Tag: predictive modeling

predictive modeling

A Definitive Guide to Predictive Modeling

What is Predictive Modeling?

The process that uses statistics and data to predict results with the help of data models is said to be predictive modeling. Predictive modeling is used to predict anything from advances in technology to corporate earning. Predictive modeling methods use data mining and advanced analytics that are likely to influence results in the future. Predictive modeling techniques employ a complex network or a simple linear equation to analyze the datasets and then such data is run through algorithms to determine what kind of products or services users might be interested in.

What are the Different Predictive Modeling Techniques?

Technique #1: Behavioral Clustering

The path leading a prospective customer to convert into a buyer is incredibly valuable for a marketer who focuses on leveraging predictive modeling solutions. Behavioral clustering is one of the predictive modeling techniques that empowers marketing teams to determine trends and commonalities that help to create a new target segment. It can help marketers enhance conversions and predict the impact of marketing campaigns more precisely.

Request a FREE demo to find out how our predictive analytics solutions can help you gain actionable insights into the performance of your marketing campaigns.

Technique #2: Product-Based Clusters

This is a predictive modeling technique that segment customers together based on similar engagement and activity with the brand. Product-based clusters focus specifically on tracking specific buying trends among target demographics. These product-based clusters also help in forecasting the lifetime value of a customer. Such predictive modeling techniques help companies in identifying services that they can actually sell to the customer over a period of time. 

Technique #3: Share of Wallet Estimation

“Share of wallet estimation” is a predictive modeling technique that assists companies to predict how much of their customer’s budget is with competitors. Also, it unveils what specific products or services you can sell to that particular customer in order to increase your share of wallet percentage. In order to attain the desired outcome, companies must leverage this technique along with the product-based clustering techniques.

Our predictive analytics solutions can help you curate, process, and analyze  data sets precisely. Request a FREE proposal to know more about our portfolio of services.

Technique #4: Likelihood of Churn

Predictive analytics models are used by most of the marketers for lead generation. But this is a mistake because predictive modeling techniques have strong defensive capabilities that can help in measuring the likelihood of churn. For most companies, customer churn can be a big issue but predictive modeling techniques can help by measuring customer’s propensity to churn by analyzing their behavior. It can help in identifying “red flag” behaviors from previously churned customers and their troubling behavior. Such information can be followed up by the customer service teams and this can be further transitioned into a nurture campaign to reduce churn. The impact can be significant when it comes to retaining current customers and boosting revenue by minimizing churn.

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A Leading International Bank Improves New Account Activity and Customer Retention Using Predictive Modeling

Overview

The banking and financial services sector has transformed tremendously over the past few years. The recent advances in analytics and predictive modeling techniques have further propelled businesses by offering powerful analytics tools to gain insights into the changing customer needs and behaviors. With the rise in the use of advanced analytics and data visualization techniques, these analytics advances have begun to accelerate rapidly across industries. The potential benefits of these sweeping new advances and predictive modeling techniques are reflected in a variety of areas such as enhanced anticipation and prediction of possible customer churn, improved effectiveness of cross-selling and marketing activities, and greater efficiency and accuracy in anti-money laundering, and other compliance initiatives.

In such a complex business scenario, satisfying the growing customer base turns out to be a daunting task even for well-established banks. Though banks have been adopting various tools to address these challenges, factors such as ensuring long-term loyalty, customer retention, fraud detection, and credit risk management have always been key areas of concern. Facing similar challenges the client in this study realized that predictive modeling would help them address such issues. The client chose to partner with Quantzig to effectively address their challenges and to expand their knowledge of how modern tools and predictive modeling techniques could improve the efficacy of their existing business models.

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Predicaments Faced

The banking client had been running a new customer acquisition program that focused on new customers relocating near branches with a cash incentive to open new bank accounts. Although the program had generated reasonably good results, the bank was anticipating reductions in available marketing budgets and wanted to lower program management costs while improving overall business outcomes.

Our Solution

Our experts worked closely with the client to develop a new predictive modeling process that makes accurate forecasts to best serve their business budget and operation planning needs. The devised predictive modeling process helped the client to identify influential attributes of new responders by categorizing the prospects into five groups based on their probability of response. The client targeted the top five categories that consisted of 60% of the new and most responsive user groups.

The use of a new predictive modeling approach delivered detailed insights and accurate predictions that helped resolve business uncertainties into profitable probabilities. To take advantage of the insights offered by the new predictive modeling approach and to make better, more profitable decisions, the client wanted to deploy predictive analytics models in their operational systems. A new business plan coupled with a robust predictive modeling platform delivered:

  • A collaborative environment and shared framework for problem definition to ensure the analytics is solving the right problem
  • A repeatable, industrial-scale predictive model

Our Predictive Modeling Solutions Can Help You Gauge Business Success

Business Impact

The solutions offered resulted in a stable predictive model with a performance that exceeded the client’s existing system, despite the considerable effort that had been invested in their existing model. Also, it’s essential to note that by focusing on the most responsive, new targets the client significantly increased customer acquisition rates and associated transactions while cutting down on their marketing costs. The predictive modeling solutions also empowered the client to fine-tune the audience based on various criteria to accurately predict acquisition campaign results. This, in turn, enabled the bank to optimize program performance on a continuous basis.

The adoption of predictive modeling techniques offered the following outcomes:

  • Customer acquisition rates increased by 25%
  • New account activity improved by 30%
  • Significant reduction in marketing costs

What are the different types of predictive models?

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Predictive Analytics in Healthcare: A Pathway to Personalized Patient Care

Every industry today is transformed virtually by predictive analytics and the healthcare industry is no exception. Predictive analytics in healthcare is advancing patient care significantly by offering detailed insights into the medical history of patients. However, there are many healthcare organizations that are yet to tap into the full potential of predictive analytics in healthcare. Deploying predictive capabilities in extracting actionable, forward-looking insights from the growing pool of data is still a daunting task for many healthcare companies.

However, realizing the power of predictive analytics in healthcare is highly essential as predictive analytics platforms can help healthcare companies to transform medical wisdom into real-world insights. Leveraging predictive analytics in healthcare can help companies to move into the new era of medicine. Consequently, this can improve hospital care, help in the management of chronic diseases and enhance supply chain processes.

At Quantzig, we understand the difference that leveraging predictive analytics solutions can create in the healthcare industry. And to help companies in the healthcare sector stay ahead of the curve, our team of analytics experts have highlighted the ways in which predictive analytics can improve healthcare and also have explained a few use cases for predictive analytics in healthcare.

Are you looking for ways to gain consolidated insights to mitigate risks and resolution of issues for medical cost management and improved patient care? Get in touch with our experts now!

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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

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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.

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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.

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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:

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