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/

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

Predictive analytics involve mining database for actionable insights on your customers and it aims to find patterns that will suggest sales strategies or tactics that will make your sales efforts more successful. It is a new tool for CRM. There are a number of third-party programs available that will enable you to apply predictive analytics to your CRM data, but, the cloud-based solutions are more attractive to small businesses. Read more at: http://it.toolbox.com/blogs/insidecrm/predictive-analytics-for-crm-70768

Customers nowadays, use apps and for a product owner it is important to know what value his app is generating to the customers. Product owners should connect with their customers and to connect with customers at a deeper level, you must take advantage of predictive analytics. Predictive analytics helps to discover core product value, and product managers can chalk out an efficient roadmap for delivering core product value to users as quickly as possible. Read more at: http://www.business2community.com/mobile-apps/how-product-owners-can-use-predictive-insights-to-optimize-the-app-experience-01345327#EhVGic3TUeHPzH71.97

Customer Relationship Management is important in any sales department. But, if CRM is integrated with predictive analytics, then it maximizes sales productivity. Predictive analytics take the help of big data to predict customer behavior and they take the help of historical trend to predict customer-specific behavior. It helps the sales department in providing the right customized content at the right time to push leads into conversion. In a nutshell, the sales team benefits from CRM integration by improving productivity and generating leads. Read more at: http://it.toolbox.com/blogs/insidecrm/are-you-coupling-predictive-analytics-with-your-crm-68571

Social media and networking sites are helping businesses and companies to grow. Social media not only helps as a platform to communicate with the customers and build up a brand reputation, but it also helps the way business is run. It also helps business enterprises to collaborate across departments, offices, countries, and with other business houses as well.  According to a recent studies, social media analytics along with predictive analytics is going to be the most effective technology for business development. Its impact will be greater than internet of things and mobile payments. So, embracing this aspect will be necessary for a business to survive. To know more, read: http://www.cio.com/article/2937401/social-collaboration/how-collaboration-tools-can-turn-your-business-into-a-social-enterprise.html