Concept of human intelligenceAlgorithm
Simply put, an algorithm is a set of mathematical instructions. Each algorithm acts like the step-by-step instructions of a computer program. While algorithms once had to be explicitly programmed, today some are designed to allow computers to learn autonomously (see definition of machine learning below). Algorithms already permeate our everyday lives. For example, they determine what content we watch on Facebook, Netflix, Amazon, and Google. Experts estimate that 75% of what we watch on Netflix is suggested by an algorithm.
Marketers have already put algorithms to use in many ways, as they can bring together data from a variety of sources to predict and influence consumer behavior. The application of algorithms will also bring new accountability to marketing and media initiatives, as marketers will be increasingly better able to link their efforts to business objectives.
Automation
The International Society for Automation (ISA) defines automation as “the creation phone numbers philippines and application of technology to monitor and control the production and delivery of products and services.” The introduction of automation in marketing has revolutionized the industry, freeing up professionals to spend their time on meaningful, value-added activities rather than repetitive tasks.
But even the conventional “set it and forget it” approach to automation is still fairly manual. A human must set up the process to be automated, and changes to that process must also be defined, initiated, and executed by a human. The future of automation will be driven by artificial intelligence, which will eliminate the need for additional human oversight (see below for the definition of intelligent automation).
Big data
In 2001, Gartner defined Big Data as “data containing greater variety arriving in ever-increasing volumes and at ever-increasing velocity.” That “three Vs” definition remains the most widely used and accepted. In the years since, marketers have been faced with an ever-increasing volume of data from ever-increasingly diverse sources; 90% of all data in the world has been created in the past two years, and that volume continues to grow at accelerating rates.
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In fact, Big Data has forced marketers to rethink how to meaningfully assimilate seemingly endless amounts of information. In the coming years, marketing teams are likely to work more closely with IT teams to implement the technologies needed to manage large amounts of data. They will also need to make decisions about which data actually contributes to better insights.
Bots
Bots get a bad rap. Unethical marketers use bots to inflate website traffic by mimicking page views or clicking on ads. But bots have a lot of positive power for marketers. A bot is simply a software application that runs automated tasks over the internet, and you likely encounter bots online every day. Chatbots have popped up on websites all over the internet and have proven to be an excellent way to engage customers. But you can also use bots to conduct research, track projects, and even sell products themselves.
Bots are becoming more sophisticated thanks to AI. For example, implementing a chatbot that can hold increasingly natural conversations will give organizations the power to engage with more website visitors and gather even more information about those visitors before a human jumps into the conversation.
Deep learning
Deep learning is a machine learning technique that relies on a neural network to help a computer learn by example, just as a human would. Deep learning technology is the basis for self-driving cars and voice-controlled consumer devices. The “examples” needed for deep learning are incredibly large sets of labeled data, which the computer uses to learn relevant classifications.
The performance of deep learning devices can outperform that of humans. In the marketing world, deep learning is likely to revolutionize our approach to SEO as machines become increasingly better at predicting ranking algorithms.
Intelligent Automation (AI)
Intelligent automation (AI) is the result of combining automation with artificial intelligence, resulting in automated data collection, analysis, and decision making. For example, let’s say someone fills out the contact form on your website and includes a short note or comment.
Automation alone allows your marketing team to notify the right people when the form is submitted and send an autoresponder right away. But couple that capability with AI, and now the software can actually “read” the note to decide how urgent it is and who should follow up. In some cases, thanks to natural language processing (defined below), the software can even respond appropriately.
With AI, personalization won’t be limited to just including recipients’ first names in the subject line or greeting. It can go much further. Imagine sending an email to each contact at their ideal time based on past behavior. Or think about automatically sending the right offer based on a person’s recent search history.
We tend to think of artificial intelligence (AI) as tangible technologies, like robots or even Watson. But most AI-based technology exists in a much more abstract form, in the form of algorithms. And that technology is essential for marketers who want to stay ahead of the game.
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Internet of Things (IoT)
The Internet of Things (IoT) refers to the connection of everyday devices to the Internet. It has already had a significant impact on the global supply chain because it allows machines and other devices to “talk” to each other. For marketers, IoT promises to provide much more context for customers’ habits, needs, wants, product usage, and purchase intent.
This year, IoT devices have surpassed 7 billion, according to IoT Analytics. This is a boon for marketers, who can gain incredible insights about their customers at every stage of the buyer’s journey . But it also poses a significant challenge: all of these connected devices already produce mountains and mountains of data. As the number of devices increases, so will the amount of data. Marketers will turn to AI-powered technology to sift through it all and draw meaningful conclusions.
Machine learning
In the past, robots and computers could automate tasks that fit a specific set of rules. The device was programmed to operate within the rules, no more, no less. But machine learning is exactly what it sounds like: machines can learn from the data they analyze, making predictions and forecasts using many more data points than humans could do on their own. For example, while humans can analyze data from a single source, the website says, machine learning means that insights can be drawn from website usage patterns, social media, email, and social media, along with customer responses to past campaigns.
Machine learning has multiple promising applications for marketers. It is the ideal technology for sophisticated customer segmentation, allowing you to identify small groups of customers who share preferences and tend to have similar behavioral patterns. Machine learning can also predict customer churn, allowing marketers to proactively reach out to high-risk customers and reduce the likelihood of churn.
Natural Language Processing
Machines used to be only able to understand code. But that has changed thanks to Natural Language Processing (NLP), which refers to a machine's ability to understand human language through machine learning and AI. NLP has evolved in recent years, and in some cases, this technology can understand the emotion or connotation behind human language. NLP is often accompanied by Natural Language Generation (NLF), where the machine processes human language and generates a response in human language – voice recognition systems like Amazon's Alexa and Apple's Siri work this way.
Many marketers already use some form of NLP for sentiment analysis. But it also holds promise for personalization. Most organizations target audiences that are geographically—and therefore linguistically—diverse. These linguistic differences can be subtle (such as regional variations in American English), more obvious (such as the differences between American English and the Queen’s English), or even entirely different languages. NLP can help you personalize communications based on these differences to deliver messages that are more likely to capture the attention of each recipient.
Neural network
A neural network is essentially designed to mimic the human brain. The network is made up of a series of layers. Each layer includes many nodes, or individual processing units, connected to other nodes in that layer along with nodes from the layers above and below. The lowest layer of the network receives the data and passes it on to the next. Although the purpose of a neural network is to simulate a brain, the network will not function exactly as a human mind would. They are best used for pattern recognition, forecasting, trend prediction, and even generalization.
Microsoft used BrainMaker, a neural network software system, to optimize its direct mail campaigns. The software identified the most critical variables in the success of direct mail campaigns so that future campaigns could be optimized. According to a company spokesperson, using BrainMaker increased the response rate from about 4.9% to 8.2%, a considerable improvement. This use of neural networks for campaign optimization holds great promise for marketers.
Predictive analysis
A branch of advanced analytics, predictive analytics is used to predict future events using data mining, statistical modeling, and machine learning techniques. To maximize the benefits of predictive analytics, you need to start with a large historical data set. Rigorous data collection in marketing is relatively new—think about how the role of analytics has changed in just the last decade—so predictive analytics has only emerged as a widely applicable technology in the past few years.
Amazon and other online retailers widely use predictive analytics to model likely customer behavior. It can also be used for predictive lead scoring, so that marketing and sales teams can tailor campaigns and offers to the prospects most likely to convert. Furthermore, applying predictive analytics to data about your current, high-value customers can help you develop strategies to reach more new customers like them.
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