Pushing Boundaries with Predictive Analytics
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.
#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.
#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.
#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.
#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.
#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.
#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.
#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.
#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.
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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.
There have been several speculations about the stationary growth of the pharmaceutical industry, but recent technological advancements are expected to encourage the growth of this industry in the years to come. Click To Tweet
The recent population shift to urban areas and increasing accessibility of people to healthcare services has opened the doors for companies in the pharmaceutical industry to a broader target market. These emerging markets are becoming extremely important for pharma companies. However, for companies in the pharmaceutical industry, it becomes very essential to shift from a sales and marketing-focused model to an access-driven commercial model. Additionally, with healthcare becoming a priority for governments in several countries, the pharmaceutical industry seems to have a bright future. But on the flipside, there are several factors like consumer attitudes, drug availability, affordability, policies of governments, which might not entirely be in the favor of pharma companies and are challenging the growth of pharmaceutical industry. In this article, we have talked about several critical challenges faced by the big players in the pharmaceutical industry. and have highlighted steps that will help companies to combat each of those challenges.
WP – The Rise and Value of Predictive Analytics in Enterprise Decision Making
The telecom industry is one of the fastest growing sectors in the world. Companies in the telecommunications industry have shifted from being mere providers of infrastructure, bandwidth and capacity to enablers of communication, information, and interaction. As technology advances, service and pricing plans evolve, and the market becomes more saturated, telecom companies face increasing competition for customers. But the good news is that there is an abundance of customer data that is available to telecom companies today. Players in the industry can get the best out of this data with the help of advanced capabilities such as predictive analytics. By using predictive analytics, companies in the telecom industry can learn more about their customers’ preferences and needs, which will eventually make them more successful in this highly competitive industry. Here is why we think that every provider in the telecom industry must leverage predictive analytics:
Satisfy customer expectations
One of the guiding principles of customer experience management is to look at how customers are engaging at every stage with the organization. This includes interactions before they sign on as customers, all the way through the end of their engagement with the company. The goal is understanding the customer’s experience and taking measures to shape it in the most positive way possible. In other words, it’s about anticipating needs and delivering services that keep customers happy, rather than reacting to problems. With the help of predictive analytics, telecom companies can accurately identify the trends in customers’ needs. This will help providers to alter their services accordingly and improve the customer experience.
Predict and prevent customer churn
Did you know that certain predictive analytics software even recommends ways to reverse trends such as churn? This can be taken into account when companies in the telecom industry are devising strategies to reduce or avoid churn. For instance, Cox Communications, a leading player in the telecom industry had built predictive models that enabled them to quickly and precisely poll millions of customer observations and hundreds of variables to identify issues including the likelihood of churn. They then personalized offers across 28 regions. By acting upon the insights and recommendations, the provider was able to reduce their customer churn.
Fraud is a key root cause of lost revenue in the telecom industry. Efficient fraud detection systems can help telcos save a significant amount of money. Fraud detection systems depend on data mining algorithms to identify and alert telcos to fraudulent customers and suspicious behavior. While data mining techniques help only in the areas of subscription fraud, it is useful to remember that there can be several methods of fraud, requiring other analytic models to aid detection. Risk management teams are the largest users of fraud management systems.
Cross-selling and up-selling
Cross-selling and up-selling activities can be supported by predictive analytic in the telecom industry by tracking on association rules and transaction histories. Analytics-driven cross-selling and up-selling campaigns are known to provide comparatively higher returns. By moving beyond financials, they also increase stickiness and reduce the number of contacts required for cross-selling and up-selling.
To know more about the opportunities and challenges of predictive analytics in the telecom sector
The healthcare industry is flooded with an overabundance of raw data. The problem lies in figuring out the best way to manage this data. But that aside, all of this raw data can actually be used to solve many day-to-day problems in the healthcare industry. Predictive analytics is rapidly becoming one of the most-discussed, perhaps most-hyped topics in healthcare analytics. Predictive analytics is used for extracting vital information from existing data. This helps to understand various patterns and also predict future trends and outcomes. It has a bright future in the healthcare industry because of factors such as the important need to understand the raw data and its meaning, and a significant number of business-oriented applications to which data analysis can be applied. However, predictions made solely for the sake of making a prediction are a waste of time and money. In healthcare and other industries, the prediction is most useful when that knowledge can be transferred into action. In order to implement a predictive analytics solution in the healthcare industry successfully, it’s important to have a clear vision of the desired outcomes, have IT systems in place that are interoperable, and have a strong commitment to knowledge-sharing across departments. Here are some of the key steps for setting up predictive analytics for your organization:
Understand organizational requirement
Predictive analytics is designed to make a real impact on existing organizational processes and to improve efficiency. The healthcare industry possesses an abundance of data, but the major challenge most organizations face is determining what to do with all of the data and how to make sense of it. There is too much data available in almost every aspect of healthcare, but this large amount of data also opens up the potential to implement a predictive analytics system. A predictive analytics platform can provide a strong pathway for providing answers as to what to do with this data.
Collect and cleanse data
Once companies in the healthcare industry have identified their analytics requirement, the next step is to sort the data. It’s important to define where exactly the data is coming from in order to cleanse it. This process includes a variety of tasks such as dealing with redundant data, missing data, and unformatted data. The most important thing is that the dataset is standardized so that the process can be automated.
Focus on end-user
In the healthcare industry, analytical results must be made available to nurses, physicians, and insurance analysts who mostly deal with the day-to-day operations. Therefore, predictive analytics platforms must be easy-to-use and accessible across an organization. Professionals in the healthcare industry deal with large amounts of data every day, so the results obtained from an organization’s predictive analytics platform must be integrated smoothly into their existing daily workflows to be truly beneficial.
To know more about the use cases of predictive analytics in the healthcare industry
The Future of Data Management