Tag: predictive modeling

predictive modeling techniques

Weekly Round-up: A Storyboard on the Benefits of Predictive modeling Techniques

LONDON: Quantzig, a leading analytics services provider, has announced the release of their latest storyboard on predictive modeling techniques and how it can help businesses in proactive decision making and pre-emptive planning.  With the availability of data in enormous volumes, it has become quite difficult for businesses to rapidly identify, objectively evaluate, and confidently pursue new market opportunities.

By using predictive modeling techniques, companies can enhance decision making and gain the ability to optimize, direct, and automate decisions, on demand, to achieve defined business goals. By applying predictive analytics models, businesses can not only better manage the present, but can also improve the probability of success in the future. In doing so, such companies become predictive enterprises.

The goal of predictive modeling techniques is to turn data into information and information into insights.

Quantzig’s analytics solutions have helped more than 55 Fortune 500 companies to make data-driven decisions and augment revenues. Below, we have rounded up some of Quantzig’s recent success stories for this week and have also highlighted ways in which predictive modeling techniques have helped businesses to become market leaders.

#1: Predictive modeling techniques improved 85% accuracy in demand forecasting: Providing seamless customer experience and complete customer satisfaction is a challenging task for any business. Don’t you agree? But predictive modeling techniques can help as it helped one of Quantzig’s clients. By utilizing predictive modeling techniques, companies can conduct analysis on historical information on sales, products, and inventory levels. Furthermore, it can reduce irregularities and streamline order management, resulting in better demand forecasting.

85% accuracy in demand forecasting achieved by a leading computer hardware manufacturer through predictive modeling techniques

#2: Predictive modeling techniques precisely forecasted the roll-out of a new initiative: Predictive analytics models are the best way to improve business operations and predict future trends. This is clearly evident in Quantzig’s latest success story, where the client managed to make accurate predictions about the success and failure points of their new initiative. Moreover, it enabled optimal budget allocation.  

Leveraging Predictive Analytics to Forecast the Roll-out of a New Initiative for a Leading Grocery Retail Chain

#3: How can clustering algorithms uncover relations between invisible variants? If you are looking to upsell your products, this predictive modeling technique can certainly help. Clustering algorithms use data mining techniques to find out the relation between different invisible variants. Furthermore, this helps businesses to obtain desired outcomes.

Top 3 Predictive Modeling Techniques That You Must Know

#4: Predictive modeling techniques optimized inventory management: Optimizing inventory for better customer service is very essential for the success of any business. By leveraging predictive modeling solutions, companies can build better inventory and can discover capabilities to drive continuous market excellence. Furthermore, predictive modeling techniques can be successfully implemented, either independently or in tandem, to offer actionable insights into the performance of the inventory.

Improved inventory management achieved by a leading oil and gas company through predictive modeling techniques

#5: How predictive modeling techniques can boost business outcomes? : Changing customer dynamics poses critical challenges before businesses such as managing customer demands. Don’t you feel the same? This is where predictive analytics plays the role of a savior. By leveraging predictive analytics solutions and utilizing predictive modeling techniques, companies can deal with a humongous set of data, which can aid decision-making.

3 Ways Predictive Analytics Solutions Can Help Businesses Derive Successful Outcomes

#6: Utilizing predictive modeling techniques to boost customer retention dramatically: Managing the huge amount of customer data is not an easy task for businesses and we understand that. This success story is an excellent example of that as one of our clients faced the same issue and approached Quantzig to leverage its expertise in offering predictive analytics solutions to reduce customer churn rate.  Also, our predictive modeling techniques proved beneficial for the client in integrating traditional and digital data sources to correlate the data and identify potential churners.

Telecom company improves customer retention through predictive churn model

#7: Why you can’t afford to ignore predictive modeling techniques?  No matter which industry you are in, retail, transport, or healthcare, you cannot ignore the importance of predictive modeling. It helps you to gain a comprehensive understanding of the market trends, customer behavior, or competitor’s approach. Furthermore, by utilizing predictive analytics models, companies can easily track the volatility of different categories, brands, and products. This can result in better business outcomes.

Is Predictive Analytics All Set to Drive Success in the Transportation Industry?

#8: Predictive modeling models identified inefficiencies and improved ROI: Analyzing customer lifecycles to devise better marketing strategies and improve customer service is one of the common issues that every business faces, so don’t be surprised if you are facing the same challenge. If you have a proper predictive modeling technique in place, you have nothing to worry about. A leading firm in the healthcare industry was facing the same issue but witnessed a great change in their approach to risk assessment after leveraging Quantzig’s predictive modeling solutions. Moreover, it reduced inefficiencies drastically and improved their ROI.

#9: Predictive modeling techniques enhanced fraud detection and customer satisfaction: For some businesses, anticipating fraud risk is a herculean task. This impacts their customer service  and the customer ends up having a bad experience. So, for such companies, utilizing predictive modeling techniques is a necessity as these techniques can help anticipate potential suspicious claims and fraud risks.

Claims predictive analysis delivers reduction in frauds and enhanced customer satisfaction for insurance company

#10: How can predictive modeling increase accuracy and improve efficiency of marketing strategies?: Accuracy in forecasting the impact of several factors on the business outcomes is essential if you want your business to grow. Predictive models, in this context, hold the key to success. These models have the potential to assess and predict the performance of different components and their tentative impact on the future of the business. Furthermore, this can allow companies to make smarter decisions and exit declining markets.

How Healthcare Industry is Leveraging Predictive Analytics to Improve Patient Outcome


predictive 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.


predictive modeling

Top 3 Predictive Modeling Techniques That You Must Know

Predictive modeling is the phenomenon of creating, testing, and validating a model to predict the probability of an outcome in the best possible way. The main objective of predictive modeling is to anticipate an event, behavior, or output through the use of various predictors. But before going into the predictive modeling techniques, let’s first decode predictive modeling in simpler terms.Contact US

What is predictive modeling? 

Predictive modeling is a process where data mining and probability are used to forecast outcomes. Each model in this technique is made up of a number of predictors or variables that influence results in the future. After the collection of data for relevant predictors, a statistical model is created. The model may be a simple linear equation or a complex neural network and this is mapped out by a sophisticated software. After the availability of additional data, the statistical analysis model is revised or validated. Simply put, predictive modeling techniques involve artificial intelligence to create a profile of predictions (algorithms) from past individuals. The model that is formulated is then deployed so that any new individual can get an instant prediction for his need, whether a bank loan or an accurate medical diagnosis. Here is the list of few popular predictive modeling techniques:

Predictive Modeling Techniques 

These techniques may vary according to varying objectives. Some coGet More Infommon modeling techniques are:

Regression algorithms

When there is an abundance of data, then regression algorithms might help to predict trends in the future based upon that data. For example, if any raw material is being purchased for the manufacturing process, then the monthly price data can be collected to forecast seasonal fluctuations in those prices.

Clustering algorithms

Clustering algorithms help find the relations between variants, which are not easily visible. Product relationships can also be discovered using this technique. This technique is very useful in bundling services and upselling purposes.

Ensemble model

This is one of the predictive modeling techniques which is considered to the best for successful predictive analytics. In this technique, a mechanism is formulated to collect all the output from various models. The information fetched is then used for final analysis. Each model has different specifics like decision trees, queries, scenarios, etc. Therefore, it is very essential to understand what works well for your data and the problems you are targeting.

Predictive modeling in healthcareRequest Proposal

For improving the quality of healthcare, minimizing costs and improving the health of the population, the use of predictive modeling in healthcare becomes quintessential. Its role in healthcare can be listed as:

  • It has immense potential to drive future models of healthcare and is a key step towards personalized medicines
  • Assimilating domain knowledge and real-world evidence to address the issues of data quality is one of the most important roles of predictive modeling in healthcare
  • Modeling techniques like feature selection and smart ensemble methods can be used to predict and improve patients’ risk of infection, length of stay, utilization of services, the risk of readmission, and many others
  • Modeling technique in healthcare can help in harmonizing data elements across data collection systems
  • Modeling technique in healthcare helps in automatically mining the data and examining the strength of the relationships among variables to obtain the desired outcome.

Oscar Predictions

4 Tips to Get Your Oscar Predictions Right: Is Predictive Analytics the Answer?

The Oscars, also known as the Academy Awards, is a set of 24 awards for artistic and technical brilliance in the American film industry. This year, the 90th Academy Awards broke many records – crowning the oldest Oscar winner ever and the first black screenwriter for the best original screenplay, selecting a science fiction film to win top honors, and picking a Chilean film as the best foreign language film. But with this year’s Oscars already out of the way, most of us are already on the lookout for ways to get next year’s Oscar predictions right and profit out of the Oscar ballots that we find ourselves addicted to. A recent trend that has been picking up some traction among betting enthusiasts is that of predictive analytics. Recent evidence suggests that the day is not far where we can predict the outcomes of such award ceremonies and other events accurately. So, how exactly can you get your Oscar Free demopredictions right using predictive analytics?

  • Firstly, you have to look at a lot of factors which signals how the movie was received by the audience, such as its financial performance and likeability.
  • Next, cross-analyze such factors to generate accurate predictions.

Sounds complex? Fret not, here’s a simple list of factors that you have to pay attention to get your Oscar predictions right:

Buzz and fanfare

To get Oscar predictions right, take a movie’s relative buzz along with its fanfare is taken into consideration. In most cases, companies analyze which movie was the most talked about in social media platforms and the film that received the best review. Prediction algorithms are built on the belief that the public’s opinion and attention influences the votes. For the calculation part, data analysts take into account Google search volume, IMDb ratings, tweets, and other similar ratings of the movie to create an aggregated score to predict the success rate.

Prior awards

Another way of getting accurate Oscar predictions is to analyze all the previous awards the movie has earned. For instance, numerous awards ceremonies take place before the Oscars, including Golden Globes and Screen Actors Guild Awards. Since such awards are also likely decided by people who cast votes for the Oscars, it makes sense to factor in such awards to predict movie success. The higher the number of previous awards won by a particular movie, the higher its chances of winning the Oscars.

Financial performance

Although many artists value the quality of their art with the number of awards won by it, studio and media houses rely more on the amount of money it brings in. Spending millions of dollars in making a movie should give them fruitful results. As a result, financial performance matters and to a certain extent influence the votes. Consequently, many prediction models take into account the box-office revenue generated by the movie in relation to its overall budget to improve the success rate of their Oscar predictions.

Critic reviews

The closest thing to the voters of the Oscar awards are the movie critics. Even the public and fans eagerly wait for critics’ reviews before watching a movie. Although critics and publications don’t possess the sole voting rights, they can certainly influence the public and industry leaders. As a result, predictive analytics models factor in critic reviews to improve their Oscar predictions.

To know more about how predictive analytics can help in Oscar predictions:

Get more info

Related posts

banking sector

How Banks Use Predictive Analytics to Remain Competitive

Sustaining a business in the service industry has always been challenging. The customers are more demanding, forcing companies to fight hard to sort out operations, improve products and services, and maintain profitability. Such a phenomenon holds true for the banking sector as well. In a quest to provide minimal interest rates and extended services, banks are fighting to remain profitable. To solve such issues, the banking and financial industry is turning towards predictive analytics to predict consumer behavior and maximize revenues from each customer. Analyzing factors such as customer loyalty, spending patterns, purchase frequency, and other buying behavior helps banks and financial institutions adjust their services and promotions to build their revenue base.Free demo

Cross Selling and Upselling Opportunities

The competition in the credit card business has increased at such a phenomenal rate that banks have started providing credit at 0% interest rate, extended credit period, and offer higher bonus points on purchases made through cards. Amongst all these services, one would be perplexed as to how banks remain profitable. Well, that is because they use customer data to cross-sell and upsell their other products like housing loans, auto loans, locker services, or a platinum credit card. Analyzing behavioral data of the consumers can paint a picture for the banks as to whom should they offer a specific product to and at what rate. This, in turn, increases the bank’s share in customer’s wallet and builds brand loyalty.

Customer Retention

A generally agreed upon adage in the service industry states that acquiring new customers is ten times more expensive than retaining existing ones. As a result, banks are focusing their energy on retaining their customer base and lowering attrition rate. Since banks deal with thousands of customers on a daily basis, it is almost impossible to identify dissatisfied customers. Additionally, they would not know if the customer they are about to lose is profitable or not. Adopting predictive analytics by analyzing customer’s historical data, spending patterns, and other behavioral data can help identify customers who are likely to churn. Predictive models can accurately identify such sets of customers, and automated systems can be built to send out lucrative promotions to retain such customers.

Enhanced Customer Screening

Banks and financial institutions have embraced advanced analytics solutions, which help them assess customers on various parameters such as creditworthiness and credit score. Banks can now generate every single detail about the customer including spending pattern, monthly billing, and spends across different shops. This way predictive models can be built to trace their spending pattern. Such screening can be helpful in multiple ways. For instance, if their card is stolen and misused by others to make a significant purchase, banks can verify the purchase by calling the customer. Additionally, predictive analytics can also help them identify a customer who might default from their payments so that timely measures could be put in place to increase collection.

To know more about how banks and financial institutions use customer data to carry out predictive analytics along with predictive models, cross-selling, attrition rate, and customer relationship:
Get more info

Related Articles:

Cognitive Computing in Oil and Gas Industry

Reinventing the Oil and Gas Industry with Cognitive Computing

The future has arrived, people’s prediction that one-day computers will be smarter than humans exceeding human cognition is no more a science fiction. The power of cognitive computing coupled with human intuition and insights is capable of changing the landscape for oil and gas industries. Cognitive computing will allow oil and gas companies to make better, faster, and more informed business decision by leveraging historical as well as real-time data. Such computing power will give humans the freedom to use the saved time in high-value activities; thereby, driving profits, quality, and ensuring safety.Free demo

Improved Decision-Making Through Cognitive Computing
Cognitive computing involves self-learning algorithms that mimic human thinking by using data to recognize patterns to automate the problem-solving process without human assistance. Oil and gas companies must address issues such as energy-price collapse, rising resource development cost, new safety and environmental responsibilities, and reserves replenishment and extraction to improve productivity and agility. For instance, companies can save millions of dollars by accurately estimating the potential for a stuck drill-bit by monitoring all the drilling sensor data and comparing it with drilling reports and geological data. Additionally, it can greatly improve the way oil companies visualize and map oil plays by helping them make strategic decisions on exploration and production activities.

Benefits of Cognitive IoT

  • Companies can save millions of dollars in downtime maintenance by using cognitive IoT to predict incidents like stuck drill-bit accurately
  • Analyzing machinery data such as spec sheets, work order history, next scheduled maintenance, and current condition with cognitive computing can inform system engineers to take best actions to enhance maintenance operations
  • Companies can use satellite, aircraft, drone and historical data to predict optimum time to drill for oil in order to mitigate weather risks
  • Instead of waiting for sensors to trigger an alarm when reaching threshold levels, companies can use predictive analytics to identify anomalies in the data and optimize operations
  • Companies can draw vast engineering information and enhance their problem-solving abilities by learning from past behaviors, experiences, data, and resolved incidences

For more queries on how oil and gas industries can benefit from cognitive computing:

Get more info

Related Articles:

Connected Cars

How Predictive Analytics is Driving the Future of the Connected Car Industry

The availability of a massive amount of data and exceptional mobile computing power has transformed the connected car technology. The traditional focus on vehicle management and infotainment systems has shifted towards developing the car’s ability to connect with the outside world. Multiple streams of communication such asFree demo IoT sensors, infotainment systems, and telematics systems in the connected car generate a colossal amount of data. Such datasets pave the way for the efficient use of predictive analytics tools to improve technologies associated with connected cars.

Ensure Passenger Safety

The AI system in a car has advanced to such a level that it can identify road signs, nearby cars, and road conditions to prevent a collision. The AI’s ability for collision prevention is augmented by employing predictive analytics in a connected car system. This can be possible by sharing vital information with nearby cars to warn each other when making abrupt maneuvers. Manufacturers are devising new ways to enhance collision avoidance systems by utilizing predictive analytics tools that utilize driver behavior data as an input.

Predictive Maintenance

The connected car system records and analyzes large data sets gathered from actuators, sensors, and machines. Such data sets can be useful in predicting requirement of full servicing, parts replacement, or other repair works. Apart from this, the data generated from connected cars can be shared with the manufacturer so that they can arrange logistics to deliver the spare parts to the required location by predicting its need.

Reinforcing Cyber Security

The idea of being able to control cars remotely may seem likely to invite security breach for malicious purpose and even terrorism. Predictive analytics is a powerful tool for identifying such cyber threats in its early stages. It utilizes user data, analyzes driver behavior to recognize patterns, and identifies behavior patterns that are inconsistent or different than the authorized user to stop unauthorized access.

Enter Quantzig:

Today, managers have access to a large stream of data, and decision-making on the basis of gut-feeling, the rule of thumb, and guessworks are largely eliminated with the advent of data analytics.

“Without big data analytics, companies are blind and deaf, wandering out onto the web like a deer on a freeway,” said a leading data analytics expert from Quantzig.

For more than 14 years, we have assisted our clients across the globe with end-to-end data management and analytics services to leverage their data for prudent decision making. Our firm has worked with 120+ clients, including 55+ Fortune 500 companies. At Quantzig, we firmly believe that the capabilities to harness maximum insights from the influx of continuous information around us is what will drive any organization’s competitive readiness and success. Our objective is to bring together the best combination of analysts and consultants to complement our clients with a shared need to discover and build those capabilities, and drive continuous business excellence.

For more information on connected car industry:

Get more info

Related Articles: