Tag: advanced data analytics solutions

online entertainment industry

Online Entertainment Industry: Unlocking Million Dollar Business Opportunities Using Advanced Analytics

Downloading a movie or song has never been so fast, but at times it can become cumbersome to store these downloaded files and access them on the go. Online media service providers bring it right into your device in a fraction of a moment. What made it change so drastically?

The online entertainment industry has made it possible for us to consume content without downloading a file. Such methods involve the exchange of millions of bits of data at a given moment so that one can enjoy their favorite movie on the go. Hence it wouldn’t be wrong to say the traditional entertainment industry is shifting towards an online streaming model. With the growing popularity of the internet of things, overall data volumes have increased to a hundredfold, and engineering advancements in this field have also given rise to newer and better ways of collecting, processing, and analyzing data.

online entertainment industry

As in other industries, advanced data analytics has bought in waves of transformations in the online entertainment industry. Advanced analytics also plays a pivotal role in enabling online media service providers to analyze customer preferences by tracking usage patterns. This helps online media service providers to target users with customized offers and discounts for specific product offerings. These collective benefits of advanced data analytics have been very important in helping the online entertainment industry make a successful transition from renting DVDs and downloading files to delivering digital content at the click of a button.

The value of advanced data analytics and algorithms is immense and the use of advanced data science in the online entertainment industry has become an art.  Request a free demo to discover the true potential of data in transforming the online media and entertainment industry.

Advantages of Advanced Data Analytics for Online Entertainment Industry

  1. Provides comprehensive insights through data visualization: Visualization of data can help online media providers to manage their key performance indicators on a daily basis. Using data dashboards key performance indicators can be analyzed in real-time at any given point of time. Data can help in reducing costs, improving sales, and identifying errors to mitigate upcoming risks. Offers insights into consumer behavior: Advanced data analytics allows online media providers to gain access to real-time customer preferences. Leveraging these insights, the online entertainment industry can rapidly respond to customer needs and increase profit margins by cross-selling of their services.
  2. Helps to stay ahead of competitors: Online media service providers can easily identify trends and performance benchmarks with the help of advanced data analytics which enables the online entertainment industry player to reduce the external threats and keep up with industry trends which in a way helps them to stay ahead of their competitors.

Top use cases of advanced data analytics in the online entertainment industry

  • Personalized marketing
  • Customer sentiment analysis
  • Real-time analytics
  • Recommendation engines
  • Media content usage analysis and interpretation
  • Customer insights analysis

Advanced data analytics enables your company to coordinate data initiatives across every vertical of your business and helps you stay ahead of your competitors. Request a free proposal to understand how you can transform your business by leveraging advanced data analytics.

Online entertainment industry players are now adopting advanced data analytics for various benefits. Rather than just assuming the customer behavior, businesses should leverage advanced data analytics to better understand their customers and devise suitable strategies to target potential customers.

Advanced data analytics helps in analyzing and tracking real-time events. Speak to our analytics experts for more insights.

Insurance fraud

How Big Data and Analytics Helps Prevent Insurance Fraud?

The insurance industry is witnessing a consistent annual growth but companies in this industry are struggling when it comes to insurance penetration among consumers. Globally, insurance fraud has become a major concern for insurance companies. To tackle these challenges insurance service providers are investing huge resources and manpower in insurance fraud detection.

Data analytics solutions can help insurance companies to reduce exposure to fraud, and credit losses by the timely identification of all insurance risks.

Companies in the insurance industry are under tremendous pressure to ensure adherence to claims decisions while striking the right balance under regulatory pressures, growth of digital channels and proliferating competition. This is the reason they are turning their attention towards big data and analytics solutions to identify misrepresentation, detect insurance fraud, and prevent identity theft. 

Talk to our analytics experts to know how our data analytics and visualization solutions can empower you to identify and tackle fraudulent activities.

INSURANCE FRAUDRole of Data Analytics in Insurance Fraud Detection

SWOT Analysis

Insurance companies can leverage big data and analytics to analyze their strengths and weaknesses first, to, detect insurance fraud. This can help them to identify gaps in their existing fraud detection framework. As a result, they can improve their insurance fraud detection techniques and modify strategies.

Quantzig’s analytics solutions can help companies in better management of insurance claims, underwriting, actuarial, pricing and marketing functions and insurance fraud detection. Request a FREE proposal to gain in-depth insights into our portfolio of analytics solutions.

Consistent monitoring

Data analytics helps in identifying new trends and patterns in the process of insurance claims as they emerge. This can help insurance companies to seamlessly update their models and rules in the monitoring system to prevent insurance fraud. With this fundamental approach to insurance fraud detection, insurance companies can continuously monitor the billing and patterns off-line as well to identify abnormal insurance claims.

By leveraging our analytics solutions insurance companies can better understand customers’ credit behavior to determine the level of risk in claims and relevant policies to suit and benefit customers. Request a FREE demo below to know more.

Data Integration

Big data and analytics play a crucial role when it comes to data integration. It can help insurance companies in building effective insurance fraud detection capabilities by combining data from disparate sources. Also, data analytics helps companies in the insurance industry to integrate internal data with third-party data. By leveraging big data analytics insurance companies can integrate data sources with derogatory attributes such as bankruptcies data, criminal records, and foreclosures into a model and can address the transient behavior. Furthermore, big data analytics can help in uncovering anomalies in insurance billing to identify insurance fraud.

To learn more about how data analytics can help you prevent insurance fraud and quantify predicted loss severity from claims, request more information below.

data and analytics

3 Ways Predictive Analytics Solutions Can Help Businesses Derive Successful Outcomes

With the shifting key values of every industry from customer-focused to customer-centric, there is an immense growth in data and information. This has changed customer dynamics and as a result, every business is facing numerous challenges such as market uncertainties, driving efficiency, and effectiveness in their marketing productivity, immense competition, customer demands, fraud detection, and risk management.

Many businesses across all industries, in order to cater customer demands, are trying to utilize the data that their customers leave behind while interacting with the company. Harnessing this pool of data can offer several benefits to organizations. However, many companies still have not realized the importance of data mining and have not gone beyond gathering and storing their data. Although it is difficult to deal with an unstructured set of data by leveraging predictive analytics solutions businesses can fetch optimum results from such data. 

At Quantzig, we understand the impact that predictive analytics solutions can have on your business. And to help companies derive actionable insights from large and complex data sets, our team of experts has highlighted three important ways in which predictive analytics solutions can help in managing large volumes of data and setting up analytical frameworks to derive real-time insights that facilitate more informed and wise decisions.


Role of Advanced Analytics in Reducing Health Care Costs

There are several questions asked by healthcare professionals like – Are there enough measures available that must be taken in case of flu? Or are the patients operated yesterday likely to catch an infection? How can recruiting staffs and providing facilities be done in a cost-effectiveContact US manner? These questions can be answered accurately using advanced analytics, which, in turn, can help in reducing health care costs. As the number of patients keeps increasing along with the associated costs, there is a dire need for adopting advanced analytics in healthcare. Advanced analytics has become a tool in reducing health care costs for many healthcare organizations. It can help in reducing health care costs through various segments, ranging from stock management to patient care to staff deployment. But before delving into the correlation between advanced analytics and health care, let’s first understand what is advanced analytics.

What is advanced analytics?

There is a simple difference between analytics and advanced analytics. Traditional analytics is used to get insights on the current happenings; whereas, advanced analytics helps understand the future to forecast upcoming behaviors and trends. This tool can be categorized into data mining and big data. Advanced analytics uses various mathematical techniques and statistical modeling techniques to analyze current and past data and predict future scenarios.

How can advanced analytics reduce healthRequest Proposal care costs?

There are several ways in which advanced analytics can aid in reducing health care costs. Predicting demands of operating rooms, reducing the rate of readmissions, adding intelligence to pharmaceuticals, and optimizing staffs are some ways in which advanced analytics can help reduce costs.

#1 Predicting demands of operating rooms

Operating rooms are quite expensive to maintain. So, every hospital tries to optimize the operating room without compromising on patients’ health. This goal can be accomplished by recognizing the role of advanced analytics in better understanding the relationship between the operating rooms that can lead to mismanagement of effective scheduling. Thus, advanced analytics can help in streamlining the operating room schedule and reduce health care costs. 

#2 Reducing rate of readmissions

Unnecessary readmissions are very frequent in the U.S and it leads to confusion of discharged patients who fail to understand how to take care of their health or take precautions after they get back to their home. Due to this, an unnecessary burden of cost is also created. This is where advanced analytics comes into the picture.

Advanced analytics helps in reducing health care costs effectively. New advanced analytics algorithms take into account various clinical factors, which helps identify patients who need to spend less than two nights in the hospital. This tool also helps doctors to know when a patient requires observation and, thus, helps in reducing health care costs to a large extent. 

CTA QZ#3 Adding intelligence to pharmaceuticals

This is one of the most powerful features of advanced analytics since it can helps investigate every corner in detail and unveils available opportunities and forthcoming challenges. The historical data available in the clinics and hospitals can help in creating predictive models that can subsequently help the pharma companies to respond to the expected and unexpected changes. Advanced analytics can also be used to uncover the opportunities for internal savings caused by inventory standardization and, thus, help in reducing health care costs.

#4 Optimizing staffs

Advanced analytics can help in trimming costs of labor and predict demand in advance to match resources and staff; thereby, minimizing the last-minute unnecessary expenses. Optimizing the staff skill using advanced analytics can be of great help, especially in reducing health care costs.

Want tips on reducing health care costs with advanced analytics


VoC analytics

A Consultant’s Guide to Sell Analytics to Skeptics

Analytical data greatly affects the business decisions made by companies. Therefore, there are high chances of decision-makers at the top-level hierarchy of a company to be skeptical and cautious while choosing the consultant to assist them in providing analytical services and arrive at a mutual plan of action. So how do you get corporates to turn to you for assistance in decision making? Here are some tips and tricks to sell analytical services to clients:

Mining Gold from DataFree demo

Analyzing a client’s historical transaction data will provide a quantitative estimate of the potential benefits that a client could gain by undertaking a certain activity. The best way to gain the trust of a skeptic is to provide them with information that they were not previously aware of.  The analytical services provided to the client should help them discover unexplored facts about the business and should also add substance to their suspicion regarding improving business processes.

Partnering with Companies

Once a client gains confidence in the analytical services provided to them by a consultant, the chances of them offering more projects are higher. In such cases, the analytical services provider can partner with these companies by providing services at a lower price instead of a benefit-sharing agreement. This helps to create a long-lasting relationship with the clients.

Provide Testimonials

Testimonial stands as an unbiased recommendation of the analytical services provided by a consultant. Providing testimonials and instances of business success of past clients gives potential clients the confidence to entrust their faith in the analytical data that is provided to them.

Clear Cut Why and How to Substantiate Data

Top level managers employ analytical services to assist them in key decision-making processes. Therefore, it is essential to clearly state how and why a particular decision will impact or prove beneficial for the business. Making blank business predictions out of thin air is a big “no” for consulting companies providing analytical services.

Grabbing the attention of potential clients?

As in every industry, there is heavy competition among analytical services providers. So how do you differentiate yourself and effectively grab eyeballs of potential clients?

  • ReferencesA good reference can work wonders especially for a small analytical services provider. From clients who have used our services to industrial experts who are familiar with our work, all of them can help open doors for new assignments
  • ConferencesBeing an active participant at either attending or giving speeches at notable conferences can prove beneficial for a consultant. Networking with many top-level managers at these conferences can generate business for an analytical services provider
  • Websites, publications, and interviewsLeads can be generated with the help of content and other data that are published on the consultant’s website or any popular business publications. Client interviews about their success stories can also generate leads from businesses facing similar issues.


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Marketing Attribution

Transforming Customer Journey with Predictive Sales Analytics

Predictive sales analytics hold the potential to anticipate and understand consumer behavior and identify shopping patterns that help facilitate positive customer engagement. Earlier, organizations followed a transactional approach towards its customers, thanks to the automated software systems that Free demomechanized tasks, reduced costs, and streamlined operations. The old ways of managing customers by making use of intuition and guesswork instead of data are redundant in today’s competitive market. With the increase in consumer awareness levels, easy accessibility, and wide availability of similar products and services, consumer loyalty has decreased. Here’s where organizations can leverage predictive sales analytics to transform customer journey at every stage in the sales process.

Predictive Sales Analytics and Stages in The Sales Process

Fact: Application of predictive sales analytics proves beneficial at every stage in the sales funnel, right from pre-sales activities to post-sales services. Predictive and business analytics help develop a sales strategy based on insights that optimize the customer experience and journey through every stage. Data analytics tools enable the business to create a loyal consumer base and lower the customer churn rate. But how is this possible, you ask? Here’s how.

  • Pre sales and leads – Predictive sales analytics tools help in understanding the various traits and aspects of the current customer base and identify the potential customers who would be interested in the products and services offered by the business
  • Sales – Advanced data analytics tools help in developing cross-selling and up-selling sales strategies that enhance customer experience and boosts profits. Businesses can strategically implement these sales tactics by bundling their products and offering product purchase suggestions to provide value to customers by making informed yet personalized suggestions
  • Post salesPredictive sales analytics solutions help businesses to build consumer loyalty and lower customer retention rate by keeping in touch with various communication messages and loyalty programs. The advanced analytics tools help determine the best channel to communicate and engage with your customers to ensure success. Businesses usually interact with their existing customer base through feedback surveys, account upgrades, and discount offers that help in retaining them with the brand.


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Data and Analytics Essential for Building a Smart City

Beyond identifying and analyzing the sales capabilities and market landscape, data and analytics can also be leveraged to build a smart city. But what is a smart city, you ask? A smart city can be defined Free demoas an urbanized area where various entities cooperate to achieve sustainable outcomes through insights gained from real-time information across domains, sectors, and systems. Smart cities are a combination of buildings, public services, education, healthcare, transportation, and utilities that leverage technology and systems, thereby ensuring connectivity and generating real-time information.

Building a Smart City with IoT and Analytics

A smart city or an urbanized region consists of IoT sensors and network devices that help improve the quality of life and ensure smart usage of resources such as time, energy, and money. Due to the recent surge in the usage of connected devices and the growing popularity of internet of things, big data, and analytics, local authorities have started capitalizing on real-time information to curb traffic, pollution levels, crime rates, and energy consumption. Predictive analytics is a form of advanced data analytics that enables decision makers to make future predictions using various analytical techniques such as predictive modeling and data mining to study past patterns and forecast the future. How is this possible, you ask? Here are the top four applications of predictive analytics in building a smart city.

  • Intelligent weather adaptive lighting enables smart power and electricity; thereby, eliminating energy wastage and ensuring optimum utilization of resources
  • IoT enabled sensors and networks use cloud based architecture and big data automation to offer real-time traffic support, creates flexible and efficient public transport, monitoring driver behavior, and decreasing road accidents. Smart transportation management enables drivers to locate the nearest available parking slot and receive notifications through a mobile interface
  • Predictive analytics helps local authorities and police department to predict crime rates and locations by leveraging past data and location history. Analytics is a key enabler to ensure smart security and create safer cities through insights gained from various data streams
  • For building a smart city, local authorities must ensure an efficient and robust city planning strategy. Data and analytics can be used to gain insights on building zoning, amenity creation, and other infrastructural elements in the city. Smart city planning ensures the implementation of city models that maximizes area accessibility, minimizes overloading risks, and ensures optimum usage of infrastructural spaces with high level of accuracy and flexibility


smart city


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