AI continues to dominate the marketing conversation, but what do marketers really understand about it? What we do know is when it comes to artificial intelligence the terms machine learning, deep learning, and neural networks are often used interchangeably. We also know that this causes confusion. What we need to better understand is, in the context of marketing, what do these terms mean and how do they relate to AI?
The explanation is more straightforward than you might think.
- Simply put, AI is the overarching term for machine intelligence.
- Machine learning, one type of AI, is the field of that science that focuses on enabling systems to learn from the data they experience, without being explicitly taught how to learn.
- Deep learning is a specific type of machine learning, one that’s proven to excel at dealing with the messy world of unstructured data, such as video, audio, and all the information that doesn’t neatly fit into spreadsheets — and that makes up 80% of all data in the world today.
- Neural networks are the algorithms that make deep learning possible, a connected system inspired by the way our own brains experience data.
What makes deep learning so exciting is that it can take what have traditionally been human processes, such as watching and comprehending a video, and synthesize those processes to find nuances and patterns that would normally have been missed. As important, they can do so at scale. In essence, deep learning neural networks can think like a human and scale like a machine. In the advertising and marketing landscape, this allows more room for optimization and the ability to predict the success of campaigns.
Focusing on the technologies can get confusing. What marketers really need to understand is how to apply solutions such as deep learning to their campaigns. There are five essential things every marketer should know about deep learning before beginning their journey.
You need to know the outcome you’d like to predict before you start
Marketers know that outcomes matter – we put campaigns together with the goal of achieving specific results. The same is true when thinking about the application of deep learning. You need clarity on the specific outcome you’d like to predict. This is the goal you will give to the algorithm for training. Outcomes will differ by product and brand. For some marketers, conversions (downloads, sign-ups, or sales) are the only metric that matters. For others, the outcome might be solely focused on brand awareness or reach. Regardless of the outcome you would like to track, having clarity on it is essential to predicting its success.
The more data available, the more accurate your algorithm will become
Historical campaign and customer data are important, but as brand priorities change, it is also vital to collect data in real time. We have used over a decade of data to help train and refine our own neural networks. Luggage brand Tumi recently leveraged machine learning to improve and personalize its outbound marketing approach. By collecting data from a variety of sources including email activity and social posts over time, Tumi was able to cull emails from three to one and keep them personalized for individual customers.
AI is iterative and gets smarter the more information you feed it
This feedback loop works best if the algorithms are fed a constant flow of campaign data and go through rigorous testing and refinement. This approach can be a game changer for optimization and automation and can lead to significant improvements in your ability to predict outcomes such as views, clicks, engagement, and even sales. It is also important to know how your data is going to change over time. Consumers get older, move, and go through lifestyle changes such as marriage and having kids. If we’re constantly mining for data, we must remain vigilant about the data we’re capturing and remain flexible in our approach.
Unstructured data is essential to predicting outcomes
It is crucial for marketers to make use of unstructured data; that is, information that has not been previously defined or modeled. There seems to be an endless supply of content for marketers to sift through. That can be everything from large blocks of text or emails to audio or video data taken from an influencer’s Instagram to a clip from a film or TV show. With the sheer amount of content currently available, marketers focusing only on structured data are doing themselves a disservice. It isn’t enough to understand whether a campaign worked; deep learning can help dissect why it worked, so marketers can strengthen future campaigns.
Deep learning is most useful when combined with human intelligence
Though it may seem obvious, simply relying on an algorithm won’t work. It needs to be trained, monitored, and informed by humans who are experts in their field and open to learning alongside machines. These experts can then take the results and put them into actionable, strategic implementation.
For years, marketers have been skilled in identifying trends, understanding their audience, and then planning and executing campaigns for said audiences. Deep learning is starting to change that system and make what was once a long process relatively shorter. Thanks to deep learning’s ability to digest, analyze, and contextualize data so quickly, according to Accenture, it is predicted to increase labor productivity by 40 percent. That does not mean we can throw expertise out the window. Instead, we can work smarter to empower campaigns and drive the right results for customers.
For many marketers, deep learning can be intimidating or daunting. Marketers willing to take the time to understand how they can use it properly to optimize their marketing, success will follow.
We as marketers now have access to almost unlimited amounts of data. Thanks to deep learning and neural networks we can process it faster and more accurately to better deliver what every marketer truly desires: results.
Ricky Ray Butler is CEO of Branded Entertainment Network