There have been great advancements in marketing. Over the years, marketing trends and technologies continue to rise and evolve. Marketers are improving the strategies they use to reach customers. Top marketers and innovative web designers are adapting.
Machine deep learning artificial intelligence (AI) has evolved and sparked huge disruptions. Marketing is no exception. Deep learning AI is a branch of the broader field of machine learning. It uses several layers of algorithms to process vast amounts of data. It can also mimic human cognitive functions.
Deep learning algorithms came about in 1943 by two innovators. They were Walter Pitts and Warren McCulloch. They developed a computer model imitating neural networks found in the human brain. Pitts and McCulloch combined algorithms and mathematics through a process called threshold logic. They managed to create a model of the human thought process.
In this article, we define deep learning and distinguish it from machine learning. We’ll also outline how it works.
Finally, we can explore its application in current and future marketing strategies.
What is deep learning?
You should know that deep learning is part of machine learning. Deep learning enables computers to carry out human brain functions such as learning.
Deep learning artificial intelligence is the technology at the core of self-driving cars. The technology makes it possible for cars to recognize road signs.
They can even distinguish between a pedestrian and another car.
Deep learning technology is also behind voice assistants. Examples are Amazon Alexa, Siri, Google Assistant, and Cortana. It’s also embedded in some mobile devices, smart TVs and smart speakers.
Machine deep learning has gained plenty of attention because of its revolutionary applications. It's becoming critical in cybersecurity, defense, healthcare, recruitment, and marketing among other industries.
Through deep learning AI, algorithms learn how to interpret text, images, and sound. They do this in a similar way to how the human brain does but using artificial neural networks.
Massive volumes of data get fed into neural network algorithms. The data is then used to train deep learning models.
After countless iterations, the neural networks learn the desired human accuracy. They can then to achieve accuracies that are comparable to that of the human brain.
Deep learning vs. machine learning
Machine learning is a subset of the broader field of artificial intelligence. Deep learning is one aspect of machine learning. That makes the difference between machine learning and deep learning.
There’s a difference in deep learning vs. machine learning performance.
Basic machine learning models become more effective at mimicking certain human cognitive functions. When a machine learning model fails to make a prediction, an engineer steps in to tweak it.
In contrast, deep learning AI models can determine if their predictions are inaccurate. They then make adjustments to the algorithms without the intervention of human programmers.
Algorithms of machine learning get used in on-demand music streaming services. Examples are Apple Music, Spotify, Google Play Music, and Pandora. The algorithms in these apps use machine learning. They make recommendations based on the preferences of a user and others of similar taste.
Google’s AlphGo is an example of a deep learning algorithm. It taught itself to play Go. Go is an abstract board game known for needing powerful intuition and intellect. AlphaGo drew a lot of attention when it beat many world-class Go masters.
AI deep learning machines
Through deep learning, computers can learn the same way humans do: by example.
Deep learning computer models learn how to interpret a text, images, and sounds. This is the technology behind driverless cars and voice assistants.
Deep learning produces remarkable results because of technology’s ever-increasing accuracy. Advancements in deep learning artificial intelligence have reached a critical point. The technology can now outperform humans. Examples are in cognitive functions such as image recognition.
While the concept itself is old, the applications are novel. Deep learning requires vast volumes of data and considerable computing power. Driverless cars came to be thanks to thousands of hours of video and millions of images.
Parallel architecture of high-performance GPUs provides the efficiency needed for deep learning. These GPUs can work with cloud computing algorithms. The combination can reduce training time for neural networks from weeks to hours.
Deep learning AI applications have found their way into a variety of industries. These include autonomous driving, defense and aerospace, medical research, and marketing. In the following sections, we take a look at applications of deep learning in marketing.
Future marketing technology
Thanks to deep learning, machines can now do complex human tasks. Applications of these deep learning models have found their way into marketing. Some of them are already in use.
The concepts of deep learning neural networks might seem complex. Marketers need not be familiar with the technical details of how they work to use them. Marketing agencies only need to tell the software what to do.
Examples of common deep learning applications include:
- Voice assistants: Google Alexa, Siri, and Bixby among others
- Automatic image captions
- Chatbots that rely on Natural Language Processes (NLP) to mimic human conversations
- Ad purchasing systems that use Real Time Bidding (RTB) software
- Copywriting AI that generates copy based on the style of other content
- Multi-language translation software
Deep learning operates in major customer relationship management (CRM) and future marketing solutions. AI-based marketing tools are becoming standard in the industry.
There are global corporations that have invested in deep learning AI. These include Google, IBM, Baidu, Microsoft, Facebook, Twitter and Amazon among others.
Future marketing ideas
Hyper-personalization is a promising tool that relies on advancements in deep learning AI. Today, personalization is among the top future marketing trends that are gaining popularity. Personalized emails have far higher conversion rates than generic ones.
AI-based personalization solutions face a few challenges. These are insufficient data-collection, resources, and consumer privacy issues.
Thanks to the Internet of Things (IoT), marketers can collect invaluable consumer data. The data comes from mobile devices, wearables, computers, smart TVs among others. Using deep leaning technology, marketers can use this datum repository for various applications. Hyper-personalization is an obvious candidate to help reach consumers.
The film Minority Report envisioned a future with the machine and deep learning algorithms. The systems had nuanced conversations with customers. They followed up on previous purchases and present recommendations. The tech applies to a specific consumer’s preferences and needs.
That future is already here, led by companies like Spotify, Amazon, and Netflix. Their provision of personalized and relevant content and product recommendations is irresistible.
AI experts and futurist may not agree on how fast machine deep learning AI is growing globally. But, there’s no doubt that it’s making steady advancements. Experts agree that deep learning artificial intelligence technology will make a significant impact.
The marketing and technology landscape overall will change with the emergence of tools that outperform humans. Specific will be in the areas of interpreting a text, images, and sounds. As a result, machine-human interactions will be indistinguishable from human-to-human interactions. This shift will open a whole new dimension on the marketing landscape.