influence maps

For decades, marketers have relied on the traditional funnel, a neat, linear model of awareness, consideration, and purchase to guide strategy and measure success. But today’s consumers don’t move in straight lines; they stream, scroll, search, and shop in seemingly unpredictable ways, jumping between platforms and touchpoints as their interests, attention, and needs evolve.

This shift in behavior demands a new approach: influence maps. Unlike rigid funnels, influence maps reflect the true complexity of modern decision-making, mapping the web of interactions, triggers, and trust points that shape each unique customer journey. Embracing this model means marketers can finally align their strategies with how people actually behave- adaptive, nonlinear, and driven by real patterns of attention and influence.

Real-World Behaviors Define Influence Maps

BCG recently proposed a shift from the linear marketing funnel to influence maps, which aligns closely with the principles of scientific observation and includes critical updates for the AI era. Here’s how observational research integrates with this new framework to create more adaptive, impactful marketing strategies:

1. Observing Real-World Behavior to Define Influence Maps
Influence maps emphasize that modern consumer journeys are nonlinear and fragmented across four key behaviors: streaming, scrolling, searching, and shopping. Scientific observation directly supports this model by:

  • Capturing authentic interactions with these touchpoints (e.g., tracking how users scroll past ads, pause videos, or linger on product pages).

  • Identifying patterns in how consumers switch between behaviors (e.g., moving from streaming an ad to searching for reviews).

  • Mapping influence pathways by observing which touchpoints actually drive decisions, not just which ones marketers assume are influential.

For example, BCG’s “impulse strategist” journey (streaming → in-store display → search → purchase) could be refined through observational data showing how long users engage with each touchpoint or what triggers transitions between them.

2. Enhancing AI with Observational Insights
AI is essential for executing bespoke strategies across complex journeys. Scientific observation feeds AI models with ground-truth behavioral data, enabling:

  • Better prediction of influence: Observational data (e.g., eye-tracking, heatmaps) helps AI prioritize touchpoints that command attention and trust.

  • Dynamic content optimization: By observing real-time reactions to ads or product pages, AI can adjust messaging, imagery, or offers to match observed preferences.

  • Validation of AI outputs: Observational research acts as a check against AI’s potential biases, ensuring recommendations align with real human behavior.

GenAI accelerates content production, but observational data ensures this content resonates by reflecting how consumers engage (e.g., shorter videos for scrollers, detailed guides for searchers).

3. Moving Beyond Surface Metrics to Measure True Influence
Traditional reach metrics, like impressions, reach, and clicks are only part of the story. Attention, relevance, and trust define influence and scientific observation directly measures these factors:

  • Attention: Tools like eye-tracking or dwell-time analysis quantify whether users actually engage with content (vs. passively scrolling past).

  • Relevance: Observing which touchpoints prompt actions (e.g., saving a product, sharing a post) reveals what resonates contextually.

  • Trust: Ethnographic studies or social listening can uncover trusted platforms or influencers that surveys might overlook.

For instance, BCG’s “smart saver” journey (search → social media → purchase) gains depth if observational research shows users spend more time on influencer posts with verified reviews versus generic ads.

4. Enabling Bespoke Journeys Through Continuous Learning

Influence maps require constant adaptation to shifting behaviors. Scientific observation supports this by:

  • Providing real-time feedback loops: Monitoring behavioral changes (e.g., new scrolling habits on emerging platforms) allows marketers to update influence maps dynamically.

  • Uncovering micro-segments: Observational data can reveal niche audience behaviors (e.g., late-night streamers who shop impulsively), enabling hyper-personalized strategies.

  • Testing hypotheses: A/B testing combined with observational metrics (e.g., click paths, hesitation points) validates which touchpoints drive outcomes.