Every business wants to improve its customer experiences and they want to turn its ordinary customers into a loyal customer. In order to make a fabulous customer experience there are 4 ways to attract customers and foster lifelong loyalty. 1) Use Technology to provide Top-Notch Customer support- Customer is needed to provide with mobile customer support, live chat, self-service management, social media support and Omni channel support.  2) Create Personalized Experiences-Customers are needed to be treated in the more personalized manner, or by catering messaging, offers and creating more communication. 3) Leverage Big Data to Get to Know your customer on an Individual level- Understanding the customer on different levels- behavioral, contextual, service-interaction ,social and consumption  4) Reward Customer Loyalty to Foster Brand Ambassadorship- Customers are needed to be given loyalty awards through brand leveraging loyalty programs. These all over enhance the customer experience and the business will able to get loyal customers. Read more at: https://www.ngdata.com/ways-to-improve-customer-experience/

Nowadays companies are on the hunt for analytics executives and organizations in the industry have created hundreds of executive roles in analytics. Employers are hunt for leaders who understand the data and threats and opportunities related to it. In order to identify for the analytics initiatives the males and females are divided according to age groups and experiences which lead them to their roles. The executives landed into their current positions by one of their career paths- 1) Linear- It’s an upward movement of the analysis function who has experience in computer experience, statistics and analytics and ability to gain customer attraction 2) Nonlinear- it’s a movement between and within analysis function and are experience in IT, marketing and accounting.  3) Parachute- there is no previous work experience needed in engineering and technology and should have knowledge of project management, telecommunications, mobile systems, project management and security. Read more at: http://www.cio.com/article/3182718/analytics/3-paths-to-analytics-leadership-roles.html

Both terms web analytics and digital analytics are interchangeable. But there is a difference between two of them. When Web analytics association changed its name to the digital analytics association then the word digital analytics came up. During the early days of the internet, Web analytics were analyzing the website data, such as users, visitors, links and many more alike. When other forms of online came like emails, search, social, etc., then a new term called digital analytics came into being where all these channels were analyzed. Now all the online channels have been transformed from web analytics tools to digital analytics tools. Web analytics is the analysis of website data, whereas digital analytics is an analysis of all data from digital channels that includes websites also. But till now web analytics are still searched more than digital analytics according to Google Trends chart. Read more at: http://webanalysis.blogspot.in/#axzz4hutH4lMG

Few challenges being faced in translating the lessons of predicting analytics from other verticals in manufacturing. The objective of this predictive analytics is to get the correct business decisions and it will impact the design and service of the product. The data is being updated continuously through their supply chain. The predictive models are used to connect the real world data to digital twin models of the virtual world. This helps in better understanding and working of their business plus with the on the factory work. Predictive analytics help to find the issues related with the product quality, performance and its features. These helps in better designing the product features and make it to optimum use of it. The predictive model is quite accurate in giving information about the risk failure, improving the machines to put in a better use as well as it gives the best correlation between job characteristics and job failure. Models are being trained through environmental data and IoT data and few factors which affect such data too such as environmental hazards, weather and many more. Its benefit for the business to take predictive analytics into consideration. https://www.ngdata.com/ways-to-improve-customer-experience/

Business analytics and business intelligence are like two sides of a coin. But there is a difference between them. Business analytics is like an umbrella term and intelligence is a part of it together with other aspects of business applications. Once a person runs a business, that person will be able to understand the difference between them, that is, business intelligence is like accessing to all kinds of business related data and software's and put them into the analysis. Business analytics is something using your business intelligence into your data and optimizing the performance of the business. To have a successful business, it is important to follow both. Business intelligence is generally used to look over the previous data, whereas business analytics look to the future needs of the business. It's necessary for a businessman to understand its difference for making any business decisions or predicting for the future. Read more at: http://www.analyticbridge.com/profiles/blogs/business-analytics-and-intelligence-compared