The growth of artificial intelligence and machine learning is picking up pace, and those who thought adoption was a few more years away are finding the reality much different. AI is already embedded in our everyday lives, and forward-thinking marketers are embracing machine learning technology efficiency to automate and scale their marketing programs.
However, little has been mentioned of deep learning, the gem in the AI armory that provides even more powerful insights to marketers.
In my last article, I focused on AI applications and explained why not showing up in 2018 without an AI system or application in your martech stack could leave CMOs lagging. Taking that premise a step further, not utilizing deep learning technology means that marketers are losing out on essential insights that I predict will fuel marketing technology development in 2018, 2019 and beyond.
Did you note how I used the terminology “application” with AI and machine learning and “technology” with deep learning?
The reason for that is to make a key distinction, as deep learning is a combination of big data sets, machine learning, computer processing power and neural networks that make applications smarter as it learns. It’s important that technology marketers distinguish between AI and the subcategories that make up AI.
Artificial intelligence (the umbrella term) is the science of making machines smarter, which, in turn, augments human knowledge and capabilities. Artificial intelligence is any computer program that does something smart.
AI is normally categorized into narrow AI and general AI (where machines can do exactly what humans do — think robots and the film “Ex Machina”). Today, only narrow AI applications are available, and these can be either supervised or unsupervised as applications grow in intelligence.
Machine learning is a subset of AI where machines take data and begin to learn for themselves. Large data sets feed algorithms that are programmed to learn and improve without the need for human data input and reprogramming. Machine learning allows a system to learn to recognize patterns on its own and make predictions.
Deep learning is another subset of AI that is better described as a technique. Deep learning technologies train themselves and are based on the biology of the human brain and neural networks. Massive data sets are combined with pattern-recognition capabilities to automatically make decisions, find patterns and power self-learning.
Going deeper with deep learning
Deep learning is the gem in the AI armory — it is the brains behind it — and has enabled many practical applications of machine learning and powered the growth of AI. Deep learning is a technology that makes applications smarter and more natural as it makes sense of big data via immense computer processing speed.
One of the biggest differences between machine learning and deep learning is the number of data sets used and data points involved. Machine learning uses thousands of data points, whereas deep learning uses millions.
Think about that for a second. For a marketer, the potential output and insights from deep learning are potentially staggering. In the marketing technology space, where competition is fierce and marketers are always looking for an edge, this may be just it.
Deep learning originally was associated with self-driving cars, surveillance and science fiction, but it’s now being associated with AI, machine learning and major marketing technology players. Deep learning is basically an advanced type of machine learning.
The evolution of deep learning
Back in 2014, Google bought AI startup DeepMind for more than $500 million, and in 2016 Microsoft set up the Microsoft Ventures VC fund, which focuses on investing in AI companies. Microsoft also joined with other tech giants such as Apple, Amazon, Google, Facebook and IBM to work on the Partnership on AI, a consortium aimed at conducting research and establishing best practices.
Between $26 billion and $39 billion was spent on AI in 2016, according to McKinsey, the bulk of which included investments in R&D and deployment by the likes of Baidu, Amazon and Google. That same year, Uber signaled its deep learning intent by acquiring Geometric Intelligence, an AI startup working to explore beyond the boundaries of what machine learning has to offer.
Meanwhile, Facebook is creating deep learning AI which aims to find out what matters most to Facebook users, while Salesforce.com is working with MetaMind, an AI startup it acquired that specializes in deep learning. IBM has Watson, and Adobe has Sensei, both of which focus on machine and deep learning.
Machine and deep learning intelligence is being built all around us. (See image below.)
Deep learning and marketing technology applications
The biggest challenge that marketers face today is the overabundance of data.
- There is simply too much data out there to process, segment and sieve learnings from. At least 99 percent (PDF) of big data is not analyzed, according to the IDC. Take a moment to think about that if you can.
- Data Management Platforms (DMPs) try to tackle this problem by providing insights from large volumes of data, but the reality is that many tasks still need to be performed (and supervised) by humans.
Deep learning hits this challenge dead-on by taking big unstructured data (unsupervised), at massive scale, and providing the most powerful, valuable and relevant insights in actionable chunks. Marketers can use this in so many ways.
A few martech-relevant examples are listed below:
Information retrieval: For applications like search engines — text search, voice and image search.
Translation: For use with text recognition and image scanning to get important and necessary information in real time — the deep learning combination of hardware, software and AI.
Pattern recognition: Finding layers of patterns to reveal new data relationships and insights and predictive analysis.
Audience targeting: For user preference profiling and predictive modeling.
Sentiment analysis: For the detection, optimization and automation of processes that help determine people’s feelings/sentiment based on the text they write.
Personalization: To target consumers, incorporate content and present people with choices and promotions at the right time based on their past preferences. This is one of the biggest areas where deep learning can have a massive impact.
Automation: For applications like email marketing, ad targeting and personalization.
Natural Language Processing: For applications like sentiment analysis and targeting beyond demographics and moving into intent.
Social media mining: Looking at how people interact with each other, mining speech and providing social media and customer intelligence and intelligent agents.
Organic search and content performance: Intent modeling, content generation and recommendations and voice search.
Brand and product differentiation: With deep learning, marketers can now identify both text and objects in images and videos to allow analysis of consumer activity at scale.
In short, deep learning produces superior results with unsupervised learning to allow marketers to make use of the massive amounts of raw and unstructured data. This lets marketers unlock new insights with speed and ease. It also reduces the time experts need to sift through data and run A/B tests and trial-and-error processes.
Conclusion
The growing AI trend and the massive buildup of data that marketers are garnering mean that making the most of AI and machine learning is going to be a necessity, not an option, for CMOs in 2018. Personalization is one of the keys to martech, and deep learning is set to have a massive impact in this area. It may be true that the full extent of deep learning’s potential is yet to be realized, but you can almost imagine the endless possibilities.
Martech providers that want to be on the cutting edge of building their martech stacks need to take note now. The scale and accuracy that deep learning can bring will make a massive impact on how marketers use data. It could be the game-changer.
Opinions expressed in this article are those of the guest author and not necessarily MarTech Today. Staff authors are listed here.
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