5 Financial Analytics Secrets: Keys to Improve Business Decision Making
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
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.