In a world where consumers are increasingly trusting brands less, Dr. Cecilia Dones, founder at 3 Standard Deviations, argues that the key to competitive advantage lies in detecting “Signals of Care”: the subtle behavioural indicators that reveal authentic customer engagement and long-term loyalty, and provides a framework for identifying these signals.
In an era where consumer trust is increasingly scarce, brands that authentically demonstrate care and reliability are not just surviving—they’re thriving. According to the Edelman Trust Barometer 2025 Global Top 10 Findings, widespread grievance and distrust now define public attitudes toward business leadership, with CEOs experiencing a significant decline in trust across global populations since 2021. This erosion of trust presents a major challenge for businesses, but also a unique opportunity: organisations that can identify and act on ‘Signals of Care’: subtle indicators of empathy, trustworthiness, and alignment, can gain a lasting competitive edge.
Why Signal Detection Matters
Brands today generate unprecedented volumes of data. Every click, swipe, and interaction creates signals about customer attention, sentiment, and preferences. Yet quantity alone is not the challenge. The real difficulty lies in distinguishing meaningful signals from noise; those markers that indicate authentic engagement and long-term loyalty. As highlighted in McKinsey’s 2025 State of the Consumer report, successful brands must focus on identifying these genuine markers to build lasting customer relationships in an increasingly complex digital environment.
Traditional marketing metrics such as clicks, views, impressions measure marketing activity, but rarely reveal relational trust or genuine connection. In an AI-driven landscape, engagement can be easily manufactured: bots inflate reach, AI-generated content floods feeds, and trend-chasing becomes the default strategy. The organisations that master signal detection can anticipate audience needs, respond proactively to emergent behaviours, and design experiences that competitors cannot replicate.
In the complex digital environment described by the Stanford Youth Safety and Digital Wellbeing Report 2025, an over-reliance on aggregated engagement metrics can mask important nuances of social media harms. The report emphasises that focusing solely on superficial usage statistics without tailored measurement risks overlooking critical individual harms, misallocating resources, and potentially causing reputational damage.
Defining Signals of Care
Signals of Care are behaviours and interactions that indicate a brand genuinely prioritises its customers’ well-being and ethical values. They manifest across several dimensions:
Brands that communicate transparently, openly sharing information about product sourcing, pricing, or operational practices, signal reliability. Responsive customer service that is timely, empathetic responses to questions or complaints demonstrates attentiveness. Ethical business practices, such as fair labour policies or sustainability initiatives, reinforce alignment with societal expectations. Consistency across brand messaging, where marketing campaigns reflect core values and purpose, signals authenticity rather than opportunism.
These cues are often subtle, embedded in repeat interactions, customer advocacy behaviours, qualitative feedback, or nuanced engagement patterns. For instance, Duolingo’s adaptive learning reminders demonstrate care for learners’ progress.
The Role of AI in Signal Detection
Artificial Intelligence plays an increasingly central role in detecting Signals of Care, particularly when analysing vast datasets that humans alone cannot process efficiently. NLP tools, sentiment analysis, and predictive models allow brands to detect patterns in social media posts, customer reviews, surveys, and support interactions revealing emerging trust, concern, or unmet needs in real time.
Across industries, the applications are diverse. Retailers can analyse product reviews and returns to identify dissatisfaction, enabling proactive guidance and personalised recommendations. Fintech companies leverage predictive analytics to alert users about potential overdraft risks, signalling attention and ethical responsibility. SaaS and tech products monitor onboarding behaviour or feature adoption to detect where users struggle, prompting supportive nudges. Even hospitality platforms examine review and communication patterns to identify hosts who consistently deliver exceptional service.
Functionally, AI supports multiple organisational touchpoints. In customer support, sentiment analysis flags frustrated or high-value customers for immediate intervention. Marketing teams track shifts in brand perception or trust across social media to adjust messaging. Product and UX teams analyse behavioural data to reveal friction points or unmet needs, while HR departments can gauge employee sentiment to proactively address engagement and morale concerns.
However, AI has limits. While it excels at identifying correlations and patterns, it cannot fully interpret ethical or emotional context. A viral post may appear to signal engagement but could erode trust if perceived as manipulative or insensitive. Biases in training data, cultural nuances, and subtle sentiment cues such as sarcasm or long-term dissatisfaction can be missed entirely.
Effective signal detection therefore relies on human-AI collaboration. AI identifies candidate signals at scale, and cross-functional teams review and contextualise them to ensure that actions align with brand values and customer care. Integrating AI into the broader detection framework: mapping what matters, collecting multi-source data, prioritising patterns, and validating outcomes enables brands to scale insight while maintaining judgement and ethical oversight. Emerging AI capabilities, including emotion analysis and predictive modelling, offer further potential, but the guiding principle remains: AI accelerates detection, but humans interpret, decide, and act.
A Framework for Detecting Signals
Organisations can operationalise signal detection using a structured approach:
Map What Matters: Identify behaviours, interactions, and outcomes that reflect trust and care in your context. Retail brands might focus on return experiences, complaint resolution, and repeat purchases, while SaaS companies might track feature adoption, helpdesk responsiveness, or community engagement.
Collect Multi-Source Data: Combine quantitative inputs (engagement metrics, NPS, advocacy indicators) with qualitative feedback (reviews, social media mentions, direct customer insights) to capture nuanced signals.
Prioritise Signals by Strategic Impact: Focus on indicators that correlate with long-term loyalty rather than short-term clicks or superficial engagement. A positive review highlighting proactive support is more meaningful than a viral social media mention.
Spot Emergent Patterns: Look for recurring behaviours that signal authentic connection. Are customers returning because they value reliability? Are they recommending your brand because they trust its values?
Validate Through Experimentation: Use pilot programmes, A/B testing, or targeted engagement experiments to confirm whether identified signals truly predict desired outcomes.
By following this approach, brands can transform complex data into actionable insights, fostering trust-driven engagement that is difficult for competitors to replicate.
What Executives Need To Do
Signal detection is not simply a marketing tactic; it is a leadership capability. Organisations that fail to detect Signals of Care risk investing in initiatives that generate attention without trust, misreading audience sentiment, and ultimately losing competitive advantage. Conversely, companies that excel at detecting signals can build trust-based differentiation, respond more effectively to market shifts, and create compounding advantages as care signals reinforce loyalty and advocacy across touchpoints.
Leaders must ask themselves: Which signals in my organisation genuinely reflect care, and which are mere noise? The answers will define the premium a brand can claim in the market.
The first step in navigating the AI-saturated, trust-deficient marketplace is not more metrics or content; it is better signal detection. Executives who identify Signals of Care, interpret them thoughtfully, and act in ways that reinforce trust will gain a sustainable competitive edge. In the next essay, we will explore Signal Interpretation, translating detected signals into actionable narratives and strategies that resonate with audiences.
