Using Machine Learning to Optimize Content Display Rules

When and how content is presented where content is displayed is no longer a rules-based logical solution set in stone by developers. As more digital experiences become personalized and data-driven, companies turn to machine learning (ML) to assess on the fly the optimal solution for content presentation. Thus, with an understanding of user patterns and situational analytics, content can be delivered~at any point in time~in a more effective manner across all channels for better engagement, relevance, and efficiency.

What Content Display Rules Typically Look Like

Traditionally, in most content systems, for example, display rules are manually created by developers or marketers. They are conditional if-then-statements based on who is supposed to see what content, when for example, "serve banner A to new visitors" or "do not render the form if the audience member already subscribed." While somewhat effective and certainly powerful at times, such rules are rigid, not easy to scale, and fail to account for nuances of engagement or nuances of engagement that change over time. Furthermore, they need to be constantly maintained, updated, and tested to ensure they're not outdated, as audience member preferences change at an ever-increasing frequency.

Where Machine Learning Changes the Game for Rule-Based Delivery

Machine learning makes rule-based delivery more fluid and intelligent. Instead of relying on a static if-then content display as being the most appropriate, for example, ML models take the analytics and figure out the best content variant for the situation. This means that ML models can break down all the relevant signals from device type to full history to geo-location to time of day to scroll depth to likelihood to convert and determine what content, instead, should be delivered. For businesses aiming to create highly personalized and conversion-driven storefronts, the best CMS for ecommerce website should integrate seamlessly with machine learning systems to optimize content in real time. It allows for a more contextual experience over time, a type of experience that learns and improves at successful content delivery over time without having to require human intervention for every incremental decision.

Feeding Models With Historical Engagement Signals to Inform the Process

Of course, for ML interventions to work effectively, they need accurate historical data. Signals of engagement clicks, scrolls, bounce rates, time on page, conversions across content types/combinations and audience segments need to be captured so that machine learning models can train themselves to determine which display rules/content type combinations yield positive results before actually rolling it out. Employing trained ML models to gauge the likelihood of which display rules/content combinations will work best based on past similar behaviors or contexts allows content systems to optimize in almost real-time.

The Shift from Fixed Utility Display Rules to Probabilistic Scoring

One of the greatest benefits afforded through machine learning is the ability to shift from fixed, binary utility display rules to probabilistic scoring. Where one might have a strict rule of "show a CTA to returning users," a model provides a probabilistic scoring value for every content block which indicates its probability of rendering optimal action for this specific user session. This scoring value can be used for placement, visibility and rendering actions. Probabilistic scoring allows for a far more complex approach to personalization and removes the rigidity of branching rule trees that lead to overly simplistic logic or content fatigue.

Personalize Layouts Based on Behavioral Clusters Rather than Selection

Where content blocks can be selected one at a time, machine learning can personalize entire layouts by understanding behavioral clusters. These clusters are defined as segments of users who behave similarly with navigation, interest and engagement history. For instance, if a user is constantly clicking on technical documentation, they may be rendered a layout with a larger share of resources; if they are marketing qualified leads, they may be shown a larger share of success stories or product videos. Machine learning models can suggest layout templates in real-time, aligning the makeup of the experience with intention in ways that static rules could never.

Show the Content That Leads to Business Goals

Users want different content for different reasons email subscriptions, final purchases, tutorial education, problem resolution with support. In order to effectively facilitate these potential goals, machine learning can drive displays that understand what blocks are most useful in achieving them. For instance, if the pricing panel always leads to conversions when shown after the demo video, the engine may learn to input that direction by default. The reverse can occur as well; machine learning can hide/display content that doesn't lead to ideal results or for specific users so that display priorities reflect business goals and not just overall user comfort.

Integrating ML into the Headless CMS Pipeline

When machine learning is integrated into the content delivery pipeline, it's typically via deployment of middleware between the headless CMS and front-end applications. This layer exists to invoke the request and assembly of content in real time, based on learned predictions. For instance, when content is requested, this can be intercepted by a middleware layer that checks user context, calls to a model inference API and before transmitting the necessary blocks to the front-end, reorders or filters them. This way, the integrity of a decoupled architecture of content and presentation exists but the additional layer provides for intelligent orchestration that can be scalable in any channel.

Analyzing to Adjust What Gets Displayed and How

Machine learning is not a static undertaking. Analyzing what's going to adjust how something gets displayed. Real-time dashboards can be built to monitor engagement increases vs decreases, conversions inconsistencies, prediction success rates and model drift. Logging can record what content was served to which audiences and for what purpose. This allows for teams to adjust and retrain what was once learned, for discrepancies that, for accuracy, fairness and even effectiveness, need to ensure that personalized content remains consistent over time and adaptable only when truly necessary as audiences evolve alongside brand goals.

Explainable AI Offers Editorial Overrides

Yet there's always a need for humans in the loop. While suggestions for display and suitability can come from machine learning, editorial teams must have the final say. Explainable AI allows for models to establish a link between certain signals and predicted outcomes; it makes the rendering of results transparent so that editorial teams can make informed choices before content goes live. In fact, some of the more sophisticated headless CMS solutions boast editorial previews that show what content will display, but why it's even being suggested so that the balance of machine learning capability and human control remains in check, based on qualitative efforts and brand voice.

Using Rule-Based Overrides Where Machine Learning Is Not Needed

Although machine learning brings advanced understandings and decision making to the table, there are still instances where basic display rules apply. When legal copy, branding, or campaign requirements necessitate that certain copy displays regardless of what an algorithm thinks, it should be able to override machine learning capabilities. Thus, it can exist within a tiered decision making system where rules are overrides. The people responsible for compliance and editorial quality will still have that authority while a larger, necessary percentage of content display logic will remain trained by professionals.

Machine Learning for Personalization Across Channels and Business Units

When personalization reaches beyond a website to an app, email, etc. voice-driven experiences are also on the rise. Machine learning can help bridge the gap. One model can be the logic layer across channels, offering recommendations for one or the other via API. Similarly, when personalization extends beyond a website to business units and departments, countries, or even regions, one consistent model can keep multiple teams in line and on-brand while providing it with localized data to inform behavior. This is how enterprise-scale personalization becomes possible with efficiency, efficacy, and intelligence.

The Future: Reinforced Learning and Context AI

Reinforced learning is a type of machine learning that is even further advanced, allowing systems to optimize based on real-time experimentation and not only historical outcomes. For example, instead of waiting for a sample size to provide a certain outcome to implement action, this type of machine learning will provide insights and then learn from whatever action it inspired and whether clicks through were a positive sign or abandoned renders means otherwise. Also, more contextualized AI will use real-time signals to assess relevance, proximity or emotional signals gauged from the user's device. This type of machine learning can change rules on a millisecond basis.

When to Show Informed by Patterns Over Time

Time based patterns enable the machine learning to acknowledge when it makes sense for users to interact relative to their behaviors with systems over time. For example, it may be easier to show a promo block on a Monday morning instead of a Sunday afternoon or vice versa, but long-form editorial may perform better over the weekends. Knowing how your audience has engaged with your content in the past by determining when they've engaged with it the most can be the basis for clearer dynamic rules that make display options more feasible (and effective) at anytime across all time zones and audience habits.

Rules Can Be Reactive Based On Changes Made in Real-Time

For rules to be reactive, they need to account for changes made in real time, no matter how minute. If someone sees a content blip and clicks X, that action should be accounted for. Ultimately, the machine learning model's precision is increased by what it can accomplish in real-time efforts instead of only during subsequent adjustments and retraining cycles. If it can acknowledge actions while they're being taken and then apply logic to those determinations immediately, the model becomes a real-time functioning entity where adjustments made in real time allow for successful results come tomorrow.

When To Show Supported by Extended Campaigns As Conversion Funnels

When treating the larger campaigns with multiple components as conversion funnels, certain pieces of content are like steps along the way. Machine learning can determine what is most effectively traversed when it comes to showing certain blocks of content in relation to each other. For example, if showing a pricing comparison block after a demo video increases trial signups, that can be prioritized moving forward. Letting predictions let something dictate when it's shown gives it the best chance of success and better conversion rates or ROI for a campaign.

Linking Machine Learned Guidelines with Multivariable Experimentation

Where multivariable experimentation gives brands the opportunity to experiment with combinations of content elements to discover which variations work best with each other, machine learning takes multivariable experimentation findings and provides guidelines across learned combinations. Rather than waiting for an analytics team to assess through A/B testing which image works best with which headline, a machine learning algorithm can perpetually test and notify the brand of the best images/headline/CTA pairings. This information can be integrated into content rendering logic down the line to automatically choose the best variation combination without additional analysis while relying on statistical efficacy.

Summary: Machine Learning Allows Content to be Smarter to Deliver Better

Ultimately, machine learning allows brands to deliver smarter content, eschewing the reliance upon strict guidelines that dictate when and how content should be delivered for more fluid approaches that learn and adapt just like their end users. With access to historical rendering, comprehension of behavior and predictive capabilities, brands can deliver better content that is more pertinent, captivating and worthwhile without overexerting human resources. As headless content capabilities become more prevalent over time, being able to slot in ML into content delivery processes will become a necessity rather than an option. When content knows where it belongs and how it should be displayed and who should see it through machine learning, content transitions from content to an experience.