Simply put, an algorithm is a set of mathematical instructions. Each algorithm acts as step-by-step instructions for a computer program. Although algorithms once had to be explicitly programmed, today some algorithms are designed to let computers learn on their own (see the definition of machine learning below). Algorithms already permeate our everyday life. For instance, they determine the content we see on Facebook, Netflix, Amazon and Google. Experts estimate that 75% of what we watch on Netflix was suggested by an algorithm.
Marketers have already been putting algorithms to work in myriad ways because algorithms can pull together data from a wide variety of sources to predict and even influence consumer behavior. Application of algorithms will also bring new accountability to marketing and media initiatives, as marketers will be increasingly empowered to link their efforts to business objectives.
The International Society of Automation (ISA) defines automation as "the creation and application of technology to monitor and control the production and delivery of products and services.” The introduction of automation to marketing revolutionized the industry, freeing up professionals to spend their time on meaningful, value-add activities instead of repetitive tasks.
But even the "Set it and forget it" approach of conventional automation is still quite manually intensive. 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 powered by artificial intelligence, eliminating the need for additional human oversight (see the definition of intelligent automation below).
Back in 2001, Gartner first defined Big Data as "data that contains greater variety arriving in increasing volumes and with ever-higher velocity." That definition in terms of the "three V's" is still the most widely used and accepted. In the ensuing years, marketers have been faced with an ever-growing body of data, from ever-more diverse sources; 90% of all the world's data has been created in the past two years, and that volume continues to increase at accelerating speeds.
Indeed, Big Data has forced marketers to rethink how to meaningfully assimilate seemingly endless information. In the coming years, marketing teams will likely collaborate more closely with IT teams to implement the technologies necessary for handling vast quantities of data. They will also have to make choices about which data truly contribute to better insights.
Bots have gotten a bad rap. Unethical marketers used bots to inflate traffic to a website through imitating page views or clicking on ads. But bots have plenty of positive power for marketing professionals. A bot is simply a software application that runs an automated tasks via the internet, and you probably encounter bots online every day. Chat bots 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 on their own.
Bots are getting more sophisticated thanks to AI. For example, chatbots are capable or more and more natural-sounding conversation, which gives organizations to power to engage with more website visitors and collect even more information about those visitors before a human intervenes in the conversation.
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 do. Deep learning technology provides the underpinning for driverless cars and voice-controlled consumer devices. The "examples" required for deep learning are incredibly large labeled data sets, which the computer uses to learn relevant classifications.
The performance of deep learning devices can surpass that of humans. In the marketing world, deep learning will likely revolutionize our approach to SEO, as machines will get better and better at predicting ranking algorithms.
Intelligent Automation (IA)
Intelligent automation (IA) is the result of combining automation with artificial intelligence, resulting in automated data gathering, analysis and decision-making. For example, say that someone fills out the Contact form on your website and includes a brief note or comments.
Automation alone allows your marketing team to notify the right people when the form is submitted, and to send an autoresponder right away. But couple that capability with AI, and now 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 might even be able to respond appropriately.
Thanks to IA, personalization won’t just mean including your email recipients’ first names in email subject lines or salutations. You’ll be able to get much more granular. Imagine sending an email to each contact at his or her ideal time, based on previous behaviors. Or think about automatically sending just the right offer based on a person’s recent search history.
Check back next week for Part Two, which will dive into terms like the Internet of Things (IoT) and predictive analytics.