Quigley-Simpson CEO Carl Fremont addresses the marketing data insights gap

The inability to convert high-volume data into actionable strategy suggests that the streaming and digital advertising sectors are over-invested in collection but under-invested in interpretation. As digital media now accounts for over two-thirds of total spend, the lack of process maturity cited by Gartner creates a bottleneck for AI-driven optimization. For the streaming ecosystem, this disconnect threatens the ROI of sophisticated ad-tech stacks if outcomes are not defined before measurement begins. Watch for a shift in agency remits toward 'impact-obsessed' models that prioritize enterprise-wide outcomes over siloed media metrics.

Quigley-Simpson operates in a market where the disconnect between data volume and actionable intelligence has become a quantifiable problem. Gartner's 2025 CMO survey found that only 52% of marketing leaders believe their organizations have the data infrastructure needed to support AI-driven decision-making, underscoring the structural gap Fremont identifies. The firm's positioning as an "impact-obsessed" agency reflects a broader industry shift toward outcome-based measurement models that tie media spend directly to revenue rather than intermediate metrics like impressions or viewability. This matters for streaming advertisers specifically because addressable TV and CTV campaigns generate granular exposure data that often lacks the attribution frameworks needed to prove incremental sales lift.

The competitive landscape around marketing data intelligence is consolidating rapidly. Emarketer projects that US digital ad spend will reach $306 billion in 2026, with measurement and attribution tools capturing an expanding share of that budget, creating pressure on agencies like Quigley-Simpson to demonstrate proprietary analytical capabilities rather than relying on platform-reported metrics. Forrester's Wave report on marketing measurement and optimization platforms, published in early 2026, identified data interoperability and cross-channel attribution as the two most requested capabilities among enterprise buyers, a finding that aligns with Fremont's argument that the bottleneck is interpretive rather than technical. The streaming sector faces a specific version of this challenge: CTV measurement remains fragmented across device-level IDs, household graphs, and panel-based estimates, making unified attribution across linear and digital inventory a persistent pain point for brands allocating budgets.

On the technology side, AI-driven marketing intelligence platforms are beginning to close parts of the gap, though adoption remains uneven. Generac Holdings, a Quigley-Simpson client, reported a 23% improvement in media efficiency after implementing a unified measurement framework that connected CRM data to digital ad exposure across CTV and social channels, according to a case study presented at the 2026 Advertising Week New York conference. The result illustrates Fremont's thesis that the data exists but the connective tissue between systems does not. For streaming platforms selling ad-supported tiers, this dynamic means that advertisers increasingly demand closed-loop attribution before committing incremental budgets, pushing platforms to invest in first-party measurement infrastructure or partner with third-party verification providers that can bridge the gap between exposure data and purchase behavior.