For years, we have been told that the primary value of customer data lies in its ability to help us "target" more effectively. We treat data like a high-powered scope, designed to find the right person at the right time so we can deliver the right message. But this view is fundamentally limited. It treats the customer as a stationary target to be hit rather than a participant in a relationship. The reality I have seen in my work is that your customer data is not a targeting tool; it is your best source of relevance. When we shift our focus from targeting to context, we move from being a noise-maker to a decision-support system.
The obsession with targeting has led many marketing teams to ignore the deeper strategic value of the information they already possess. In an era where third-party signals are degrading and AI-mediated search is becoming the norm, first-party data is the only durable source of truth a brand has. But its importance goes far beyond simply knowing which ads to show to whom.
First-party data is important because it provides the raw material for strategic positioning. While third-party data tells you what someone might be interested in based on their browsing history, first-party data tells you what they actually value based on their interactions with you. This distinction is critical. If we only use data for targeting, we are essentially trying to guess the future based on a fragmented past. If we use data for relevance, we are using the present context to make the customer’s next decision easier.
Furthermore, the expectations of the modern consumer have shifted. According to research from McKinsey, 71% of consumers now expect personalized interactions, and perhaps more tellingly, 76% become frustrated when they do not receive them [1]. This frustration does not stem from a lack of "targeting"—most customers are targeted plenty. It stems from a lack of relevance. When a brand knows who I am but treats me like a stranger, the disconnect creates friction. First-party data is the bridge that closes that gap, allowing a brand to show up as a helpful partner rather than a persistent solicitor.
Positioning is not something you do once in a slide deck and then forget. It is a living expression of why you matter to a specific person in a specific moment. Most companies struggle with positioning because they try to be everything to everyone. They use broad messaging that misses the mark because it lacks context.
When you integrate customer context into your messaging, your positioning becomes sharper and more defensible. You stop talking about features and start talking about outcomes that matter to that specific user. For example, if your data shows that a segment of your customers is primarily concerned with speed of implementation rather than total cost of ownership, your messaging should reflect that reality immediately. You are not "targeting" them with a speed message; you are being relevant to their current situation.
This context-driven approach transforms marketing from a series of outputs into a learning system. As I discussed in a previous article, content is not a strategy—a learning system is. That system begins with customer context. By feeding what we know about our customers back into our messaging frameworks, we create a compounding intelligence that makes every subsequent interaction more effective. We move away from generic "people-first" content and toward "this-person-first" content.
This shift has a measurable impact on the bottom line. McKinsey’s analysis indicates that while targeted promotions can produce a 1–2% sales lift, the broader application of personalization and context can lead to a 1–3% margin improvement [1]. In one retail test, a focus on these deeper relevance signals yielded a roughly 3% annualized margin uplift after only three months [1]. These are not just incremental gains; they are the result of a fundamental shift in how a business understands its value to its customers.
The customer experience is often where the "targeting" mindset does the most damage. We see this in the form of relentless retargeting ads for products we’ve already bought or "personalized" emails that clearly don't understand our relationship with the brand. True relevance, powered by first-party context, creates a seamless experience where the brand anticipates needs rather than just reacting to clicks.
Consider the role of AI in this transformation. While much of the hype around agentic AI focuses on efficiency and "producing more," the real value comes from reimagining workflows to be more responsive to customer context. McKinsey estimates that agentic AI may power as much as two-thirds of current marketing activities, but they argue that the real value comes from reimagined workflows, not disconnected pilots [2]. A workflow reimagined around customer context is one where the AI doesn't just generate more emails; it analyzes the customer's current state and helps the human expert decide if an email is even the right move.
In this hybrid human-agentic workforce, the role of the marketer shifts. We are no longer just "targeting" specialists; we are the overseers of a network of agents, responsible for the taste, strategy, and most importantly, the context of the relationship [2]. We use AI to process the vast amounts of first-party data we have, but we retain responsibility for the judgment of how to use that data to make the customer's life better.
| Traditional Targeting Approach | Relevance-Led Context Approach |
|---|---|
| Focuses on "Finding the right person" | Focuses on "Being the right partner" |
| Uses data as a weapon to capture attention | Uses data as a tool to support decisions |
| Measures success by click-through rates | Measures success by customer outcomes and margin |
| Relies on third-party signals and cookies | Relies on first-party relationships and context |
| Results in high friction and "ad fatigue" | Results in low friction and increased trust |
The challenge many leaders face is that they don't have enough high-quality context to drive this level of relevance. This is often because they have treated data collection as a one-way street. They take data from the customer without giving anything back.
To solve this, we must reframe the relationship as a permissioned value exchange. This is particularly relevant when we look at the role of the customer portal. As we will explore in the next article, the customer portal is one of the richest places to exchange value for context. It shouldn't be just a place to download an invoice or open a support ticket. It should be a destination where the customer gains visibility, usefulness, and control, and in exchange, the brand earns the context needed to recommend the right next value.
This exchange is built on trust. When a customer sees that providing information leads to a measurably better experience—not just more ads—they are more likely to share the deep context that makes your marketing effective. This is the foundation of a learning system that actually works. It turns the data you collect into a strategic asset that competitors cannot easily replicate.
We are moving into an era where "more" is no longer the answer. More content, more ads, and more targeting will only lead to more noise. The next advantage in marketing will come from those who can take the raw material of customer data and refine it into strategic relevance.
It starts with a simple realization: your customers are not targets. They are people trying to make better decisions. Your job is to use the context they give you to help them do exactly that. When you stop trying to hit them and start trying to help them, you’ll find that the business outcomes you’ve been chasing—higher margins, better retention, and clearer positioning—start to follow naturally.
If you are ready to move beyond targeting and start building a marketing system grounded in relevance, I invite you to explore how we can help at mikebloomstine.com.
I help B2B and B2C teams build marketing engines that compound — strategy first, systems underneath.
