As ChatGPT and other generative A.I. language models take the world by storm there has been a lot of talk about what Artificial Intelligence systems can do within any particular field. While AI has been foundational to Marketing efforts for many years, thanks to programmatic advertising and advanced audience targeting through search, social and programmatic display channels, they have been limited by their task-based orientations. Deep A.I. is quickly emerging as a marketing game changer.
Traditional Machine Learning in Marketing
In Marketing, A.I software has been designed to perform tasks that would typically require human intelligence at speeds not possible when done manually. For businesses focused on growth, especially startups and e-commerce businesses, this is incredibly helpful from a soft-costs perspective. In essence, traditional machine learning algorithms enabled a marketing team with limited staff to automate some of the most time-consuming (and necessary) elements of their work, from data analysis to simple campaign optimizations. This all made machine learning a huge step in the right direction from a results point-of-view…but limited in its potential.
Deep A.I. is altogether different. And that difference represents an enormous advantage for marketers and growth-phase businesses who are tired of limited data for decision making, wasted budgets and lackluster results.
The Deep A.I. Difference
Deep A.I. is a specific subfield of A.I. that expands upon the orientation of basic A.I. through deep learning algorithms designed to achieve specific goals, rather than merely complete specific tasks. These algorithms are essentially multi-layered neural networks that approach goal-based performance through the real-time analysis of huge amounts of data. In other words, rather than simply doing what the original algorithm instructs it to do, Deep A.I. actively looks for opportunities to improve upon the original algorithm.
How Traditional Machine Learning Compares to Deep A.I.
Traditionally, Marketers would utilize data sources to identify the target audience and establish a blueprint that was statistically designed to drive results. Machine learning systems would then execute that blueprint in the most efficient way possible. To really emphasize the differences, let’s fully define traditional machine learning:
First, It’s Blueprint-Driven
Think of it like building a detailed map: Imagine you’re exploring a new city. Traditional machine learning analyzes data (think street signs and landmarks) to identify patterns and relationships (think common routes and traffic patterns). Using these insights, it builds a comprehensive “blueprint” (aka the model) of the city landscape. This map helps navigate and predict behavior – in this case, predicting how your target audience might respond to your campaign strategies.
Limitations: While helpful, blueprints can be static. If the city changes (think new roads or construction), the map might be inaccurate. Similarly, traditional machine learning models, built on initial data patterns, might struggle to adapt to real-time shifts in audience behavior or market trends.
Feature Engineering: Key to Machine Learning
Imagine carving meaningful shapes from raw clay: Data is raw clay, full of potential but unusable in its original form. Feature engineering involves manually extracting and refining relevant pieces of information (features) from the data. This is like carving specific shapes (features) from the clay, making it useful for building (the model).
Challenges: This process requires significant human expertise. Identifying the right features to extract is subjective and time-consuming. If you pick the wrong features, it’s like building with the wrong clay blocks – your model won’t be sturdy or accurate.
Powerful, But Limited Adaptability
Think of a rigid train schedule: Once the blueprint (train schedule) is set, trains follow it religiously. Similarly, traditional machine learning models, based on initial data patterns, can be inflexible. They might struggle to adapt to:
Real-time changes: New data points or unexpected events might deviate from the initial patterns, throwing the model off track.
Nuances in the data: Subtle variations in audience behavior or market trends might be missed by the model, leading to inaccurate predictions.
How Deep A.I. Takes Things Much Further
With Deep A.I., this process is taken to entirely new level, as thousands of data sources are measured throughout campaign execution, enabling the system to recognize real-time opportunities to break from the anticipated blueprint.
Predictive Analytics
Utilizing predictive analytics, Deep A.I. can predict the future behavior of individual consumers, modifying campaign targeting and marketing channels in real-time. Instead of relying on pre-defined features, deep AI models can automatically learn complex patterns and relationships directly from raw data. This can uncover hidden insights that traditional methods might miss.
End-to-end learning
One of the most impactful elements of Deep A.I. is the ability to handle feature extraction and model training simultaneously, reducing the need for manual intervention. What does this mean? These systems continuously learn and adjust to new data and real-time feedback, initiating responsive changes to the core programming, while continuing to execute against the roadmap.
Data-driven
Instead of relying on pre-defined features, deep AI models can automatically learn complex patterns and relationships directly from raw data. This can uncover hidden insights that traditional methods might miss.
Breaking the Mold
Remember when we compared feature engineering to molding clay? Well, Deep A.I. can handle feature extraction and model training simultaneously, reducing the need for manual intervention. In this way, it can continuously learn and adjust to new data and real-time feedback, making it far more flexible and responsive. In other words, rather than merely being designed to mold the clay into the predefined patterns, it is capable of identifying and creating completely new molds.
To take that thought one step further. Traditional machine learning is like building a house with a detailed blueprint, specifying every brick and nail. Deep A.I. is like building a house with a self-learning architect who constantly analyzes the environment and adjusts the design based on feedback from the materials and construction process.
Cross Channel Marketing Solutions
There are several ways in which all of this lends itself to exciting new pathways for marketers. Perhaps most powerfully, through its analysis of consumer behavioral patterns, dynamic cross channel marketing solutions move beyond traditional marketing engagement metrics to optimize ad delivery across channels based on the quality of leads generated and the cost of acquiring a new customer.
To really define what we mean by “optimize ad delivery”: first, think personalize experiences at scale. With Deep A.I. messaging can be tailored to each individual consumer, with recommendations, visuals and even landing pages generated in a way that speaks specifically to their preferences and behavior.
What’s more, these campaign adjustments are made on the fly. Rather than merely identifying the ways in which the original blueprint might be failing, Deep A.I. models continuously analyze campaign performance and rework the original plan to maximize results, leading to better ROI. Perhaps the system reveals new customer segments not originally within the campaign target. Or content preferences never anticipated. Or potential brand risks that could derail performance.
It all adds up to identifying patterns within complex data sets that humans are likely to miss.
Wrapping Up
In short, while all Deep AI is artificial intelligence, not all artificial intelligence is Deep. While, traditional machine learning algorithms will continue to provide hugely beneficial systems for marketers, for startups and other companies focused on scaling into growth, Deep A.I. represents a powerful new marketing advantage. For the first time, we’re seeing machine learning that really learns, rather than merely finding the most efficient way to complete a desired task. It’s an exciting step forward.
