From the printing press to Deep Learning, the history of marketing shadows all our great leaps forward in communications technology. It is the marketer’s job to identify how these novel technologies can be used to reach potential customers, and educate them about products and services that enrich their lives.
The printing press let us create posters, radio and television enabled us to speak to people in their homes, and the internet gave us new ways to sell to people. Today, we’d like to take a moment to dive into how marketers can take advantage of the next big leap forward in technology: Deep Learning.
In this article you will learn about:
- Artificial Intelligence isn’t designed to replace humans but supplement their efforts.
- Deep Learning is excellent at understanding complicated and unstructured datasets.
- Deep Learning powers many of today’s coolest innovations, including self-driving cars.
- Deep Learning technology empowers marketers to connect with customers.
Table of Contents:
- What is artificial intelligence, and why does it matter?
- What is Machine Learning?
- Why is Deep Learning so important?
- Optimization is needed for Deep Learning technology to reach its potential
- How is Deep Learning used in marketing?
- Deep Learning can help boost any kind of marketing campaign
What is artificial intelligence, and why does it matter?
Artificial Intelligence (AI) is probably one of the most misunderstood technologies today. In popular discourse, it conjures up images of robot uprisings, mass unemployment, and the dreaded singularity. Fortunately for humanity, this is broadly fiction, at least for now.
In reality, our brains are the product of millions of years of evolution and are significantly more sophisticated than any AI we are capable of producing. However, while the human brain is great at problem-solving, it tends to struggle when given repetitive tasks and large datasets.
This is where artificial intelligence is truly useful, not as a replacement for humans, but as a way to free us from tasks we don’t enjoy and that would be far too time-consuming for a human, for example, big data-sets. This empowers us to concentrate on what we do well. Artificial intelligence covers a huge variety of use cases, and it’s used in everything from video games to spell-checkers and even cars.
For our purposes, one of the most important forms of AI is called Machine Learning.
What is Machine Learning?
Machine Learning is often used by scientists and other researchers to search large datasets for patterns. These patterns can then be reviewed by humans in order to curate meaningful insights.
Rather than requiring a developer to enter specific parameters, Machine Learning is able to use algorithms and statistical models to “learn” and adapt without explicit instructions from the user, and make increasingly accurate insights about the data you feed it. However, Machine Learning has to be taught how to learn and analyze the best outcome. Deep Learning solutions don’t require this kind of support.
Machine Learning is most useful when dealing with large, structured datasets like Excel spreadsheets or curated databases. However, it is generally ill-suited to more complicated tasks, such as gathering data from images or videos. This is where we come to the next great leap forward: Deep Learning.
Why is Deep Learning so important?
Unlike machine learning, Deep Learning attempts to model the same neural networks you see in the human brain. This is more complicated to design, but it enables us to generate much more sophisticated insights from any dataset, including unstructured datasets.
Without diving into techno-speak, Deep Learning is designed to mimic the same structures that allow our brains to recognize patterns and understand insights. This allows it to process data through varying layers of complexity, and derive meaningful insights faster than traditional machine learning solutions can.
Deep Learning is particularly useful for two reasons. The first is that it can understand complicated, unstructured, datasets from multiple sources. Secondly, it is capable of learning from inputted data without human intervention. This allows it to react to new data, and become smarter the more often it is used.
One of the most well-known examples of Deep Learning is Tesla’s self-driving technology, which has proven a massive challenge for the company and involves solving many of the problems with generalized AI, which would be impossible without Deep Learning.
Haha, FSD 9 beta is shipping soon, I swear!
Generalized self-driving is a hard problem, as it requires solving a large part of real-world AI. Didn’t expect it to be so hard, but the difficulty is obvious in retrospect.
Nothing has more degrees of freedom than reality.
— Mr. Tweet (@elonmusk) July 3, 2021
Aside from self-driving cars, Deep Learning is behind many of the coolest modern inventions. Whether that’s Apple and other manufacturers using AI-powered cameras, the ability to color historical video and photos in an instant, or the ability to show customers adverts they actually want to see.
Optimization is needed for Deep Learning technology to reach its potential
Deep Learning technology is generally quite complicated to build, and even more challenging to scale successfully. There are ways around this. For example, at RTB House, we have followed the Apple model by building software that is specifically optimized for the hardware we use.
This allows us to understand exactly what our specs are, and ensure that our solution uses every bit of potential power on offer. With this, we can run Deep Learning algorithms in the seconds required to successfully respond to a bid request, or select the right creative for the right user.
How is Deep Learning used in marketing?
Any marketer will tell you that they deal with a lot of data, from many different sources. Understanding and interpreting this information is the key to deciding what ads to place, and where to place them, in order to hit key metrics and maximize return on ad spend (ROAS).
Deep Learning is particularly useful for companies with offerings that will appeal to multiple demographics for different reasons. For example, a bank may have an offering with multiple terms and conditions that would appeal to different target audiences, or an automotive company might have a car that appeals to both speed-lovers and tech enthusiasts.
In the past, marketers would need to manually interpret this data, which took up a significant amount of time and energy. Deep Learning offers a way to automate this process and generate better insights than a human would be able to.
Audience Construction:
Deep Learning has a number of specific applications in a marketing context. The first is in audience construction. Deep Learning is able to take data from a variety of sources, including first- and third-party data, and use that to construct a profile for an individual, or a group of individuals.
This allows marketers to construct granular audiences and serve them highly personalized adverts, without compromising user privacy. Given the rising user awareness surrounding privacy issues in advertising, this is a very useful feature.
Personalized Retargeting:
How often have you gone to make a purchase, added something to the cart, and then become distracted and forgotten about it? Personalized retargeting can be used to identify users who didn’t follow through with a purchase, and remind them why they loved that product in the first place.
It can be used to encourage registration, revive lapsed users, or reduce cart abandonment.
Contextual Targeting:
Contextual targeting is often overlooked as old-fashioned, but Deep Learning technology has breathed new life into it. RTB House’s ContextAI solution is powered by Deep Learning, and is able to determine the content that target users engage most with, and decide what kind of creatives to display on the site to entice them to click. This approach is rarely used in retargeting but is an invaluable part of our branding campaigns.
For example, if your data shows that a specific audience is interested in a holiday in Greece, you can use this data to show them adverts with items they might need for their trip such as swimsuits or water sports gear.
Cookieless Targeting:
The key strength of Deep Learning is data interpretation. This will make it particularly useful in the near future as markers are compelled to find new sources of data in order to replace third-party tracking cookies.
Deep Learning is able to understand diverse, non-standard data sources. This will allow companies employing it to get an edge on the competition as we move away from tracking cookies.
Additionally, we can use this technology to derive better insights from less data. This enables marketers to make the most of whatever information they have on hand, which will be particularly useful as the number of reliable data sources shrinks in the future.
Deep Learning can help boost any kind of marketing campaign
Whether you’re looking for better ways to target users, alternative tools for the cookieless future, or a way to understand what your customers are actually looking for, Deep Learning can probably help you boost your marketing efforts and free you up to concentrate on the tasks that you excel at.
Every single one of RTB House’s products is enhanced by Deep Learning. This makes us a great way for marketers to take advantage of Deep Learning technology, without needing to build the solutions themselves.
If you’d like to learn more about Deep Learning, and how you can implement the technology into your workflow, our team would love to speak to you – just head to the contact page here.
