It’s back-to-school season. Knowing your target audience is an essential piece of planning a successful back-to-school marketing campaign. To get the most out of your marketing investment this back-to-school season, it’s important to understand how to identify and segment back-to-school shoppers so you can make sure that the right message reaches the right group at the right time.
In this blog post, we’ll cover how you can segment your target audience to create and deliver custom messaging tailored to individual groups. We’ll discuss segmentation methods that uncover:
- Who they are
- Where they live
- What type of person they are
- How they behave and spend
Here are our tips to accurately define and target your back-to-school marketing audience.
Maximize back-to-school marketing with customer segmentation
Customer segmentation is the process of dividing your audience into smaller groups based on common characteristics such as demographics, behaviors, psychographics, geographics, and more. The purpose of customer segmentation is to create a more personalized and effective approach to marketing. By understanding the unique needs and preferences of each segment, you can tailor your messaging, campaigns, and content to resonate with your customers on a deeper level.
Benefits of customer segmentation
Three benefits of customer segmentation include:
- Improved audience targeting
- Higher engagement rates
- Increased ROI
Instead of addressing your entire customer base with generic messaging, segmentation enables you to deliver custom campaign messaging that speaks directly to each group. This personalized approach helps build trust and loyalty with your customers over time.
Customer segmentation also allows you to better understand your customers, their motivations, and pain points, ultimately leading to more effective marketing campaigns.
Types of customer segmentation
When it comes to segmenting your customers, there are several methods to consider. By experimenting with different approaches, you can find the best fit for your business. Keep in mind that the most effective customer segments will differ depending on the industry.
Let’s review four types of customer segmentation that you can implement as part of your back-to-school marketing strategy.
1. Demographic segmentation
Demographic segmentation categorizes consumers into groups based on shared demographic characteristics such as age, gender, income, occupation, marital status, and family size.
For example, targeting college students during the back-to-school season with promotions on laptops is likely to be more effective than targeting retirees who may have less interest in such products.
2. Behavioral segmentation
Behavioral segmentation divides customers into groups based on their demonstrated behaviors. This method sorts customers by their knowledge of products or services, attitudes toward brands, likes/dislikes about offers, responses to promotions, purchasing tendencies, and usage of products/services.
Behavioral segmentation can help you identify the highest-spending customer segments, so you can budget and target more effectively. Through this type of segmentation, you can analyze each group’s patterns, discover trends, and plan informed marketing moves for the future.
In a back-to-school campaign, you could use behavioral segmentation to identify students who prefer to shop locally. You could then target students who value supporting local businesses and emphasize the importance of buying from local retailers during the back-to-school season.
3. Geographic segmentation
Geographic segmentation involves dividing your target market into groups based on their physical locations. Geographic segmentation reveals aspects of a local market, including physical location, climate, culture, population density, and language.
In a back-to-school campaign, you could use geographic segmentation to identify target audiences in colder climates who may be more interested in winter clothing and gear. You could also use geographic segmentation to target students living in college towns with messaging that speaks directly to campus life.
4. Psychographic segmentation
Psychographic segmentation groups customers based on psychological factors such as lifestyle, interests, personality, and values.
In a back-to-school campaign, you could use psychographic segmentation to target students who value sustainable practices, promote eco-friendly products, or offer incentives for recycling and reusing items.
Watch our 2024 video for tips from industry leaders for back-to-school
In our new Q&A video with Experian experts, we explore changing consumer behaviors surrounding back-to-school shopping in 2024. In the video, we discuss:
- Anticipated shifts in consumer behaviors and shopping habits
- Tactics we predict marketers will employ to navigate signal loss
- Which channels will be the most successful
- And more!
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Why an identity framework matters more than any single identifier The challenge facing marketers today isn’t a single identifier on a deprecation timeline. It’s the increasing fragmentation of signals and identifiers across browsers, devices, apps, and platforms. This shift introduces complexity into how audiences are reached and measured, as signals behave differently in every environment, and it becomes more complex to piece together a complete view of the consumer. Each environment contributes to its own set of visibility gaps, making identity less predictable and more uneven. The result is a patchwork of inconsistent identity signals rather than a single, predictable decline. While you can’t control how platforms evolve, you can control how you respond to fragmentation. The future won’t be defined by the loss of any single identifier, but by your ability to unify, interpret, and activate the many signals that remain. Marketers who adopt a flexible, identity framework will be best positioned to create consistency in an otherwise fragmented landscape. At Experian, we believe flexibility starts with intelligence. For decades, we’ve used AI and machine learning to help marketers understand people’s behavior more clearly, respect their privacy, and deliver messages that drive business outcomes. Our technology brings identity, insight, and intelligence together, so even as the number of signals grows and becomes more varied across environments, marketers can reach the right people with relevance, respect, and simplicity. This intelligence acts as the connective tissue across fragmented ecosystems, ensuring marketers can recognize and reach audiences consistently wherever they appear. What forces are driving fragmentation in identity and signals? Changes to traditional IDs: Since Apple introduced ATT, access to IDFA has become inconsistent across apps and devices. Google’s evolving Android privacy roadmap adds another layer of variability, fragmenting mobile addressability. Safari and Firefox have long restricted third-party cookies, while Chrome continues to support them for now. This creates different signal availability across browsers, contributing to an uneven and increasingly fragmented identity landscape on the open web. Shifts in signals: IPv4 to IPv6 migration introduces mismatched identity structures that complicate continuity across environments. Platform-driven fragmentation: Closed ecosystems and uneven adoption of evolving RTB standards (like OpenRTB 2.6 updates designed to support new identifiers and consent signals) create differences in which identifiers and consent signals are shared in the bidstream. At the same time, the rise of alternative or “universal” IDs—often developed by individual platforms, publishers, or technology companies—means that multiple ID types can appear within the same auction, each with its own structure, rules, and level of support. These differences reduce interoperability across platforms and contribute to a more fragmented activation landscape. Each change creates an identity silo. Together, they form an ecosystem defined by fragmentation rather than absence. Without an identity framework, these environments operate as disconnected identity islands. A multi-ID world requires a unified identity framework Alternative IDs play an important role, but they also expand the number of signals marketers must reconcile. Without a consistent identity layer, more IDs often mean more complexity—not more clarity. Common alternative IDs in use today: UID2: The Trade Desk’s UID 2.0, an iteration of their original Unified ID 1.0, which was still reliant on third-party cookies, creates persistent IDs with user-provided email addresses and phone numbers. ID5: This independent identity provider builds an identity infrastructure that powers addressable advertising across channels. It can create an ID based on both deterministic and probabilistic data. Hadron ID: Hadron ID is a unique, interoperable identity system (including first-party, audience-based, contextual, deterministic, and probabilistic) developed by Audigent, now part of Experian, to drive revenue for publishers by making their audience data and inventory actionable for media buyers. Industry reports suggest roughly one-third to two-fifths of open-auction traffic carries alternative IDs, sometimes multiple per request. Among Experian clients, adoption of alternative IDs rose 50% year over year, with a 30% increase in IDs resolved to individuals via our Digital Graph. Identity isn’t disappearing; it’s multiplying. A modern identity framework resolves these identifiers into a single, privacy-safe consumer view.
Year after year, CES signals where marketing is headed next. In 2026, the message was clear. Progress comes from connecting data, intelligence, and outcomes with discipline, not spectacle. Across AI, programmatic media, and measurement, the same priorities surfaced again and again. Under the bright lights of Las Vegas, three themes cut through, and each one pointed to a future where data, intelligence, and outcomes move in lockstep. Here are the three themes that defined CES 2026. 1. Agentic AI proved that it’s only as good as its data inputs AI was once again the star of the show. At CES 2026, marketers focused less on demos and more on proof that AI improves decisions, reduces friction, and drives outcomes. Every credible use case traced back to accurate, privacy-first data. What changed at CES was how that intelligence is being applied. Agentic AI systems designed to act autonomously are moving beyond insights and into execution. From media buying to optimization, these agents are increasingly expected to make decisions at speed and scale. That shift raises the stakes for data quality. When AI is operating campaigns, not just informing them, accuracy and privacy are non-negotiable. Without accurate, privacy compliant data, AI agents struggle to reflect real behavior or support responsible personalization. A reliable, privacy-first data foundation is what turns AI from an interesting experiment into an operational advantage. That advantage gets even stronger when it’s anchored in an identity graph that understands people and households across channels. When identity and intelligence move together, AI becomes more accurate, accountable, and effective at driving outcomes. In an AI first world, the strongest signal isn't scale. It's data quality. 2. Curation goes mainstream Curation is no longer experimental. At CES, it showed up as an mandated capability for buyers and sellers navigating fragmented signals and complex supply paths. Marketers want intentional media buys they can explain, defend, and repeat. AI is accelerating this shift. As AI systems take on more responsibility for planning, packaging, and optimization, curation provides the guardrails. It defines what “good” looks like (premium supply, trusted data, and clear performance goals), and allows AI to operate within those constraints driving the optimal outcomes for marketers. Rather than maximizing inventory access, curation prioritizes control, transparency, and performance. Buyers want premium supply aligned to specific goals. Sellers want clearer paths to demand. They can play the odds or own the outcome. When data leads, they own it. When curation is powered by high-fidelity audiences and a connected identity framework, it becomes even stronger. That’s what allows curated deals to deliver clarity, confidence, and repeatable performance. This shift reflects a broader move away from probability-based buying toward outcome ownership, where AI-driven systems are measured not on activity, but on results. 3. Activation and measurement finally shared the same stage Activation and measurement are now coming together around shared data and identity. CES 2026 marked a turning point where closing the loop felt achievable, not aspirational. Both the buy- and sell-sides face pressure to show that media investment drives outcomes. Agentic AI was a quiet driver of this optimism. As AI agents increasingly manage activation decisions in real time, marketers need measurement systems that can keep up. That requires a shared data and identity foundation. One that allows AI-driven actions to be evaluated against outcomes consistently, across channels and partners. "The companies leading in alternative data aren't just optimizing for growth, they're setting a new standard for inclusion, precision and responsible lending." – Ashley Knight, SVP of Product Management, Experian Achieving that requires a consistent identity spine that connects planning, activation, and outcomes across channels. And that spine is strongest when it’s built on accurate, privacy-first data and audiences that understand people and households. That connection allows marketers to move beyond proxy metrics and evaluate performance based on tangible results. When campaigns and measurement rely on the same data foundation, AI driven platforms can optimize toward outcomes such as new customers, account growth, or in-store activity, not just delivery metrics. That’s the connective layer that turns disconnected touchpoints into a measurable, outcomes-based system. The takeaway CES made one thing clear: agentic AI is moving marketing from intention to execution. But only for teams with the right foundation. AI is maturing, but only for teams with accurate, connected, privacy-first data that AI agents can act on responsibly. Curation is scaling, giving both humans and AI systems clearer paths to quality, control, and differentiation. Activation and measurement are aligning, allowing AI-driven decisions to be judged on outcomes, not assumptions. We’re building for that world today. One where agentic AI operates on a trusted data and identity foundation, curation defines the rules, and outcomes determine success. With the right foundation and the deep data inputs, you can move faster, reduce risk, and let intelligence (human and artificial) work together to deliver results that last long after the neon lights fade.