While content teams used to operate with a static persona and a manual targeting process, the future will rely on real-time audience segmentation an automated, educated, real-time process of assessing who users are based on their behavior, what they like, and how they’re engaging with your brand. This is already possible in an API-driven content landscape. In this article, you’ll learn all about dynamic audience segmentation in API-driven content workflows.
If connected content relies on an ever-changing repository of who your audiences are, it’s easy to get the best content to the right people at the right time in the right place across devices and platforms.
Moving from Fixed Segmentation to Segmentation in Real-Time
Before, segmentation of the audience was fixed; meaning that groups would be created with a broader brush that helps determine who would be targeted for what content. Often, age, gender or job title would be the buckets in which someone was categorized regardless of whether that fixed information accurately reflected what the person might be interested in now and how that intent changed over time.
With segmentation in real-time, audience groups are adjusted based on current data signals like what they’ve searched, how they’ve engaged in the past, their location, device type or time spent interacting. How headless CMS enhances flexibility is evident in this approach, as an API-first architecture enables segments to be resolved and queried dynamically, facilitating relevant and contextualized delivery of information at scale.
Content Structure Requirements for Delivery Based on Segments: Dynamic Audience Segmentation in API-Driven Content Workflows
In order to achieve segmentation in real time, the content model must be reusable and flexible. A headless CMS retains content as structured, modular blocks not only allowing unique audience segments to receive specific blocks but entire pieces of content can be distributed, as well. For example, if Acme Corp has a white paper and an infographic and related CTAs, they can designate some blocks for repeat visitors and some for those visiting for the first time.
Conditional fields and tags easily denote whether a piece should exist or remain hidden after certain configurations like the assignment to a specific segment ID. Access through API calls allows frontends to render the proper version based on anticipated goals as suggested through the API.
Responding to Segments Based on Behaviors in Real Time
Real-time segmentation requires response to engagement over time. Everything from clickstreaming to what was viewed, how much someone scrolled, when they logged in, when they left and conversion actions impact how someone will be segmented. Even how long a reader has stayed on an article might eventually matter. This real-time collection is often captured via client-side scripting and analytics tools that allow for observability or event management.
Once information is aggregated, insights are sent to analytics or a customer data platform (CDP) for segmentation rules to be established and user profiles to be enhanced. In an API-first workflow, however, this logic exists outside of the platform, allowing services to request the segment by user to generate personalized content in real-time.
Content APIs should include engines: Dynamic Audience Segmentation in API-Driven Content Workflows
Segmentation engines should exist as part of content APIs to allow the insertion of dynamic segmentation into the content delivery process. This is done largely through middleware. When the request comes to the content API, middleware can assess which segment a given user is currently a part of and modify the resultant query for the API. It can add a filter for XYZ segments, for example, or just ask for all blocks associated with that particular audience.
This way, the segmentation logic is agnostic to both the CMS and frontend code and instead, created as a layer that’s flexible and scalable so that in-the-moment signals can segment and render only the relevant pieces of content without muddying the experience for editors in the backend 24/7.
Dynamic segments provide omnichannel personalization
Dynamic segments cannot be limited to one channel. If a user is on a brand’s website and identified in a segment, if they receive an email from that brand three days later, get a push notification from the brand’s mobile application, or interface with customer support, they should automatically and seamlessly receive the same treatment.
This is where an API-based architecture fits the bill. Any system that is pieced together with a brand’s entire ecosystem via API should have access to that dynamic segment information. Therefore, a mobile app can understand someone’s segment, via an API call, and adjust its UX or content feed accordingly.
Editors should have segment-aware authoring capabilities: Dynamic Audience Segmentation in API-Driven Content Workflows
While segmentation logic may live in the code or through external applications, the ability for content creators to author and preview for segments must be accessible. This is based on how many of today’s headless CMSes operate. Segment selectors, preview options and visibility rules can all exist within an editorial interface.
For example, editors can check boxes or select from drop-down menus to assign content to dynamic segments and preview environments show what this content looks like for disparate audiences. This gives editors control of all aspects of this functionality without having to go searching through integrated data sources where it may be hidden or harder to comprehend.
Ensuring Compliance with Consent and Privacy Laws
Data-driven personalization via dynamic segmentation can also become problematic relative to data compliance. GDPR, CCPA and the like require certain consent and accountability that must be upheld even if the means are via API. Developers must ensure any consent flags are triggered and followed through on within segmentation logic meaning defaulting to no segmentation should a user not consent or at the very least, filtering out personal data associated with the opportunity for segmentation.
Middleware is helpful for triggering such options as it intercepts requests and applies logic as needed based on consent. However, if privacy by design is inherently part of the dynamic segmentation process, it’s clear that the action will be compliant, above board, and effective for good relations with users.
Applying Machine Learning for Future Intent: Dynamic Audience Segmentation in API-Driven Content Workflows
While much of dynamic segmentation comes from conditional logic after the fact, applying machine learning can elevate segments to another level. Rather than waiting for user action or flagging attributes to respond, machine learning can analyze past group behavior or similar profiles to predict future behavior and intent.
Segments could be offered or content served before a group self-identifies or intention is clear. In order to apply machine learning for this purpose, models must be scalable within an API driven infrastructure as predictions need to be offered almost instantaneously and integrated seamlessly into efforts to serve content inferences APIs or cloud-based engines, for example.
Managing Segment Overload With Scalable Taxonomies
Dynamic segmentation gives businesses more control over how they’d like their audiences segmented for more tailored experiences; however, where dynamic segmentation gets complex is when you need control over control.
That’s why a scalable taxonomy hierarchy should be established to note all segment names, definitions, parent segments, and fallback logic to avoid conflict with other segmentation efforts. For example, a segment that triggers for first-time visitors should take precedence over a generic low-engagement segment; segments that are region-based should include language variants as sub-segments. A scalable taxonomy ensures content developers across multiple systems work off the same identifiers and naming conventions across services. A scalable comprehensive also allows for better testing, reporting and governance.
Tracking Segment Performance and Evolving Over Time
Segmentation should not be a static effort. Segments may fall out of grace or not perform over time. Therefore, it’s critical to track performance of content by segment.
Click through rate, conversion, engagement, retention and more should be measured on a segmented basis so that teams can adjust their rationale for segmentation, change content strategies, even kill underperforming segments. Integrated analytics dashboards and feedback loops with analytics, CMS and personalization engines allow for teams to gauge success and pivot so segmentation rationale becomes more refined over time for stronger results.
Implementing A/B Testing Within the Segment Process: Dynamic Audience Segmentation in API-Driven Content Workflows
In addition to the segment process, A/B testing should be part of the equation to see what message or content blocks render the best response from which segments. Content management systems can deliver different types of blocks for A/B testing based on randomized segment inclusion or experimentation flags.
Robust middleware or frontend logic can determine which piece of content is shown to whom and the analytics system can record how each variant did. Segmentation helps teams experiment both within the segments and across segments to hone in on messaging and learn more about the audience to make personalization more proactive instead of reactive.
Integrating Campaign Management Systems with Dynamic Segmentation
Finally, to fully leverage dynamic segmentation, audiences must connect to campaign management systems. This allows marketing teams to create and plan campaigns in which the logic is applied to automatically change upon seeing what the audience is doing right now or what attributes it has in that moment. Campaign management systems can reach out to users’ segments via API to trigger messages at the precise moment it should go out, whether that’s email, push notification, social media post; this way, all channels are communicating with the same timeliness and relevance.
Promoting Continued Marketing and Dev Team Communication
Marketing and dev teams should continue to communicate post-segmentation as successful segmentation techniques come from trials and tribulations. The marketing team knows the audience response, how to deploy campaigns, and the brand voice; the developers have the insight into what’s possible tech-wise and how things may be integrated into the content delivery system.
Integration meetings, access to shared dashboards, and collaborative documentation will resonate with internal processes. The more the two units can attempt to coexist, the easier instant segmentation will take hold digitally on project-focused plans and within intentioned avenues.
Segment Templates for Repeatable, Quick Execution
As companies expand and scalable personalization becomes part of standard operating procedure of projects, the ease of generating new segments will be essential. Make segmentation templates to duplicate. These are existing collections of filters, rules, and content designations that allow for quicker campaign launches and reduce the necessity of creating each segment from scratch.
A template library could be included in a master library to allow for cross-campaign access; the more the process can be duplicative and streamlined, the less miscommunication occurs across teams and platforms.
Making Sure the Company is Edge-Ready for Real-Time Segmentation Execution
Companies should be prepared to execute segmentation logic at the edge where execution closer to the user allows for quicker content delivery. Edge computing allows for edge-triggered or edge-based personalization wherein dynamic segments can be resolved and implemented at an edge node instead of going back and forth to reduce round trip latency for near-instantaneous experiences.
This is critical for time-sensitive campaigns where geo-targeting is required; working with various CDN providers can troubleshoot how to apply middleware to platforms within content management systems. Edge-ready segmentation is the future of real-time content personalization that can be digitized at scale.
Conclusion: The Ecosystem for Dynamic, Scalable and Intelligent Segmentation
Dynamic segmentation of audiences is part and parcel of modern-day content development. It establishes the possibility of API driven integrations and real-time, responsive personalization that adapts to in-platform engagement and use. From middleware integrations to editorial platforms to machine learning to compliance with privacy regulations, everything must work together behind the scenes to foster the possibility of impactful, scalable personalization.
Thus, when an ecosystem of dynamic segmentation exists, brands are poised to access their ideal personalized universe, delivered at the proper time, for the proper person, attuned to their needs in every single experience and every single stage of the customer journey.
