Key Marketing AI Concepts (In Plain English!)

Data Basics, In Plain English Nancy Koleva

Today’s marketing teams have no shortage of business questions they want to solve. However, they run into all kinds of challenges when trying to make AI a reality. One of them is wanting to democratize the use of AI and data. They are unfortunately unable to do so due to a lack of knowledge and transparency around how AI-based technology and AI tools work. 

We're excited to share that this blog post kicks off our revamped “In Plain English” blog series. The series aims to make data, data science, machine learning (ML), and AI topics accessible to non-technical experts. It will provide a basic understanding of the topics in a digestible way.

This first article introduces key marketing AI concepts. It also provides concrete examples of how these concepts relate to marketing use cases and challenges.

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What Is Marketing AI?

Discussing AI marketing can be challenging. This is because AI is frequently used along with ML and deep learning (DL). In some cases, these terms are even used interchangeably.

Why do people use these terms relatively interchangeably, and what are the distinctions? DL is a subset of ML, which is itself a subset of AI.

Thus, marketing AI is any system that leverages human capacities for learning, perception, and interaction to solve marketing problems at a level of complexity that supersedes human abilities.

For instance, AI-enabled recommender systems can analyze large amounts of data. This data includes the properties of different products and user behavior for an online store. The system can then match users to products that they may be interested in purchasing.

AI models can complete tasks at a much faster and more accurate rate than any manual effort from savvy marketers. This would be a huge time and energy waste, not to mention the marketers' skills would be underused.

Machine Learning for Marketing

As mentioned above, ML is a subset of AI. It involves AI systems performing a specific task without being explicitly programmed to do so with rule-based instructions. Instead, those rules are inferred or “learned” from the previously collected data, thus the term machine learning. 

Standard AI and ML in marketing have different uses. For example, churn prediction tries to predict when a customer may leave in order to try to keep them.

A standard AI algorithm can indeed analyze vast amounts of customer behavior data and generate a churner profile. The marketers and/or data experts using it, though, would need to rely on their own knowledge of the business and essentially hand-code rules that determine how exactly the different factors in the data impact whether a customer is likely to churn or not. 

ML requires marketing and data experts to identify which elements may affect a customer's likelihood to churn. These elements, known as "features," are then combined in the optimal way by the algorithm. In other words, you "train" a model. The key question is: Based on my data, what is the best rule I can create to solve my business problem? 

Standard AI algorithms can be well suited to and deliver great results for a number of tasks. They, however, cannot adapt to new problems or changing factors unless a developer rewrites them. For example, if a new pricing or subscription plan affects churner behavior in a new way.

In contrast, ML algorithms can adapt to a series of different problems. They use data to determine how to tune themselves to the task, without all the human intervention that standard algorithms require.

Deep Learning for Marketing

What is the role of deep learning in marketing? A DL algorithm is able to learn hidden patterns from the data by itself, combine them together, and build much more efficient decision rules. That’s why it can deal with problems that a human brain could not understand. All the value of deep learning is this automatic pattern identification capability. 

This involves dealing with intricate issues. For example, grasping concepts in images, videos, texts, sounds, time series, and all other unstructured data.

Deep learning has enabled a variety of marketing activities. For example, we can now distinguish an image for visual trend forecasting or sentiment analysis. We can also understand customer comments, translate or generate custom content, and provide deeply personalized websites.

Deep learning algorithms can be sophisticated and perform well with highly complex tasks. However, deep learning is not always the best solution for marketing AI problems.

If we compare different AI techniques to means of transportation, deep learning would be the airplane. It is one of the fastest and most advanced technologies available and highly efficient for overseas travel. For simpler tasks such as commuting to work, it would be much more efficient (not to mention less expensive) to drive, bike, or walk.

Deep learning is ideal for complex marketing tasks. These tasks involve complicated relationships and abstract meanings in the data, such as a consumer's habits or preferred product categories. Additionally, they often require a large volume of data, such as a database of 100,000 customers instead of just 100. For simpler and more straightforward data problems, however, traditional ML would do just fine.

Natural Language Processing (NLP) 

Natural language processing (NLP) is a set of processes and techniques that create a link between human language and computer understanding. It makes it easier for computers to process words and phrases for statistical analysis, ML algorithms, and deep learning. Deep learning enables us to create conversational AI agents. These agents can effectively interact with customers, or create contexts for communication.

Automated Machine Learning (AutoML)

At a very high level, AutoML is about using ML techniques to automatically do ML. Or in other words, it means automating the process of applying ML.

AutoML was initially used for selecting the most effective algorithms for a task. It was also used for optimizing the performance of those algorithms. Recently, its development has grown to cover the entire data-to-insights pipeline. This includes cleaning the data, tuning algorithms with feature selection and creation, and even operationalization.

At this larger scale, it’s no longer AutoML. Augmented analytics has the potential to impact the entire process. It can empower marketing teams to scale data efforts and implement AI solutions at a larger scale.

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