How can we use AI to better understand the future? What will our clients and the marketplace need and want one year, two years, five years from now?” This is a shift from analytics that automatically collect and analyze data from past interactions (buying behavior, customer service engagements, etc.) to leverage AI to provide solutions that address both current and future challenges. 

Now, instead of using chatbots and other AI tools as communication devices to interact with customers at various phases of their shopping and decision-making process, many companies are exploring how bots can collect and analyze customer needs and business data for solution development. 

Read more at : http://www.cio.com/article/3200667/artificial-intelligence/robots-are-not-coming-they-are-already-here.html

Satisfying the customers mean retailers needs to be more accurate on the business context, needs more granularity in terms of data and insights, and the ability to respond closer to or in real time. This is where big data technologies provide practical solutions that deliver performance and economies at scale. Understanding customer behaviour and its impact on shopping decisions is a relatively underutilized science, therefore only a combination of big data, machine learning and predictive & prescriptive analytics can tap into the real potential of how organizations understand and respond effectively to customer needs today. Read more at: http://www.computerworld.in/interview/big-data-machine-learning-and-analytics-can-help-us-understand-customer-needs-seema-agarwal-manthan

 Customers get to know about any product from the sales professionals, and sales people use certain key tips to attract the customers. Now, as the technology is advanced, people get to know about all the products, specifications, alternatives and the reviews from the customers who already used it through social media. Sales people use big data have to research on customer behavior. Big data have transformed customer behaviors. Nowadays, sales and marketing professionals also take advantage of these conversions to study the customer behavior and plan accordingly for the promotions and advertising events. Read more at: 

http://bigdataanalyticsnews.com/big-data-has-proved-to-be-the-absolute-enhancer-of-business-sales/

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/

Big data analytics can analyze the past behavioral data of humans. It is highly correlated to future outcomes.  The most important task is to understand how much emotional extent can be predicted accurately. By analyzing critical reasoning and judgmental decision, there will be higher chances to predict more efficient human behavior. Social media plays an important part by providing more sensitive data having higher emotional quotient. Search dynamics also help to predict future trends. Therefore, combining big data, transaction data and social data along with shopping behavioral data opens a new horizon which enables marketers to influence human behaviors and perceptions. To read, follow: : http://www.forbes.com/sites/forbesinsights/2016/02/02/has-big-data-taken-the-human-out-of-human-behavior/#2280493b170c 

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 Intelligence is becoming a trend in today’s world. But, we need to know what predictive intelligence is all about. It is a method of delivering experiences unique to each individual. It also enables marketers to observe customer behavior, and build customer preferences. This information is then used across any web-based channel like the web, email, mobile or even a call center. Read more at: http://www.business2community.com/brandviews/salesforce/what-is-predictive-intelligence-and-why-should-every-marketer-care-01312444

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

All major corporates employ customer call centers to incline to the needs of their customers. A client usually calls the customer center and the first impression he receives is that of the service tended to him by the customer care representatives. So, organizations have started using automatic voice response systems to reduce the workload of their agents, attain to their customers round the clock and reduce operational costs. However, it has been found that many customers are disappointed with the automatic voice response systems due to various reasons. Companies need to design effective voice response systems that are brief and serve the needs of the customers. Find the detailed view point here: 

According to the MillwardBrown's AdReaction 2014 report, “multiscreen consumers usually prefer micro-video, interactive TV ads, and TV ads promoting mobile apps, Facebook pages, and brand websites.”  Multiscreen marketing is a storytelling strategy that brands employ to reach audiences across multiple platforms. It should be a part of an overarching strategy. And you have to incorporate a creative mix of hashtags, follow-up social engagement and user content re-purposing across multiple screens. Some tips are:

•        Use cross-screen analytics to know where your audiences live and work.

•        Choose the most efficient media to achieve your reach and frequency objectives.

To know more tips and information about multiscreen strategy, follow this link: http://www.marketingprofs.com/opinions/2015/27762/four-quick-tips-for-a-winning-multiscreen-strategy

Data analytics, especially the predictive ones, enable companies to obtain forward-looking insights they wouldn't otherwise have been privy to -- such as forthcoming changes in customer behavior and buying patterns, or the formation of new market segments as-of-yet unserved by the industry. The problem is that while you typically can't achieve the competitive edge you're looking for without analytics, you can't achieve it when you over-analyze, either which becomes a balancing act. Optimization is certainly a critical part of what companies should seek to derive from analytics, so too is innovation. Analytics becomes exclusively about finding ways to cut costs whereas also about finding ways to be cutting edge. It becomes about protecting today's existing revenue stream and should also be about finding tomorrow's new one. Thus, focusing too much on optimization can have the unintended effect of making organizations afraid to take on any risk. To know more, please read this interesting article by Joanna Schloss at: http://www.cmswire.com/cms/analytics/why-companies-cant-afford-to-go-overboard-with-analytics-029179.php

Mobile devices have become very important in any business. Research tells us that 66% of the UK population owns a smartphone and its users check it around 150 times a day. Businesses can analyze customer behavior by using a mobile engagement platform, integrated with the CRM. It will help them to target different customers. To know more, follow Atila Madakbas (GM UK & Ireland at Netmera)’s article link: http://digitalmarketingmagazine.co.uk/digital-marketing-analytics/digital-marketing-through-analytics-of-consumer-behaviour/1657

Marketers can understand customer behavior and also tell how consumers act at the point-of-sale. At the same time, they also want to analyze vast volumes of data to analyze and segment different groups and target customers and prospects. Read more about tips that will help marketing departments to deliver successful big data projects at Yves de Montcheuil (VP of Marketing at Talend)’s article link: http://digitalmarketingmagazine.co.uk/digital-marketing-data/mining-for-gold-5-top-tips-for-marketers-to-get-the-most-from-big-data-projects/1146