Using Predictive Marketing to Demystify AI and Machine Learning

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 timeElements 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 analysisML 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 buyers 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 systemsThese 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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Frequently Asked Questions

At its core, this is a ‘garbage in, garbage out’ question. By automatically – and accurately – bringing all of your customer data together into a data platform you will realize several benefits:

  1. Easily Access All Data Sources and Types: Redpoint easily unifies customer data from all sources, in any format, without coding. Integrate it all: first-, second-, and third-party sources; structured and unstructured; active; and dark data to get a complete and precise view of every customer.
  2. Integrate Complete and Accurate Data at Unbeatable Speed: you can leverage Redpoint to automate data quality with zero latency, so you make the best decisions that enable you to deliver a more relevant customer experience.
  3. Integrated Analytics: feed your analytics systems with data from all environments across the organization using data-preparation technology that economically scales with your business to deliver accurate insights at the speed of business.
  4. Data Stewardship: create highly accurate, comprehensive data profiles, including streaming digital behavior and IoT (Internet of Things) data, to drive the highest quality interactions with customers.
  5. Accurate master customer data: using advanced matching algorithms to resolve identities, our industry-leading data quality delivers precise, automated master customer data.

Data quality is about making sure that all data owned by an organization is complete, accurate and ready for business users to analyze, share, turn into decision-making insights, etc. The quality of data has always been important. But the strategic value of data quality has risen dramatically as companies gather ever-growing volumes of data from more and more sources, and in various formats. Organizations today collect data from multiple enterprise applications, Web sites, mobile devices, and social networks. The volume of data is likely to increase even more with the growth of the Internet of Things (IoT) and its countless connected objects all generating and sharing information.n

Evolutionary programming is the heart of Redpoint’s automated machine learning approach because it ensures that the model ties directly to the metric a marketer is trying to push during development. It is called the “fitness function” because it is the metric the model must “fit” to or optimize. It guarantees that a model will be highly relevant and effective in moving the metrics the marketer intends to move – without having to rely on error-prone human judgment and experience, and the attendant resources.

Companies with a clear strategy of core metrics to use as they measure ROI will benefit the most from AML solutions. AML with Redpoint addresses the processes needed to build predictive models in a way that also reduces or even eliminates the challenges that marketers – and data scientists – typically encounter when trying to scale predictive models across the enterprise. The operational marketer can target virtually any metric, using hands-free evolutionary programming to solve any problem. While it’s impossible for a human to make sense of a graph with 20 or more dimensions, algorithms built for a hyper-dimensional space allow for nuanced, complex groupings. By putting complex automated machine learning into the hands of the everyday marketer, AML gives organizations the power to create and scale a personalized customer experience that drives new revenue.

Definition: Automated machine learning (AML) automates the process of applying machine learning to real-world problems. AML covers the complete pipeline from the raw dataset to the deployable machine learning model. The high degree of automation in AML allows non-experts to make use of machine learning models and techniques. (Wikipedia)

Automated machine learning (AML) in the Redpoint rgOne solution scales beyond proofs of concepts and special projects with code-free models and automated algorithmic optimization that democratizes the utilization of analytics for the everyday operational marketer. These models are predictive and must have the ability to optimize and adapt the model at all stages. Companies tend to invest heavily in the initial building of models, but because the world is dynamic they are then faced with having to repeat the process again and again as time goes on and new data brings new patterns to be detected. A static model constructed for today’s dynamic customer journeys loses predictive value – goes “stale” – quickly, which will entail an expensive rebuild/retrain of the models in a few short months. Most important is automation, the ability to scale these models and evolve over time. An evolutionary programming model is key to ongoing growth and value to any enterprise.





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