That’s what’s required as digital content delivery grows more and more dynamic and modular. The latest and greatest new infrastructural content delivery system from web development is the headless CMS. This system increases speed and availability for multi-channel publishing; however, it also complicates the ability to assess whether or not content will be effective. Because with such vast systems there are an unfeasible amount of content units to test and validate, human testing is not timely or possible. Enter the predictive model. The predictive model relies on artificial intelligence to streamline and ensure reliable processes across the board by learning and determining whether specific content units will perform better or worse in specific situations and flagging errors pre-release. When a team can implement a content testing, validation, and automation system via the predictive model, they leverage numerical efficiency, project precision, and user satisfaction.
How Content Testing Changes with Headless
Content is inherently tied to presentation in the traditional CMS world. If teams need to test a change in copy, they’ll want to validate it visually or through SEO tools across the page. Yet with headless systems, content merely exists as blocks within the overall technology stack and is served by API to various front-end experiences, live and in real-time. The more channels, the more nuanced the content testing and the more difficult for teams to manage. Gone are the days where assessing one view sufficed; now, teams have to understand how blocks translate across devices, within other layouts and more, beyond expected layman’s capabilities. Nextjs preview mode offers a critical solution here, allowing teams to preview unpublished content in real time as it would appear across different channels and layouts. Where manual QA used to reign supreme, demand for intelligent automated solutions at scale to ensure testing across the board becomes necessary.
What are Predictive Models?
Predictive models rely on machine learning to assess risk and opportunity before they occur. Based on historical performance and behavior patterns, content testing predictive models use past patterns to see how other, previously published pieces performed to learn what worked and what didn’t and deduce how new content might fit for better or worse. For example, if highly engaged, significant content blocks generally do not work or receive low readability scores within a particular system for a specific genre/audience type, the predictive model will caution experts and prompt alternative approaches sooner than later.
Where Do They Look to Train their Models?
To create such conditions, acceptable quality assessments need to exist. In a headless type environment, this means accessing performance metrics from past efforts across websites, landing pages, blogs, SEO content, etc. Historically high click-through-rates (and average dwell times) help bolster certain pieces as successful outcomes. Conversely, low engagement numbers and poor usability serve as low-performance reductive qualities. This is the content testing that creates a training set over time. The more content created moving forward will be assessed against this data the more effective the content testing and predictive risk and quality assessments will be.
Content Testing Predictive Capabilities During the Writing Process
Perhaps the biggest advantage of predictive content testing is that it has the power to function within the writing process. As content teams are writing entries within the CMS, predictive powers can run concurrently and evaluate headlines, structure, legibility and readability, along with topical authority or semantic relevance. When a piece is predicted to engage poorly, the software can recommend changes to tone and structure, or word count before it even reaches a staging folder. This kind of iterative response fosters editor and marketer empowerment as they can make informed changes without relying on secondary analytics post-publication.
Predictive Possibilities at the Block Level for Customization Within Construction
In headless CMS environments, one piece of content might be broken apart into various pieces or blocks that exist independently but are utilized and repurposed. While blocks can form content in any sequence, not every combination is best. Therefore, with historical data gleaned from successful predictions, predictive assessments can say which block combinations worked best relative to retention, lead gen, product discovery, etc. and assess other combinations based on that success. For example, if a feature video block works best when placed after a feature list, the predictive software can inform teams when they’re doing the same for another assignment. Predictive assessments help content teams both assess content and construct it for better performance down the line.
Using Predictive Assessments for Omnichannel Preview and Assessment
Content won’t just exist on a website generating a headless experience; it can be disseminated to mobile applications, smart solutions, kiosks and more. With so many potential touchpoints, using predictive assessment models can determine whether or not content will work across all of them factoring in size of screen, methods of engagement, user intent for each opportunity. This can allow teams to distinguish when something that works well on web may not function as well on mobile or vice versa. Thus, applying predictive assessments for omnichannel viability ensures consistency across the entire digital experience universe without needing manual testing efforts across many different dissemination methods.
AI-Driven Predictions for SEO and Accessibility Audits
Compliance and optimization, too, are made easier by predictive analytics. Content can be assessed for its ability to rank based on the prediction of whether it can achieve search intent, if it’s keyword optimized enough, or if meta content holds value based on prior rankings and CTRs. The same is true for accessibility; predicting whether something will fail WCAG compliance through natural language processing and visual recognition can predict if alt text, color contrast and heading hierarchy will be problematic. Thus, AI-driven predictions offer a second layer beyond human review to pinpoint problematic areas early on and allow for continuous adjustments without the need for all-encompassing manual audits.
Predictive Scoring for Brands with CI/CD Workflows
Brands who work within CI/CD pathways can similarly leverage a predictive assessment of content before it even goes live. Instead of waiting for deployment to assess performance, anything that goes live can trigger a predictive assessment and return a quality score for whether the content is structurally sound, adheres to best practices and is likely to perform well. If the content does not meet a threshold agreed upon by the team, it automatically will be flagged for revision and/or sent to human review assuring that only the best content that literally aligns strategically with brand needs will ever go to production and it’s all done seamlessly without holding up progress within development cycles or straining editorial teams.
Reporting Features to Justify Predicted Performance Over Time
Buy-in for predictive capabilities comes from visibility into the opportunity. Editorial dashboards can help those working on content see the predictions in one place “engagement score,” “SEO prediction,” “mobile readiness,” “readability score,” and more can all be visual representations that help content teams understand what AI believes and how they should adjust. Over time, these dashboards can also help keep track of accuracy for the model there’s no better way to illustrate a need for change than showing predictive performance isn’t in line with actual short results or consistent downward trends.
Building the Predictive Model With A/B and Multivariate Testing
While giving teams a heads up is invaluable, the reality of effective prediction will come from testing down the line. Teams can do A/B or multivariate testing to either confirm or deny the prediction, thus, training the model over time. For instance, if a predictive model predicts a specific hero image will increase conversion for a landing page and the team subsequent does an A/B test that validates that increase in conversion (with statistical significance), then, the prediction has been validated. The outcome of the prediction (increase in conversion or not) is also considered training data for future versions of the predictive model to learn from. Therefore, using the A/B tested result of what was predicted bolsters the predictive model and creates a feedback loop for future endeavors.
Avoiding Model Drift Over Time to Predict Effectiveness
People are not always predictable. Something that appealed to someone six months ago is irrelevant today. That’s why it’s SO important to ensure predictive models are retrained and validated over time so model drift does not occur. Features may not have the same weight over time, either. Predictive testing can be policed with clear coaching processes to state when validation and retraining should occur, such as quarterly so seasonal changes and diminishing returns of content can be accounted for. There can also be insights taken from performance track record over the last few weeks/months to determine if a predictive model is performing well enough to leave it alone, or if it’s performed poorly according to quality benchmarks showing some adjustments need to be made.
Tailoring Predictions Based on Content Type and Content Intent
Not all content is equal. Thus, customizing and calibrating predictive models and their correlating features based on content type and marketing intent goes a long way. For example, a predictive model that is used to score blog content for SEO personalization will have different features than predictive models for product descriptions designed to drive eCommerce conversion. The more companies can segment their ability to predict per content type landing pages versus help articles or marketing pop-ups the more accurate scoring logic will be applied and the resulting insights will be relevant, actionable and meaningful to drive operational success for that one piece of content.
Educating and Onboarding for Cross-Team Buy-In
Predictive testing can only make sense and be trusted by both technical and non-technical team members if everyone uses and relies on the findings. Through cross-team onboarding, in-app overlays, and internal documentation facilitate a deeper understanding of the models’ underpinnings and how content teams can comprehend and utilize the results. For example, popup teaching moments in the content CMS and example-based how-tos allow teams to use the predictive testing features reliably to integrate them into their expected workstreams.
Allowing Human Talent and Creativity to Make The Final Decision
As empowering as predictive models can be, they are not meant to replace human creativity. The best content teams will factor predictions into their considerations but ultimately rely upon their expertise and talent when deciding boundaries. Editors apply emotional tenacity; marketers have a keen sense of brad voice; designers possess specialized abilities that no machine can appropriately change or replicate. Predictive models serve as reinforcement to help ensure the content remains intact structurally and in context.
Enabling Expansive Channels Like Voice and New AI Mediums
As interfaces broaden to include audio apps, chatbots, digital assistants, and even AI-generated opportunities for engagement, the need for predictive testing thrives. While these channels may not be booming now, content in the future needs to be structured, contextualized, and ready for engagement opportunities beyond just words on a page. Thus, predictive testing assesses where content may lack in narrative voice or conversational opportunities, and by allowing testing to focus on these potential channels, predictive testing helps future-proof your headless CMS environment.
Conclusion: Predictive Content Makes Smarter, Faster and Better-Quality Posts
Predictive content testing provides an element of intelligence, speed and volume to content testing features in a headless CMS environment. By predicting outcomes, validating format, etc. and offering edits within the content creation phase instead of waiting for a post-publishing phase, such a solution reduces the potential for failure, increases audience engagement and makes editorial efforts easier. As digital content creation teams start to leverage automation and other tools to meet demand for today’s content operations, using this type of content testing is a no-brainer. Don’t let testing be the headache champion from reactive publishing; use prediction as the offense to put testing on defense so that opportunities for successful content creation moves from reactive publishing to proactive advantages.
