Data-Driven Over Demographics: Rethinking Senior Living Marketing Strategy
by Tim Duer
More than 75% of adults aged 50 and older want to remain in their current home as they age (AARP, 2024). In response, senior living organizations are evolving to meet the expanding expectations of this diverse and growing population. Once centered almost exclusively around campus-based care models providing independent living, assisted living, and memory care, many now offer a broad spectrum of services - from in-home healthcare and wellness programming to hybrid models that blend community and autonomy. This diversification allows providers to reach a wider range of individuals with care options that reflect the varied ways older adults want to live.
While this diversification brings new flexibility for older adults and families, it also presents a significant challenge for marketers: How can you target audiences effectively when neither the services nor the consumers fit a one-size-fits-all model?
To address this complexity, healthcare and senior living marketers must move beyond traditional demographic segmentation and embrace more advanced tools that reveal behavioral patterns, decision-making dynamics, and personal motivators.
Why Age and Income No Longer Define the Audience
Historically, marketers in the senior living space have relied on age and income as core targeting criteria:
Independent Living campaigns typically target the older adult directly, using messages that emphasize lifestyle enhancements and amenities.
Assisted Living or Memory Care efforts are more likely to target adult children, focusing on safety, support, and peace of mind; typically with no awareness of whether or not they even have an aging parent.
This segmentation remains helpful in broad strokes, but it doesn’t reflect the more nuanced realities of today’s consumers. A 74-year-old retiree may want access to wellness programming through a senior living provider but still prefer to age in place with occasional support services. A 68-year-old couple might be exploring independent living for its social and maintenance-free benefits, while simultaneously assisting a sibling with complex medical needs at home. A 60-year-old adult child may be considering memory care for a parent, but only after exhausting at-home care options. Others in the same demographic band may be researching senior housing not for family member, but for themselves, years in advance. Approaching all of these individuals as the same audience and crafting broad, one-size-fits-all messages in an attempt to cover every need often leads to diluted engagement and missed opportunities for connection.
The Challenge of Non-Recurring Decisions: Why Traditional Data Falls Short
One reason marketing in this space is particularly challenging is the nature of the decision itself. Choosing a senior living option, whether for oneself or a loved one, is not a recurring transaction. It's a major life decision - often made only once - and it rarely comes with a relevant “purchase history” that marketers can analyze for guidance.
This reality calls for targeting strategies that can identify likely future behavior based on present-day signals, rather than past transactions. Understanding who might be open to in-home services versus full-time care - before they begin their search - requires predictive, not just retrospective, insight.
From Demographics to Drivers: Modeling Preferences and Motivations
Advanced audience modeling can help marketers differentiate between individuals who may appear identical on paper - same age, income, geography - but hold very different attitudes or preferences:
Is safety or independence a greater concern?
Is proximity to family a priority?
Do they see senior housing as empowering or limiting?
These are not questions demographics alone can answer, but they are critical in shaping effective outreach strategies. Predictive modeling offers a way to infer likely behaviors and motivations based on observable traits and patterns. While no model can predict every individual’s preferences with certainty, it allows marketers to make more informed assumptions - identifying "best fit" segments for specific messages or offerings.
For example, someone who values autonomy and remains socially active may respond more favorably to messaging around campus-based wellness programs or community involvement. Conversely, someone whose consumer behavior signals concerns about cost may be more receptive to messaging that emphasizes flexibility, financial transparency, or family proximity.
The goal is not perfect prediction, but improved alignment - delivering messages that are more likely to resonate, based on behavioral indicators rather than assumptions.
Segment Smarter: Traits That Matter Beyond Age
Marketers can also benefit from considering less obvious, but highly relevant, traits:
Activity preferences: Interests like gardening, woodworking, or fitness can guide how services are framed.
Decision-maker identity: Not everyone aged 50–70 is actively supporting a parent, but identifying those that do greatly improves campaign efficiency.
Perceived barriers: Understanding what deters someone from considering care - stigma, cost, loss of independence - can help shape more resonant messaging.
These kinds of insights help marketers refine who they speak to, what they say, and how they say it. Rather than diluting a message across all channels and audiences, teams can craft targeted content that aligns with specific values, lifestyles, or concerns.
Smarter Targeting Supports Smarter Spending
In an era of tighter marketing budgets and rising expectations, refining audience segmentation isn’t just a strategic advantage - it’s a financial one. Precision targeting reduces wasted impressions and enables marketers to concentrate their spend on the audiences most likely to engage or convert. It also allows for meaningful frequency: showing fewer people more relevant content more often, rather than spreading the message thin.
Ultimately, the goal is not simply to reach more people, but to reach the right people - with the right message, at the right moment in their journey.
Final Thoughts
As senior living providers diversify their service offerings, marketers must evolve their strategies in parallel. The complexity of these services - and the uniqueness of the individuals they’re meant to serve - demands more than age-and-income targeting. It calls for a deeper understanding of behavior, motivation, and decision-making context.
Tools like predictive modeling and behavioral segmentation offer promising ways forward. Not because they replace human insight, but because they support it - helping marketers ask better questions, refine their assumptions, and connect more meaningfully with their audience.
At Causeway Solutions, we’ve built senior living audience segments that reflect these deeper insights—combining lifestyle indicators, behavioral signals, and modeled preferences. Whether you're trying to identify adult children currently navigating care decisions or 65+ individuals signaling interest in aging-in-place solutions, our activation-ready audiences are designed to help.
For example, marketers can activate segments like:
• Prefer to Stay in Own Home or Community
• Likely Decision Maker for Another Adult or Senior
• Concerned that Senior Housing Poses Health Risk
• Favorable Opinion of Senior Housing
These segments are available across major digital platforms and built to help marketers reach the right people, with the right message, at the right time—no guesswork required.
Need something more tailored?
Contact our Healthcare Strategist, Patrick Upton, at patrick.upton@causewaysolutions.com to build a smarter, high-performing campaign.
Reference: AARP. (2024, December 10). New AARP report: Majority of adults 50‑plus want to age in place, but policies and communities must catch up [Press release]. AARP. https://press.aarp.org/2024-12-10-New-AARP-Report-Majority-Adults-50-plus-Age-Place-Policies-Communities-Catch-Up