Abstract
This article introduces the CAIC framework (Consumer journey mapping, AI integration, Innovative media mix, and Collaboration) as a pedagogical response to fundamental shifts in digital media planning driven by Artificial Intelligence (AI) and platform proliferation. It outlines the framework’s implementation through a structured curriculum, applied exercises, and experiential client projects designed to cultivate the essential media planner competencies. The paper presents CAIC as a foundational model to advance advertising education for an increasingly automated and complex media landscape.
Keywords
Introduction
We are in a new era of advertising, where AI-driven tools are poised to reinvent how advertisers plan, buy, and optimize campaigns (Ford et al., 2023; Kotler et al., 2024; Russell, 2025). Media planning, long a discrete function within agencies, now demands a new approach. A digital media strategy is a comprehensive plan for using digital channels to achieve marketing goals, yet the rapid evolution of technology, particularly artificial intelligence (AI) and the proliferation of online platforms, presents significant new challenges in developing these strategies (Kingsnorth, 2025).
In recent years, major advertising sellers like Google, Meta, and Amazon have launched AI-based products, such as Performance Max (or PMax), Advantage+, and Performance+. These tools promise to generate creative assets dynamically and optimize spending across ad inventory autonomously. Their rise reflects key marketer priorities: a focus on outcome-based KPIs (e.g., sales), the automation of creative and planning processes, and improving AI capacity (Madison and Wall, 2025). While transparency remains a concern, a growing number of marketers are adopting these platforms, finding commercial success as the platforms gradually provide more control and transparency.
Within this rapidly transforming ecosystem, the traditional processes and workflows of advertising and media planning are poised for revolutionary change. Specifically, three pivotal shifts are emerging: First, consumer journeys have grown vastly more sophisticated (Yu et al., 2025). Today’s audiences navigate a fragmented landscape of streaming, scrolling, searching, and shopping. Consequently, a core skill in teaching digital media strategy is identifying the true points of influence within these complex paths and planning media engagements around them. Second, driven by advances in marketing technology and AI, the practice of managing customer journeys has evolved beyond simple linear models (e.g., awareness, interest, decision, and action) (Kotler et al., 2021). This evolution requires marketers to possess an upgraded understanding of the underlying technologies and a critical grasp of how AI augments, rather than merely automates, the strategic planning process (Qin & Jiang, 2019). Third, as most media platforms now offer rich video, audio, and text formats, optimizing the cross-platform media mix has become paramount. However, attributing specific outcomes to individual platforms is increasingly difficult. This makes methods like marketing mix modeling and multi-touch attribution essential for quantifying the relative impact of each channel. Therefore, a central challenge for modern media planning is optimizing this mix to balance short-term performance with long-term brand-building goals.
Given these three paradigm shifts, educators require a structured pedagogical model that directly addresses the resultant challenges: navigating sophisticated consumer journeys, harnessing AI integration, and innovating the cross-platform media mix. In response, this article proposes the CAIC (pronounced “Cake”) framework as a novel approach for teaching digital media strategy in the AI era. This framework is designed to translate these industry disruptions into core, teachable competencies.
The framework comprises four key elements, each engineered to address a specific facet of the new planning paradigm. The first, Consumer (C), directly confronts the shift towards non-linear, sophisticated consumer journeys. It moves students beyond theoretical funnel models by entailing an in-depth exploration of the consumer journey through multiple research methods and the practical mapping of these pathways to strategic media touchpoints. The second element, AI Integration (A), is the direct pedagogical response to the proliferation of AI-driven tools. This component emphasizes not only technical familiarity but also the development of human–AI collaboration skills. Students are guided to engage with AI systems as strategic partners in decision-making: interpreting outputs, questioning assumptions, refining recommendations, and integrating algorithmic insights into broader campaign strategy. By foregrounding human–machine teaming rather than passive tool usage, this element ensures that AI is positioned as a form of strategic augmentation rather than a black-box automation (Huh et al., 2023).
The third pillar, Innovation (I), targets the critical challenge of cross-platform optimization. It emphasizes the innovative analysis and allocation of the media mix, teaching students to navigate a landscape where attribution is complex, and to launch integrated campaigns that balance performance and brand objectives across channels. Finally, the fourth element, Collaboration (C), serves as the essential, unifying mechanism for experiential learning. Distinct from human–AI collaboration embedded within the AI Integration component, this dimension centers on collaboration with industry professionals, advertising agencies, and real-world clients. Specifically, it creates the authentic environment necessary for students to synthesize and apply the other three competencies. It is within these collaborative projects that the exploration of consumer journeys, the application of AI tools, and the innovation of media mixes are tested and refined. Figure 1 presents the proposed framework. The CAIC (“Cake”) framework
Notably, a growing body of concerns surrounds AI integration in education, including overreliance on AI-generated outputs, the reproduction of biased content, and the erosion of independent thinking (Jose et al., 2025). The CAIC framework directly addresses these risks by embedding structured critical evaluation throughout the course, ensuring AI augments rather than replaces human judgment. Students are required to assess, question, and revise AI-generated recommendations (especially in the “AI integration” component), with the framework explicitly defining the boundaries of human and AI responsibilities to cultivate the analytical independence essential for responsible AI use in digital media strategy.
The remainder of this paper is structured as follows. First, it provides a detailed explanation of the CAIC framework within the context of course design strategy. Next, it describes the course structure itself, followed by an overview of key assignments and assessments. Last, it offers a discussion on the future of media planning education.
Course Strategy: the CAIC (“Cake”) Framework
This course applies four pillar strategies: consumer journey mapping, AI integration, innovative media mix, and collaboration with real-world clients and advertising agencies. Notably, the CAIC framework is conceptualized not merely as a curricular structure, but as a staged pedagogical model designed to reflect the evolving logic of contemporary media planning practice. Rather than presenting four parallel topic areas, CAIC represents a developmental sequence of competencies that progressively integrate analytical reasoning, algorithmic augmentation, strategic synthesis, and professional execution. The sequence begins with consumer journey mapping (C), which establishes the analytical foundation of influence-based thinking. By diagnosing how influence operates across fragmented touchpoints, students learn to identify critical intervention moments and develop structured reasoning about audience behavior. Building on this foundation, AI integration (A) introduces algorithmic systems as strategic collaborators. Students are trained to interpret and refine AI-generated outputs, understand optimization logic, and distinguish between automated execution and human judgment, thereby positioning AI as augmentative rather than substitutive.
The framework then advances to innovative media mix (I) development, where students synthesize consumer insights and AI capabilities into coherent cross-platform allocation strategies that balance performance objectives with long-term brand building. Finally, collaboration (C) situates these competencies within authentic professional environments, requiring students to negotiate constraints, justify strategic decisions, and respond to real-world feedback from agencies and clients. In this progression, each pillar reframes and deepens the previous one, moving students from diagnostic analysis to technological mediation, to strategic orchestration, and ultimately to accountable professional enactment. CAIC thus operates as a scaffolded competency model aligned with contemporary algorithmically mediated advertising practice, offering a replicable instructional architecture for AI-integrated media planning education.
“C” for Consumer: Consumer Journey Mapping
The first strategy focuses on developing an understanding of the increasingly sophisticated consumer journey by teaching students how to examine and analyze the consumer path, a crucial component of effective media strategy development. This strategy thus focuses on teaching students practical methods for developing a meaningful consumer path analysis.
To guide this process, three consumer path frameworks are introduced. The first is the widely used classic AIDA model (attention, interest, desire, action), introduced by E. St Elmo Lewis. AIDA has long served as a simple and intuitive checklist for advertisers to explain how consumers progress from awareness to action, and it has historically shaped media budget allocation. However, AIDA does not account for factors such as customer service, user experience, distributor relationships, or word-of-mouth effects. As a result, brands that rely solely on this model may overlook important opportunities for consumer engagement.
The second framework is the 5A model (aware, appeal, ask, act, advocate), developed by Philip Kotler (Kotler et al., 2016). This model reflects changes brought about by increased connectivity, emphasizing “appeal” shaped by social influence and expanding the notion of loyalty through “advocacy,” which goes beyond mere repeat purchase to include active recommendation.
Recognizing the limitations of traditional linear models such as AIDA and the 5A’s, both of which assume a sequential path through fixed stages, the course introduced a third framework developed by the Boston Consulting Group (BCG) in collaboration with Google (Yu et al., 2025). This influence-based approach reconceptualizes the consumer journey as dynamic and non-sequential, emphasizing what drives “influence” rather than movement through predefined steps. BCG identifies four core consumer behaviors—streaming, scrolling, searching, and shopping—as the primary drivers of influence across touchpoints. Importantly, “influence maps” vary across individuals, reflecting heterogeneous, AI-mediated consumer journeys shaped by algorithmic personalization, real-time data signals, and platform-specific optimization systems (Yu et al., 2025).
This shift from funnel-based logic to influence-based mapping is made possible by AI technologies that analyze large-scale behavioral data and dynamically adjust media delivery. Rather than targeting consumers at fixed stages, AI-enabled systems identify high-impact moments across fragmented journeys, allowing campaigns to adapt to individualized paths in real time (Yu et al., 2025). As such, consumer path analysis is no longer solely about sequencing touchpoints, but about understanding how algorithmic systems interact with consumer behavior to amplify or redirect influence.
To translate this conceptual shift into instructional practice, students were required to conduct a comprehensive consumer path analysis using all three models: AIDA, the 5A’s, and the BCG influence map (see Appendix A for model construction and example student outputs). This comparative exercise was designed to cultivate targeted competencies aligned with AI-era media planning, including: (1) diagnosing when linear versus dynamic models are strategically appropriate; (2) identifying how algorithmic optimization alters consumer decision pathways; and (3) integrating influence-based thinking into downstream media allocation decisions. The learning objective of this component was therefore not merely model comprehension, but the development of analytical flexibility and AI literacy as foundational skills for contemporary media planning. Students are instructed to use three primary databases (i.e., Mintel Reports, Statista, and Simmons Catalyst) to support their consumer research for the path analysis.
A practical limitation of implementing the influence map framework in the classroom is the instructor’s lack of access to real-time consumer behavioral data and proprietary platform analytics. Because influence-based modeling in industry settings relies heavily on algorithmic signals and large-scale behavioral datasets, students were unable to fully replicate data-driven influence mapping processes. Instead, the instructional application of the framework focused on guiding students to analyze the four core consumer behaviors (i.e., streaming, scrolling, searching, and shopping) across the consumer journey and to identify the most influential behavior based on available secondary research and structured primary research exercises. While this approach does not simulate AI-powered optimization at scale, it enables students to conceptually understand how influence operates within dynamic journeys and to approximate strategic decision-making in the absence of proprietary data.
“A” for AI: AI Integration
The second strategic pillar, AI Integration, focused on embedding artificial intelligence within the media planning process. This component was structured around three core topics: (1) Foundational AI Concepts in Advertising, (2) AI for Consumer Research, and (3) AI in Media Planning and Buying. The module began by establishing a foundational understanding of AI itself. Drawing on Yang (2023), the instruction explored definitions of “intelligence,” delineated types of human intelligence, and introduced core concepts such as artificial intelligence (AI), its inherent limitations, and the distinctions between artificial narrow intelligence (ANI) and artificial general intelligence (AGI), along with an overview of machine learning.
Subsequently, the focus shifted to the theoretical and practical application of AI in advertising. Theoretically, key frameworks, including the Computers Are Social Actors (CASA) theory (Nass et al., 1994), the uncanny valley theory, Theory of Mind (Minton et al., 2021), and AI anthropomorphism, were examined, accompanied by a review of pertinent academic literature (e.g., Ford et al., 2023; Kietzmann et al., 2018). On the practical front, the course reviewed specific AI applications within the advertising domain, including automation and optimization, predictive analytics, AI-driven content creation, AI-powered customer research, search engine optimization (SEO), and the deployment of AI agents (Huh et al., 2023).
The segment dedicated to AI for Consumer Research aimed to develop student understanding of how AI systems collect, classify, and derive actionable insights from consumer data (Yang, 2023). These insights include brand sentiment, trending topics, consumer segmentation, identification of key opinion leaders, and predictive modeling of customer preferences (Yang, 2023). To translate theory into practice, industry tools such as Displayr (a market research tool), Perplexity AI (an answer engine based on real-time web search, good for consumer research), Resonate (a consumer research tool), Brandwatch (a social media listening tool), and Zappi (an ad testing tool) were demonstrated (Pollitt, 2025).
Beyond demonstration, students engaged in structured, hands-on exercises designed to simulate applied consumer research workflows. For example, students were tasked with using ChatGPT’s Data Analytics feature to analyze a provided dataset containing consumer survey responses and social media comments (Francis, 2025). They were required to identify emerging themes, segment audiences based on behavioral and attitudinal patterns, and generate preliminary consumer personas grounded in data interpretation.
In a separate exercise, students conducted a structured secondary research assignment using AI-assisted search tools such as Perplexity AI. Each team was assigned a specific research focus (e.g., Gen Z beverage trends). Students were required to document (1) their exact prompts, (2) the sources cited by the AI system, and (3) the key statistics or claims generated in each response. To prevent overreliance on a single output, students were required to triangulate findings across at least three independent sources and identify inconsistencies, unsupported claims, or potential overgeneralizations in AI-generated summaries. They then submitted a brief evaluation memo assessing source credibility, completeness of synthesis, and possible biases (e.g., recency bias or limited source diversity). Finally, students explained how validated insights would inform targeting, positioning, or media allocation decisions within their campaign plan. This exercise positioned AI-assisted research as a starting point for critical analysis rather than an authoritative endpoint, reinforcing human oversight within the research process.
Within the segment on AI in Media Planning and Buying, instruction began with the fundamentals of programmatic advertising and how AI has automated its core processes, via experimentation, adaptive sequencing, and real-time optimization. The focus then shifted to major AI-powered media planning platforms, specifically Google’s Performance Max, Meta’s Advantage+, and Amazon’s Performance+. Recognizing both the technical sophistication and industry relevance of these systems, the instructional approach prioritized conceptual understanding over operational mastery, aiming to develop students’ foundational knowledge of algorithmic optimization and automated decision-making processes. To further bridge theory and practice, a guest lecture was incorporated, inviting industry professionals with hands-on experience in these platforms to share practical insights and real-world applications. Last, additional industry tools (e.g., Simpli.fi) were examined to demonstrate how AI aids in strategic budget allocation, workload minimization, and media goal achievement (Pych, 2025; Reyes, 2025). This applied learning culminated in a practical task where students utilized ChatGPT to generate a basic media plan.
Notably, while the course frames AI as an augmentative rather than automative force in media planning, this distinction is operationalized through a clear division of cognitive and technical responsibilities. AI systems are positioned as supporting functions that excel in large-scale data processing, predictive modeling, real-time optimization, and cross-platform budget allocation (Gao et al., 2023; Wu & Andrews, 2024). For example, when students examine tools such as PMax or Advantage+, AI is understood to automate bidding strategies, identify high-probability audiences, and dynamically adjust media delivery based on performance signals. In contrast, human planners are explicitly trained to exercise judgment in areas that require contextual interpretation and strategic reasoning, such as defining campaign objectives, evaluating whether AI recommendations align with brand positioning, balancing short-term efficiency with long-term brand equity, and explaining decisions to stakeholders (Mariani et al., 2022).
AI integration strategies for structured media planning instruction
“I” for Innovation: Innovative Media Mix
The third pillar, Innovative Media Mix, targets the critical challenge of cross-platform optimization in contemporary media planning. It emphasizes the analytical and strategic allocation of media budgets, equipping students to navigate a landscape where attribution is complex and to architect integrated campaigns that balance performance and brand-building objectives across channels. The execution of this strategy within the course focused on two primary objectives: (1) introducing students to emergent media platforms and their associated digital advertising strategies and (2) developing their competency in creating an effective cross-platform media mix.
Regarding emergent platforms and strategies, the course introduced seven core digital approaches: email marketing, social media advertising, content marketing, video marketing, influencer marketing, interactive and immersive advertising (e.g., augmented and virtual reality), and e-commerce marketing. Each was selected for its distinct strategic role.
Email marketing was included as a foundational, reliable channel for personalized communication and nurturing long-term brand-consumer relationships. Social media advertising was examined as an essential tool for brand awareness and engagement, with a specific focus on how platforms like TikTok, Instagram, YouTube, and LinkedIn drive trends, foster community, and facilitate direct dialogue, particularly among Millennial and Gen-Z audiences.
The course defined content marketing per the Content Marketing Institute (2018) as “a strategic marketing approach focused on creating and distributing valuable, relevant, and consistent content to attract and retain a clearly defined audience and, ultimately, to drive profitable consumer action.” This strategy was framed as critical for generating electronic word-of-mouth, building trust and credibility, and achieving significant organic reach and impact (Lou et al., 2019; Xie & Lou, 2020). Students explored how different types of branded content serve as key mechanisms for delivering value and retaining consumers.
While often overlapping with content marketing, video marketing was treated as a distinct discipline focused on leveraging video for storytelling, education, and demonstration. Its prominence was underscored by data indicating that 89% of businesses use video as a marketing tool (Wyzowl, 2026). The course addressed the complexity of managing diverse video formats—such as explainer videos, social shorts, testimonials, and video ads—and highlighted the growing role of AI in scaling video production, making its inclusion essential for a modern curriculum.
Influencer marketing, which has propelled social media to become the world’s largest advertising channel, was also examined in depth. With global spend projected to reach $266.93 billion by the end of 2025, it is cemented as the epicenter of modern consumer influence and engagement (Influencer, 2025). Consequently, understanding its strategic deployment is essential. Last, the course addressed interactive and immersive advertising (e.g., AR and VR) and e-commerce. The former represents a key modality for creating engaging, interactive consumer experiences, while the latter encompasses the critical digital touchpoints for product discovery and sales conversion.
The pedagogy for these seven strategies was designed to be both efficient and applied. Each was introduced through a consistent framework covering its core strategic rationale, planning considerations, executional requirements, and measured effects, with concepts illuminated through relevant case studies. The primary instructional goal was to build student awareness and functional understanding, thereby equipping them with the foundational knowledge needed to evaluate the unique advantages of each channel for cross-platform synthesis.
Building on this foundational knowledge, the course advanced to the critical task of synthesis. While this pillar introduces students to a range of emerging media platforms and digital marketing strategies, its core focus is on cultivating students’ cross-channel strategic reasoning abilities, specifically, how to evaluate, prioritize, and integrate diverse media options throughout the consumer journey, merging them into a coherent solution that serves both performance goals and brand building objectives. Also, notably, the Innovative Media Mix pillar is intentionally designed to provide strategic breadth rather than tactical depth. Channel-specific executional expertise is developed in subsequent courses within the curriculum, whereas this course focuses on helping students understand each platform’s strategic role within an integrated media architecture.
“C” for Collaboration: Client and Agency Collaboration
Experiential learning is an approach in which students actively engage in “learning by doing,” recognizing that meaningful learning emerges through cycles of experience, reflection, conceptualization, and experimentation (Richards & Marshall, 2019). According to Kolb (2014), these four stages—concrete experience, reflective observation, abstract conceptualization, and active experimentation—together form a continuous learning cycle. When students move through this cycle repeatedly, they achieve “deep learning.” This approach is especially effective in advertising education, where the application of theoretical knowledge to real-world contexts is essential. By engaging students in hands-on activities and reflective processes, experiential learning enables them to connect classroom theories with authentic industry challenges and take an active role in shaping their educational experience.
Guided by this pedagogical framework, the final pillar strategy of the course emphasizes collaboration with agencies and clients to provide students with real-world media planning opportunities. Over the semester, students complete three team-based projects, with the third involving external collaboration. Two modes of collaboration have been implemented: partnerships with advertising agencies and partnerships with real-world clients.
The first type of collaboration involves working directly with executives from local advertising agencies. The instructor begins by identifying agencies with strong digital media practices and inviting them to share actual client briefs or tasks. These briefs typically include project background, key consumer insights, assignment details, media objectives, target audience information, key messages, KPIs, mandatories, media timing, and budget. After receiving the brief, The instructor collaborates with the agency partners to refine it into a formal project guide for students. Student teams then develop a comprehensive media plan, which they present to the agency executives at the end of the project. The authenticity of the briefs ensures that students work on problems that mirror real-world media planning challenges, complete with concrete goals and constraints. Because agency executives are working on the same client brief themselves, they are able to offer nuanced, industry-informed feedback that helps students better understand professional expectations. At the same time, because students present to agency professionals rather than actual clients, the experience remains rigorous but less intimidating, creating a supportive environment for learning.
The second type of collaboration involves working directly with a real-world client who seeks to address a specific marketing problem through the development of a digital media plan. In these partnerships, clients visit the classroom to deliver the project brief, and students often visit the client’s organization to conduct primary research. Students then build a media plan that responds to the client’s needs, culminating in a final presentation delivered directly to the client. This form of collaboration places greater responsibility on students, as it requires sustained communication with the client and a heightened level of professionalism throughout the planning process. Although the pressure is higher, the intensity of the experience often accelerates student learning. This approach also requires that clients be highly engaged and responsive, as students depend on timely feedback to guide their work. Unlike agency executives, clients may have limited familiarity with media planning concepts, which means they often require additional explanation and guidance from both students and the instructor to facilitate a productive collaboration.
While both agency and client collaborations are grounded in the same experiential learning philosophy, they differ in pedagogical intensity, stakeholder dynamics, and levels of professional accountability. Agency collaboration functions as a professionally scaffolded simulation in which students engage with authentic client briefs while gaining insider perspectives on how agencies interpret client demands, develop strategic plans, and navigate industry workflows. Because agency professionals are experienced media practitioners, they are often able to provide structured guidance, nuanced feedback, and a supportive learning environment that helps students better understand internal agency decision-making processes. In contrast, client collaboration represents a higher-stakes enactment of professional practice. In this model, students assume direct responsibility for identifying client needs, diagnosing marketing challenges, and proposing actionable solutions to real decision-makers. The pressure is more immediate and authentic, as students must independently interpret business problems and communicate strategic recommendations in a manner that is both persuasive and professionally accountable. The distinction, therefore, lies not in the experiential foundation itself but in the degree of real-world responsibility, autonomy, and role immersion afforded to students.
Together, these two models of collaboration bring authentic professional practice into the classroom and extend students’ learning beyond theoretical understanding. By working with agency partners and clients, students gain deeper insight into the complexities of media planning, develop stronger strategic and communication skills, and experience firsthand the iterative nature of solving real-world marketing problems.
Summary
Importantly, while specific AI tools and media platforms may evolve rapidly, the CAIC framework is intentionally designed to remain adaptable rather than dependent on any single technology. The framework emphasizes enduring competencies, such as influence-based thinking in mapping consumer journeys, human–AI collaboration, strategic synthesis, and professional accountability, that can be applied across emerging platforms. By focusing on transferable cognitive and strategic capabilities rather than platform-specific technical mastery, CAIC is structured to accommodate ongoing technological change while preserving its pedagogical integrity.
Course Structure
Building on the “CAIC” framework, the course was organized into four sequential modules throughout the semester: (1) The Digital and AI Advertising Landscape, (2) Strategic Foundations of Media Planning, (3) Consumer Path Analysis, and (4) Digital Media Tactics. An overview of each module’s core content is provided below. It is important to note that while the four “CAIC” strategies were integrated throughout the curriculum, they were not taught linearly. Instead, their introduction was sequenced to align with, and progressively build upon, the students’ developing understanding of digital media planning. This section, therefore, provides a chronological description of the course structure, while explicitly noting how each “CAIC” component was incorporated within the modules (see Appendix C for a course calendar). Figure 2 presents the four modules. Matching course structure with the CAIC strategies
Module 1: The Digital and AI Advertising Landscape
The semester began by establishing the context of the evolving, AI-driven marketing environment. This foundational module focused on three key areas: emerging consumer trends within the new technology-driven paradigm, an overview of cutting-edge marketing technologies and AI platforms, and the transformative role of AI in media planning and consumer journey management. The goal was to provide students with the essential macro-level understanding needed for the rest of the course. Given the rapidly changing ecosystem, it is critical for students to first comprehend the broader landscape, shifting consumer trends, and how new technologies are fundamentally reshaping industry practices. Within the “CAIC” model, this module served as the primary vehicle for introducing the “A” (AI Integration) strategy.
Module 2: Strategic Foundations of Media Planning: Goals, Audiences, and Channels
The second module of the course is to review the strategic foundations of digital media planning. During this week, students were introduced to the core components of the media planning process, including setting media goals, understanding the consumer path, and mapping the consumer journey across media channels. After establishing these fundamentals, we examined how AI is applied in media planning and buying, ensuring students not only understood the traditional foundations but also how these processes have evolved in today’s digital landscape. Additionally, a complementary lecture on pitching a media plan was included. Since students participate in three team projects requiring them to develop media plans, this sector ensures they have a strong, practical understanding of the digital media strategy planning process. Within the “CAIC” model, this module integrated concepts from both the “A” (AI Integration) and “I” (Innovative Media Mix) strategies.
Module 3: Consumer Path Analysis
The third module focuses on teaching students the methods for studying and analyzing the consumer path, a crucial component of developing an effective media strategy. As such, it served as the primary instructional vehicle for the “C” (Consumer) strategy within the “CAIC” framework. By the end of this module, students could conduct a practical consumer analysis, construct a coherent consumer journey map, and identify pivotal media touchpoints to directly inform subsequent media strategy development.
Module 4: Digital Media Tactics
The final module introduced students to a spectrum of digital media tactics, with a deliberate emphasis on emerging and innovative approaches. This module served as the primary platform for delivering the “I” (Innovative Media Mix) strategy of the “CAIC” model. Pedagogy in this segment was characterized by a case-based learning approach. Instead of traditional lecture formats, students engaged deeply with multiple case studies for each tactic, analyzing how these tools are effectively deployed to build media strategies and solve real-world marketing challenges. In particular, each case study follows a structured format in which detailed campaign background information is provided, including brand overview, marketing problem, research insights, campaign objectives, media plan, and measurable outcomes. Students are required to evaluate whether the media strategy effectively addressed the stated objectives and to propose strategic improvements based on consumer journey logic and cross-platform integration principles. Learning objectives focus on strengthening students’ ability to diagnose strategic alignment, assess media effectiveness, and identify optimization opportunities. Student deliverables typically include small-group in-class discussions and short written responses addressing guided analytical questions designed around the case.
Moreover, industry collaboration was intentionally embedded within this module to connect conceptual learning with professional practice. Guest speakers from ad agencies and brands provided practitioner perspectives on different strategies and real-world decision-making constraints. In several cases, agency partners also shared recent campaign materials, which were adapted into classroom case studies featuring authentic objectives, targeting logic, media allocation decisions, and performance outcomes. The module culminated in final project presentations evaluated by real-world clients and guest agency judges, whose feedback reinforced professional standards and the iterative, stakeholder-driven nature of media planning.
In sum, this module’s objective was to ensure that, upon completion, students were not only familiar with emergent digital tactics but also proficient in applying them confidently within their collaborative group projects.
Course Assignment and Assessment
Student assessment in this course comprised three primary components: (1) individual exercises and case studies, (2) three group projects, and (3) individual attendance and participation. The following section details the design and purpose of each component.
Individual Exercises and Case Studies (30%)
Throughout the semester, students completed six individual assignments, structured as in-class exercises or brief case studies. These tasks were designed to reinforce learning and provide a practical reflection on module content. For instance, within Module 3: Consumer Path Analysis, one assignment required students to develop a path analysis that revealed a consumer journey and its key touchpoints using methodologies taught in class. In Module 4, exercises included using ChatGPT’s Data Analytics to derive insights from a consumer dataset and prompting ChatGPT to generate a basic media plan for a specified scenario. These individual tasks were typically administered during class sessions to assess comprehension and the immediate application of concepts. Students were encouraged to present their approaches, and formative feedback was provided in real time to solidify learning.
Group Projects (60%)
The central assessment component of the course consisted of three group projects assigned throughout the semester. All projects required the creation of a mock or real-world media plan for a client, progressing from a simple to an increasingly sophisticated scope. This design enabled students to iteratively refine their media planning process to solve marketing problems within constrained timelines, thereby mimicking authentic industry workflows. Grounded in an experiential learning approach, these collaborative projects served as the primary means of evaluating students’ understanding and their competence in applying course concepts to practical digital media planning, effectively replacing traditional examinations.
The first project (15%) tasked student teams with developing a digital media plan for a self-selected local business, such as a retail shop, bakery, or café. The plan focused primarily on email marketing and social media advertising, requiring deliverables that included a content calendar and sample video content. By engaging directly with a small business, students gained experience crafting a small-scale digital strategy reliant on accessible channels like email, social platforms, organic content, and video marketing. This relatively straightforward project established a foundational understanding of integrated media planning.
For the second project (15%), teams developed a mock plan centered on paid media strategies. Students were provided a brief outlining core research insights, media goals, budgets, and deliverables. The focus was on formulating paid media strategies and designing an organic social support plan to amplify paid content. A key requirement was the integration of AI tools to aid in the plan’s development.
The final project (30%) was a comprehensive, real-world engagement, typically conducted in collaboration with an advertising agency or an actual client. A detailed brief was provided by the external partner, and student teams had the opportunity to consult with agency or client representatives to clarify objectives and conduct deeper research. The culminating deliverables included a comprehensive digital media plan and a formal media plan book. The project concluded with a final pitch presentation judged by the agency or client representatives, offering students authentic professional feedback.
Attendance and Participation (10%)
The final assessment component evaluates students’ individual attendance and active participation in class sessions. Given that each class integrates theoretical foundations, industry practices, and case study analysis, proactive engagement in discussions is essential for deepening comprehension and fostering peer learning. This component, therefore, assesses consistent attendance and the quality of contributions to in-class dialogue throughout the semester, reinforcing the collaborative and interactive ethos of the course.
Thoughts on Future Digital Media Strategy Education
Over the past decade, media planning has grown increasingly complex as consumers forge highly individualized paths to purchase across an expanding network of touchpoints, from streaming services and social platforms to generative AI interfaces. While this multi-touchpoint landscape offers unprecedented opportunities to influence consumer behavior, traditional linear journey models fail to capture its nuanced reality (Yu et al., 2025). A digitally savvy consumer might discover a product through a YouTube ad and purchase it on Amazon, while another might move from a price-comparison search directly to a social media “shop now” button. Critically, however, reach alone does not equal influence. As Boston Consulting Group indicates, true influence is determined by three converging factors: a consumer’s level of attention in a given moment, the relevance of the content to their immediate needs, and their degree of trust in the platform (Yu et al., 2025). Together, these insights underscore the need for educators to equip students with the skills to identify high-impact touchpoints and align content and resources with specific consumer needs across complex, non-linear journeys.
Consequently, media strategists and planners today shall prioritize planning for influence. This requires a deep understanding of how touchpoints shape consumer journeys, a holistic view of all media channels a brand could access, and the ability to build customized plans tailored to the needs and preferences inherent in each unique journey. In this evolving paradigm, AI emerges as a critical enabler. Generative AI can accelerate the creation of customized, even real-time content, while broader AI systems optimize strategic resource allocation and media placement. Together, these capabilities can ensure maximum relevance and impact at every stage of the consumer journey.
Therefore, the core responsibility of future media strategists will evolve toward cultivating a profound understanding of consumer-brand relationships and identifying the most meaningful ways to connect (Madison and Wall, 2025). Their strategic value will lie in orchestrating a holistic view of all potential brand engagements. They should be on the frontlines in defining appropriate campaign goals and, crucially, in evaluating the role of both online and offline channels to identify optimal cross-channel mixes. For this strategic vision to be realized, planners will need to upskill significantly. As data volume expands, the imperative grows not only to analyze this information but to interpret its implications compellingly. Education in digital media strategy must actively reflect and embrace this necessary evolution.
Given the above discussion, the author proposes the following five skill sets for future digital media strategy education.
Consumer Journey Mapping
This requires moving beyond traditional linear models to chart the non-linear, individualized paths consumers forge across an expanding touchpoint network. The planner must identify which moments truly shape decisions, where attention, relevance, and trust converge, to tailor strategies that align content and resources with specific consumer needs at each stage.
Proficiency in Leveraging AI
Effective media planners must harness artificial intelligence as a strategic partner. This involves employing broader AI systems to analyze data, generate consumer insights, and automate the optimization of media buying and budget allocation across complex campaigns.
Optimizing Cross-Channel Media Mix
The skill lies in orchestrating a holistic strategy that integrates both digital and offline channels. Planners must evaluate and balance the entire spectrum, from social and search to sponsorships and outdoor advertising, to create synergistic mixes that serve both immediate performance goals and long-term brand building.
Evaluating Influence
Planners need to shift from measuring mere reach to assessing true impact. This means analyzing qualitative factors, such as a consumer’s level of attention, the contextual relevance of content, and their trust in a platform, to prioritize high-value touchpoints and demonstrate how marketing efforts drive meaningful business outcomes.
Strategic Storytelling
This skill focuses on interpreting complex data and synthesizing cross-channel performance into a compelling narrative. Planners must explain the “why” behind consumer behaviors, justify strategic choices, and connect tactical execution to overarching campaign goals for clients and stakeholders.
Conclusion
This article has detailed the design and implementation of a digital media strategy course built upon the CAIC framework (Consumer journey mapping, AI integration, Innovative media mix, and Collaboration) developed in response to the fundamental shifts in the advertising landscape. The course structure, assignments, and experiential learning projects are presented as a pedagogical model for equipping students with the five critical skills required for modern planning: consumer journey mapping, leveraging AI, optimizing cross-channel media mix, evaluating influence, and strategic storytelling.
Beyond describing a single course implementation, this paper makes several pedagogical contributions to advertising education. First, it advances the CAIC framework as a scaffolded instructional model that integrates consumer influence analysis, AI literacy, cross-platform synthesis, and professional collaboration into a coherent developmental sequence. Second, it operationalizes human–AI collaboration within media planning education by positioning AI not merely as a tool for automation, but as a strategic partner requiring interpretation, oversight, and critical judgment. Third, it offers a replicable assignment architecture that balances conceptual AI understanding with applied experimentation, thereby addressing both the practical constraints and the rapid technological evolution shaping contemporary advertising curricula. Together, these contributions provide educators with a structured yet adaptable approach for integrating AI into media planning instruction while preserving the centrality of human strategic reasoning.
Preliminary student feedback and observed learning outcomes further suggest the effectiveness of this instructional approach. Across course evaluations, students consistently highlighted the value of AI-facilitated, project-based learning, collaborative assignments, and real-world engagement with agencies and guest speakers. Many noted that developing full campaign plans enhanced their ability to think strategically about media allocation, structure persuasive presentations, and translate analytical insights into coherent storytelling. Students also emphasized that working in teams and iterating on projects throughout the semester deepened their understanding of how media planning decisions evolve in practice. While these reflections are qualitative in nature, they indicate that the CAIC framework not only supports conceptual understanding of AI-integrated media planning but also fosters practical confidence and strategic communication skills.
The proposed approach is, by necessity, a work in progress. A primary limitation is the inherent challenge of maintaining curricular relevance given the blistering pace of change in both AI technologies and media platforms. Also, while the framework introduces strategic AI use, the depth of technical execution in areas like predictive analytics or automated optimization can be limited within a single semester. Future iterations would benefit from deeper partnerships with technology providers and data platforms to give students hands-on experience with enterprise-grade tools.
As AI technologies continue to evolve at an accelerated pace, advertising education must balance exposure to applied tools with the cultivation of enduring competencies. Although this course incorporates demonstrations of AI-powered platforms (e.g., PMax and ChatGPT), its primary emphasis is not on platform-specific technical training, but on developing students’ conceptual understanding of how AI facilitates and transforms media planning. By prioritizing algorithmic literacy, human–AI collaboration skills, and strategic interpretation over tool mastery, the CAIC framework is intentionally designed to remain adaptable amid rapid technological change and varying institutional access to proprietary systems. In doing so, the model seeks to prepare students not merely to operate current platforms but to critically engage with emerging AI systems as strategic partners in an evolving advertising ecosystem.
The evolution from planning for reach to planning for influence demands more than new tools; it requires a shift in mindset. Therefore, ongoing course development must continually reinforce the analytical and strategic storytelling skills that allow students to interpret AI-driven outputs, justify strategic choices, and articulate cross-channel influence. This endeavor cannot be undertaken in isolation. It is the author’s hope that this exposition of the CAIC framework invites collaboration, critique, and experimentation from fellow educators, industry practitioners, and scholars to refine the pedagogy needed to prepare strategic thinkers capable of guiding brands through an increasingly complex and AI-augmented media ecosystem.
Footnotes
Funding
The author received no financial support for the research, authorship, and/or publication of this article.
Declaration of Conflicting Interests
The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.