decisions about marketing, inventory, promotions, and customer experience—leading to higher profitability and retention. How Do Retailers Analyze Customer Behavior? Retailers analyze customer behavior by integrating data from multiple channels into a unified view. 1. Identify and unify data sources Retail behavior data includes: POS & transaction history Loyalty program activity E-commerce browsing and conversion data Mobile app activity In-store traffic and heatmaps Customer support interactions Email and push engagement These are merged into a Customer 360 profile. 2. Apply behavioral analytics Retailers use methods such as: Basket analysis Product affinity scoring Cohort analysis Price sensitivity modeling Path-to-purchase mapping 3. Use AI/ML for predictions Models forecast: Purchase probability Churn risk Category expansion likelihood Discount responsiveness 4. Activate insights across channels Analytics power personalization in: Website and app recommendations Email and SMS journeys Loyalty program offers In-store clienteling and POS prompts How do retailers collect customer data? Most data comes from POS systems, loyalty programs, website/app tracking, in-store sensors, and marketing platforms. Tools Retailers Use for Customer Analytics Here are the main category tools used in enterprise retail analytics ecosystems: 1. Cloud Data Platforms (CDPs + Warehouses) Used for centralized data storage and modeling: Snowflake Google BigQuery AWS Redshift Databricks 2. Customer Data Platforms (CDPs) Used to build unified customer profiles: Segment Tealium mParticle Adobe Real-Time CDP 3. BI & Visualization Tools Used to analyze and visualize customer insights: Tableau Power BI Looker 4. AI/ML and Personalization Engines Used for real-time personalization and recommendations: Salesforce Marketing Cloud Adobe Experience Platform Insider Dynamic Yield 5. Retail-specific analytics applications Used for merchandising, pricing, and loyalty analytics. Looking to deploy CDPs, CLV models, or real-time personalization? Explore our Retail Analytics Consulting Services for a transformation roadmap. Customer Segmentation in Retail Customer segmentation groups customers based on shared attributes to support more relevant messaging and offers. Why Segmentation Matters for Retail Segmentation helps retailers: Personalize marketing Optimize promotions Reduce churn Improve loyalty program performance Increase CLV Types of Retail Segmentation 1. Demographic Segmentation Age, gender, income, and household size. 2. Behavioral Segmentation Purchase frequency, basket size, and channel usage. 3. Psychographic Segmentation Lifestyle, values, and interests. 4. RFM Segmentation (Recency, Frequency, Monetary) A widely used retail model for ranking customer value. 5. Predictive Segmentation ML models categorize customers by churn risk, conversion probability, price sensitivity, etc. How does customer segmentation help retailers? Segmentation helps retailers target audiences efficiently, personalize offers, and improve marketing ROI. What Is CLV in Retail and Why It Matter? Customer Lifetime Value (CLV) measures the total profit a retailer can expect from a customer over time. Why CLV Is Critical Shows which customers are most valuable Helps optimize acquisition and retention budgets Improves loyalty program strategy Supports long-term revenue forecasting How Retailers Use CLV Create high-value customer segments Set personalized offer tiers Predict churn and intervene early Improve marketing profitability What is CLV, and why is it important? CLV helps retailers identify profitable customers, reduce marketing waste, and build long-term growth strategies. How Retailers Use Customer Analytics to Drive Personalization & Growth Retailers use customer analytics to optimize the end-to-end consumer journey. 1. Personalized Recommendations Dynamic product recommendations on site Personalized merchandising AI-powered upsells and cross-sells 2. Smarter Promotions & Pricing Predictive discount optimization Elasticity modeling Personalized offers based on value and behavior 3. Loyalty Optimization Segment-based reward structures Personalized loyalty tiers Churn prediction-based outreach 4. Inventory and Demand Planning Predictive demand forecasting SKU rationalization Real-time replenishment 5. Omnichannel Journey Optimization Align online behavior with offline purchasing Improve friction points Enable personalized in-store experiences Frequently Asked Questions 1. What is customer analytics in retail? It’s the use of data and analytics to understand behavior, personalize experiences, and improve profitability. 2. What tools do retailers use for customer analytics? They use CDPs, cloud data warehouses, BI platforms, and AI-powered personalization systems. 3. How does segmentation help retailers? Segmentation improves targeting, reduces marketing inefficiencies, and increases customer satisfaction. 4. What is CLV? CLV stands for Customer Lifetime Value — the total revenue or profit a retailer expects from a customer over their lifetime. 5. How do retailers analyze customer behavior? By combining transactional, digital, loyalty, and in-store data into predictive and prescriptive insights. Transform Your Retail Customer Analytics Strategy Visit our Retail Customer Insights Hub or request a C-suite analytics strategy assessment to jump-start your transformation.

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The impact of AI and data analytics in pharma research with futuristic elements.

The Impact of AI and Data Analytics in Pharma Research

November 11, 2025

The pharmaceutical industry is on the cusp of a seismic transformation. No longer simply buzzwords, AI and data analytics have emerged as mission-critical technologies powering the next wave of data-driven drug discovery and pharma R&D efficiency. From predicting disease progression to identifying molecular drug targets, AI transforms pharmaceutical research. Data analytics for pharma is rapidly evolving as companies adopt AI for pharmaceutical analytics to improve drug development and patient outcomes. In this blog, we explore how AI and analytics are revolutionizing pharma, highlighting recent breakthroughs and tools, and providing expert perspectives on what’s next. Generative AI and pharma are increasingly shaping the breakthroughs highlighted in recent pharma AI news. Data analytics in pharma R&D is being rapidly transformed by AI for pharmaceutical analytics, enabling deeper insights and faster decision-making throughout the drug development lifecycle. AI in Pharma R&D: Redefining the Research Life Cycle Conventionally, R&D in Pharma has been a lengthy process with high investments. This paradigm changes with AI by: Accelerating compound screening: Machine learning models analyze large chemical libraries much faster than classical wet-lab approaches. By integrating AI customer insights for pharma with advanced data analytics for pharma, companies can better understand patient needs and optimize commercial strategies. Predictive modeling by AI can anticipate the behavior of molecules in biological systems, hence reducing the need for expensive clinical trials. Recent pharma data analytics news often highlights growing challenges in pharma AI data strategies for drug discovery, especially around data quality, integration, and regulatory compliance. Optimizing clinical trials: Advanced algorithms help in the design of trials, improved cohort recruitment, and early detection of side effects. Dr. Anjali Mehra, Chief Data Scientist at BioSynthAI, said, AI isn’t replacing scientists; it’s making scientists more efficient by transforming billions of data points into actionable insights in seconds. Data-Driven Drug Discovery: The Power of Predictive Analytics Data-driven drug discovery currently enables pharma companies to accelerate innovation: It helps analyze complex genomic data sets to find new targets for therapy. Real-world data: RWD involves mining data from patient health records, wearables, and even social data to spot patterns and risks. Digital twins: simulated models of human organs are tested with drugs, reducing the need to conduct trials on live ones. Visual Insight: Below is the comparative chart for 2025, depicting the reduction of time in varied R&D stages due to AI integration: R&D Phase Avg Time (Pre-AI) Avg Time (With AI Tools 2024) Time Reduction Target Identification 2 years 6 months 75% Compound Screening 1.5 years 4 months 78% Clinical Trial Design 1 year 3 months 70% New Pharma AI Tools in 2025 You Should Know Some of the most sophisticated pharma AI tools at the forefront of the race in 2025 include: DeepMind’s AlphaFold 3 Predicts protein structures with unprecedented accuracy. Insilico Medicine’s Pharma.AI It automates the complete drug discovery pipeline. BenchSci It uses machine learning to decode scientific experiments and suggest the best pathways. Atomwise Deep learning-based drug design based on structure. BioSymphony Indian startup that makes use of AI to synthesize generative compounds with higher efficacy. Ready to Leverage AI for Your Pharma Innovation? Partner with INT Global to develop and deploy AI and data analytics solutions tailored towards your pharma enterprise’s needs. Benefits of AI in Pharma: What Makes It a Game Changer? Shorter development cycles mean faster drugs to market. Lower R&D costs = Higher ROI. Better targeting leads directly to better treatment outcomes. Better reporting and safety profiling are a result of regulatory compliance. Challenges & Ethical Considerations Despite the benefits, challenges like Data Privacy and Patient Consent Algorithmic bias  Integration with legacy systems  Regulatory uncertainty Clearing these hurdles will necessitate sheer AI governance, multidisciplinary collaboration, and continuous regulatory evolution. By combining data analytics in pharma R&D with AI-driven customer insights, pharmaceutical companies can accelerate innovation while delivering more personalized healthcare solutions. Generative AI continues to reshape drug discovery, a trend highlighted in recent pharma AI news reporting breakthroughs in molecule design and clinical trial optimization. Future of AI in Pharma: What Lies Ahead? The future is promising and fast-evolving: AI-driven personalized medicine will become the norm. Quantum computing and AI will boost simulation speeds. Collaborative AI models across pharma giants will improve global research. Innovate Smarter with AI & Data-Driven Strategies The integration of AI and data analytics is no longer optional—it’s the cornerstone of modern pharma research. Whether you’re a biotech startup or an established pharmaceutical leader, the time to act is now. 👉 Transform your pharma R&D with INT Global. Let’s build AI solutions that save lives. 🔗 Get in Touch with Our Pharma AI Experts Frequently Asked Questions Q1. How is AI used in pharma R&D today? AI is used to analyze chemical compounds, design clinical trials, predict drug efficacy, and more. It helps speed up research, lower costs, and improve accuracy. Q2. What are some of the best pharma AI tools in 2024? Top tools include AlphaFold 3, Pharma.AI, Atomwise, and BenchSci. Q3. Is AI replacing human scientists in pharma? No. AI is an augmentative tool that enhances human decision-making, not a replacement. Q4. What challenges do pharma companies face with AI adoption? Major challenges include data integration, regulatory compliance, bias in models, and ethical concerns around patient data. Q5. Can AI reduce drug development time significantly? Yes, AI can reduce time across drug discovery, clinical trials, and compound testing by over 60–70%.

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Key Metrics

Key Metrics Every Business Should Be Tracking with BI

September 3, 2024

BI (business intelligence) metrics help companies and leaders unearth invaluable data and insights related to several key metrics/parameters. It can thus be said that business analytics and metrics have a symbiotic relationship when it comes to enabling data-driven business decisions, along with helping companies and leaders understand operations, customers, and organisational health better. Naturally, when there are more informed decisions along with regular tracking of KPIs for business growth, the end-result is a boost in organisational performance. Here’s looking at some of the key metrics that you should be tracking with the help of BI. Business Intelligence Key Metrics for Companies Nowadays, AI, data analytics, and other technological tools like unified and integrated CRMs have automated the measurement and tracking of several of these metrics. Businesses now view everything across unified and centralised dashboards while getting help with BI implementation strategies for evaluating multiple aspects of business performance and productivity. Here are a few of the KPIs for business growth that should be regularly tracked and analysed in order to enable data-driven decision making and eventual results. It should be noted that KPIs may vary across businesses, depending on their type and other parameters. Business Analytics and Metrics are Invaluable for Companies In a growingly competitive business landscape, companies should invest in proper BI implementation strategies with cutting-edge AI and analytics-driven processes/systems. BI will enable them to regularly track these invaluable metrics in order to get a grip of organisational performance, employee productivity, customer satisfaction, financial output, and of course, overall efficiency. At the same time, companies can also track KPIs that are customised as per their particular goals. It can thus be said that BI-enabled tracking of metrics is invaluable for almost any company today. FAQs 1. What are the most important key performance indicators (KPIs) for businesses to track using BI? There are several crucial key performance indicators (KPIs) that businesses can track with the help of BI (business intelligence) tools, including CLV (customer lifetime value), cash flow, enterprise productivity, revenue, and gross profit margin. 2. How can tracking customer acquisition cost (CAC) with BI tools benefit my business? CAC (customer acquisition cost) is a vital metric that can be tracked with BI tools. This is evaluated in relation to the CLV (customer lifetime value) and hence tracking it is important. This metric is what helps companies build the right balance between customer acquisition costs and potential revenue generation from that customer over his/her lifetime. 3. What role does BI play in monitoring and improving customer lifetime value (CLV)? Business intelligence (BI) has a crucial role to play in helping companies track customer lifetime value (CLV), which indicates the value derived in terms of revenues from the average customer over his/her lifetime/association with the brand. Looking at the historical data and patterns for this metric, companies can work on better customer retention strategies to improve CLV in case it has come down. 4. How can BI tools help in tracking and reducing churn rate? Strategic BI tools can be immensely helpful with regard to tracking the churn rate and also lowering it based on patterns and inputs for the same. The objective will be to lower churn and scale up CLV (customer lifetime value in turn). Companies will depend on these tools for data-based decision making and multi-channel inputs on customer feedback that will help them find and fix pain points to reduce churn. 5. What financial metrics should businesses be tracking with BI to ensure profitability? Some of the important financial metrics that businesses should track with BI include revenue, gross profit margin, and customer lifetime value, to name a few. Many other metrics can be customised based on the specific business requirements.

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Rise of parametric insurance

The Rise of Parametric Insurance: Paying Out Based on Data, Not Damage

August 27, 2024

Use cases for data analytics in insurance have evolved considerably in recent years. One of the biggest such innovations is parametric insurance that is steadily rising in importance, with the increasing unpredictability of the world and environment today. Instech reports have already highlighted how parametric solutions saw record growth in 2022 and the trend is set to continue over this decade as well. What is Parametric Insurance? Often labeled as climate risk insurance or disaster insurance, parametric insurance can be classified as the following: Why Parametric Insurance is Beneficial? Insurtech players have already started realising the value of parametric insurance solutions, particularly for helping communities build financial resilience in the light of unpredictable and volatile climate risks. Some of the key benefits include the following: Are There Any Disadvantages? While the advantages of parametric insurance clearly outweigh the disadvantages, there are still a few moot points in this case. How It All Stacks Up Parametric insurance is an innovative product that is well-positioned to take-off in the current global scenario, despite its potential drawbacks. Technology will play a vital role in data-based payouts in the future. This will include real-time tracking from ground-based sensors and satellite/radar imagery and data. The aim of the insurance product in this case is to minimise risks as much as possible based on advanced data and technological prowess. In fact, it can well transform into a more effective and robust climate risk insurance model, thereby incorporating the costs of climate change in a better way. With the certainty of these products, a higher portion of premiums that buyers spend on coverage come back to them as claims, instead of being spent through disputes and frictional expenditure. Parametric insurance is also customisable to various industries and corporate clients, since it is not concerned about the type of asset, rather wider financial losses due to triggering events. Clients in the corporate sector can thus tackle the bigger financial impact on operations in case of climate events, including vendors, suppliers, customers, and logistics. IoT and other new technologies will keep powering parametric solutions, with warning systems that can inform customers about potential risks. This will help them take preventive measures in advance. LLMs or large language models are also positively impacting the sector and will completely change underwriting in the future. They will process vast datasets swiftly, including claims history and historical occurrences, thereby enabling underwriters to predict future claims more efficiently. So, to sign off, it can be said that parametric insurance should be around in the coming decade and even beyond. With more insurance companies, underwriters, customers, and brokers recognising it as a good solution for risk transfer, there will be more evolution into secondary complexities and events like floods, hail, thunderstorms, wildfires, and more. With data being leveraged comprehensively for pricing, this form of insurance will make underwriting easier and lower the time to quote and finalise policies or even settle claims. This will help insurance companies save more time and money, which will prove hugely beneficial in the long run. FAQs 1. What is Parametric Insurance and How Does It Differ from Traditional Insurance? Parametric insurance is a form of insurance where payouts are triggered based on certain pre-determined thresholds/parameters. It is different from traditional insurance, since it is not concerned with the actual loss/damage. 2. How Does Parametric Insurance Work? In case of any event/disaster that triggers any specific thresholds/parameters like wind speed, earthquakes of a certain magnitude, and so on, pre-fixed payouts will be given to policyholders, irrespective of their actual losses and whether they have suffered losses or not. Claims processing will be near-automatic due to pre-fixed agreements. 3. What Are the Benefits of Parametric Insurance for Policyholders? Communities and people without access to conventional insurance can benefit from higher financial resilience against climate change with parametric insurance. It enables swift payouts and more agile operations, while saving time and resources. It also lowers the chances of disputes with policyholders and fraudulent claims. 4. What Types of Events Are Covered by Parametric Insurance Policies? Parametric insurance policies can cover a wide range of events including earthquakes, poor crop yields/harvests, natural disasters like hurricanes and cyclones, and so on. 5. How is the Payout Determined in Parametric Insurance? The payouts in parametric insurance are determined based on whether specific thresholds have been triggered in the case of any event. There is no consideration for the actual loss of the policyholder. If these parameters are triggered, then payouts are released to policyholders and are pre-fixed amounts. These amounts are estimated by policyholders in most cases, depending on their calculations of the potential financial cost of damages due to varied natural disasters and other such events.

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coding for clarity

Coding for Clarity: How Developers Use Analytics to Demystify Complexity

June 10, 2024

Software development has become increasingly intricate in today’s fast-paced world. With technological frameworks and methodologies constantly changing, developers often find themselves lost in mazes of code. To help negotiate this complexity, developers are starting to use analytics in their coding practice. But, that is not all. This article will explore the challenges of understanding complexity in software development, and the utilization of analytics to simplify coding. The Intersection of Human Resources and Software Development One of the key factors determining whether or not software development projects meet their goals is not just in technology application but lies with the people coding for the same. In creating this environment, human resources are of particular importance. They provide a framework in which developers can excel and grow. For example, by understanding the distinct talents and skills of every member on a team, HR must make certain that people of appropriate caliber are assigned to tasks in their area. For taking on the difficulties of software development, it is essential to ensure that skills match tasks. Moreover, human resources can help teams communicate and work together, giving everyone a chance to bring their own different viewpoints and experiences to bear on complex problems encountered inside such an organization. Encouraging innovation and nurturing talent will certainly help to stake out a clearer path for the future, whether in coding or any other field. To adapt this model requires also recognizing and nurturing the diverse abilities and virtues of each team member, thereby encouraging them all to participate actively in targeting software development complexity. Understanding Complexity in Software Development Software development is a multifaceted process that involves managing an intricate web of variables and interdependencies. As projects expand in size and scope, the complexity of the codebase increases, creating a challenging landscape for developers. To deliver high-quality software within reasonable timeframes, it’s crucial to comprehend and effectively manage this complexity. Developers are tasked with navigating elaborate algorithms, intricate data structures, and sophisticated architectural patterns, all of which significantly contribute to the intricate nature of software development. Utilizing Analytics to Simplify Complexity By utilizing analytics, developers can gain valuable insights into their code, allowing them to navigate the intricacies and better understand the inner workings of their software. Through the analysis of metrics like code complexity, code churn, and defect density, developers can effectively pinpoint specific areas within the codebase that are susceptible to problems. Armed with these insights, developers can then strategically prioritize refactoring efforts and enhance the clarity and maintainability of their code. Additionally, analytics can offer visibility into the performance of development teams, providing managers with the data needed to make informed decisions that support and optimize their teams’ productivity and efficiency. Collaborating for Clarity When it comes to achieving clarity in coding, analytics can offer valuable insights, but it’s the power of collaboration that truly takes it to the next level. Successful collaboration involves developers working closely with one another, drawing from their diverse skill sets and experiences to untangle complex problems and create elegant solutions. Practices such as pair programming, where two developers work together on the same code, code reviews to add another layer of scrutiny, and cross-functional teams that bring together different perspectives, all play a significant role in promoting clarity in coding. Through these collaborative efforts, developers can collaboratively share their knowledge, brainstorm and identify better solutions and collectively simplify intricate codebases. This approach not only yields cleaner and more efficient code but also fosters a stronger sense of teamwork and shared accomplishment within the development process. Best Practices for Clarity in Coding In the pursuit of clarity and efficiency, developers can adopt a set of best practices to streamline their coding processes. This includes writing clean and concise code, following established coding standards such as naming conventions, code structure, and architectural patterns, and documenting code effectively by providing clear comments and documentation. Additionally, developers can leverage design patterns and principles, such as MVC (Model-View-Controller) or SOLID principles, to architect solutions that are easier to comprehend and maintain. By adhering to these best practices, developers can proactively reduce the complexity of their code, improving readability, maintainability, and scalability of their software systems. Nurturing Talent for Clarity To effectively address the challenges posed by complexity in software development, it is crucial to prioritize the growth and development of talent within development teams. This can be achieved by creating a culture of continuous learning and skill development, which empowers individuals to take on intricate coding challenges with confidence. Implementing mentoring programs where experienced developers can share their knowledge and provide guidance to junior team members can significantly contribute to the professional growth of the entire team. Additionally, organizing training workshops focused on the latest tools, technologies, and best practices in software development can further enhance the expertise of the team members. Furthermore, promoting knowledge-sharing initiatives such as tech talks, code reviews, and collaborative problem-solving sessions can facilitate the exchange of ideas and foster a culture of learning within the team. Overall, these efforts are instrumental in cultivating the expertise needed to simplify coding and promote clarity in software development projects. Signing Off In conclusion, the journey towards demystifying complexity in software development is a multifaceted endeavor that demands the combined efforts of human resources, analytics, collaboration, best practices, and talent nurturing. By recognizing the intersection of human resources and software development, leveraging analytics to gain insights, embracing collaboration, following best practices, and investing in talent, developers can navigate the complexities of coding with clarity and confidence. As the software development landscape continues to evolve, the pursuit of clarity in coding remains a timeless aspiration for developers around the world.

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Predictive Data Analytics

A Practical Guide to Predictive Big Data Analytics

March 29, 2024

Predictive big data analytics are making waves worldwide and with good reason. They are fast becoming growth engines for companies across sectors, particularly for their penchant to drive better decision-making. At the same time, implementing predictive analytics is becoming more of a competitive advantage for industries and businesses these days. Hence, there’s no denying their importance. However, as with all good things, there are several intricate layers of complexities surrounding all the hype around them. It is no different in the case of predictive analytics. Here’s demystifying the concept or tool, whichever way you perceive it. Diving Into Predictive Analytics When we talk of predictive big data analytics, it should be understood that big data and predictive analytics should first be understood separately and in context before interrelating them. Big data indicates the massive volumes of complex information/data gathered by businesses. Predictive analytics leverages big data for generating valuable insights and discovering relevant patterns for forecasting future trajectories or events. Big data thus encompasses not only this voluminous data but also the techniques for gathering, processing, and storing the same. Predictive big data analytics is thus a set of operations and models which make use of data mining and machine learning among other technologies for forecasting or predicting future events/trends. How It Actually Works Predictive analytics revolves around predictive modeling and this, in turn, covers two kinds of machine learning algorithms. These are supervised and unsupervised. The former help in predicting any targeted outcomes and are primarily used for predictive analytics operations. Coming to supervised machine learning algorithms, there are two kinds that you should know more about. Classification models help in forecasting whether the observations can come under any specific class, segment, or category. To cite an instance, it may help identify a customer as one likely to stay with the company or whether a customer will churn. Some classification models/techniques include logistic regression and decision trees. Regression models are those which help predict any value. For example, the click-through rate of an online advertisement can be predicted in this manner. Some models/techniques include polynomial regression and linear regression. Returning to unsupervised machine learning algorithms, they do not forecast, but only identify data patterns which can be leveraged for grouping/labeling similar data points together. One of the popularly used algorithms is known as k-means clustering and this helps group customers into segments or other similar data points into such clusters. Predictive analytics may also use other data mining or statistical models for the identification and forecasting of future trends and outcomes. Why Prescriptive Analytics Is Different You should not confuse predictive big data analytics with prescriptive analytics. The latter actually develops upon the results enabled by predictive models in an earlier stage. Predictive analytics informs why any event is taking place and what may take place later, prescriptive analytics is about the experimentation and optimization of models already in place. It will answer questions regarding the outcome or event of something actually happening and enable companies to move ahead with best possible scenarios. How Predictive Analytics Is Used Here are only a few instances that are worth citing: Core Aspects of Predictive Analytics These are the ways in which predictive big data analytics offers greater value to almost every industry or company out there today. Who wouldn’t want the power to understand where things are going and what shape they can take in the future. In fact, preventive action and risk management can also be improved considerably for companies in most sectors by leveraging predictive analytics. However, it should also be stated here that investing in the right talent to manage these processes and setting up the right big data infrastructure are also pre-requisites for the successful deployment of the above-mentioned models. FAQs Is predictive big data just a fad, or does it have real-world applications? Predictive big data is not a fad anymore. It has several real-world applications, helping organizations with various functions from identifying consumer fraud to managing inventory, and also understanding consumer preferences and behavioral patterns. Can predictive analytics be used for real-time decision-making? Predictive analytics can be used for real-time decision making by companies. This can be done through the application of predictive models to real-time data feeds. However, it requires suitable infrastructure, expertise, and analysis models. What is predictive big data analytics, and how does it differ from traditional analytics? Predictive big data analytics revolves around leveraging big data to generate insights that businesses can use to improve decision-making, enhance productivity, and cut losses. It is different from traditional analytics since the former uses structured information in smaller and more manageable amounts, while big data indicates unstructured and vast information. What industries can benefit most from predictive big data analytics? Predictive big data analytics can benefit several industries immensely, including manufacturing and production, banking and financial services including insurance, retail, healthcare, and more. Where is predictive big data analytics headed? Predictive big data analytics has firmly stamped itself as the future source of insights and real-time decision-making for businesses. Based on a study by Allied Market Research, the global market for predictive analytics is expected to reach a whopping US$35.45 billion by the year 2027, posting a CAGR (compound annual growth rate) of 21.9%. Demand will rise for more informed and data-based decision-making instead of intuition.

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Data Analytics in Health Insurance for Better Risk Management

2024 Trends: Data Analytics in Health Insurance for Better Risk Management

January 8, 2024

The health insurance industry has witnessed a rapid evolution in terms of digitisation, which has also swiftly transformed several other sectors worldwide. One of the biggest contemporary trends in health insurance (and poised to last for the foreseeable future) is the harnessing of data analytics to ensure superlative risk management among other benefits. Pharmacy managers, insurance companies, healthcare providers, and other industry stakeholders are already leveraging analytics to tackle issues of fast-growing healthcare costs. Insurance companies are already using them to identify high-risk patients and operate accordingly. Why Data Analytics is Indispensable From an operational standpoint, data analytics is indispensable for health insurance companies. They keep generating huge data volumes internally through sales and engagement but also get sizable chunks from varied external sources. When this data spreads out through multiple systems, it becomes an uphill task for insurers to effectively use and track the same. This is why analytics-based solutions that can fuse and consolidate data from multiple touch points and sources have gained relevance in recent times. A centralised data gathering system with consistent analytics and actionable insights is thus one of the key trends in health insurance currently. How Data Analytics Enables Better Risk Management Data analytics is undoubtedly contributing towards enabling insurers to manage their risks better. Here are some aspects that should be highlighted in this regard. These benefits have gained even more traction in recent years if one considers how insurers lose a whopping $40 billion per annum on account of fraudulent claims as per Gartner reports. Many insurance companies also estimate 10-20% of claims to be fraudulent while identifying less than 20% of the same. Suspicious and fraudulent behaviour and patterns can be identified with data analytics, with insurers creating diverse models to enable swift detection based on historical data and activities. For instance, analytics has reportedly helped the Czech Republic’s Allianz Insurance to save a whopping US$4.5 million annually by lowering fraudulent claims paid by it. This is just one of the instances that testify to the need for insurance companies to rapidly adopt data analytics into their operational frameworks. Analytics can be used to conduct risk evaluation in real-time which will help organisations respond swiftly in volatile scenarios. For example, in the case of auto insurance, an accurate assessment of risks posed by specific drivers will help insurers create more competitive premiums. Cars connected to the internet will help them gather large volumes of data accordingly. Insurance firms can now predict the chances of drivers being involved in accidents by analysing driving habits and behavioural data. Some other advantages of data analytics include easier customer lifetime value (CLV) prediction and prospective claim forecasting. FAQs How can data analytics contribute to more precise risk assessment in the health insurance industry? Data analytics can enable more accurate risk assessments in the health insurance sector. It can help identify fraudulent behavioural patterns and flag the same for review before the actual payout. What impact do 2024 trends in health insurance data analytics have on customer experiences and personalised offerings? 2024 trends in health insurance data analytics will have a positive impact on customer experiences. Insurers will be able to personalise their offerings for customers based on their behavioural data and also offer custom premiums with incentives for recommended and healthier actions.

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The Impact of AI and Data Analytics in Pharma Research

The Impact of AI and Data Analytics in Pharma Research

January 3, 2024

AI in pharma research has the potential to be a veritable game-changer for the entire sector. Data analytics in pharmaceuticals along with other innovations like data-driven research and AI/machine learning in pharma have made it comparatively easier to develop new drugs and tackle emerging diseases. Biopharma research remains expensive and lengthy although AI can play a vital role in enabling higher probabilities of success and boosting productivity.  How AI and Data Analytics are Indispensable for Pharma Research Here are a few ways in which AI in pharma research can be indispensable for the industry soon. AI in pharma research will enable the creation of feedback loops for further refining the predictive abilities and stability of AI algorithms. They will also inform experimental design functions accordingly. Through analytics and data science tools, pharma can capture the entire value of the present portfolio and create mechanisms and IP for driving research in the future. AI-drug discovery is already taking place with several companies building their pipelines. Biopharma entities are also developing top-down and executive strategies where AI-backed discovery can be a vital indicator and enabler of performance in the future. Automated image analysis or lead optimisation will be bolstered along with the collection of experimental data in a reusable manner, automated screening algorithms linking molecular descriptions with hits or desired outputs, blueprinting, enabling better testing and learning solutions for product delivery and designing new screening protocols. AI is already transforming the research space through the application of machine learning and data science to huge data sets, enabling swifter discoveries of newer molecules. It enables cross-referencing of published scientific literature with alternate sources of data (clinical trial data, conference abstracts, public databases, and unpublished data) to surface therapies that are promising. Medicines can be delivered in months at times instead of several years as a result. AI can also help lower clinical trial costs and cycle times while enhancing overall clinical development outcomes considerably. ML and AI are already being used for automatically generating study protocols while NLP (natural language processing) is being used to scale up manual tasks. AI algorithms can also ensure continual clinical data cleaning, coding, aggregation, management, and storage. Through automation and centralisation of intakes for adverse event reports backed by AI-backed technologies like NLP and OCR (optical character recognition), case documentation workloads are considerably reduced for expediting investigative processes. These are only a few of the widespread benefits that data analytics, AI, and ML can bring to the table for life sciences and pharmaceutical companies, especially in terms of research and development. FAQs What role will AI play in optimising clinical trials and research methodologies, and how is this expected to impact the pharmaceutical industry in 2024? AI will play a huge role in the optimisation of research methodologies and clinical trials in the future. This will have a positive impact on the pharmaceutical industry in 2024 and beyond since AI will optimise patient recruitment, predict the efficacy of treatments, automate data analysis, and boost safety tracking. It will also accelerate trial procedures while lowering costs and enhancing data quality. This will lead to more personalised and successful clinical trials. How will integrating AI and data analytics accelerate drug discovery processes within the pharmaceutical industry in the upcoming year? Drug discovery processes within the pharmaceutical industry can be accelerated in the upcoming year through the integration of data analytics and AI. This will be possible through the prediction of drug-target interactions, evaluation of the safety and efficacy of drugs that are repurposed, and identification of newer options for treatments. Potential biomarkers can be identified while researchers can easily analyse big data sets and design new molecules while forecasting the efficacy levels of potential drug candidates accordingly.

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Enhancing Underwriting Precision: The Role of Data Analytics in Insurance Broker Decision-making

Enhancing Underwriting Precision: The Role of Data Analytics in Insurance Broker Decision-making

December 28, 2023

Data analytics in insurance has been a veritable game-changer for the industry in recent times. It has become a major solution for several issues while enabling a more personalised experience for customers. Insurance brokers are steadily embracing data-driven insurance intending to bolster their decision-making process. Let us look at some of the biggest advantages of deploying analytics in the insurance sector. Benefits of Data Analytics in Insurance for Brokers Insurance broker decision-making can improve considerably with the deployment of data analytics. Here are some of the biggest advantages worth noting in this regard. These are some of the core benefits that can boost insurance broker decision-making greatly. Analytics can transform the operational side of the business while freeing up brokers to focus on strategising for future growth without worrying about the operational and administrative aspects of the sector. FAQs What is the role of data analytics in enhancing underwriting precision for insurance brokers? Data analytics can greatly enhance underwriting in terms of accuracy for insurance brokers. It can estimate risks better and predict premiums more accurately for customers who are more prone to the same. In what ways can insurance brokers leverage data analytics to tailor insurance solutions for individual clients? Insurance brokers can seamlessly leverage data analytics for tailoring insurance solutions for their clients.  Data-driven insights will enable better customer understanding and an idea of preferences. This will help insurance brokers come up with genuine recommendations and more personalised products/services that will help customers meet their needs better. What types of data sources are most valuable for insurance brokers seeking to enhance underwriting precision through analytics? Insurance brokers who are looking to enhance their underwriting processes through analytics rely on varied data sources. Some of these sources include social media platforms, demographics, lifestyle, age, medical data, and more.

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2024 Trends: Data Analytics in Health Insurance for Better Risk Management

2025 Trends: Data Analytics in Health Insurance for Better Risk Management

December 20, 2023

The health insurance industry has witnessed a rapid evolution in terms of digitization, which has also swiftly transformed several other sectors worldwide. One of the biggest contemporary trends in health insurance (and poised to last for the foreseeable future) is the harnessing of data analytics to ensure superlative risk management, among other benefits. “Health insurance data analysis plays a key role in improving decision-making and driving effective health insurance analytics.” Predictive analytics in health insurance enables insurers to derive actionable insights from data, helping them design more profitable and customer-focused insurance products. “Data analytics in health insurance is transforming the industry by enabling insurers to make smarter decisions through advanced data analytics insurance practices.” Analytics in health insurance are rapidly evolving as new insurance analytics trends enable more accurate risk assessment, personalized pricing, and improved patient outcomes. Pharmacy managers, insurance companies, healthcare providers, and other industry stakeholders are already leveraging analytics to tackle issues of fast-growing healthcare costs. Insurance companies are already using them to identify high-risk patients and operate accordingly. Health insurance predictive analytics is becoming increasingly important as insurance industry data analytics evolves to improve risk assessment and customer outcomes. Why Data Analytics is Indispensable From an operational standpoint, data analytics is indispensable for health insurance companies. They keep generating huge data volumes internally through sales and engagement but also get sizable chunks from varied external sources. When this data spreads out through multiple systems, it becomes an uphill task for insurers to effectively use and track the same. Big data in health insurance is transforming the industry by enabling advanced data analytics in health insurance to improve risk prediction, fraud detection, and personalized member care. This is why analytics-based solutions that can fuse and consolidate data from multiple touch points and sources have gained relevance in recent times. A centralized data-gathering system with consistent analytics and actionable insights is thus one of the key trends in health insurance currently. Data analytics in health insurance is rapidly evolving as organizations increasingly rely on data science in health insurance to improve risk assessment, pricing accuracy, and patient outcomes. How Data Analytics Enables Better Risk Management Data analytics is undoubtedly contributing towards enabling insurers to manage their risks better. Here are some aspects that should be highlighted in this regard. By leveraging big data in health insurance, companies can enhance customer analytics in health insurance to deliver more personalized and cost-effective care solutions. Data analytics in health insurance is becoming increasingly powerful, especially as predictive analytics helps insurers identify risk patterns and improve patient outcomes. These benefits have gained even more traction in recent years if one considers how insurers lose a whopping $40 billion per annum on account of fraudulent claims as per Gartner reports. Many insurance companies also estimate 10-20% of claims to be fraudulent while identifying less than 20% of the same.  Suspicious and fraudulent behaviour and patterns can be identified with data analytics, with insurers creating diverse models to enable swift detection based on historical data and activities. For instance, analytics has reportedly helped the Czech Republic’s Allianz Insurance to save a whopping US$4.5 million annually by lowering fraudulent claims paid by it. This is just one of the instances that testify to the need for insurance companies to rapidly adopt data analytics into their operational frameworks.  Analytics can be used to conduct risk evaluation in real-time which will help organisations respond swiftly in volatile scenarios. For example, in the case of auto insurance, an accurate assessment of risks posed by specific drivers will help insurers create more competitive premiums. Cars connected to the internet will help them gather large volumes of data accordingly. Insurance firms can now predict the chances of drivers being involved in accidents by analysing driving habits and behavioural data. Some other advantages of data analytics include easier customer lifetime value (CLV) prediction and prospective claim forecasting.  FAQs How can data analytics contribute to more precise risk assessment in the health insurance industry?  Data analytics can enable more accurate risk assessments in the health insurance sector. It can help identify fraudulent behavioural patterns and flag the same for review before the actual payout.  What impact do 2024 trends in health insurance data analytics have on customer experiences and personalised offerings? 2024 trends in health insurance data analytics will have a positive impact on customer experiences. Insurers will be able to personalise their offerings for customers based on their behavioural data and also offer custom premiums with incentives for recommended and healthier actions. 

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