AI in GTM: Solving the Attribution Gap
This article explores how AI is transforming attribution for B2B SaaS GTM teams. Learn why traditional models fall short, how AI-powered solutions like Proshort provide holistic insight, and what steps are required to close the attribution gap. Discover best practices and future trends in AI-driven GTM attribution.
Introduction: The Attribution Challenge in Modern GTM
In the ever-evolving landscape of B2B SaaS, the go-to-market (GTM) strategy is at the heart of driving predictable revenue growth. Yet, even with sophisticated martech and CRM stacks, one challenge remains persistent: the attribution gap. Accurately attributing revenue and pipeline influence across a complex, multi-touch buyer journey is difficult, leading to blind spots in decision-making and resource allocation. As AI transforms every facet of business, its role in closing these attribution gaps is becoming pivotal for GTM leaders.
The Attribution Gap: Where Traditional Approaches Fall Short
Attribution is the process of assigning credit to different marketing and sales activities that contribute to a deal’s progression and closure. Traditional attribution models—first-touch, last-touch, multi-touch—attempt to map influence, but often fall short for several reasons:
Fragmented Data: Buyer journeys span dozens of channels, from social media and webinars to outbound sales and product usage. Data silos make it nearly impossible to see a complete picture.
Non-linear Journeys: Modern B2B buying is not linear. Stakeholders enter and exit at different stages. Decision cycles loop and branch unpredictably, defying simplistic models.
Manual Attribution: Relying on self-reported data or manual tagging leads to inaccuracies, bias, and missed touchpoints.
Lagging Indicators: Many attribution systems only report after the fact, making them unhelpful for course-correction in real time.
These gaps have significant consequences. GTM teams invest in channels and tactics without clarity on true ROI. Sales and marketing misalign on what’s working. Budget is wasted, and growth stalls.
AI’s Transformative Potential in GTM Attribution
Artificial intelligence introduces a paradigm shift in GTM attribution. Rather than relying on static rules or limited data, AI-powered systems can:
Aggregate data across platforms and touchpoints automatically, eliminating silos.
Analyze complex, non-linear journeys using machine learning to identify hidden patterns of influence.
Attribute influence at a granular level—by persona, account, or even intent signal—rather than just channel or campaign.
Deliver real-time insights and recommendations, enabling dynamic GTM adjustments.
By leveraging AI, organizations can move from guesswork to precision in attributing revenue, understanding which interactions truly drive pipeline, and optimizing spend accordingly.
AI Techniques Powering Next-Gen Attribution
Natural Language Processing (NLP): Uncovers intent and influence signals in unstructured data—emails, call transcripts, meeting notes, and social conversations.
Graph Analytics: Maps relationships between buyers, influencers, and touchpoints, identifying key nodes and connectors in the decision process.
Predictive Modeling: Scores the likelihood of conversion based on historical data and ongoing engagement, prioritizing the most impactful activities.
Automated Data Integration: Ingests and normalizes data from CRM, MAP, webinars, product analytics, and more, creating a unified attribution dataset.
The Evolving Buyer Journey: Why Attribution Matters More Than Ever
B2B buyers now operate in teams, conducting independent research and interacting with vendors across digital and human channels. According to Gartner, the average buying committee consists of 6–10 stakeholders, each with unique touchpoints and priorities. The buying process is:
Self-directed: Buyers spend just 17% of their time meeting with potential suppliers; the rest is independent research.
Digital-first: 80% of B2B interactions are now digital, complicating attribution further.
Consensus-driven: Multiple stakeholders must align, making it hard to track true influence.
For GTM teams, this means attribution models must account for group behavior, digital exhaust, and both direct and indirect influence. AI can process these variables at scale, revealing previously invisible drivers of opportunity progression.
Common Attribution Pitfalls (and How AI Solves Them)
Channel Bias: Traditional models often overvalue the first or last touch, underestimating the impact of nurture activities. AI evaluates all touchpoints holistically.
Data Decay: Human-entered fields and self-reported forms are incomplete or inaccurate. AI continually refreshes and validates data using external signals.
Under-Reporting Dark Funnel Touches: Anonymous website visits, social shares, and dark social are missed. AI connects these signals using advanced tracking and intent models.
Delayed Feedback Loops: By surfacing real-time impact, AI enables immediate GTM pivots, preventing wasted spend.
Building AI-Driven Attribution: Key Components
To implement AI-powered attribution, organizations must assemble several foundational components:
Unified Data Infrastructure: Centralize data from CRM, marketing automation, sales engagement, and product analytics. Data normalization and de-duplication are critical.
Identity Resolution: Accurately match buyer identities across devices, channels, and platforms using probabilistic and deterministic models.
AI Attribution Engine: Deploy machine learning models that analyze historical and real-time touchpoints to assign influence scores.
Customizable Attribution Models: Allow for flexible weighting and logic, adapting to unique GTM motions (e.g., ABM, PLG, high-velocity sales).
Actionable Dashboards: Present insights in a consumable format for sales, marketing, and RevOps teams to inform strategy.
Case Study: AI Attribution in Enterprise SaaS
Consider a global SaaS provider with a complex GTM motion involving inbound marketing, outbound SDRs, channel partners, and product-led growth. Before adopting AI attribution, the team struggled to:
Identify which campaigns drove late-stage conversions.
Align sales and marketing on pipeline influence.
Optimize budget allocation across paid, organic, and partner channels.
By adopting an AI-driven attribution platform, the provider:
Unified all touchpoint data, resolving duplicate and anonymous contacts.
Used machine learning to reveal unexpected influence from webinars and product usage signals.
Reallocated spend to the highest-impact channels, improving ROI by 22% within six months.
This transformation unlocked a new level of collaboration between GTM teams and delivered measurable revenue gains.
Key Metrics for AI Attribution Success
Attribution Accuracy: Reduction in unattributed or misattributed deals.
Influence Reporting: Ability to quantify contribution by channel, campaign, and touchpoint.
Pipeline Velocity: Improvements in deal progression speed due to better GTM alignment.
ROI Optimization: Increased returns on marketing and sales investments.
Choosing the Right AI Attribution Solution
The market for AI-powered attribution tools is rapidly maturing. When evaluating solutions, GTM leaders should consider:
Depth of data integration—can it ingest all relevant touchpoints?
Flexibility of attribution models—does it support account-based and PLG motions?
Transparency of AI logic—are influence scores explainable and auditable?
Real-time reporting—are insights delivered fast enough to drive action?
Security and compliance—are data governance standards met for enterprise scale?
One example is Proshort, which unifies GTM data sources and applies advanced AI to surface actionable attribution insights for enterprise sales teams. By providing transparent and customizable attribution models, Proshort empowers organizations to close the gap between activity and revenue impact.
Integrating AI Attribution into Your GTM Operating Rhythm
Adopting AI attribution isn’t just a technology upgrade—it requires process and culture shifts:
Cross-Functional Alignment: Involve marketing, sales, RevOps, and IT in attribution model design and validation.
Continuous Model Training: Regularly refresh models as buyer behavior and GTM tactics evolve.
Closed-Loop Feedback: Use attribution insights to inform campaign design, sales plays, and budget reallocations in real time.
Change Management: Educate teams on the value and limitations of AI-driven attribution to drive adoption and trust.
Leaders must champion a test-and-learn mindset, using AI insights to iterate and improve GTM outcomes continually.
Future Trends: The Next Era of AI in GTM Attribution
AI-driven attribution is just the beginning. The future holds even greater promise for GTM leaders:
Predictive Attribution: AI will forecast which combinations of touchpoints are likely to drive future revenue, not just report on the past.
Dynamic Personalization: Attribution insights will fuel real-time personalization of content, offers, and sales engagement by persona and deal stage.
Integration with Generative AI: Generative models will synthesize recommendations and automate playbook adjustments based on attribution data.
Deeper Dark Funnel Visibility: AI will connect the dots on untracked buyer research and intent, further closing the attribution gap.
Organizations that invest early in AI attribution will be positioned to move faster, spend smarter, and win more deals in an increasingly competitive landscape.
Conclusion: Closing the Attribution Gap with AI
The attribution gap has long hindered GTM leaders’ ability to optimize spend and align teams. AI now offers a transformative solution, enabling multi-dimensional analysis of complex buyer journeys and surfacing the true drivers of revenue. By embracing unified data, advanced analytics, and solutions like Proshort, organizations can finally close the loop between activity and impact. The result: smarter decisions, stronger alignment, and accelerated growth in the enterprise SaaS space.
Key Takeaways
Traditional attribution models are insufficient for today’s complex GTM motions.
AI enables holistic, real-time, and explainable attribution across all buyer touchpoints.
Adopting AI attribution requires unified data, the right tools, and organizational buy-in.
Solutions like Proshort empower teams to optimize spend and accelerate revenue.
Introduction: The Attribution Challenge in Modern GTM
In the ever-evolving landscape of B2B SaaS, the go-to-market (GTM) strategy is at the heart of driving predictable revenue growth. Yet, even with sophisticated martech and CRM stacks, one challenge remains persistent: the attribution gap. Accurately attributing revenue and pipeline influence across a complex, multi-touch buyer journey is difficult, leading to blind spots in decision-making and resource allocation. As AI transforms every facet of business, its role in closing these attribution gaps is becoming pivotal for GTM leaders.
The Attribution Gap: Where Traditional Approaches Fall Short
Attribution is the process of assigning credit to different marketing and sales activities that contribute to a deal’s progression and closure. Traditional attribution models—first-touch, last-touch, multi-touch—attempt to map influence, but often fall short for several reasons:
Fragmented Data: Buyer journeys span dozens of channels, from social media and webinars to outbound sales and product usage. Data silos make it nearly impossible to see a complete picture.
Non-linear Journeys: Modern B2B buying is not linear. Stakeholders enter and exit at different stages. Decision cycles loop and branch unpredictably, defying simplistic models.
Manual Attribution: Relying on self-reported data or manual tagging leads to inaccuracies, bias, and missed touchpoints.
Lagging Indicators: Many attribution systems only report after the fact, making them unhelpful for course-correction in real time.
These gaps have significant consequences. GTM teams invest in channels and tactics without clarity on true ROI. Sales and marketing misalign on what’s working. Budget is wasted, and growth stalls.
AI’s Transformative Potential in GTM Attribution
Artificial intelligence introduces a paradigm shift in GTM attribution. Rather than relying on static rules or limited data, AI-powered systems can:
Aggregate data across platforms and touchpoints automatically, eliminating silos.
Analyze complex, non-linear journeys using machine learning to identify hidden patterns of influence.
Attribute influence at a granular level—by persona, account, or even intent signal—rather than just channel or campaign.
Deliver real-time insights and recommendations, enabling dynamic GTM adjustments.
By leveraging AI, organizations can move from guesswork to precision in attributing revenue, understanding which interactions truly drive pipeline, and optimizing spend accordingly.
AI Techniques Powering Next-Gen Attribution
Natural Language Processing (NLP): Uncovers intent and influence signals in unstructured data—emails, call transcripts, meeting notes, and social conversations.
Graph Analytics: Maps relationships between buyers, influencers, and touchpoints, identifying key nodes and connectors in the decision process.
Predictive Modeling: Scores the likelihood of conversion based on historical data and ongoing engagement, prioritizing the most impactful activities.
Automated Data Integration: Ingests and normalizes data from CRM, MAP, webinars, product analytics, and more, creating a unified attribution dataset.
The Evolving Buyer Journey: Why Attribution Matters More Than Ever
B2B buyers now operate in teams, conducting independent research and interacting with vendors across digital and human channels. According to Gartner, the average buying committee consists of 6–10 stakeholders, each with unique touchpoints and priorities. The buying process is:
Self-directed: Buyers spend just 17% of their time meeting with potential suppliers; the rest is independent research.
Digital-first: 80% of B2B interactions are now digital, complicating attribution further.
Consensus-driven: Multiple stakeholders must align, making it hard to track true influence.
For GTM teams, this means attribution models must account for group behavior, digital exhaust, and both direct and indirect influence. AI can process these variables at scale, revealing previously invisible drivers of opportunity progression.
Common Attribution Pitfalls (and How AI Solves Them)
Channel Bias: Traditional models often overvalue the first or last touch, underestimating the impact of nurture activities. AI evaluates all touchpoints holistically.
Data Decay: Human-entered fields and self-reported forms are incomplete or inaccurate. AI continually refreshes and validates data using external signals.
Under-Reporting Dark Funnel Touches: Anonymous website visits, social shares, and dark social are missed. AI connects these signals using advanced tracking and intent models.
Delayed Feedback Loops: By surfacing real-time impact, AI enables immediate GTM pivots, preventing wasted spend.
Building AI-Driven Attribution: Key Components
To implement AI-powered attribution, organizations must assemble several foundational components:
Unified Data Infrastructure: Centralize data from CRM, marketing automation, sales engagement, and product analytics. Data normalization and de-duplication are critical.
Identity Resolution: Accurately match buyer identities across devices, channels, and platforms using probabilistic and deterministic models.
AI Attribution Engine: Deploy machine learning models that analyze historical and real-time touchpoints to assign influence scores.
Customizable Attribution Models: Allow for flexible weighting and logic, adapting to unique GTM motions (e.g., ABM, PLG, high-velocity sales).
Actionable Dashboards: Present insights in a consumable format for sales, marketing, and RevOps teams to inform strategy.
Case Study: AI Attribution in Enterprise SaaS
Consider a global SaaS provider with a complex GTM motion involving inbound marketing, outbound SDRs, channel partners, and product-led growth. Before adopting AI attribution, the team struggled to:
Identify which campaigns drove late-stage conversions.
Align sales and marketing on pipeline influence.
Optimize budget allocation across paid, organic, and partner channels.
By adopting an AI-driven attribution platform, the provider:
Unified all touchpoint data, resolving duplicate and anonymous contacts.
Used machine learning to reveal unexpected influence from webinars and product usage signals.
Reallocated spend to the highest-impact channels, improving ROI by 22% within six months.
This transformation unlocked a new level of collaboration between GTM teams and delivered measurable revenue gains.
Key Metrics for AI Attribution Success
Attribution Accuracy: Reduction in unattributed or misattributed deals.
Influence Reporting: Ability to quantify contribution by channel, campaign, and touchpoint.
Pipeline Velocity: Improvements in deal progression speed due to better GTM alignment.
ROI Optimization: Increased returns on marketing and sales investments.
Choosing the Right AI Attribution Solution
The market for AI-powered attribution tools is rapidly maturing. When evaluating solutions, GTM leaders should consider:
Depth of data integration—can it ingest all relevant touchpoints?
Flexibility of attribution models—does it support account-based and PLG motions?
Transparency of AI logic—are influence scores explainable and auditable?
Real-time reporting—are insights delivered fast enough to drive action?
Security and compliance—are data governance standards met for enterprise scale?
One example is Proshort, which unifies GTM data sources and applies advanced AI to surface actionable attribution insights for enterprise sales teams. By providing transparent and customizable attribution models, Proshort empowers organizations to close the gap between activity and revenue impact.
Integrating AI Attribution into Your GTM Operating Rhythm
Adopting AI attribution isn’t just a technology upgrade—it requires process and culture shifts:
Cross-Functional Alignment: Involve marketing, sales, RevOps, and IT in attribution model design and validation.
Continuous Model Training: Regularly refresh models as buyer behavior and GTM tactics evolve.
Closed-Loop Feedback: Use attribution insights to inform campaign design, sales plays, and budget reallocations in real time.
Change Management: Educate teams on the value and limitations of AI-driven attribution to drive adoption and trust.
Leaders must champion a test-and-learn mindset, using AI insights to iterate and improve GTM outcomes continually.
Future Trends: The Next Era of AI in GTM Attribution
AI-driven attribution is just the beginning. The future holds even greater promise for GTM leaders:
Predictive Attribution: AI will forecast which combinations of touchpoints are likely to drive future revenue, not just report on the past.
Dynamic Personalization: Attribution insights will fuel real-time personalization of content, offers, and sales engagement by persona and deal stage.
Integration with Generative AI: Generative models will synthesize recommendations and automate playbook adjustments based on attribution data.
Deeper Dark Funnel Visibility: AI will connect the dots on untracked buyer research and intent, further closing the attribution gap.
Organizations that invest early in AI attribution will be positioned to move faster, spend smarter, and win more deals in an increasingly competitive landscape.
Conclusion: Closing the Attribution Gap with AI
The attribution gap has long hindered GTM leaders’ ability to optimize spend and align teams. AI now offers a transformative solution, enabling multi-dimensional analysis of complex buyer journeys and surfacing the true drivers of revenue. By embracing unified data, advanced analytics, and solutions like Proshort, organizations can finally close the loop between activity and impact. The result: smarter decisions, stronger alignment, and accelerated growth in the enterprise SaaS space.
Key Takeaways
Traditional attribution models are insufficient for today’s complex GTM motions.
AI enables holistic, real-time, and explainable attribution across all buyer touchpoints.
Adopting AI attribution requires unified data, the right tools, and organizational buy-in.
Solutions like Proshort empower teams to optimize spend and accelerate revenue.
Introduction: The Attribution Challenge in Modern GTM
In the ever-evolving landscape of B2B SaaS, the go-to-market (GTM) strategy is at the heart of driving predictable revenue growth. Yet, even with sophisticated martech and CRM stacks, one challenge remains persistent: the attribution gap. Accurately attributing revenue and pipeline influence across a complex, multi-touch buyer journey is difficult, leading to blind spots in decision-making and resource allocation. As AI transforms every facet of business, its role in closing these attribution gaps is becoming pivotal for GTM leaders.
The Attribution Gap: Where Traditional Approaches Fall Short
Attribution is the process of assigning credit to different marketing and sales activities that contribute to a deal’s progression and closure. Traditional attribution models—first-touch, last-touch, multi-touch—attempt to map influence, but often fall short for several reasons:
Fragmented Data: Buyer journeys span dozens of channels, from social media and webinars to outbound sales and product usage. Data silos make it nearly impossible to see a complete picture.
Non-linear Journeys: Modern B2B buying is not linear. Stakeholders enter and exit at different stages. Decision cycles loop and branch unpredictably, defying simplistic models.
Manual Attribution: Relying on self-reported data or manual tagging leads to inaccuracies, bias, and missed touchpoints.
Lagging Indicators: Many attribution systems only report after the fact, making them unhelpful for course-correction in real time.
These gaps have significant consequences. GTM teams invest in channels and tactics without clarity on true ROI. Sales and marketing misalign on what’s working. Budget is wasted, and growth stalls.
AI’s Transformative Potential in GTM Attribution
Artificial intelligence introduces a paradigm shift in GTM attribution. Rather than relying on static rules or limited data, AI-powered systems can:
Aggregate data across platforms and touchpoints automatically, eliminating silos.
Analyze complex, non-linear journeys using machine learning to identify hidden patterns of influence.
Attribute influence at a granular level—by persona, account, or even intent signal—rather than just channel or campaign.
Deliver real-time insights and recommendations, enabling dynamic GTM adjustments.
By leveraging AI, organizations can move from guesswork to precision in attributing revenue, understanding which interactions truly drive pipeline, and optimizing spend accordingly.
AI Techniques Powering Next-Gen Attribution
Natural Language Processing (NLP): Uncovers intent and influence signals in unstructured data—emails, call transcripts, meeting notes, and social conversations.
Graph Analytics: Maps relationships between buyers, influencers, and touchpoints, identifying key nodes and connectors in the decision process.
Predictive Modeling: Scores the likelihood of conversion based on historical data and ongoing engagement, prioritizing the most impactful activities.
Automated Data Integration: Ingests and normalizes data from CRM, MAP, webinars, product analytics, and more, creating a unified attribution dataset.
The Evolving Buyer Journey: Why Attribution Matters More Than Ever
B2B buyers now operate in teams, conducting independent research and interacting with vendors across digital and human channels. According to Gartner, the average buying committee consists of 6–10 stakeholders, each with unique touchpoints and priorities. The buying process is:
Self-directed: Buyers spend just 17% of their time meeting with potential suppliers; the rest is independent research.
Digital-first: 80% of B2B interactions are now digital, complicating attribution further.
Consensus-driven: Multiple stakeholders must align, making it hard to track true influence.
For GTM teams, this means attribution models must account for group behavior, digital exhaust, and both direct and indirect influence. AI can process these variables at scale, revealing previously invisible drivers of opportunity progression.
Common Attribution Pitfalls (and How AI Solves Them)
Channel Bias: Traditional models often overvalue the first or last touch, underestimating the impact of nurture activities. AI evaluates all touchpoints holistically.
Data Decay: Human-entered fields and self-reported forms are incomplete or inaccurate. AI continually refreshes and validates data using external signals.
Under-Reporting Dark Funnel Touches: Anonymous website visits, social shares, and dark social are missed. AI connects these signals using advanced tracking and intent models.
Delayed Feedback Loops: By surfacing real-time impact, AI enables immediate GTM pivots, preventing wasted spend.
Building AI-Driven Attribution: Key Components
To implement AI-powered attribution, organizations must assemble several foundational components:
Unified Data Infrastructure: Centralize data from CRM, marketing automation, sales engagement, and product analytics. Data normalization and de-duplication are critical.
Identity Resolution: Accurately match buyer identities across devices, channels, and platforms using probabilistic and deterministic models.
AI Attribution Engine: Deploy machine learning models that analyze historical and real-time touchpoints to assign influence scores.
Customizable Attribution Models: Allow for flexible weighting and logic, adapting to unique GTM motions (e.g., ABM, PLG, high-velocity sales).
Actionable Dashboards: Present insights in a consumable format for sales, marketing, and RevOps teams to inform strategy.
Case Study: AI Attribution in Enterprise SaaS
Consider a global SaaS provider with a complex GTM motion involving inbound marketing, outbound SDRs, channel partners, and product-led growth. Before adopting AI attribution, the team struggled to:
Identify which campaigns drove late-stage conversions.
Align sales and marketing on pipeline influence.
Optimize budget allocation across paid, organic, and partner channels.
By adopting an AI-driven attribution platform, the provider:
Unified all touchpoint data, resolving duplicate and anonymous contacts.
Used machine learning to reveal unexpected influence from webinars and product usage signals.
Reallocated spend to the highest-impact channels, improving ROI by 22% within six months.
This transformation unlocked a new level of collaboration between GTM teams and delivered measurable revenue gains.
Key Metrics for AI Attribution Success
Attribution Accuracy: Reduction in unattributed or misattributed deals.
Influence Reporting: Ability to quantify contribution by channel, campaign, and touchpoint.
Pipeline Velocity: Improvements in deal progression speed due to better GTM alignment.
ROI Optimization: Increased returns on marketing and sales investments.
Choosing the Right AI Attribution Solution
The market for AI-powered attribution tools is rapidly maturing. When evaluating solutions, GTM leaders should consider:
Depth of data integration—can it ingest all relevant touchpoints?
Flexibility of attribution models—does it support account-based and PLG motions?
Transparency of AI logic—are influence scores explainable and auditable?
Real-time reporting—are insights delivered fast enough to drive action?
Security and compliance—are data governance standards met for enterprise scale?
One example is Proshort, which unifies GTM data sources and applies advanced AI to surface actionable attribution insights for enterprise sales teams. By providing transparent and customizable attribution models, Proshort empowers organizations to close the gap between activity and revenue impact.
Integrating AI Attribution into Your GTM Operating Rhythm
Adopting AI attribution isn’t just a technology upgrade—it requires process and culture shifts:
Cross-Functional Alignment: Involve marketing, sales, RevOps, and IT in attribution model design and validation.
Continuous Model Training: Regularly refresh models as buyer behavior and GTM tactics evolve.
Closed-Loop Feedback: Use attribution insights to inform campaign design, sales plays, and budget reallocations in real time.
Change Management: Educate teams on the value and limitations of AI-driven attribution to drive adoption and trust.
Leaders must champion a test-and-learn mindset, using AI insights to iterate and improve GTM outcomes continually.
Future Trends: The Next Era of AI in GTM Attribution
AI-driven attribution is just the beginning. The future holds even greater promise for GTM leaders:
Predictive Attribution: AI will forecast which combinations of touchpoints are likely to drive future revenue, not just report on the past.
Dynamic Personalization: Attribution insights will fuel real-time personalization of content, offers, and sales engagement by persona and deal stage.
Integration with Generative AI: Generative models will synthesize recommendations and automate playbook adjustments based on attribution data.
Deeper Dark Funnel Visibility: AI will connect the dots on untracked buyer research and intent, further closing the attribution gap.
Organizations that invest early in AI attribution will be positioned to move faster, spend smarter, and win more deals in an increasingly competitive landscape.
Conclusion: Closing the Attribution Gap with AI
The attribution gap has long hindered GTM leaders’ ability to optimize spend and align teams. AI now offers a transformative solution, enabling multi-dimensional analysis of complex buyer journeys and surfacing the true drivers of revenue. By embracing unified data, advanced analytics, and solutions like Proshort, organizations can finally close the loop between activity and impact. The result: smarter decisions, stronger alignment, and accelerated growth in the enterprise SaaS space.
Key Takeaways
Traditional attribution models are insufficient for today’s complex GTM motions.
AI enables holistic, real-time, and explainable attribution across all buyer touchpoints.
Adopting AI attribution requires unified data, the right tools, and organizational buy-in.
Solutions like Proshort empower teams to optimize spend and accelerate revenue.
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