Machine learning has become today’s “it” technology.
It is fueling the advancement of marketing clouds with solutions driven by artificial intelligence (AI), such as IBM’s Watson or Salesforce’s Einstein, and is becoming an important component of many marketing technologies.
In fact, machine learning has become so buzzy that its exact purpose and precise benefit for marketers has become obscured by the hype.
Why Machine Learning Exists
To get to the root of the hype, it’s useful to think about why machine learning exists and how it has evolved for marketers.
Think about the origin of machine learning this way: Computers are good for mundane problems, such as payroll tax calculations, counting votes or serving web pages. Tasks like those are easy problems for structured programming, but they are impossibly hard for humans to do quickly and accurately.
Humans are good at complex problems, such as sentiment analysis, image recognition and facial recognition. Computers struggle with those types of complex problems because they involve the following attributes:
- Parameters (degrees of freedom).
- Changes to underlying systems over time.
- Representation (topology).
However, computers can be trained to handle those complexities through artificial intelligence.
Let’s assume this: intelligence = assimilation + adaptation. If computers can assimilate customer data through data cleansing and standardization, while handling the inherent noise of all the data, then they can learn from that data and adapt, just like humans do. At its core, that is what machine learning is. But it requires a clean data set.
Human intelligence includes these attributes:
- Anchoring (comparing past to future).
- Availability (calculation of probabilities).
- Representativeness (grouping things together for patterns).
- Loss aversion (the misery people experience when they suffer a loss is twice as intense as the happiness they experience when they gain something).
- Status quo (people will stick to what they do).
- Framing (information is presented against a background).
Machines can learn to behave in ways that are understandable to humans through the following learning models:
- Supervised learning: Train using targets.
- Unsupervised learning: Find patterns in data and label them.
- Semi-supervised learning: Learn using some targets.
- Reinforcement learning: Learn based on feedback.
Models are foundational for machine learning’s basic application in marketing: understanding patterns in consumer behavior in order to improve outcomes.
Related Article: Put Machine Learning to Work on Your Ecommerce Taxonomies
Machine Learning for Understanding Patterns in Consumer Behavior (Analytics)
Machine learning is a class of algorithms designed to mimic human intelligence without explicit instructions (unlike regular programming, which repeats fixed instructions). This approach is the closest to how humans recognize patterns and correlations and arrive at a higher understanding of their surroundings.
Machine learning can help by intelligently segmenting customers — identifying relevant insights about customers’ needs, preferences, attitudes and behaviors regarding products, services, communications and interactions — and using the insights to optimize marketing and sales actions (by breaking down heterogeneous groups into smaller homogeneous groups around which marketing actions are driven).
Machine Learning for Consumer Segmentation (Predictive Analytics)
As marketers, if we use machine learning on all the information we collect on our customers through their interactions with our brands, we can begin to develop loyalty programs, manage and understand the customer life cycle, market in real time, create highly personalized and relevant communications, acquire new customers and retain valuable customers for longer periods of time.
Machine learning has become a critical component of customer data platforms (CDP) because it enables the advanced segmentation required for one-to-one personalization. If a company has a goal of improving the customer experience and heightening the relevance of each interaction, CDPs could hold the key — as long as the customer identities have been resolved into single, clean IDs and machine learning in the CDP can operate on that reconciled data set.
Once you’re working with a cleansed, deduplicated, stitched set of customer data, you can use machine learning to segment customers based on attributes like these:
- Life cycle.
- Behaviors (one-time shoppers, store preferences, frequent returners).
- Needs or product based attributes (professional RC car drivers, grandfathers).
Related Article: 5 Drivers of Personalized Experiences: A Walk Through the the AI Food Chain
Recommending Actions Based on Trends (Prescriptive Analytics)
What follows from being able to predict customer segments is being able to prescribe to the marketer what to do based on this data by looking for sizable segments that warrant attention and action.
Here’s a retail example: If the Duchess of Cambridge starts wearing red skirts and causes a surge in red skirt searches and sales, machine learning could recognize the trend and identify all customers who performed those searches or bought red skirts, and the marketer could learn in real time that this is a high-trending product and act accordingly.
In other examples, prescriptive machine learning can also be used to identify excess inventory of a product through pattern recognition based on product sell-through rates and inventory levels. Or it can be used to detect a low-conversion product and prescribe action based on the attributes of those who engaged with the product but did not make a purchase. Or it can be used to identify a potential early adopter of a new product.
Machine learning has many applications. For marketers, machine learning is the key to understanding intricate and important customer segments, their behavior and propensities, and how to engage with these customers in a relevant way that improves their profitability for the business.
Machine learning-powered CDPs can make marketers’ jobs easier by enabling advanced segmentation on a cleansed, deduped, stitched master customer data set — allowing them to effectively use machine learning to understand customers and increase the relevance of customer engagement across all channels of marketing and interaction.
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