Attribution vs. Signals: How to Use Both for Better Marketing and Sales Decisions

Attribution and signals are two powerful tools in the marketer’s and sales strategist’s toolkit, yet their roles and benefits are often misunderstood. Over the years, attribution has been heralded as the gold standard for understanding customer journeys, but its shine has dimmed in the past few years. Meanwhile, signals—a dynamic, data-driven approach—have gained momentum, offering a fresh perspective on how we interpret and act on customer behavior.

In this blog, we’ll explore the differences between attribution and signals, their respective strengths and weaknesses, and how to use each effectively to drive better decisions. Whether you’re in marketing, sales, or rev ops, this discussion will give you clarity on when and how to deploy these tools to improve performance and outcomes.

What Is Attribution?

Attribution is a structured approach to identifying and crediting touchpoints in the customer journey. It answers questions like:

• Which ad or email led to a lead?
• What campaign generated a sale?

Attribution relies on models—such as first-touch, last-touch, or multi-touch attribution—to assign value to specific actions or channels. The goal is to provide insights into the effectiveness of marketing and sales efforts.

Strengths of Attribution:

• Clear metrics: Helps determine which channels or campaigns contribute to success.
• Historical analysis: Provides a retrospective view of customer behavior.
• Strategic insights: Supports broad strategy decisions, such as which campaigns to scale.

Limitations of Attribution:

• Incomplete data: Many touchpoints, especially those in "dark social" or offline interactions, go untracked.
• Linear bias: Attribution often oversimplifies the customer journey by treating it as a straight line.
• Delayed actionability: Attribution is primarily backward-looking, making it less useful for real-time decisions.

What Are Signals?

Signals represent dynamic data points that indicate a prospect’s or customer’s intent, interest, or engagement at a given moment. Think of signals as spikes (positive interest) or dips (negative disinterest) in the behavior of your target audience.

Examples of signals include:

• A prospect downloading a whitepaper.
• A customer visiting pricing pages multiple times.
• A lead opening an email but not clicking through.

Signals blend behavior, intent, and performance data to predict future outcomes. They provide actionable insights that help teams respond quickly and appropriately.

Strengths of Signals:

• Proactive guidance: Enables teams to act in real-time based on current behavior.
• Comprehensive view: Combines behavioral, intent, and demographic data for a fuller picture.
• Scalable decision-making: Supports granular and aggregate analysis for both individual leads and broader trends.

Limitations of Signals:

• Complexity: Requires integration of multiple data sources, which can be resource-intensive.
• Blind spots: Similar to attribution, signals may miss certain data points, especially in unmeasured areas.
• Data quality variability: Not all signals carry the same weight or accuracy, so prioritization is critical.ç

How to Use Attribution and Signals Together

While attribution and signals are distinct, they complement each other beautifully. Attribution excels at answering “what worked,” while signals focus on “what’s next.” Together, they provide a full-spectrum view of the customer journey, enabling data-driven decisions at both strategic and tactical levels.

1. Recognize Their Roles

• Use attribution for long-term strategy and campaign optimization.
• Use signals for real-time decision-making and individual engagement.

2. Address Data Gaps

Both approaches have blind spots. Be mindful of:

• Dark social: Conversations and touchpoints that aren’t easily tracked.
• Data accuracy: Validate the quality of your signals and attribution data.

3. Don’t Expect Perfection

Neither attribution nor signals will give you a 100% accurate view of your customer journey. Accept this limitation and focus on making decisions with the best available data.

4. Prioritize High-Quality Data

Not all signals or attribution models are created equal. Invest in reliable sources and ensure your data is clean, accurate, and actionable.

Conclusion

Attribution and signals aren’t competitors—they’re allies. Attribution provides a map of the past, while signals act as a compass for the future. By leveraging both, you can develop a comprehensive strategy that drives growth, optimizes performance, and empowers your team to make smarter decisions at every stage of the customer journey.

If you’re struggling with messy data or unclear customer insights, start by integrating these tools into your workflows. By focusing on both the big picture and actionable details, you’ll position your team for success in a data-driven world.

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