Tag: predictive analytics techniques

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Predictive Analytics in Healthcare: Benefits and Challenges

The healthcare industry is going through transformations as it moves from a volume-based business to a value-added business. Organizations in the healthcare industry are facing the heat to provide value-based care with optimum outcomes to the patients. With the huge influx of data in the healthcare systems, it is really becoming very difficult for healthcare organizations to draw meaningful insights from it and utilize it to treat patients and improve the quality of services. This is where predictive analytics in healthcare comes into the picture. Also, with the emergence of value-based reimbursement, many healthcare systems have now realized the importance of predictive analytics in healthcare. It has become an important key to manage population health and deliver care more cost-effectively. At the same time, it helps healthcare organizations in reducing readmission rates and predict different types of healthcare trends. In this article, our team of healthcare analytics experts has highlighted a few benefits of predictive analytics in healthcare. Also, they have discussed the challenges that healthcare organizations face in implementing predictive analytics in healthcare.

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Benefits of Predictive Analytics in Healthcare

Benefit #1: Predictive analytics in healthcare can increase the accuracy of diagnoses

By leveraging predictive analytics solutions, physicians can utilize predictive algorithms to make more accurate diagnoses. Predictive modeling and machine learning can provide real-time information to doctors that can fuel the accuracy of predictions and can lead to better patient outcomes. Also, with the use of predictive analytics in healthcare, multiple datasets from hundreds of patients can be analyzed to build tools that find patterns in patient journeys to facilitate early diagnosis and predict negative outcomes. Furthermore, predictive analytics solutions can help in monitoring diagnosed patients and assessing how their disease status progresses in real-time. Such alerts can facilitate early intervention that can make a real difference in helping a patient avoid complications or long-term physical damage.

Benefit #2: Predictive analytics will help preventive medicine and public health

Predictive analytics in healthcare facilitates early intervention that can help in the prevention or amelioration of many chronic diseases. Predictive modeling, particularly within the realm of genomics, can also help primary care physicians to identify at-risk patients within their practice. With that knowledge, patients can be advised to make changes in their lifestyle to avoid future risks or complications. Also, predictive analytics solutions help to develop a profile for patients at risk of substance abuse and help providers anticipate how their prescription decisions might affect those patients.

Are you finding it difficult to reap clinical and financial rewards from predictive analytics? We can help. Get in touch with our experts now.

Benefit #3: Predictive analytics can provide employers and hospitals with predictions concerning insurance product costs

Healthcare organizations providing healthcare benefits to employees can input characteristics of their workforce into a predictive analytic algorithm to predict future medical costs. Such predictions can be based upon the own data of the organization or the organization may even collaborate with insurance providers to generate the predictive algorithms. Furthermore, healthcare organizations working with insurance providers can synchronize databases and actuarial tables to build predictive models and subsequent health plans. Organizations might also use predictive analytics in healthcare to determine which providers may give them the most effective products for their specific needs.

Our advanced analytics solutions help healthcare organizations to improve patient outcomes and reduce the cost of care. Want to know how? Request a FREE proposal now.

Challenges in the implementation of predictive analytics in healthcare

Challenge #1: Developing a comprehensive patient profile

Most healthcare providers already possess the data assets of their patients that are required to build a predictive model. But utilizing such information can certainly be a challenge. The difficulty increases even more when patients move between providers, health systems, or even geographical regions. As a result, healthcare organizations find it difficult to map the profile of such patients. Therefore, implementing predicting analytics solutions becomes challenging.

Challenge #2: The challenge of clinical application

As predictive analytics in healthcare has become increasingly available for real-world applications, healthcare professionals diagnosing and treating patients encounter multiple data elements apart from classical clinical data. When new data is added into the patient narrative, this information needs to be put into a framework that physicians can understand and recognize as credible. Therefore, it is important that an alert not only states that a patient likely has a certain disease, but also includes a detailed rationale as to why the analysis is making a specific prediction.

Challenge #3: Data aggregation challenges

Data of patients are often spread across many file cabinets, servers, hospitals, and government agencies. Pulling all these data together and collaborating them all for the use in the future requires a lot of planning. Every participating organization must agree and understand upon the types and formats of big data they intend to analyze.  Also, the quality and accuracy of such data of patients need to be established. This requires not only data cleansing but also a review of data governance processes.

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Predictive Analytics in Marketing: Key to Drive Business Profitability in 2019

What is Predictive Analytics?

Predictive analytics is an approach that helps in predicting unknown future events. There are many techniques that are used in predictive analytics such as machine learning, data mining, data modeling, and artificial intelligence to examine current data and make future predictions. Also, it helps in finding patterns in both structured and unstructured data sets, thereby assisting in the identification of future risk and opportunities. Furthermore, predictive analytics has the potential to integrate management and technology together to drive better business outcomes. By leveraging predictive analytics solutions, businesses can become dynamic and can anticipate behaviors and outcomes based on the known facts and data and not merely upon assumptions.

How can predictive analytics solutions help businesses derive successful outcome and boost ROI? Read Quantzig’s recent blog to find out.

3 Ways Predictive Analytics Solutions Can Help Businesses Derive Successful Outcomes

Leveraging Predictive analytics in marketing can help businesses refine their marketing strategies and provide personalized services to customers. Want to know how? Get in touch with us right now!

Predictive Analytics in Marketing Realm

How can predictive analytics in marketing drive profitability for business? Are you thinking the same? The answer to it is, any tool, process or technique that can guide marketers to identify the buying habits of consumers is nothing less than a boon to their business. This is because if the past buying habits of a customer are identified and analyzed well, it can help in projecting the future buying habits, thereby helping in future decision-making based on those projections. Predictive analytics in marketing helps to ensure that these predictions are precise and accurate.

Here are a few things that a business can do when the available data is mined and predictive analytics in the marketing realm is applied:

Analyze and predict the seasonal behavior of customers

Today most of the products and services are sold online. Application of predictive analytics in marketing especially helps in this case. It helps in highlighting the products that are on high demand and those that customers prefer to buy at any given time.

Target the most profitable product category

The second benefit that businesses gain by applying predictive analytics in marketing is that they can target the most profitable products and services.  By administering the technique of artificial intelligence and machine learning, it is easy to identify affluent customers who prefer high-end products. This is an integral part of effective and predictive marketing strategy too.

By applying predictive analytics in marketing businesses can gain insights into new profits streams, better ways to conduct the business, and ultimately lead the game. Request a free proposal to know more.

Employ the most suitable marketing strategy for winning repeat business

Predictive analytics in marketing can inform businesses about customers who are most likely to be repeat customers. Owing to the high competition, businesses need to allocate resources on targeting such customers that are likely to profit the business the most. And applying predictive analytics in marketing is the best step to achieve this.

How can predictive analytics help in forecasting consumer demand precisely? Read our latest success story here to gain better insight.

Forecasting Consumer Demand with the Help of Predictive Analytics – A Quantzig Success Story

Prioritize customers

Finally, predictive analytics in marketing helps in prioritizing customers. It helps in identifying factors that indicate that a particular customer s most likely to become a repeat customer. It guides to recognize customers who buy the highest-margin products and are most likely to initiate returns.

Use-Cases of Predictive Analytics in Marketing

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Use Case #1: Refine segmentation for better campaigns

Applying predictive analytics in marketing helps in refining customer segmentation and creating customized campaigns. It allows to mine behavioral and demographic data to push quality leads further down the sales funnel.

Use Case #2: Improves content distribution strategy

Sometimes even the good content fails to drive business and the reason behind this is an ill-defined content distribution strategy. By applying predictive analytics in developing a marketing strategy, this problem can be tackled head-on. Using predictive analytics in marketing makes it easier to analyze the types of content that resonate most with customers of certain behavioral or demographic backgrounds. Furthermore, this helps in distributing similar content to such customers sharing the same demographic or behavioral habits.

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Use Case #3: Precise prediction of customer lifetime value

Artificial intelligence and machine learning can make predictive analytics in marketing more efficient. It can enable businesses to gauge the historical lifetime value of existing customers that match the backgrounds of new customers. Consequently, this can help in making a fair and precise estimate of the lifetime value of new customers.

Use Case #4: Better insight into the propensity to churn

Protecting your bottom-line becomes much easier by leveraging predictive analytics in marketing. How do you ask? By analyzing and learning from the mistakes committed in the past. By applying predictive analytics in marketing, businesses can analyze the behavioral patterns of previously-churned customers. Furthermore, this can help in identifying the warning signs from current customers. Consequently, businesses can take measures to plug such customers into a churn-prevention nurture campaign.

Use Case #5: Optimization of campaign channels and content

By leveraging predictive analytics in marketing, businesses can optimize their campaign channels as well as the content. With the entry of new customers in the business pipeline, there is an availability of their data which can be utilized for the various purpose. These purposes include identification of most suitable marketing channels, content type and even data and time to target specific and potential customers.

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