Many executives in the healthcare industry suggest that predictive analytics will cut cost in their organization. A survey conducted in February 2017 forecasted that predictive analytics processes will reduce 15% of the cost for more than five years. A majority of healthcare industries already use them. Lack of budget is a biggest challenge for the implementation of predictive analytics. Lack of skilled employees, too much data, lack of confidence in the accuracy of data and lack of support of technology and executives are some of the important challenges healthcare industry faces of implementing predictive analytics. Read more at :

https://www.information-management.com/news/predictive-analytics-seen-as-cost-cutter-by-healthcare-execs?feed=0000015a-13e1-deb4-ab5e-9bfd65d50000

The present business environment is volatile and full of uncertainties. Therefore, a need arises to improve efficiency and profitability. Though many organizations rely on traditional techniques, predictive analytics is the new trend of managing risks and monitoring frauds which eliminates all the guesswork. Predictive analytics help us in reaching the source of fraudulent transactions and in dealing with future plausible attacks. Lack of corporate transparency and missing public trust should be dealt with by using advanced tools for managing huge data and ensuring accountability. Predictive analytics helps in building the customer profile to know his credibility which is useful for banks. Read more at : https://blogs.metricstream.com/ready-predictive-analytics-revolution/

The present business environment is volatile and full of uncertainties. Therefore, a need arises to improve efficiency and profitability. Though many organizations rely on traditional techniques, predictive analytics is the new trend of managing risks and monitoring frauds which eliminates all the guesswork. Predictive analytics help us in reaching the source of fraudulent transactions and in dealing with future plausible attacks. Lack of corporate transparency and missing public trust should be dealt with by using advanced tools for managing huge data and ensuring accountability. Predictive analytics helps in building the customer profile to know his credibility which is useful for banks. Read more at : https://blogs.metricstream.com/ready-predictive-analytics-revolution/

Predictive analytics incorporate an assortment of measurable strategies from predictive modelling, machine learning and data mining that investigate present and chronicled certainties to make forecasts about the future. There are numerous components that show churn: drops in item utilization, debasing assessment in client communications. But the issue is predictive analytics depend on complex models that consider numerous "variables" that could possibly be independent. Key steps that can make client progress with prescient examination: 1. Quit attempting to rethink the whole. 2. Understand that it is just an expectation, not an assurance. 3. Move from imagining a scenario where to what's next. 4. Make it an agreeable, shut learning circle. For more read: http://www.cmswire.com/analytics/leveraging-the-power-of-predictive-analytics-to-control-churn/

The advanced age is loaded with examinations, estimations and learnings. It has brought streaming information in real time. In today's predictive analytics world, advertisers can see the future effect before spending a dime. Advertisers can hope to know how likely it is for a specific occasion to happen in the life of a customer. Customers are focused on forecasts, which depends on their computerized impression. For more read: http://www.cmswire.com/analytics/predictive-analytics-makes-marketing-dollars-work-harder/

Having access to information doesn't mean consumers will correctly understand and interpret the data. Real estate entrepreneurs also must gain a better understanding of this data to make it useful for customers. Real estate leaders should have knowledge of something when they use predictive analytics i.e. 1. Fortify the foundation, 2. Draw up a blueprint, and                                                                                                              3. Entertain your guests.    For more read: https://www.entrepreneur.com/article/275805

By utilizing technology & traditional data analysis, wholesalers can use predictive analytics: 1. Forecast future customer needs, 2. Discover trends to foresee future business scenarios, 3. Predict changes in customer segments and the impact to the organization,  4.develop future pricing strategies, 5.improve upcoming marketing campaigns, 6.project customer profitability, 7.develop strategies to maintain customers, 8. evaluate their exposure and risk profile, 9. identifies potential new customers, markets and segments. By leveraging the use of predictive analytics, wholesalers can gain the upper hand over competitors in today's digital economy. For more read: target=_blankhttp://clarkstonconsulting.com/blog/wholesale-distribution-and-the-digital-economy-the-importance-of-predictive-analytics/

Predictive analytics help you how to make money. Angela Hausman, PhD (marketing professor at Howard University) writes in her article about some possibilities to improve bottom line by using predictive analytics: 1. Recommendation algorithms
2.Manage the customer journey
3.Segmentation based on CLV (customer lifetime value) or other variables
4.Optimize deployment of company resources
5.Hire the best employees for a job
6.Detect fraud. Using predictive analytics, firms segment their customers on more influential variables. Predictive analytics can help to optimize deployment of resources. For more read: http://www.business2community.com/business-intelligence/want-buy-using-predictive-analytics-01543154#eTFpvvsFFDwam4MG.97

Predictive marketing uses machine learning to deliver more accurate insights to encourage sales. The primary objectives are measuring customer behavior and audience insights, campaign effectiveness, calculating and improving customer lifetime value and customer retention. Predictive analysis can achieve these goals by learning from patterns within the data that are derived from customer touch points. Read more at: http://www.emarketer.com/Article/Using-Data-Predictive-Analytics-Helps-B2Bs-Throughout-Funnel/1013868

Predictive analytics is one of the most useful tools to analyze customer behavior on a particular product or services. This process helps identify customers’ needs and also chalk out a correlation matrix which helps to understand the additional demands. Companies monitor interactions of their clients to predict attrition. Negative consumer sentiment in social media, looking out for issues on the retailer's online knowledge base, and repeat calls to contact center may indicate attrition. It facilitates the next best interaction, monitor transaction details and analyze fraudulent activities. Neither business operations, nor business analytics have the complete information to make data-driven decisions, hence there exist a gap between customer needs and Delivery Company. To overcome this, customer centric ideas have to be taken in consideration. Businesses need a continuous and well-defined program to measure data quality. Establishing data quality standards and monitoring data quality quotient in real-time makes predictive analytics reliable.

To read, follow: http://www.cmswire.com/analytics/what-customer-centric-predictive-analytics-looks-like/

All new on-line communication that both consumers and retailers access on a regular basis is creating even more data for retailers to store. It is time to extract valuable information from all the existing data in order to meet customer demands, increase sales and improve business performance.

In the simple terms, predictive analytic is a technique that is used for forecasting. It uses past data like how many products were sold and at what rate? Predictive Analysis is a big help in the retail industry. It doesn't mean that the whole process has to work on automation. Human decision making (sometimes gut feeling or intuition) and software like Predictive Analytic would give even better results. Some constraints in adapting this type of analytics is that decision power is transferred to a machine. This is always a difficulty because downsizing or resistance can also be a result. Analytics work on big data; which is difficult, expensive and can fluctuate.

To read more:http://www.in.techradar.com/news/world-of-tech/What-is-predictive-analytics-ndash-and-should-we-fear-it/articleshow/51945739.cms

One of the major difficulties for organizations is the accumulation of data.  According to a research, it was found that only 1% of all apps use prescriptive analytics. This number is set to rise by 2018 to 50%. Organizations need to have high quality, rich insights into their data usage and is important for forecasting, tracking physical and performance capacity. This is where predictive analytics plays an important part; allow real time feedback, provide advantage of tools for capacity and performance planning, lower the cost of ownership, and improve the quality of support services. For more read the article written by Tim Jones (Technical Specialist) : http://www.mis-asia.com/blogs/blogs/predicting-your-storage-capacity-with-analytics/

The need for wearing body cameras for police personnel has increased. Police departments are also taking the help of specialized data mining solutions to predict and prevent misconduct. The problem with this, is that, it leads to officers being treated differently based on actions they are yet to take, and might never take at all. Predictive analytics is playing an important part in modern policing and in ending crimes which are less serious than police misconduct. For more read the article written by Graham Templeton ( Writer ): http://www.extremetech.com/extreme/224560-new-analytics-can-predict-and-possibly-prevent-police-misconduct

The best way to improve the probability of desirable outcomes is to predict the unknown future results. Predictive analytics help organizations to become forward looking and proactive. It uses a number of predictive modelling and analytical technique for the prediction of the future. One of the modelling techniques is a response model which doesn't predict influence. It only predicts the desirable outcomes of one method without making any prediction of alternative method. Healthcare organizations will be more successful if the predictions for treatment decision results in the desired outcome. For more read the article written by Eric Siegel (founder of the Predictive Analytics World Conference):

http://data-informed.com/drive-influence-with-uplift-modeling/

401(k) is a retirement savings plan sponsored by an employer. It is found that retirement planning and wealth management firms are upgrading themselves with client segmentation. Nowadays, segmentation and predictive analytics projects are important to organizations. Segmentation is an important as it help firms continue to add resources. Firms are also trying to progress with the help of big data. For more read the article written by John Sullivan : http://401kspecialistmag.com/segmentation-critical-fully-understand-clients-across-channels-march-2016-boston-new-research-cerulli-associates-global-analytics-firm-finds-firms-approaching-client-segmentat/

Analytics help retailers move to a unified commerce experience and allow for more targeted, cost-effective marketing. Advanced analytics is used for the optimization of merchandise planning. Real-time analytics is integrated with customer and product information across all channels. Retailers have the ability to use science and algorithms to optimize pricing strategies to reduce markdowns, improve demand forecasting to optimize inventory, localize assortments and optimize space planning. Advances in software and the occurrence of cloud-based solutions enable retailers to think about accelerating their upgrade/replacement cycles. It will help limit overstocking and discounting. With real-time analytics, retailers can market to consumers on a 1:1 basis based on customer context. Retailers have the ability to know what customers have in their closet, purchase behavior and preferences. For more, read the article written by Forrest Cardamenis : 

http://www.luxurydaily.com/analytics-offer-solutions-to-merchandising-and-marketing-problems-brp/

Predictive analytics and statistical analysis are based on the concept of relationships between observed and future actions. When analyzing people, we observe a small sample of data on people and build a predictive model to identify a number of shared traits they have. Micro-targeting is the idea of finding relationships among variables to recognize the target audience's shared traits. It helps to identify the right people. The steps included to predict purchase behavior and design a campaign to expand customer base are: 1. Create dataset. 

2.  Once we have a dataset loaded, we will analyze the people who have purchased the cloud solution and find what they have in common with one another. 

3.  To do this, our first step is to create a dependent variable. 

4. Then, we build a predictive model which takes that variable containing cloud purchase information, and compares it to other variables in our data set. 

5.  The regression model we build then compare our cloud purchase variable to whichever other variables used for analysis, and then gives us statistical correlations for each variable. 

6. Initiating the cloud solution licensing, then identify more people who fit this demographic.

For more read the full article at:

http://www.econtentmag.com/Articles/Column/Marketing-Master-Class/Why-Predictive-Analytics-and-Microtargeting-is-a-Game-Changer-for-Marketers-109377.htm

Silent customers create major risks to companies. They don't express their dissatisfaction. Companies can avoid these issues through the proper use of technology with predictive analytics.  Predictive analytics can stop the silent customer attrition by identifying four ways to retain customers:

1. Recognize customers who make a detailed analysis before they determine.

2. Determine the most effective actions to reduce the attrition

3. Distinguish between the best time, message, and channel to reach the customer.

4. Identify the full path to retention rather than one single action.

Companies that adopt predictive analytics to identify who is likely to leave and determine the best plan of action to stop attrition.

For more read :

http://www.information-management.com/news/big-data-analytics/using-predictive-analytics-to-stop-silent-customer-churn-10028295-1.html

These days, faster diagnostic and machine learning on large sets of data promise a real-time understanding of health. Predictive analytics help to decrease costs, and helps in preventive disease management. Precision medicine is an approach to treatment and prevention considering individual variability in genes, environment and lifestyle for each person and also classify people precisely, based on susceptibility, microbiology and/or prognosis, at a considerably higher resolution. Health data analysis provides useful perspectives to predict the future. Precision medicine makes true predictive analytics possible. Prediction requires precision, but it does not require precision alone. Predictability comes from a wide and narrow gap which requires a data sets with high-accuracy. For more read : http://hitconsultant.net/2016/02/22/31535/

Big data analytics plays an important role in supply chain management. 97% of supply chain executives have reported how big data analytics helped them to grow their business and only 17% of any particular industry have implemented this process. This process generates higher visibility and deeper insight to the customer behaviour and demand supply scenario. It also helps to discover and manage supplier relationships more effectively. Big data help to understand customer needs and make a 360 degree analysis regarding marketing channel, segmentation and acceptability. Predictability helps to create more efficient supply chain progress (increased ~10%). It identifies supply chain risk by considering the previous demand, supply scenario almost accurately. Supply Chain Traceability and recalls are data-intensive and highly correlated to supply chain risk. The ability to quickly meet customer fulfilment is an important driver. It helps in competitive advantage across all industries which can be achieved by big data analysis.

To read, follow: http://www.computerworld.com/article/3035144/data-center/overcoming-5-major-supply-chain-challenges-with-big-data-analytics.html