With the healthcare sector beginning to leverage advanced technologies such as predictive analytics and AI, healthcare organizations, health care agencies, and primary health providers must be aware of its benefits and risks.
With the increasing pool of business data, there is a need for more effective, efficient business processes. The flood of big data into business has uncovered new ways for sales and marketing teams to collaborate and add value to each other’s work. Predictive sales analytics helps businesses across industries to forecast and develop campaign designs intended to generate higher-quality leads. Sales analytics has helped in identifying new avenues for marketing to derive desired results from sales efforts.
Predictive sales analytics offers detailed insights into the salesforce of an organization. Not only does it provide insights into which prospects to target, but it can also help sales teams to determine which marketing campaigns will be most successful based on buyers’ personas. In this article, our analytics experts have highlighted important ways in which predictive sales analytics can help businesses boost profitability and uncover new sales opportunities.
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Benefits of Predictive Sales Analytics
Streamlined lead scoring
Predictive sales analytics can help in determining lead scoring. Marketers can use predictive analytics to accurately forecast the potential customer’s likelihood to convert over time based on their previous activity and buying behavior. Predictive analytics tools can help businesses to measure customer’s demographic and behavioral data and find out whether or not the lead truly need their product or service. Also, it can help in analyzing the timeline of the buyer’s journey and the way they respond to particular offerings.
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Faster sales cycle
Predictive sales analytics can help in triggering the right content based on a lead’s behavioral and psychological data. This can further help in answering questions, managing objections, and moving the lead through the sales process more efficiently. Predictive analytics tools can help sales teams to move the lead down the sales funnel faster by analyzing their content consumption and behavior at each touchpoint throughout the journey.
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Better upselling opportunities
Predictive sales analytics can help marketers and account managers to maximize their existing and potential customer’s value. With the help of sales data analysis, marketers can easily understand and analyze the factors responsible for buying decisions of a particular lead. This can also help in improving upselling opportunities resulting in better business outcomes.
Improved customer retention capability
Predictive sales analytics can help businesses to track the activity of customers who have opted out of business. This insight can help marketers to forecast potential churn risks. Also, predictive analytics platforms can help sales teams across industries to understand factors of potential churn such as inactivity, substandard performance metrics, or even mentions on social media. This can, further, help in taking immediate action against the same.
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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.
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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.
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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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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.
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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.
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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.
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.
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.
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.
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
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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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!
What is Prescriptive Analytics?
Prescriptive analytics applies mathematics and logic to data to describe a specific course of action. Also, prescriptive analytics has the huge potential to suggest options in decision making that further helps in taking advantage of a future opportunity or mitigating risk in the future and explaining the impact of every option of the decision. Prescriptive analytics techniques can automatically process fresh data to enhance the accuracy in forecasting and can make the decision-making process more efficient. Additionally, prescriptive analytics assists organizations to achieve business objectives such as high profits, better customer service, and operational efficiency. Prescriptive analytics solutions utilize optimization technology to solve complicated decisions with millions of decision constraints and variables. They provide actionable insights into every aspect of the decision-making process.
Prescriptive analysis helps companies to improve business decisions and the effectiveness of actions. Want to know how? Get in touch with our experts now.
Pushing Boundaries with Predictive Analytics