Analytics is the discovery, interpretation, and communication of meaningful patterns in data; and the process of applying those patterns towards effective decision making. In other words, analytics can be understood as the connective tissue between data and effective decision making, within an organization. Especially valuable in areas rich with recorded information, analytics relies on the simultaneous application of statistics, computer programming and operations research to quantify performance.
Organizations may apply analytics to business data to describe, predict, and improve business performance. Specifically, areas within analytics include predictive analytics, prescriptive analytics, enterprise decision management, descriptive analytics, cognitive analytics, Big Data Analytics, retail analytics, supply chain analytics, store assortment and stock-keeping unit optimization, marketing optimization and marketing mix modeling, web analytics, call analytics, speech analytics, sales force sizing and optimization, price and promotion modeling, predictive science, credit risk analysis, and fraud analytics. Since analytics can require extensive computation (see big data), the algorithms and software used for analytics harness the most current methods in computer science, statistics, and mathematics.
Sebastian Raschka, researcher of applied Machine Learning and Deep Learning at Michigan State University, thinks that the future of Data Science does not indicate machines taking over humans, but rather human data professionals embracing open-source technologies.
It is common understanding that future Data Science projects, thanks to advanced tools, will scale to new heights where more human experts will be required to handle highly complex tasks very efficiently. However, according to McKinsey Global Institute (MGI), the next decade will witness a sharp shortage of around 250,000 Data Scientists in the U.S. alone. The question is whether machines can ever enable seamless collaboration between technologies, tools, processes, and end users. Automated tools and assistants can aid the human mind to accomplish tasks more quickly and accurately, but machines cannot ever be expected to substitute for human thinking. The core of problem-solving is intellectual thinking, which no machine, no matter how sophisticated it is, can replicate. (further information)
Check out the Google Machine Learning Glossary
Check out the Google Machine Learning Glossary
More info: link
More info: How Businesses can Benefit from Chatbots
The ethics of artificial intelligence is the part of the ethics of technology specific to robots and other artificially intelligent beings. It is typically divided into roboethics, a concern with the moral behavior of humans as they design, construct, use and treat artificially intelligent beings, and machine ethics, which is concerned with the moral behavior of artificial moral agents (AMAs). (more info)
Natural language processing (NLP) is a subfield of computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data.
Challenges in natural language processing frequently involve speech recognition, natural language understanding, and natural language generation.(more info)
In 2009, futurist Ross Dawson predicted that in the coming decade we could see “augmented humans” with AR glasses or contacts allowing us to control machines. Instead, Google Glass and Snap Spectacles both made Business Insider’s list of “Worst Tech of the Decade.”
Something about the tech just didn’t resonate with people, beyond the few superfans who tried them. Shortly after Google Glass was released, Google even had to warn wearers not to be “creepy or rude (aka, a ‘Glasshole’).” The company ended consumer sales of Glass in 2015.
Futurist Gerd Leonhard predicted that tablets would usher in an era of augmented reality’s dominance, which would be a “huge boon” to content production. AR has allowed for fun Snapchat effects and games like Pokemon Go, but it hasn’t changed daily life in the way that people thought it would — at least not yet.
Autonomous vehicle technology from companies like Tesla has definitely improved, and reports of drivers falling asleep at the wheel have mostly been without injuries as the cars were able to compensate. But Tesla still says that autopilot mode requires “active driver supervision,” and a Tesla in autopilot mode earlier this month crashed into a police car, proving that the system is far from perfect. Self-driving tech from Alphabet and Uber have also yet to see a wide launch, and largely remain in the testing phase.
The last decade has seen plenty of highs and lows for cryptocurrencies. Investing in bitcoin early could have made you very wealthy by now, but many analysts see it as a bubble or niche financial product.
Cryptocurrency exchanges have been hacked, sometimes leading to investors losing their holdings. Some early investors have made millions, others trying to get in on the craze have seen their investments fall to a fraction of their value as crypto prices fluctuate wildly.
Facebook is working on the launch of its Libra cryptocurrency, and CEO Mark Zuckerberg testified on the subject before the House Financial Services Committee in October. Lawmakers have been critical of the project, and many major backers including PayPal and Visa have dropped out.
Incorporating AI into sectors like policing was predicted to to help us avoid prejudice, but even as AI plays an increasingly important role in daily tasks, bias among AI exists. In fact, researchers keep finding evidence that AI is far from perfect and can introduce similar biases as those held by people. From an AI algorithm that kept black patients from getting the same quality of medical care as white patients, to hiring algorithms that learned to prefer male candidates, it’s clearly early days for the technology.
The past decade has already forced a shift in the professional skills required of workers. New technologies like collaboration apps and document and knowledge capture tools have had a wide-ranging impact on what people can do: speeding up communication, enabling faster access to and dissemination of information, and multiplying reach. Yet even among all this progress, nothing promises to be more disruptive to the future of work than the introduction of artificial intelligence.
Recent data from McKinsey suggests that almost every occupation will be touched by automation. But the firm forecasts that intelligent technology is likely to automate away just 5% of roles, meaning that most of us will live in a world where AI helps us by taking on just part of our current jobs. McKinsey thinks that most occupations will experience around 30% of their tasks being ultimately automated. Most of us will find our roles changing, and we will find ourselves working alongside virtual colleagues. Having been on the front lines of this revolution for some time now, it is my belief that for the foreseeable future, technology will continue to take on tasks, not entire jobs. Change is hard for people, but I find that getting people to see this truth and learn to trust these new kinds of intelligent machines opens the door to remarkable transformations.
Marketing is an area in organizations that is already changing due to advances in automation and AI. Unlike previous generations of rules-based technologies that improved speed or enhanced existing processes, AI brings the promise that it can actually become a collaborative team member. In a 2019 Forrester Consulting survey commissioned by my company, we found that while 88% of marketers say they are using AI, just 50% or less say “their current marketing stack supports their top objectives very well.” Why isn’t the transformative benefit of AI being realized? It turns out that of those using marketing AI, 74% are using their AI like old technology, to surface insights and recommendations that they consider and then manually take action on.
This “assistive AI” approach leaves most of the value of intelligent technology out of the equation. The beauty of an intelligent machine is its ability to be flexible and dynamic and to operate with success in mind-bogglingly complex environments. “Autonomous AI” that understands, predicts and can actually take action in real time is the answer to getting full value out of AI. But using it requires a significant shift in mindset from marketers. They need to move from operating machines to collaborating with them.
AI To Amplify Human Capacity
When used in a truly autonomous fashion either within or across channels, AI has the power to both drive campaign execution at scale and create deeper business value. However, there are two important things to remember: AI is not a cure-all, and AI will never come up with ideas.
Consider findings recently debuted in the book Lemon. How the Advertising Brain Turned Sour, from Orlando Wood, chief innovation officer of System1. Despite the benefits that come from better adtech, including wider reach, more data and faster execution, Wood finds that “a golden age of advertising technology has not led to a golden age of advertising effectiveness.” Short-termism and media industry changes have shifted the marketer’s focus from inspiration and creativity to simply keeping up, but tools that can automate the more time-consuming, tactical parts of marketing can help.
AI is best thought of as a tool to amplify human capacity in tasks like number-crunching, identifying patterns in vast amounts of data and automating large, complicated and multivariate tasks. The optimal way to work with AI is to let it take over technological skills such as big data processing and prediction (in other words, supercharging marketing fundamentals) so that marketers have the bandwidth to devote to the social, emotional and cognitive skills needed to create inspiring, branded moments that resonate with customers. After all, only humans can bring the creativity and critical thinking needed to make meaning from data. I believe this will always remain the purview of the human workforce.
AI + Humans = The Second Golden Age of Marketing
Our survey with Forrester found that respondents who are already using AI solutions in an autonomous rather than assistive manner see a number of contributions to their marketing efforts beyond digital advertising. They tend to use data more effectively and engage their customers in a more personalized fashion — this alongside the improved returns on their digital advertising investments you might expect. Even marketers who have yet to embrace an autonomous AI-powered marketing solution understand the value: 95% of them find an autonomous marketing solution appealing for their organizations, according to our Forrester study.
Clearly, the future is coming. So, how do we prepare for it? Consider these steps to make sure your organization is equipped to embrace artificial intelligence:
1. Foster creativity in the workplace. There will be an increased emphasis on abilities like creativity, judgment and critical thinking that complement technology. Encouraging these skills now will set your teams up for success.
2. Define where a human and machine team can add the most value. By outlining where a machine’s role ends and a human’s role begins, you can identify organizational shifts that may need to occur or new roles that need to be defined.
3. Offer training on AI for the entire company. AI cannot be truly implemented well if your organization does not have a data-driven mindset.
4. Keep people in the loop. It’s critical for people to know what decisions the AI makes. This builds transparency and trust between technology and people while allowing those whose jobs are changing to play an important role in these human and machine teams.
5. Share AI’s insights to augment team relationships. Sharing learnings from AI with other team members can inform new strategies and plans, enhancing what people do well.
By playing to the unique strengths of humans and artificial intelligence, marketers can make better and quicker decisions, increasing productivity, revenue and outcomes.
Author: Avi Ben Ezra is the Chief Technology Officer (CTO) and Cofounder of SnatchBot and SnatchApp (Snatch Group Limited)
Marketers, today have a variety of tools available to them to offer consumers and business buyers the convenience, relevance and responsive engagement expected. Customers are now better connected than ever before, they have presence and interact across a broad range of media. The fast pace with which marketing continues to move forward means that it is extremely easy to be left behind and the fast developments in marketing technologies are not something to be ignored. They not only make predictions easier, but also take a load of work off the shoulders of the employees in the marketing department but also increase the way that the rest of the company interacts with its customers, giving them the personalization and interaction that they demand. A good marketing program necessitates that marketers make use of these modern technology trends, since they understand the needs and behaviors of their consumers better than everyone else, presenting initiatives that will continue transforming their company campaigns to meet the challenges ahead.
With the second quarter of 2019 already gone, what are those technology trends that will continue to shape the way marketers plan ahead?
Taking the statistics from previous years and comparing then with the present, there is a significant rise in marketing tech users and companies are seeing noteworthy results in their competitive advantage.
It is estimated that the sharing of marketing metrics with sales teams has grown at a rate of 21% in the last year and a half and similarly, so have the number of data sources used by marketers. The adoption of AI is increasing at an even faster rate and marketers are making more use of coordinated channels too.
What is driving these increases?
The driving force behind the increases is the connected customer, who expects convenience, relevance and responsive engagement. Consumers judge a company from their overall experience and never separate interactions with various departments. Therefore, it is important that they see the company that they buy from as one with the company that they interact with and that they get the same level of service across all departments.
According to consumer surveys, today’s consumers don’t only have high standards for product quality, but also expect these to be matched in other interactions with other services that they might need. Competition is stiff and consumers have all the information about competitors available in seconds through their Smartphones and laptops.
Gone are the days when each department worried about its own performance. Today, sales, customer service, commerce and marketing are responsible for ensuring the entire consumer experience – no department can afford to act independently from the other. The initiatives for these efforts always fall onto the marketing department since it completely understands customer needs and behavior.
How can marketers lead their companies to success?
These three essential technology trends will enable marketers to continue to direct businesses in a successful direction.
The majority of customers, more than 75%, expect that companies understand their needs and expectations. At the same time, over 50% expect that any offers they receive are personalized. Therefore, personalization is a crucial element and comes with important benefits which include: brand building, lead generation, customer acquisition, up-selling; customer retention and customer advocacy.
Artificial intelligence (AI) is one of the most important marketing tech tools for personalization because it aids marketers to unlock the data needed.
The capabilities of AI are expand continuously and so do the ways that marketers use it: predictive journeys, real-time offers, improved customer segmentation, personalized channel experience, automated social and messenger app interactions, dynamic landing pages and websites, media buying, and offline/online data experience facilitation and programmatic advertising.
Personalization is important to customers, but so is transparency about how that data is used. Regulators also worry about transparency and recent data breaches have also shaken consumer confidence.
High percentages (over 75%) of customers do trust companies with personal information if it is to be used to fully personalize their experience, but they demand clarity on how the information will be used.
In the past two years, marketers are more mindful about how privacy and personalization can be balanced in order to fully satisfy their customer needs.
Data sources for marketers abound as they go about tallying email open rates, ad click rates and more. This information allows them to engage the right individual with the right information and also at the right time. In 2017, the median data sources available to marketers were 10 and the number has shot up by 15% already in a period of just 2 years.
More data does not necessarily mean a more unified view, as many marketers struggle to make sense of all the data that becomes available. A mix of various solutions is often the preferred set up for some marketers, where they unite data from marketing databases to email service providers. Another one of the fast growing areas in marketing tech is data management platforms (DMP) which are offering a wide range of solutions for this problem and for many others too.
Originally, DMP was mostly used for monitoring ad performance and optimizing media campaigns. Organizations have evolved its uses and now often include the management of customer identity and other solutions. DMP use is on the rise and it will continue to offer marketers a unified solution to their customer identity challenges and vital opportunities in marketing management.
The top challenge and priority for marketers is to have the ability to engage customers in cross-channel, real-time conversations. This is where proper use of RPA and omni-channel chatbot solutions are valuable.
Even though cross-channel marketing is not a new concept, it is not always easy to achieve since most customers now use an average of 10 channels in their communications with companies. Standard expectations are to have two-way communication with customers. Most messages across channels are duplicates, and there is no coordination between them. The ideal communication, something which most companies are aiming to achieve, is to engage customers dynamically across channels through messages that evolve across each communication channel.
The channels of communication are mostly: website, mobile app, social advertising, video advertising, social publishing, Email, mobile messaging, banner ads, paid search (SEM) and voice activated personal assistants.
Marketers need to start meeting the expectations that customers have for cross-channel engagement. Marketing technology allows for such collaboration between marketing and the other teams within the business, allowing it to be more competitive.
The integration of new technologies will be the way forward for businesses in order for them to be able to better enhance their customer experience. Of the marketing technologies that are expected to have the biggest impact on interactions, data collection, and personalization are the technologies of artificial intelligence and augmented reality.
Marketers need to take time to start understanding how the digital landscape and the technology it offers can improve the performance of their campaigns. Perhaps, starting with the implementation of one trend to may be enough, but incorporating them cohesively can lead to even greater results. Suggested reading: Avi Ben Ezra on Chatbot deployment in Europe.