People and patterns and predictions, oh my.
For marketers, a simple way to think about deep learning is that it’s ultimately about presenting customers with exactly what they want, whether or not they know yet that they want it. That could mean an experience, a bit of information, an ad, or a suggestion for a specific product. But what is deep learning?
Deep learning is a subset of artificial intelligence (AI) derived from the science of neural networks. And neural networks are simply an attempt to mimic the way scientists think our own brains process and make sense of the world. Basically, a neural network self optimizes its performance on a desired task based on exposure to structure and unstructured data.
I spy with my AI…
For example, let’s imagine we’re creating a deep learning based image recognition system designed to spot a product––a specifically branded can of soda—in photos posted on social media because we’d like to give a shout out, through our own social accounts, to the poster for their brand loyalty.
The first thing we would need to do is train the deep learning neural network using a number of verified positive and negative sources—e.g., photos containing said soda can, pre-tagged as a hit as well as photos with no can correspondingly tagged as a non-hit. Next, the system would be fed untagged positive and negative photos. The digital patterns in those photos would be compared to whatever digital patterns emerged from reviewing the initial guided positive and negative inputs.
If the system recognizes what it has determined is the pattern for, “branded can,” it marks that photo as a positive hit. At this stage, the system will require human feedback to determine whether that positive hit was, in fact, positive and whether other photos were falsely tagged as hits or non-hits. Each iteration, every data point, refines the neural network to better identify its proper target. And with data sets that span the internet, you can imagine how refined those algorithms can get.
But here’s the interesting part. Humans generally can’t read or understand those algorithms. We don’t know what the criteria the network is using, per se. We only know it’s getting better (or worse) at identifying the branded can. And there are plenty of times the technology fails completely, not to mention offensively.
How this “portrait” was made:
- generate random polygons
- feed them into a deep learning, neural net face detector
- mutate to increase recognition confidence until the neural net is reasonably sure it is “seeing” a face
A synthetic portrait “recognized” among random overlaid polygons by deep learning AI at http://iobound.com/pareidoloop/
Marketers love patterns, too.
That ability to recognize patterns is an obvious benefit to marketers. What is segmentation besides recognition of patterns. Demographic patterns. Psychographic patterns. Behavioral patterns. Spending patterns. But where we all used to divine these patterns in a more general and collective fashion across the aggregate population, now powerful deep learning AI can make continuous, deft pattern related decisions on an individual by individual basis, thousands of times a second. It can, and it does, let’s take a look at how.
Real-time media targeting and buying.
Gone are the days when media purchasing was planned months in advance. Programmatic media and real-time bidding platforms are using deep learning AI to assess, in real time, the level of intent or interest a user may have for a product, service or experience. Again, don’t think of this as testing against a static target profile. The system is learning in real time as well, refining its model and iterating—ultimately looking to optimize levels of desired behavior generated (clicks, purchases, et al) per media dollar spent. All the while, the system is developing both a detailed predictive model for intent as well as a more accurate program for moving those customers from intent to conversion. This also allows for marketers to scale campaigns more precisely as well as increasing their ability to track media ROI.
Truly personalized experiences.
All UX designers strive to create as intuitive an experience as possible—minimizing the time and effort required of a user to connect with whatever it is they desire. Deep learning systems driving those interactions can process the data surrounding users’ behaviors. Using that data, obviously, can be used to provide suggested actions correlated to the users’ past actions. That could range from something as simple as a “you might also like” shopping moment to something as complicated as proactively making dinner reservations for a customer because you know from the location of their mobile device or credit card activity that they are suddenly in an unfamiliar city and that they enjoy experimenting with more exotic foods while traveling.
Deep study still recommended.
As we’ve mentioned before, here, here and here, deep learning AI will likely become increasingly pervasive in marketing and advertising. If you want a far more detailed and thorough primer on the topic, Stanford university has placed online an amazing guide to deep learning.