MOPs and Full Funnel Diagnostics in the Era of LLM Optimization

In the rapidly evolving landscape of large language models (LLMs), marketing operations (MOPs) professionals face new challenges in tracking and optimizing marketing funnels.

Industry experts Doug Bell (Fractional CMO), Nadia Davis (Sr. Director MOPs at PayIt), and Lance Thompson (Technical Sales Operations Manager at SeekOut) recently discussed strategies to adapt to these changes, emphasizing the importance of engagement-based models and refined diagnostic approaches.

Understanding the New Funnel Disruption

Traditional funnel diagnostics, which relied heavily on measurable touchpoints like direct and organic traffic, are becoming less effective as buyers increasingly utilize AI-powered research tools such as ChatGPT and Perplexity. This shift reduces visibility into the buyer's journey.

“A lot of that organic and direct traffic you counted on is going to start migrating over to LLMs. That means your ability to track and attribute will shrink.” — Doug Bell

To navigate this disruption, MOPs professionals must focus on engagement intensity rather than traditional funnel stages, assessing how entire accounts interact across both known and unknown channels.

For more on the topic of buyer journey mapping, check out our blog post Why & How to Map the Buyer Journey.

Rethinking Funnel Measurement: From Linear to Engagement-Based Models

The emergence of LLMs necessitates a shift from linear funnel models to engagement-based approaches.

“If we still treat the website as the primary place for research, we’re missing 80% of the buyer journey. We need a new model focused on engagement intensity.” — Nadia Davis

An engagement-based funnel model evaluates:

• The volume and depth of interactions from an account
• Unstructured engagement signals (e.g., community mentions, AI-generated recommendations)
• Correlations between engagement surges and pipeline acceleration

This approach enables marketing operations teams to identify active accounts in the market, even when traditional analytics fall short.

For more on marketing analytics and metrics, check out our blog post Do Demand Generation Metrics Make Sense for B2B?

Adapting to Market and Economic Factors

External economic factors also influence funnel performance. Doug Bell points out two significant macro factors:

1. Industry-Specific Pressures: Sectors like SaaS and biotech face economic challenges affecting deal velocity.
2. Market Maturity: Emerging categories may experience inconsistent inbound funnels until broader adoption occurs.

To address these challenges, MOPs teams should leverage AI tools to analyze market maturity and determine the viability of inbound versus outbound strategies.

For more on the topic of customer acquisition cost (CAC), check out our blog post Questions to Ask When CAC is High.

Practical Steps for Optimizing Funnel Diagnostics

To remain effective in the LLM era, marketing and revenue operations teams should:

1. Consolidate Data Across Systems: Unify engagement data from various platforms, including ABM tools, CRM systems, and external sources like communities and AI-generated referrals.
2. Revamp Channel Mix Analysis: Assess how changes in major platforms' algorithms impact inbound lead quality.
3. Build an Engagement Intensity Model: Score accounts based on cumulative interactions, regardless of their presence in the CRM as known leads.
4. Leverage Reverse Funnel Forecasting: Use short-term engagement trends to project long-term pipeline health.

Final Thoughts: Embracing the Future of Funnel Measurement

Marketing operations professionals must adopt a non-linear, engagement-first approach to measuring funnel success. Traditional metrics like website traffic and CRM lead stages are becoming less reliable. Success now depends on aggregating fragmented buyer signals, analyzing engagement patterns, and utilizing AI-driven insights to refine strategies.

“We have to live in two worlds—reporting in traditional funnel terms for investors while simultaneously shifting toward engagement-based modeling for real insight.” – Doug Bell

For more insights on adapting to AI-driven marketing changes, consider joining the RevOps Co-op community for exclusive discussions and best practices and check out our blog.