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Artificial Intelligence

Ten things have been chronicled which marketers must be aware of while dealing with Artificial Intelligence (AI). First of all, AI must be used to deliver highly personalized and relevant content to optimize on its digital marketing capabilities. It also helps eliminate ‘marketing waste’. Chatbots and digital assistants have emerged as ‘the face’ of AI induced marketing. There are loads of AI based technologies that are in full swing right now developed by the tech giants such as Google, Facebook and IBM, but there is little integration between these offerings. Not all marketers are excited about AI though as evidenced by the low mere twenty six percent confidence displayed by those marketers polled in a Wakefield survey. Even CMOs themselves are facing multiple challenges in AI adoption. The same survey points out that up to three-fifths of them polled, are reluctant to merge AI with existing technologies for fear of disrupting the existing work patterns. In fact chat bots have been shown to often not click with customers as proven by the marketing research conducted by Boxever. CIOs too will have their own challenges as with marketing increasingly merging with technology, the CIO will need to offer broader marketing related services as well. The entire essence of marketing can get changed due to the micro data based analysis now being done. Marketers must try that eventually AI manages to know its customer thoroughly so that these same customer can in turn be converted to fans.

Source:http://www.cio.com.au/article/614174/10-things-marketers-need-know-about-ai/

The rise of Artificial Intelligence (AI) is making wholesale changes in several industries such as education, medicine, financial advisory and business consulting. A closer look tells us that all these plus other industries all rely upon a set of steps to execute their tasks. It starts with gathering of data, analyzing the same, interpreting the results, determining the ideal course of action before finally implementing the course of action decided. The skilled professionals from these lines command high fees due to three reasons. They go through the tasks accurately, possess experience in developing the solutions and helping their clients execute such plans. The first two can be performed better by robots that have been embedded with deep learning capabilities. The third bit though only humans do as machines lack empathy. A lot of customers are using IBM’s Watson computer’s capabilities to chart the course of action, even interacting with chat bots along the way instead of real humans. Thus instead of trying to fend off the entry of such technologies, businesses must instead embrace them to improve their capabilities. Greater investment must be made to develop emotional intelligence among employees so that solutions provided by AI can be executed using the human gift of empathy.

Source:https://hbr.org/2017/02/the-rise-of-ai-makes-emotional-intelligence-more-important?

Human beings have the ability to process some information without being specifically told. This is part of the instinct developed over millions of years’ evolution. While this is called common sense among humans, machines need to be specifically told, hand-held rather at every step. Now things are however changing thanks to the influx of Deep Learning. At Facebook’s AI Lab and on Amazon’s Echo, Deep Learning principles are being applied. Deep Learning would not have been possible without the advent of Big Data, so much of which is now available that it is easier to process cognitive information. Even Microsoft has developed a tool using Deep Learning that seamlessly translates English in to Mandarin with an error rate of barely seven per cent. Voice assistants like Siri or Alexa also use such capabilities. Deep Learning involves neural networks similar to how human beings are structured. We are going through the phase of Deep learning OS 1 version. Major innovation is taking place in software. The design of neural networks for example is evolving. A marketplace is also developing to reuse them. Similarly, the hardware is evolving to combine the optimum mix between physical assets and Cloud based resources.

Source:https://hbr.org/2017/01/deep-learning-will-radically-change-the-ways-we-interact-with-technology?

Concepts such as e-learning and Artificial Intelligence (AI) have already made massive inroads in to corporate training. In fact for every dollar invested by company on online learning, there is a thirty times increase in productivity. Two-fifths of new recruits who receive poor training during their first year at a company, end up quitting. Also two-thirds of workers surveyed claim that training and development is most important part of the workplace policy. That is why investment on training has topped seventy billion dollars in the US alone. It is also considered among the top three non-financial motivators for three-fourths of employees. This trend is particularly strong among the millennial generation, a staggering eighty seven percent of whom confirm its due importance. However a talent research assessment carried out by Raytheon confirms that a mere seven percent of learning organizations are using predictive analytics in training. Automation cannot yet be considered complete as even now computers cannot be relied upon to execute tasks without any human interference. Gut feeling still plays an important role.

Source:http://www.forbes.com/sites/steveolenski/2017/02/06/why-c-levels-need-to-think-about-e-learning-and-artificial-intelligence/#1c9fc0a71d69

Streaming analytics is the latest rage among marketers using Big Data for solutions. It helps in pricing analysis, customer segmentation, marketing campaign results and deciphering coherent business intelligence on user trends. Customer profiles get tracked against real time live events. Detailed servicing options are provided to the customers as this model takes its customer-centricity very seriously. There is even a social media engagement during the journey. Streaming analytics may be used in various fields such as insurance, retail and finance. It is even being used to identify errors in the Internet-of-Things (IoT) chain. It can even be used to detect fraud. Unlike a lot of other data based strategies, streaming is relatively economical. Amazon has already tried its pilot project with over a million unique users.

Source:https://martechtoday.com/introduction-streaming-analytics-marketing-customer-engagement-194452

Digital marketing has become much more complicated now. Earlier keyword optimization would allow any brand to get noticed by Google, but now it is robots who control the flow of content. Thus marketers will need to work with robots and understand their algorithm to succeed in this. Thus marketing automation must be used to email clients using Google or Microsoft’s software while social media automation software can have a similar impact on Facebook or Twitter. Mobile marketing automation can help companies handle Apple’s operating system. Moz does a detailed analysis on keyword ranking and even rating of pages as per Google’s listing. Google has now started gauging the power of Artificial Intelligence to provide solutions to technology vendors. Rank Brain even contends that Google now uses deep-learning capabilities. Content is then delivered accordingly.

Source:https://martechtoday.com/robots-gatekeepers-customers-194472

A Japanese life insurance company Fukoku Mutual Life has slashed the jobs of more than thirty employees, replacing them with robots armed with Artificial Intelligence (AI). This business innovation will apparently save Fukoku hundreds of millions of yen annually. The AI system is based around IBM’s Watson Explorer system. Another firm Dai-Ichi Life had earlier introduced a similar move. Due to Japan’s ageing population and high prowess in robotics, the country is primed for growth in this field. Soon robots using AI may be a factor in politics as well, helping bureaucrats cut through routine work procedures. Their introduction hasn’t been all smooth as an earlier experiment at a university’s testing system using them backfired.

Source:https://www.theguardian.com/technology/2017/jan/05/japanese-company-replaces-office-workers-artificial-intelligence-ai-fukoku-mutual-life-insurance

 

A new tool called Afiniti has been developed to help call centre employees using the power of Artificial Intelligence (AI). It records huge amounts of data to generate coherent business intelligence about thousands and eventually millions of customers as well as all its call centre employees. The data is processed to draw conclusions on tastes, preferences or requirements of the customer base. Customer’s phone numbers are used to study behavior and usage patterns across the board. Their social media usage trends on Facebook, Twitter and LinkedIn are also assessed. In addition, the demographic data on gender, age, ethnicity and parenting status are also collated. On the other hand, Afiniti also records the call details, and transaction records of the employees so that they can best be trained on their shortcomings while further leveraging their existing skills. The specific machine learning abilities of the tool, allow it to process granular bits of data to form meaningful information ready to use.

Source:https://consumerist.com/2017/01/06/calling-customer-service-an-ai-is-picking-the-agent-thats-best-for-you/

 

Artificial Intelligence (AI) and Machine Learning will eventually replace a lot of sales jobs and in fact the process has already started. But instead of completely eliminating jobs, what they will do is to modify them. Business research conducted by Demand-base throws up some startling trends. While a significant eighty percent of marketers surveyed, believe that AI will revolutionize marketing, barely a fourth of the total are confident of their own abilities. An even lower tenth of the sample confirms that AI is being used at their present organization. This provides a conundrum as marketing leaders acknowledge AI’s increasing importance, they aren’t themselves at the cutting edge of its implementation. These leaders in general also voiced some common challenges to with AI’s implementation. One was setting up corporate training programmes involving AI for its employees as most were alien to the concept. Another was the difficulty in establishing metrics to measure. Also integrating AI with existing suite of technologies was proving to be a challenge. Thus in order to leverage the full potential of AI, business leaders especially those from marketing need to take a hands-on role themselves.

Source:http://www.forbes.com/sites/steveolenski/2016/12/14/80-of-marketing-leaders-say-artificial-intelligence-will-revolutionize-marketing-by-2020/#66a3e03521e1

 

Artificial Intelligence (AI) has been around for several decades now in different forms, yet only now is it truly being used in everyday functions. The next phase of AI is just round the corner, and marketers need to prepare for the coming dawn by adopting a few basic steps. With so much of data now generated and subsequently analyzed, marketers need to beware whether the right kind of Big Data is being assessed. They could be instead tracking the wrong sources, measuring the wrong metrics, or simply backing the wrong choice. AI needs to be at the forefront of digital marketing, directing all actions and operations from a central vantage point. For that to happen, the AI channels need to be aligned with all other such data sources. Methods need to be devised so that data from all internal sources can be directed towards the AI platforms for the analysis to take place. The kind of insights gained from AI need to be instantly put into proper use.

Source:http://www.martechadvisor.com/articles/cloudiaaspaas/how-marketers-can-prepare-for-the-new-wave-of-artificial-intelligence/

 

Much has been made out about Artificial Intelligence’s (AI) impact on work and human life. Science fiction has portrayed it as akin to magic while fear mongers claim it will take away human jobs. AI is already impacting several kinds of work such as e-commerce, logistics, media and advertising. Yet, as the founding lead of Google Brain and present head of Baidu’s AI wing testifies, the kind of functions it is performing in these areas is largely confined to only one type- supervised learning. This basically implies A kind of data being fed for B kind of output to be processed. There is one major limitation though. It is, that enormous quantity of data warehousing needs to be done, often to the tune of hundreds of thousands of units to process meaningful insights. Whether it is in photo tagging, loan approvals, speech recognition or language translation, this limitation lasts. Yet once all this is done, there is enormous amount of automation that AI can facilitate.

Source:https://hbr.org/2016/11/what-artificial-intelligence-can-and-cant-do-right-now?referral=00563&cm_mmc=email-_-newsletter-_-daily_alert-_-alert_date&utm_source=newsletter_daily_alert&utm_medium=email&utm_campaign=alert_date&spMailingID=15858721&spUserID=OTY0OTMwNTk5NwS2&spJobID=902915771&spReportId=OTAyOTE1NzcxS0

 

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