Avinash Tripathi is a VP Analytics at University of Phoenix, thought leader and keynote speaker with over 20 yrs of experience in the field.
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Many executive dashboards today can still look fine when it comes to measuring advertising ROI: Traffic is up, conversion rates are stable and other leading indicators are well above benchmark.
Yet, many digital and analytics leaders are noticing a disconnect between the growth they expect and what they’re seeing. The days of easy growth are behind us, and incremental efficiency gains are hitting their limits. As AI is going mainstream, a significant portion of the customer journey takes place outside of your traditional funnel. By the time your analytics tools have registered a user, your business may have already missed the boat on the most critical parts of the decision-making process.
When we measure digital experiences, most of us start with a straightforward model. First, a user discovers our brand, then they evaluate it and finally, they convert. Instead, their journey could be from prompt to AI synthesis, to shortlist, to validation and then to conversion.
In the current model, AI systems are no longer just intermediaries directing traffic. They are interpreters and curators, compressing information, comparing options and shaping initial preferences.
By the time some users arrive at a company’s website, they may already have formed a strong initial preference and are simply confirming information they already know. As a result, the key metrics that a company should measure have evolved beyond the typical funnel-based metrics and now include pre-funnel decisioning factors such as inclusion, positioning and perception, starting before the first click.
This latent demand remains invisible to traditional site analytics, as it does not surface in reports such as impressions, sessions or overall attribution.
If AI has effectively pushed the decision point of a process upstream, then we need new methods for measurement at that point. To better identify the influences within a particular industry, a company needs to understand a metric that falls between how search engines assign weight to a webpage and the number of unpaid links a site receives.
As AI increasingly influences consumer decisions, I've been observing a measurement gap and developing a conceptual model to illustrate my observations. The Pre-Funnel Influence Index (PFI) provides a framework for businesses to develop this new metric and can be expressed as a weighted composite index:
PFI can serve as a conceptual key performance indicator (KPI) framework for evaluating how often and how favorably a brand appears in AI-mediated discovery before traditional engagement begins. It can track your brand’s identification, understanding and recommendation by artificial intelligence and machine learning at all touchpoints that occur before a customer is even in your funnel, and provides directional visibility into influence before engagement.
Your AI Positioning Score (AIR) measures your inclusion, the percentage of relevant conversations you’re mentioned in, the percentage of high-intent queries you’re visible for and the consistency of your inclusions across queries and platforms.
Your AI Positioning Score (APS) rates your brand as primarily premium, budget or generic, and assesses how you differ from your competition and whether it meets the brand message you intend to communicate.
Your Competitive Ranking Index (CRI) shows where you rank in search and in AI-generated recommendations, how often you appear in the top positions versus competitors and how visible you are overall.
And finally, your Zero-Click Impact Estimate (ZCIE) estimates potential pre-funnel demand shifts using proxy signals including a decline of non-branded organic discovery relative to branded traffic, a decline in impressions without a corresponding decline in traffic and shifts in awareness-oriented signals versus actual visits.
As AI-driven recommendations are entering the scene, organizations are moving from transactional web analytics to decision analytics. This means organizations need to measure the share of voice of their brand in AI-driven recommendations for relevant topics, as well as their positioning and sentiment, to understand how they are perceived compared to competitors.
Instead of focusing solely on traffic, clicks and conversions, organizations should evaluate whether they are being included in AI-generated recommendations, how they are being positioned relative to competitors and whether shifts in branded search, direct traffic or assisted conversions may signal changes occurring before customers ever reach their websites. The first step is not necessarily building a new metric, but recognizing that a growing portion of discovery and consideration is happening outside traditional analytics platforms. Organizations that begin monitoring these signals today will be better positioned to adapt as AI-mediated discovery continues to evolve.
Organizations also need to understand their competitive presence at a prompt level and whether they are consistently featured or occasionally excluded, and be aware of proxy indicators for pre-funnel demand, such as changes in branded search, direct traffic or assisted conversions. Any such analysis should rely on aggregated, privacy-compliant signals rather than attempts to identify individual users, reconstruct private prompts or infer sensitive attributes.
AI is making your funnel more efficient, but it is subtly shrinking your addressable market before users ever reach your site. By pre-filtering leads, comparing options and shortlisting only the most relevant choices, AI delivers highly qualified, high-intent users who are often already predisposed toward a specific brand.
However, as this filtering intensifies, the total pool of potential customers begins to shrink. Fewer users are entering consideration, and fewer brands are making it into AI-driven shortlists. While traffic and efficiency metrics may appear healthy or even improving, conversion dynamics and overall growth potential are being constrained upstream.
Traditional dashboards fail to capture this decline, leading organizations to optimize for what they can see, rather than what truly matters: being included, prioritized and preferred in the moment before the first click is even made.
Organic no longer simply means unpaid search traffic; people and algorithms are now exposing content through multiple layers of discovery. Paid, unpaid, shared and earned, every form of digital exposure has the potential to drive intent.
Influencing the buying decision is no longer about driving clicks to your website. Your brand must have an optimized surface area to be seen, mentioned or included in the consideration set of potential buyers.
Analysts also need to look at first touch, assisted conversion and direct traffic models that signal the influence of AI in online activity that is often untraceable. The end goal is to measure if your brand is even in the conversation that matters, driving the intent that leads to a purchase.
The real risk isn’t that dashboards are understated; it’s that they are right about the wrong part of the journey. Organizations may be over-investing in the bottom of the funnel because metrics show “everything is fine” while actual demand may be shifting or narrowing upstream.
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