Customers nowadays expect much more from organizations. So, every business must provide a world class customer experience that wants to win and keep customers. Hence, more and more contact centers are using analytics to collect both structured and unstructured data – from phone conversations to e-mails to social media to buying habits. This helps companies are to customize the experience for each customer. Analytics, can help organizations to go far beyond offering personalized support to each customer and also help you to understand what your customers want from you at the product and service level. Read more at: http://www.tmcnet.com/channels/call-center-management/articles/416191-benefits-customer-analytics-go-beyond-customer-relationship.htm

It is said that a very small percentage of the retailers are keen on using the customer analysis tool in spite of the retail industry being the most refined tool across sectors for analysis of customer behaviour. Dave Nash(director at consultancy West Monroe Partners), has mentioned that this tool is still not as popular as it should be for the very reason of the dearth of relevant customer data that cannot be assimilated into other operational data and also due to lack of skill and the knowledge to use them appropriately. But Elaneor McDonnell Feit (assistant professor of marketing at Drexel University) believe otherwise. According to her, the percentage of retailers investing in developing their customer data set is high. She is of the view that what is required using these data appropriately to take the right decisions for various functions of the firm and implementing them in the most efficient way. Feit has also mentioned a very innovative and out of the box tool for this purpose: Recommendation engine. The recommendation engine is a mechanized tool to facilitate the buying process of the customers, by guiding them find the thing they need. These recommendation engines are tailored for specific businesses and its customers making it unique as a retailer.
Hence, every firm should have a data driven approach within the retail industry which will help the entire economy to grow progressively and efficiently.
Read more at: http://www.cmswire.com/analytics/retail-could-make-better-use-of-customer-analytics/

Several sophisticated data techniques have been used to determine prospective services and specialties preferred at a particular geographic location. For example, psychographic data, provided by a customer analytics company, reflected local healthcare consumption trends. Demographic and marketing data, from health-planning agencies and hospital associations, as well as electronic health records (EHR) should be studied to visualize new market opportunities, i.e. new locations for health-care facilities. Healthcare strategists like retailers, are using geo-analytics—visualization tools incorporating sophisticated geographical and marketing data—to determine prospective health-care facility locations. Healthcare organizations should be analyzing data extracted from EHRs, along with health insurance claims data to understand the services used by potential healthcare customers.
Read more at: http://www.modernhealthcare.com/article/20150530/MAGAZINE/305309978/no-more-guessing-health-systems-tap-sophisticated-data-tools-to-pick

Sigmaway consultants have worked with clients in benefits solutions workspace providing analytics on healthcare plans. For more details visit http://www.sigmaway.us/

According to David Loshin (Knowledge Integrity Inc.)," big data and analytics is helping organizations to gather and analyze data in search of valuable business information and insights that can help them improve their products and services." Cost, Simplicity and Performance are the three factors helps to lower the barrier of entry for analytics and motivate organizations to adopt a big data analytics. The organizations which are planning to integrate analytics tools into their organizations need to have a data driven culture, recognize the potential of information. Key stakeholders are aware of the benefits of big data analytics and include agility in making decisions for adopting the technology. Types of big data to analyze:

• Transaction Data
• Human-generated Data
• Mobile Data
• Machine and Sensor Data.

Big data analytics tools help you in managing all these data with a reasonable investment. Helps you in analyzing these applications:
• Customer Analytics
• Sales and Marketing Analytics
• Social Media Analytics
• Cybersecurity
• Plant and facility management
• Pipeline Management
• Supply chain and channel analytics
• Prize optimization
• Fraud Detection

Read more at: http://searchbusinessanalytics.techtarget.com/feature/How-to-determine-if-big-data-analytics-tools-are-right-for-you

Companies can use data scientists to prepare data sets, business analysts to develop models using both statistical and machine learning algorithms, application developers can be used to deploy and manage predictive analytics life-cycles, and tools. There are many vendors in the categories of customer analytics, cross-selling, smarter logistics, e-commerce etc. Open source software community is driving predictive analytics into the mainstream. Many Business Intelligence platforms also offer “some predictive analytics capabilities."  Rapid Miner’s predictive analytics platform can also be integrated into the cloud. Read more about this article at: http://www.cmswire.com/cms/big-data/3-vendors-lead-the-wave-for-big-data-predictive-analytics-028684.php?mkt_tok=3RkMMJWWfF9wsRomrfCcI63Em2iQPJWpsrB0B/DC18kX3RUnJb6Wfkz6htBZF5s8TM3DVlJGXqlI4UEKTLE%3D 

In modern world, there has been an increase in communication channels and hence this customer-centric era presents both challenges and opportunities for businesses. Companies must have the skill to connect to the data sources relating to customer experience. Hence, nowadays the big data challenge has gained more importance. In case of customer experience management, the data needs to be combined with unstructured customer feedback data and this is important in order to have a complete picture of customer experience. Data governance plays a crucial role here. One big challenge of customer data is that they don't know which data is more relevant in the first place. Data governance creates the base for the common understanding of the customer across the business.

To read more about customer data governance, please follow the link :

http://www.computerweekly.com/blogs/Data-Matters/2015/05/the-growing-importance-of-data-governance.html

In the present scenario, the retailers who are combining customer analytics, internet of things (IOT) and data innovation to retrieve and analyze data of consumer preferences, are generating the maximum sales. Others are losing out. Customers now-a-days are well aware of their personal data being collected online by firms and hence expect better retail experience in return. That will only be possible when customers are segmented and served, which is done by analyzing their personal data, using data analytics. Big data can also be used in framing a product's optimal price system and inventory management, according to the prevailing or to be prevailed customer preference trends. Read more at:

http://channels.theinnovationenterprise.com/articles/grasping-the-value-of-data-analytics-in-retail

Analytics has eventually gained importance in the banking sector. From cost optimization, risk balancing to revenue growth, analytics does it all. Operational analytics: reporting, basic forecasting with data and Advanced analytics: model driven, focusing on the predictive aspects- these are used by the banking sector. Slowly customer analytics and risk analytics are also coming into the picture. These help in revenue growth, investment banking, improving customer experience and save the bank from the uncertainties of the market. Analytics is giving the banking sector well defined strategies, changing paradigms with the advancement of technology. With this evolution of analytics, the need for professionals who can bridge the gap between IT and businesses is immediate. Banks are already employing personnel to read into the data offering growth, efficiency and risk management. Read more at:http://www.businessworld.in/news/economy/analytics-&-banking/1719002/page-1.html

Customer analytics deals in the evaluation of consumer satisfaction, from the purchase of a product or service. The insights obtained from the data, assists in assessing a company's principle performance indicators, the sales division's performance and in making future sales forecasts. Customer loyalty aids in generating profits for the company. Customer relationship management (CRM) analytics, when applied efficiently, generates the best insights on customer satisfaction. CRM analytics analyses data, ranging from the profile of the consumer to customer feedback, to ensure the best possible results. Read more at:

http://channels.theinnovationenterprise.com/articles/the-secret-to-measuring-customer-experience

According to Tali Yahalom, consumers want to be treated indivually and not like any other consumer you deal with every day. Companies should realize that changing rules once in a while is less costly than losing a customer. Moreover, social mediums like Facebook, Twitter, and Yelp have become extremely useful in tracking one's reputation online. One should be considerate towards a customer's emotional state of frustration and listen calmly to him/her instead of becoming defensive. Nowadays, companies have tools for collecting consumer feedbacks and suggestions which are extremely important. A company should be optimistic towards feedbacks and try and learn from it. To know more, follow this link: http://www.inc.com/guides/2010/12/the-new-rules-of-handling-customer-complaints.html

Big data is extending the range of data types in banks that can be covered beyond those common transaction data, and it helps to address problems. Some important areas in banking like fraud analytics, customer analytics and web analytics are also enhanced by big data. Today's improved technologies and frameworks enable banks to get customer data, graph data and geo-location data easily from customers, other banking channels etc. which in turn yields significant insights that can be used in customer marketing, risk management and infrastructure optimization. Big data projects are beneficial as they enhance areas like web security, compliance checks and customer analytics and thus cause the banks to make relevant investments in it. Banks need to know and understand the characteristics of the data they need and need to capture more information beyond risk and marketing data. If the users have sound idea of the nature of the available data, their strategies of making a rough analysis and then use the results to guide them in refining the analysis, will be more effective. This approach helps banks to analyze more data and gain insights that were previously difficult to achieve, without changing the current analytical infrastructure of the banks. Read more about this in Jaroslaw Knapik (Senior Analyst, Financial Services Technology)'s article link:http://www.cloudcomputing-news.net/news/2014/jun/16/big-data-set-to-boost-the-effectiveness-of-analytics-in-banking/