John Koetsier<\/b><\/a> mentioned what Brian Solis<\/b><\/a>, Principal Analyst and futurist at Altimeter said to him as he asked experts what the most transformative technologies for marketing would be in 2018:<\/p>\n AI and machine learning will have the most profound impact on marketing in 2018 because it will fundamentally make ‘marketing’ more human\u2026Which is ironic in and of itself.<\/p><\/blockquote>\n In his survey of 350 marketing experts, the two top trends<\/b><\/a> were, to no surprise, Artificial Intelligence (better known as AI) and big data<\/strong>. AI is a broad field, so, inevitably there is a need for more specificity as to what areas of this discipline are most relevant to marketing.<\/p>\n <\/p>\n And that\u2019s where machine learning <\/b>comes into the equation because of that, along with deep learning, are the two areas of AI<\/b> more specifically highlighted to make an impact in the field.<\/p>\n <\/p>\n Machine learning<\/b> is a kind<\/b> of artificial intelligence.<\/a> So, what does this type of AI do? It essentially<\/b> \u201callows software applications to become more accurate in predicting outcomes without being explicitly programmed.\u201d Machine learning\u2019s purpose<\/b>\u00a0\u201c\u2026is to build\u00a0algorithms\u00a0that can receive input data and use\u00a0statistical analysis\u00a0to predict an output value within an acceptable range.\u201d<\/p>\n <\/p>\n Machine learning algorithms<\/b> can either be supervised or unsupervised. A supervised algorithm<\/b> needs humans to put in the inputs, the output that the data scientists want out of it, and of course, to be there to give feedback about how accurate their predictions are when getting trained<\/b>. When the algorithm’s unsupervised<\/b>, there’s no need to get it trained with any data and, with the use of deep learning techniques, is more adept at more complex processes<\/b>.<\/p>\n <\/p>\n Machine learning\u2019s all around us<\/b>; most likely you\u2019ve seen it from when you shop online, the ads that show up on your browser based on where you just visited or Googled, and on your Facebook News Feed. That\u2019s because machine learning personalizes<\/b> the content that shows up on it, and based on your activity on Facebook, the data set used to curate your News Feed adjusts accordingly. Not only is it the News Feed<\/b>; another example is \u201c\u2026Facebook\u2019s retargeting ads.\u201d<\/p>\n <\/p>\n A recent article in Entrepreneur<\/i><\/b><\/a> highlighted how machine learning could change the way marketers work in some ways. One of the most significant risks for marketers is the fact that trial and error are part of all marketing campaigns, resulting in marketing waste. Rather than relying on this not-always accurate approach attempting to get at the audience best receptive to your product or service<\/b><\/a>, \u201c[m]achine learning has the potential to reduce much of marketing’s imprecise nature. Using behavioral data, marketers can target their audiences in an efficient way that greatly improves the likelihood of converting shoppers to customers.\u201d<\/p>\n <\/p>\n Another point to note is machine learning\u2019s potential to achieve the “real-time” phase that many marketers have thrown around for years. Instead of it merely being a buzzword, machine learning can turn it into a reality. While other technologies tried to do this<\/b>, \u201c[c]onsumers see offers\u00a0change by the minute based on the virtually unlimited data their behaviors\u00a0create for machines to process.\u201d Hence, that\u2019s why after searching for something and going on a website, you\u2019ll often find<\/b> \u201c\u2026you don’t need to wait long for an ad to surface on your timeline.\u201d<\/p>\n <\/p>\n Another reason why it helps in this respect makes sense when one understands the distinction between structured and unstructured data. As the name implies, structured data<\/b> is \u201c\u2026the kind of data that is organized and displayed in a database with rows and columns, such as sales numbers and business addresses that one can easily sort.\u201d As for unstructured data<\/b>, \u201c\u2026it\u2019s variable and complex, making it much more difficult to sort, categorize and analyze.\u201d That means social media would qualify as unstructured data. When it comes to social listening, a technique perfected by tools such as Brandwatch<\/b><\/a> and Sysomos<\/b><\/a>, \u201c[n]ew<\/b> social media analytics tools, built on powerful machine learning and AI technologies, dive deep into the nuances of consumer sentiment and purchase intent in order to surface new trends and audience affinities as they evolve in real time, or even before they happen.” That lets you make the most out of the unstructured data coming out of social media, giving you better tools to achieve the real-life ideal digital marketers have been aiming to accomplish in recent years.<\/p>\n <\/p>\n Sharper insights are not only useful when it comes to monitoring campaigns, but machine learning will also help humans who outperform machines (for now)<\/b> when it comes to \u201c\u2026understanding language, interpreting words based on context, tone, and what we know about the person saying them.\u201d For copywriters tapping into insights that their workplaces or clients provide to resonate with their target. However, there still exists the imprecision in traditional marketing campaigns because those insights often rely on educated guesses. Machine learning, in contrast, \u201c\u2026provides actual means of\u00a0sentiment analysis\u00a0so marketers know what to say and how the audience is likely to react.\u201d As they simultaneously monitor social media activity to gain feedback on what messaging resonates, \u201c[b]rand specialists and copywriters then can tweak ads immediately in response to comments and trending replies. This brings the right message to the surface.\u201d<\/p>\n <\/p>\n Machine learning will also help marketers achieve the holy grail of demand forecasting since AI applications will allow for<\/b> \u201c\u2026the overwhelming possibility to give customers what they want before they know they want it. These efforts still will be mostly suggestions. But they’ll be informed by data, not presented as blind suggestions to a disinterested consumer.\u201d<\/p>\n <\/p>\n And, as the other examples have exemplified, a more precise marketing strategy will lead to reduced costs. How does it do that? Chidike Samuelson sums it up like this: \u201cMachine learning reduces marketing expense\u00a0because it requires far fewer people to be involved. It also drastically cuts\u00a0communication costs,\u00a0as a majority of\u00a0customers can be kept updated on offers via automatic emails, scheduled social-media posts and online ads or other content.\u201d<\/p>\n As John Koestier revealed in Forbes<\/i><\/b>,<\/b><\/a> the magic formula for marketing technology in 2018 is this<\/b>: \u201cAI + data = 2018\u00a0marketing success<\/b>.\u201d Get ready for this technology to have an even more significant impact on the way goods and services make it into your purview, and of course, influencing how you spend your money.<\/p>\n To learn more about the IE Data Science Bootcamp<\/b><\/a>, download your copy of our informational booklet here<\/b><\/a>. And, if you\u2019re ready to apply for our next intake, get started on your application<\/b><\/a>.<\/p>\n <\/p>\n","protected":false},"excerpt":{"rendered":" Machine Learning\u2019s poised to make an impact John Koetsier mentioned what Brian Solis, Principal Analyst and futurist at Altimeter said to him as he asked experts [\u2026]<\/span><\/p>\n","protected":false},"author":2,"featured_media":7746,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[384],"tags":[390],"_links":{"self":[{"href":"https:\/\/www.ie.edu\/lifelong-learning\/blog\/wp-json\/wp\/v2\/posts\/7743"}],"collection":[{"href":"https:\/\/www.ie.edu\/lifelong-learning\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.ie.edu\/lifelong-learning\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.ie.edu\/lifelong-learning\/blog\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.ie.edu\/lifelong-learning\/blog\/wp-json\/wp\/v2\/comments?post=7743"}],"version-history":[{"count":8,"href":"https:\/\/www.ie.edu\/lifelong-learning\/blog\/wp-json\/wp\/v2\/posts\/7743\/revisions"}],"predecessor-version":[{"id":9035,"href":"https:\/\/www.ie.edu\/lifelong-learning\/blog\/wp-json\/wp\/v2\/posts\/7743\/revisions\/9035"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.ie.edu\/lifelong-learning\/blog\/wp-json\/wp\/v2\/media\/7746"}],"wp:attachment":[{"href":"https:\/\/www.ie.edu\/lifelong-learning\/blog\/wp-json\/wp\/v2\/media?parent=7743"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.ie.edu\/lifelong-learning\/blog\/wp-json\/wp\/v2\/categories?post=7743"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.ie.edu\/lifelong-learning\/blog\/wp-json\/wp\/v2\/tags?post=7743"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}But first, what\u2019s machine learning?<\/h4>\n
That sounds complicated, but maybe I have already seen this?<\/strong><\/h4>\n
How is machine learning poised to impact marketing?<\/h4>\n
And more steps towards the future<\/h4>\n
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What does this teach us?<\/h4>\n
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Ready to crack the 2018 machine learning success formula?<\/h5>\n