These insights represent early patterns from 42 responses in our ongoing 2025 GTM Data Benchmark study. While compelling, these findings haven't yet reached statistical significance and should be interpreted as directional indicators rather than definitive benchmarks.

We're continuing to collect responses through November, with the complete analysis and report scheduled for Q4 2025. As our sample grows toward 100+ responses, percentages and patterns may shift.

Think of this as an early preview from your peers – real insights, but not yet the full picture.

Participants: 42 GTM leaders representing diverse B2B organizations

Leadership Representation:

• Vice Presidents of Sales (29%)
• Sales Operations (21%)
• Chief Revenue Officers (7%)
• Revenue Operations (5%)
• Marketing Leadership (7%)
• Additional roles including SDR leaders, enablement, and data analysts (31%)

Company Characteristics:

• Primary Industries: Software/SaaS (68%), Financial Services, Healthcare Tech, Manufacturing
• GTM Motions: Outbound-dominant (45%), Inbound-led (31%), Partner/Channel (24%)
• Team Sizes: Ranging from 5-200+ sales reps (median: 25-35)
• Data Ownership: RevOps (43%), Sales Ops (31%), Distributed/Other (26%)

The median self-reported data quality score was 6.3/10, with notable distribution:

• High performers (8-10): 19%
• Moderate quality (5-7): 71%
• Low quality (1-4): 10%

Interestingly, companies reporting higher data quality scores showed no correlation with reduced time spent on manual data tasks, suggesting quality perception may not reflect operational efficiency.

While 88.1% of respondents report strong Sales and Marketing alignment on ICP definition, only 35% indicate accurate ICP tagging in their CRM systems. This 53-point implementation gap represents one of the largest disconnects in the study.

Further analysis shows:

• 59.5% report 75-100% ICP match in closed-won deals
• 23.8% report 50-75% match rate
• 9.5% cannot determine ICP match rates
• Companies with accurate ICP tagging report higher win rates

Intent data usage has reached 64.3% adoption, distributed across:

• Website visitor tracking: 43% (first-party data)
• Job change alerts: 31%
• Funding signals: 29%
• Technology monitoring: 26%

ROI perception remains mixed:

• Positive ROI reported: 59%
• Strong positive ROI: 4%
• Uncertain ROI: 37%
• No positive ROI: 4%

Sales teams dedicate significant time to data-related activities:

• <1 hour/week: 35.7%
• 1-3 hours/week: 33.3%
• 3-6 hours/week: 21.4%
• 6 hours/week: 4.8%
• Unknown: 4.8%

Extrapolated across average team sizes, this represents 15-20% of available selling capacity.

Primary obstacles to data quality improvement:

1. Process gaps (40.5%) – Lack of standardized workflows
2. Integration challenges (23.8%) – System incompatibility
3. Tool limitations (16.7%) – Current stack constraints
4. Resource constraints (9.5%) – Staffing/skill gaps
5. Leadership alignment (4.8%) – Executive sponsorship

The research reveals a market in transition. Organizations have largely solved strategic alignment challenges (ICP definition, sales/marketing coordination) but struggle with operational execution. The 53-point gap between ICP strategy and CRM implementation suggests the limiting factor is not technology or budget, but rather the absence of systematic data orchestration processes.

The mixed ROI on intent data investments indicates a maturity curve issue rather than technology effectiveness. With only 4% reporting "strong" ROI despite 64% adoption, organizations are struggling to operationalize the signals they're collecting.

AI adoption patterns (54% actively piloting or operational) combined with poor data quality foundations (71% at moderate or low quality) suggest many AI initiatives may be built on unstable foundations.

Based on participant priorities, we anticipate focus areas including:

• Lead scoring accuracy improvements (31% priority)
• AI initiative support (26%)
• Personalization at scale (21%)
• Enhanced segmentation capabilities (19%)

These priorities suggest a shift from data collection to data activation, with emphasis on intelligent orchestration rather than additional point solutions.

This analysis represents initial findings from an ongoing research initiative. Full benchmark report including industry segmentation and company size analysis will be published in early Q1 2026.

Thank you for your participation in advancing the understanding of GTM data operations. Your contribution helps establish critical benchmarks for the industry.

This research was conducted by No Fluff Selling's GTM Pipes research division. No individual responses are identified or shared. All data is presented in aggregate.