In today’s business, buzzwords such as artificial intelligence and IoT are common. But, these technologies will soon invade CRM as most businesses won’t be mixing their customer data with such technologies in the next few months. These technologies are certainly on the way, but today’s CRM evolution is the result of the delivery of good customer experience. Today’s, better customer experience is where CRM is heading right now. CRM is now embracing CX in a variety of ways. They are: Managing CX through CRM, The Reemergence of Social Data, Deeper Data Intelligence, Reduced Data Silos and More Third Party Data. Read more at: http://it.toolbox.com/blogs/insidecrm/where-crm-is-heading-in-2018-78364

Nowadays, business is moving faster and getting more demanding due to the increased competition and consumer expectations that come from online and mobile-centred economy. Now, meeting these demands requires managing customer experience at every touch point, and that is in turn putting added importance on automation, artificial intelligence, big data and analytics. CRM is also coming up with some innovations. Here are five of the CRM innovations which are coming up and will help your business to handle today’s market demands. They are:

1.       Automatic Workflow Adjustment

2.       Chatbots and Conversational Interfaces

3.       Automatic Data Entry

4.       IoT Integration

5.       Customization

Read more at: : http://it.toolbox.com/blogs/insidecrm/five-crm-innovations-youll-see-in-2018-78395

In today’s business world, robotic process automation or RPA is everywhere. Nowadays, you can automate any business function with a software robot you wish to. It is found that the emergence of RPA in the enterprise is like an arbitrage of software i.e.  you can use the price differences between business process outsourcing and RPA as an advantage in a business. It is also predicted that the RPA market would grow to $2.9 billion by 2021 from a base of $250 million in 2016. The basic advantage of  RPA is that it can unite with existing business process management (BPM) and workflow tools. Read more at: http://www.cmswire.com/digital-workplace/robotic-process-automations-reach-expands-in-the-enterprise/?utm_source=cmswire.com&utm_medium=email&utm_campaign=cm&utm_content=nl-daily-170823&mkt_tok=eyJpIjoiTW1SbE16ZzRPVE0wT1dZeCIsInQiOiJ5WXd3XC9Ybm45d3FvUnRwZjIxcGdzcENSK0ZPNCtjNlAraTZ2c2NZMjUyMythMlwvbjJ5dXpVSDJtVXFDWnNVYnRKM2ZcLzB4eGxhbTNia0hwaWlmRWxIMmlJWlhDclhYMTl5bVlVZUdBVklZVFwvQUxTM1NYNVdEamIzTmV5Tmk2YVgifQ%3D%3D

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

There are many ways in which a first-time customer will feel enticed to be a full-time customer and maintain a long relationship with the company. One should understand the profile of the typical customer of the company and segment the group both geographically and demographically. Giving customer's a positive experience by accurately replying to their queries goes a long way. Companies should understand that customer retention is much more than the cost incurred in the advertising campaigns. While offering subscription information, companies should keep in mind not to cost their customers anything. Rather, moving subscriptions to social media can reach thousands customers within minutes. Hence social media posts should be updated according to the customers interests. Read more at :

https://www.smartdatacollective.com/customer-feedback-data-analysis-keys-good-customer-retention-rate/

Data is an important aspect of decision making and for valuable insights over the data, BI has emerged crucial in organizations. Some useful ways to manage your data and team are : 1.Invest in a robust BI service like Google Analytics or CoolaData so as to get better insights on behavioral analytics and better collaboration. 2.Break down silos - when organizations go through silo effect and information stops flowing, BI can prevent this through transparency. 3.Teams should acquire data analysis skills and grow as they work. 4.One should relate to the context, not depend solely on the numbers but on the why, how. 5.Decide on how to decide for better data implementation. 6.Drive action through insights, study how trends correlate and act accordingly. Read more at :

http://www.networkworld.com/article/3197753/big-data-business-intelligence/6-ways-to-manage-your-data-and-team.html

 In the recent days, prevention of fraud is a very difficult task and banks should keep in mind some non-financial signals that might prevent a fraud. Fraudsters take advantage of the growing technology where a simple code interception can lead a criminal to hack a person's credentials. It is difficult to suspect any change in contact details as people change phone numbers very frequently and so the bank managers should put this down as a sign of potential threat. Bank managers should have the correct  solutions for these fraudulent actions happening. Fraud managers should have the latest fraud-preventing programs in order to receive the signals of a fraud before hand and can prevent it from causing severe damage. Read more at :

http://banking.com/analysis/non-financial-events-must-play-role-fraud-prevention-program/

Internet has opened up new possibilities for communicating, from emailing and blogging to mobile video chatting. Most businesses now communicate with their customers through multiple online channels. As brands prioritize this digital experience, they often overlook communicating by voice is faster, easier, and more effective than typing messages. According to the advertising and marketing advisory firm BIA/Kelsey, calls to businesses are expected to exceed 169 billion per year by 2020. These calls are 10-15 times more likely to generate a successful sale or follow-up activity than digital form submissions, which means they are more efficient in generating revenue. Voice is central to communication, and because it’s not strictly an online channel, it presents a challenge for brands that treat digital transformation as a matter of generating clicks and website views. The challenge is particularly acute for businesses that operate in categories such as insurance, lending, travel, or healthcare. In these “considered purchase” categories, consumers typically reach out to a brand after conducting preliminary research online, and are thus well qualified to buy. Read more at https://hbr.org/2017/07/your-customers-still-want-to-talk-to-a-human-being

To all voracious readers, here is an ideal list of books to have in bookshelf of every data scientist and analyst. These books cover a wide range of topics and perspective, which would help to become a well-rounded data scientist and analyst. Here are a few:

• R Cookbook by Paul Teetor

• Python for Data Analysis by Wes McKinney 

• Web Analytics: An Hour a Day by Avinash Kaushik 

• Web Analytics 2.0 by Avinash Kaushik 

• How to Measure Social Media: A Step-By-Step Guide to Developing and Assessing Social Media ROI by Nichole Kelly

• The Visual Display of Quantitative Information by Edward R. Tufte

• Visualize This: The Flowing Data Guide to Design, Visualization, and Statistics by Nathan Yau

• The Wall Street Journal Guide to Information Graphics: The Dos & Don’ts of Presenting Data, Facts, & Figures by Dona M. Wong

• Now You See It: Simple Visualization Techniques for Quantitative Analysis by Stephen Few

Read more at https://www.analyticsvidhya.com/blog/2015/06/infographic-read-books-analytics-data-science/

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/

According to authors, innovations in the areas of Artificial Intelligence(AI),Machine Learning and Deep Learning are beginning to dominate the technology landscape but the actual user experience and concerns about privacy and security are obstacles that can prevent AI from reaching its potential. Despite it’s negative implications, a Consumer Intelligence Report by PwC, revealed, that most consumers believe AI will help humankind, by solving complex problems, like fatal diseases, city traffic, cybercrime, infrastructure and fraud, that plague modern societies , thereby helping people to live more fulfilling lives. AI could revolutionize  personalized healthcare by analyzing data thus helping in maximizing life expectancy and enhancing wellbeing.AI will expand access to financial, medical, legal and transportation services to those with lower incomes thus emerging  both as a process and a product that instills trust and transparency in consumers. Read more at https://aitrends.com/security/consumers-welcome-ai-despite-lingering-privacy-concerns/

According to authors, modern-day marvels are the result of machine learning, which are programs that combine through millions of pieces of data and start making correlations and predictions about the world , by using machines that uses cold hard data to make decisions that are sometimes  more accurate than a human’s, thereby reducing biases. Computers don’t hold any inherent biases, as machine  knows only one approach i.e. the objective analysis and are capable of analyzing massive amounts of information, thus having  a distinct advantage over humans. Utilizing the data for machine learning, can uncover contradictory and surprising results by making data more accessible, more understandable and  enabling the organization to achieve a new level of business intelligence, thus empowering decision makers at all levels with a powerful tool. Read more at https://aitrends.com/machine-learning/machine-learning-cure-decision-bias/

According to authors, the implication of big data is the quantity is paramount, the returns generated do not match the quantity of data generated. Experts point out, it is not per se the data that should be big, but the primary factor that counts is the diversity of data, the amount of richness they provide and the focus on accelerating human understanding of data , which has the potential to create output subject to increasing returns. More data retards innovation, the speed of experimentation and iteration. However IT teams helps in bringing order to chaos, in data and analytics, by managing data infrastructure, such as data warehouses and production processes . Data scientists, who’re occupying the space between IT and business consumers , have made enormous strides in getting grip on their data, analyzing and acting on it, thereby avoiding imbalance. Read more at https://aitrends.com/big-data/three-big-data-developments-no-one-is-talking-about/

According to authors, the development of Artificial Intelligence (AI) creates several opportunities for retailers to improve their customer services , insights and business processes. AI makes it possible to generate insights on a scale like never before, it does this by combining customer data  across all platforms( from social media to CRM) like past purchases, search habits, click behavior, age, gender, season of the year and various other variables and self learning algorithms. By crawling the web and aggregating various forms of customer data, AI gives retailers the opportunity to engage with customers on a more personal level. Through chat applications such as Facebook and Messenger and by using chat bots on websites, the potential customers can communicate with the retailers using speech or text, that will assess and answer customer queries, thereby assisting in the selection process and helping in the execution of simple tasks. Read more at https://aitrends.com/retail/robots-ai-retail-8-things-must-know/

According to authors, the days of journeying to a travel agency and sitting at the desk of an agent to book a trip have all but disappeared as technology is increasingly putting that level of personalised service in the palms of our hands by launching travel apps that incorporate artificial intelligence and technology .One such app is the Green Card app, that simplifies the process of getting a green card to  enter  into a foreign country, on the basis of a bot, that files out a questionnaire ,which when answered,  generates  a package of documents that can be filed with U.S. Citizenship and Immigration Services to complete the application process. This saves ample time. Another personal travel assistant Mezi,  designed for travel management companies, corporate and travel agents was launched two years ago and claims to have AI capabilities that customises travel suggestions based on past searches and online habits. Read more at https://aitrends.com/mobile/a-i-innovation-finds-a-home-on-mobile-devices/

According to authors, two of the machine learning APIs introduced by Google in the month of March, has moved forward into a new dimension i.e. open beta status. Cloud Natural Language APIs which analyses syntactically and assigns a constituent structure to the meaning of texts, helps in finding out the sentiments expressed via texts and online reviews, the syntax of the text and assists in understanding  entities like people, places, events, products, and media. New York used this techie for exploring sentiment analysis of  the stories published in The  New York Times and it was found that relevant updates about news didn’t enjoy a favourable environment, whereas, the arts stories enjoyed positive support. Read more at https://aitrends.com/business/googles-cloud-adds-machine-learning-apis/

Deep Learning(DL) is a subfield of Machine learning concerned with algorithms inspired by the structure and function of the brain called Artificial Neural Networks. It  interprets the raw data through multiple processing layers, where each of these layers, uses the output of the previous one as its input, thus  creating more abstract presentation, tackling conceptual problems ,like image classification and natural language processing  and helping to infer logically. Industries that leverage Machine Learning at present , can switch over to DL approaches in future as it’s an approach of AI , which is showing great promise when it comes to developing the autonomous, self-teaching systems which are revolutionising many industries. DL drives sales, increases engagement  and improves user experience, thus it  will be the future of Personalization, which enables a business organization to enjoy amass customer appreciation. Read more at  https://aitrends.com/deep-learning/deep-learning-personalizing-internet/