Artificial Intelligence (AI) and machine learning (ML) have been shiny objects for marketers for the past several years. Chief marketers, or their CEOs, have demanded to have it—without fully grasping what “it” is. Over that same time frame, marketers have been deluged with ever-expanding sources of customer data, leading data-driven marketing to shift from being humdrum to being as sexy as branding.
If data-driven marketers want to be true A-listers, though, they need to optimize the way they use AI and ML in their marketing. Many marketers grasp AI and ML at a high level. What they really need is a deeper understanding of what AI and ML can do to support and improve their marketing and how to get the most from them.
So, what is AI for marketing? And how does it differ from machine learning?
Redpoint Global’s senior engineering analyst Bill Porto explains it this way: AI is the replacement or augmentation of “intelligent” human tasks by a machine, whereas ML is the use of historical data to create a descriptive, predictive, or prescriptive model to be run against current data. AI might be self-driving car software, which requires hardware for sense-and-control, software to recognize details of a situation (pedestrian, light, car movements, route changes), and the “executive function” that acts on the input and drives the car. ML might be in the “recognize” and “decide” components.
AI technologies enable marketers to enhance analyses of and automate decisions based on data they’ve collected. For this reason, marketers often use AI where speed is essential; for example, to personalize communications and offers in real time. Elements of AI such as machine learning and natural language processing help marketers predict the future actions of their various audiences.
AI is not a replacement for marketing expertise; it augments marketing teams, allowing them to focus on activities such as strategic planning while the AI tools handle tasks such as serving contextually relevant messages at key customer interaction points—at scale.
Machine learning is the aspect of AI that most marketers are familiar with (though many refer to it as AI). Marketers primarily use machine learning to improve personalization at scale, predict churn propensity, and identify customer segments. Using algorithms, modeling, and statistical methods such as regression analysis, ML programs learn and improve over time as they process more and more data.
Consider this example: Marketers might use chatbots to respond to customers on a website, and those chatbots might use text analysis, sentiment analysis, customer lifetime value calculation, persona, product rankings or recommendations, next-best-actions, customer journey analysis, etc. to respond to the customer. ML is in many of those details, but the chatbot itself might be considered AI.
ML delivers startling results—when marketers feed it the high-quality, robust data it needs to analyze, learn and perform. This is especially important for marketers using ML for activities such as real-time personalization. Achieving and optimizing real-time personalization requires collecting data and updating each customer’s Golden Record in real time and then analyzing the new data in the context of any previous data in real time, which will enable marketers to then execute contextually relevant campaigns in real time.
The beauty of ML is how extensively it can help marketers increase marketing effectiveness and customer lifetime value, streamline processes such as personalization and targeting, and even reduce costs and boost revenue by improving lead scoring and marketing performance and reducing “waste.” Marketers can also see benefits with activities such as ad targeting, content optimization, recommendations, and sentiment analysis. ML can help marketers meet or exceed goals in areas such as customer acquisition and retention and cross- and upselling.
One simple example of the impact machine learning can have on a common marketing tactic is optimizing email send frequency. Without machine learning, marketers generally need to run frequency optimization tests to their full lists and follow each test with an ROI analysis. Automating frequency optimization through machine learning not only speeds and simplifies the testing process, but it also allows marketers to personalize send frequency to specific recipients.
Let’s take a closer look at some use cases and benefits.
See the future:
Marketers can automate regression modeling to track and analyze data such as changes in customer preferences, responses to campaigns, and buying habits to predict customers’ responses to future actions or communications—allowing marketers to automate those actions and communications so they can happen in real time, at the cadence of the customer.
Get personal:
One way that marketers can improve their look-alike marketing, segmentation, and preference analysis is by automating clustering to happen in real time. These models are designed to discern optimal groupings and then assign customers to them. For example, marketers can use browsing data to segment customers in real time.
Optimize customer journeys:
By automating predictive analytics, marketers can more precisely guide customers through their buyer’s journey, not only improving their experience, but also increasing conversions and purchases.
Scale up:
Marketers can deliver personalized actions, communications, and offers at scale by automating processes such as segmentation and targeting. More relevance reduces customer fatigue and increases lifetime value and revenue.
Keep it fresh:
Static models can go “stale” quickly in today’s dynamic marketplace. Automated machine learning models stay current, which ensures that marketers will get the optimal predictive value from them.
Measure and monitor:
The highest-quality customer data is fresh, accurate, robust, and detailed. So, marketers should collect all available data to create a real-time holistic view of each customer, often called a Golden Record, that feeds their machine learning activities. That single customer view should be a blend of first-, second–, and third-party data (batched or streaming) from internal and external systems. These include everything from CRM and call center data to POS and email systems to preference center and website behavior data.
With the right data feeding AI and ML systems, marketers can confidently automate key marketing tactics while leaving themselves time and energy for more strategic endeavors. And that’s where the benefits of AI and machine learning are clear.
Related Content
What is Automated Machine Learning?
The New “Birds of a Feather”: Real-Time, Dynamic Audience Selection with Automated Machine Learning
Evolutionary Programming: The “Survival of the Fittest” Data Models
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