B2B Buyer Intent: 3 Signals & 2024 Benchmarks
Explore 3 types of B2B buyer intent: Topic, Keyword, and Company Surge®. See 2024 data on how intent lifts conversion rates, pipeline, and ROI.
According to a 2024 B2B buying study, accounts prioritized with intent data converted to closed opportunities at a rate of 21.3%, compared to just 8.4% for non-prioritized accounts. Bombora's Company Surge® methodology identifies accounts showing an abnormal increase in research on specific topics. Blending this third-party data with a company's own first-party engagement signals can lift MQL-to-SQL conversion by 34%. [4]
TL;DR
- Intent-prioritized accounts convert to closed-won at 21.3% versus 8.4% for non-prioritized accounts in 2024. [4]
- Blending Bombora's third-party intent with first-party data provides a 34% lift in MQL-to-SQL conversion. [4]
- 71% of B2B marketers actively used third-party intent data in their 2024 ABM programs, up from 55% in 2022. [4]
- Zoom achieved a 36% lift in sales conversions for accounts targeted with Demandbase intent data. [19]
- The median time from signing an intent data platform contract to seeing a qualified pipeline contribution is 94 days. [4]
What Are the 3 Types of B2B Buyer Intent Signals?
B2B buyer intent signals are broadly categorized by their data source: first-party data collected from a company's own digital properties and third-party data aggregated from external sources. First-party intent is the most reliable signal, derived from direct interactions like website visits, content downloads, and CRM activity. [26, 7] Its primary limitation is scope, as it only captures behavior from accounts already engaging with your brand. [9] To see the 95% of the buyer journey that happens before a prospect lands on your site, teams turn to third-party data. This information is typically sourced from a data cooperative, such as Bombora's network of over 5,500 B2B publisher websites, which captures billions of content consumption events monthly. [23, 18] This model allows vendors to track research activities across a vast market, identifying accounts showing interest in specific topics even if they have never heard of your company. [17] The trade-off for this expanded reach is a potential reduction in signal precision compared to first-party sources, a gap that many platforms aim to close by blending both data types. [6]
Topic Intent provides a broad, contextual understanding of an account's interests by analyzing the subject matter of the content they consume, a method pioneered by Bombora. Instead of matching literal words, this approach uses natural language processing to classify content into a comprehensive taxonomy, which as of March 2025, included over 17,210 distinct B2B topics. [4, 2] For example, an article about Instagram and Facebook would be correctly classified under the 'social media' topic even if the phrase itself is never mentioned. [27] This contrasts sharply with Keyword Intent, a high-specificity signal used by platforms like 6sense, which identifies literal word or phrase matches within web content and searches. [11, 12] While Topic Intent is ideal for identifying general interest and top-of-funnel prospects, Keyword Intent allows for more granular segmentation, enabling marketers to distinguish between different buying stages, such as research versus active evaluation. [16] Many advanced B2B platforms, including 6sense, integrate Bombora's topic data alongside their own keyword signals to create a more complete picture of account activity. [3, 24]
Company Surge® Intent is a proprietary metric from Bombora that identifies when an account's research on a specific B2B topic significantly exceeds its normal behavior, signaling active buying intent. The platform establishes a historical baseline of content consumption for each company over a 12-week period; a 'surge' occurs when research intensity, frequency, and the number of individuals involved all spike above this established norm. [19, 8] This methodology provides a powerful timing signal, allowing sales and marketing teams to prioritize accounts that are actively in-market. [5] For instance, a Company Surge® score of 60 or higher indicates a statistically significant increase in research activity. [8] This data is sourced from Bombora's exclusive Data Co-op, which includes over 5,500 publisher sites where 86% of the data is shared exclusively with Bombora. [23] Integrating these surge signals into predictive models has been shown to increase accuracy, with one analysis of mutual 6sense and Bombora customers reporting a 20% lift in converting 'purchase' stage accounts into pipeline opportunities. [19]
| Signal Type | Granularity | Primary Data Source | Key Vendors / Platforms | Primary Use Case |
|---|---|---|---|---|
| Topic Intent | Broad (Conceptual) | Third-Party Data Cooperative | Bombora | Top-of-funnel account discovery and market trend analysis. [2] |
| Keyword Intent | High (Literal Match) | Third-Party Publisher Networks | 6sense | Precise audience segmentation and targeting based on specific search terms. [11] |
| Company Surge® Intent | High (Behavioral Spike) | Bombora Data Co-op | Bombora, 6sense, Demandbase | Prioritizing in-market accounts showing an abnormal increase in research. [1] |
| First-Party Website Intent | Very High (Direct Action) | Owned Web Properties (Analytics, IP Lookup) | HubSpot, Marketo, Google Analytics | Lead scoring and identifying high-intent actions like pricing page visits. [26] |
| First-Party CRM/MAP Intent | Very High (Direct Engagement) | CRM & Marketing Automation Platforms | Salesforce, HubSpot | Tracking email engagement, content downloads, and sales interactions. [26] |
| Second-Party Intent | High (Verified Interest) | Partner Properties (e.g., Review Sites) | G2, TrustRadius | Identifying late-stage buyers comparing specific products and competitors. [20] |
Topic Intent: How Broad Research Patterns Predict Market Trends
Topic-level intent signals are powered by massive datasets that track broad content consumption patterns across the business web. The Bombora Data Co-op, for instance, monitors an average of 22 billion monthly content consumption events from a network of over 5,000 B2B websites to generate its insights. [1] This cooperative model pools privacy-compliant, brand-anonymous visitor data from publishers, marketers, and technology providers, creating a vast resource that details the research habits of millions of companies. [3] This immense scale allows platforms like Bombora to establish a baseline of normal research activity for any given company. When an organization's consumption of content related to specific business subjects spikes abnormally, it triggers a signal, such as those found in the Bombora Company Surge® product. [12] This methodology provides a macroscopic view of market movements and buying committee interests, capturing the initial stages of the buyer's journey long before a prospect fills out a form on a vendor's website. The data is not just about volume; it is about identifying statistically significant changes in research behavior that predict commercial intent.
A crucial distinction in topic intent is the use of advanced modeling to move beyond simple keyword matching. Vendors like Bombora employ natural language processing (NLP) and deep learning models to analyze the context and substance of online content. [6] These models assign one of tens of thousands of proprietary topics based on what a piece of content is conceptually about, not just the literal words it contains. [4] For example, while a keyword system might simply register the word "cloud," Bombora's NLP models can differentiate whether the content is about cloud infrastructure, data management, or meteorology. [6] This contextual understanding is vital for accurately gauging a company's genuine interest. According to a 2023 guide, this process involves training the NLP models with hundreds of relevant content pieces to identify the patterns associated with a topic like 'cloud security', allowing the system to score the relevance and density of new content it encounters across the Data Co-op. [19] This results in a much higher-fidelity signal that reflects true conceptual research rather than coincidental word presence, giving revenue teams more reliable data for their go-to-market strategies.
Marketing and sales teams primarily use topic intent data to discover net-new opportunities and improve internal alignment. A 2024 analysis of intent data usage from Mixology Digital found that 44% of B2B marketers (n=unspecified) report their primary goal for using intent data is to identify new accounts to target, while 53% stated their main objective is to align sales and marketing efforts. [5] By monitoring surges on topics relevant to their products, companies can prioritize accounts that are actively in-market but may not have been on their radar. [14] This allows for more efficient resource allocation, focusing expensive sales cycles and advertising budgets on accounts demonstrating active research. Furthermore, this shared, objective data provides a common ground for sales and marketing, enabling them to agree on which accounts to pursue and what messaging to use based on observed interests. According to a 2023 study from Foundry, 91% of marketers now use intent data within their account-based marketing (ABM) programs to prioritize accounts and select appropriate content. [5]
Beyond broad content consumption, topic-level intent signals are also generated by user activity on specialized B2B technology review platforms. Vendors such as G2 and TrustRadius provide high-fidelity intent data based on how buyers research specific software and service categories on their sites. [2, 16] Actions like viewing a product's profile page, reading reviews, making direct comparisons between two solutions, or exploring alternatives within a category all generate distinct intent signals. [8, 11] For example, a signal from a G2 comparison page indicates a buyer is actively evaluating your product against a specific competitor, representing a mid-to-lower funnel activity. [17] TrustRadius refers to this as "downstream intent," which it defines as signals from in-market buyers who are closer to a purchase decision. [13, 26] This type of topic intent is highly valued because the context is explicit; a user researching the "Customer Data Platform" category on a review site is clearly evaluating solutions in that market, providing a direct and actionable signal for sales and marketing teams to engage with. [10]
Company Surge®: Pinpointing Accounts in Active Buying Cycles
A Company Surge® score of 60 or higher serves as a critical threshold, indicating an account is actively researching a topic with significantly more intensity than its historical average. [2, 3] This score, which ranges from 0 to 100, is generated by Bombora's patented model that compares an account's recent content consumption on a specific topic, observed over the last three weeks, against its own 12-week baseline. [5] A score of 60 or more signifies a statistically meaningful spike in research activity, suggesting the organization has moved beyond passive interest and into a more active evaluation phase. [1, 3] For instance, a company consistently reading one article per week about "cloud security" that suddenly starts consuming five articles, downloading whitepapers, and registering for webinars on the same topic would see its score surge. This methodology allows go-to-market teams to filter out noise and focus on accounts demonstrating a pattern of behavior that strongly correlates with a buying cycle, rather than chasing single, isolated topic interactions. [2] By setting a score threshold of 60, and often higher to further refine focus, teams can prioritize the accounts most likely to be in-market. [19]
Bombora's data is sourced from a vast, proprietary Data Cooperative of more than 5,000 B2B websites, a network that includes influential publishers, niche industry sites, and branded content hubs. [7, 12, 14] According to a 2024 report on the co-op's structure, 86% of this data is shared exclusively with Bombora, meaning competitors do not have access to these specific signals, providing a unique market view. [8, 9, 13] This consent-based, privacy-first model collects anonymized data directly from publisher partners, tracking billions of monthly content consumption events without relying on third-party cookies or scraped data. [7, 15] The cooperative nature is a key differentiator; members contribute their first-party visitor engagement data in exchange for aggregated audience insights, creating a powerful network effect that enhances data quality and scale. [7] This direct access allows Bombora to capture a complete picture of research behavior, including engagement with paywalled content, form fills, and downloads, which are then processed using Natural Language Processing to classify the content against a taxonomy of over 21,600 B2B topics as of early 2025. [12, 18]
Activating Company Surge® data involves a multi-pronged strategy to engage accounts showing heightened intent, primarily focusing on prioritizing sales outreach, personalizing digital advertising, and informing account-based marketing (ABM) campaigns. Sales development teams can transform their daily workflow by using Surge® scores to rank and prioritize their target account lists, focusing their efforts on companies that are already actively researching relevant solutions. [19] This allows for warmer, more relevant outreach. [17] In parallel, marketing teams can push these high-intent account lists directly into advertising platforms like LinkedIn via its Matched Audiences feature to run hyper-targeted ad campaigns. [6] The content and messaging of these ads can be customized to align with the specific topics the accounts are surging on, which has been shown to dramatically increase click-through rates and engagement. [17] For broader ABM strategies, this intent data is foundational for account selection, ensuring that marketing and sales resources are aligned and concentrated on accounts that are demonstrating a verifiable, active buying journey. [10, 26]
While powerful for prioritizing which companies to target, Company Surge® data is fundamentally account-level only and does not identify the specific individuals conducting the research. [2, 22, 27] This creates a critical gap: a sales team might know that 'ABC Corporation' is surging on "CRM software," but they do not know who within the 10,000-person company is on the buying committee. To bridge this gap and make the intent signal actionable, the data must be paired with a contact data provider like ZoomInfo, Lusha, or Cognism. [15, 25] These platforms enrich the account-level surge signal by providing detailed contact information, including names, job titles, email addresses, and phone numbers for likely buyers within the target organization. [25] Many go-to-market platforms, such as 6sense and Demandbase, have built entire workflows around this model, licensing Bombora's account-level data and layering their own or third-party contact data on top to provide a complete solution. [16, 23] This two-step process, combining the 'what' and 'where' from Bombora with the 'who' from a contact database, is essential for converting an anonymous intent signal into a direct sales conversation. [22]
2024 Conversion Benchmarks: How Intent Data Lifts Performance
Applying intent data yields dramatic improvements in core conversion metrics, most notably in the account-to-opportunity pipeline stage. A comprehensive 2024 B2B Buying Study, which analyzed buying journeys from January through September 2024, found that accounts prioritized using intent signals converted to closed opportunities at a rate of 21.3%. [1] This performance is a full 2.5 times higher than the 8.4% conversion rate observed for non-prioritized accounts, providing a clear quantitative case for focusing sales and marketing resources on prospects actively researching relevant solutions. [1] The performance lift extends beyond just opportunity creation, directly impacting the speed at which deals move through the funnel. The same body of research indicates that when buying signals are used to trigger orchestrated, multi-channel marketing plays, organizations see a 23% lift in pipeline velocity compared to using those same signals for siloed, single-channel responses. [1] This acceleration occurs because coordinated outreach across different platforms, such as targeted ads, sales emails, and content syndication, creates a more immersive and persuasive experience for the buying committee, reinforcing the message and compelling quicker action from accounts that are already in a buying cycle.
Blending third-party and first-party data sources creates a synergistic effect that significantly elevates lead quality and conversion rates further down the funnel. Bombora's 2024 Company Surge® Performance Report highlights this phenomenon, reporting a 34% lift in the marketing-qualified lead (MQL) to sales-qualified lead (SQL) conversion rate when its third-party topic consumption data is combined with a company's own first-party engagement signals. [1] This approach allows marketing teams to move beyond simplistic lead scoring, which often relies solely on a prospect's interaction with owned assets like a website or emails. By layering in third-party insights about an account's broader research activity across thousands of publisher sites, teams can more accurately identify which MQLs are truly sales-ready. This is a critical advantage when benchmark data shows the median B2B MQL-to-SQL conversion rate hovers around a modest 13%, a figure that reflects the common challenge of sales teams rejecting marketing leads that lack genuine, immediate purchase intent. [6, 8] Strategies that fuse external research trends with internal engagement data help ensure that the leads passed to sales are not just interested, but actively in-market, directly addressing the primary bottleneck in many B2B funnels.
Real-world applications of intent-driven Account-Based Marketing (ABM) provide concrete proof of its effectiveness in generating pipeline and revenue. A widely cited case study from Demandbase detailed how Zoom, a leader in communication solutions, transformed its go-to-market strategy by operationalizing intent data. The initiative produced a 36% lift in sales conversions for targeted accounts and, even more impressively, a 6.25x uplift in opportunities generated from its ABM campaigns. [2, 3] This success was achieved by moving away from fragmented targeting and toward a unified strategy where marketing and sales teams used shared data to identify and engage high-value accounts showing active buying signals. By leveraging the Demandbase platform, Zoom could scale personalized outreach and prioritize prospects with precision, leading to engagement rates of 90% for its top-tier target accounts. [2] This example demonstrates how a well-executed, intent-powered ABM program does more than just improve a single metric; it creates a more efficient and aligned revenue engine that delivers substantial, measurable growth in both pipeline and closed business, as detailed in a Starr Conspiracy analysis. [1]
| Conversion Metric | Baseline / Without Intent Data | Uplift / With Intent Data | Data Source / Vendor | Year |
|---|---|---|---|---|
| Account-to-Closed Opportunity Rate | 8.4% | 21.3% (2.5x Lift) | 2024 B2B Buying Study | 2024 |
| MQL-to-SQL Conversion Rate | 13% (Median) | ~17.4% (34% Lift when blended) | Bombora / Industry Median | 2024 |
| Sales Conversion Lift (ABM) | Control Group Baseline | 36% Lift | Demandbase (Zoom Case Study) | ~2023 |
| Opportunity Uplift (ABM) | Control Group Baseline | 6.25x Uplift | Demandbase (Zoom Case Study) | ~2023 |
| Pipeline Velocity (Orchestrated Plays) | Single-Channel Response Baseline | 23% Lift | 2025 B2B Marketing Benchmark | 2024 |
| Lead-to-Close Rate (Cold vs. Intent) | 5.5% (Cold ICP Match) | 18.7% (3.4x Lift) | 6sense / Demandbase Cohort Analysis | 2026 |
How Leading B2B Platforms Activate Intent Data
Leading revenue intelligence platforms activate intent data by fusing proprietary and client-side signals into a predictive engine that prioritizes accounts. The 6sense Revenue AI platform exemplifies this by combining a client's first-party data, such as CRM records and website analytics, with an extensive third-party data network called the Signalverse™. [26] This network captures trillions of anonymous research activities across the web daily. The platform's predictive models, trained on over a decade of B2B purchase data, analyze this combined signal set to identify patterns that precede a purchase. [16, 25] Based on this analysis, the AI assigns every account to a specific, dynamic buying stage, such as Target, Awareness, Consideration, or Decision. [8] An account in the Awareness stage, for instance, is just beginning its research, while an account in the Decision stage is exhibiting significant research activity across multiple sources, indicating it is much closer to making a purchase and ready for sales engagement. [11] This methodology moves beyond simple lead scoring to provide a holistic, account-level view of market readiness, allowing teams to align their marketing and sales plays with the buyer's actual journey. [25]
Other major B2B platforms leverage different data sources to identify in-market buyers, focusing on bidstream data and enriching existing contact databases. Demandbase One™ taps directly into the B2B advertising bidstream, analyzing content consumption from over one million web publishers to surface accounts researching relevant topics. [12] The platform processes trillions of signals monthly to identify companies showing active interest before they self-identify on a vendor's website. [30] This approach powered significant results for the cybersecurity firm Coalfire, which achieved a 40% increase in marketing-generated pipeline and a 25% higher lead-to-opportunity conversion rate after implementing Demandbase to refine its account-based marketing strategy. [29] In contrast, ZoomInfo primarily layers intent data as an additional feature on its core product, a massive database of contact and company information. Its intent offering, often powered by data partners like Bombora, adds a layer of behavioral signals to its rich firmographic data, allowing users to identify which companies within their target profiles are actively researching specific keywords. [4, 7] This model allows teams to start with a foundation of contact data and then append intent signals to prioritize outreach.
Activating intent data through these top-tier platforms requires a significant financial commitment, with median costs for mid-market companies typically exceeding $50,000 annually. For 6sense, whose platform includes predictive AI and orchestration, the median annual cost reported by procurement data platform Vendr in 2026 was between $55,211 and $62,820. [2, 10] Similarly, the median cost for Bombora's standalone Company Surge® intent data, which is often integrated into platforms like 6sense or purchased directly, is reported to be in the $50,000 to $60,000 range for mid-market companies, though basic packages can start around $30,000 per year. [22] ZoomInfo offers a different pricing structure; while its entry-level data plans begin around $15,000 annually, packages that include their intent data module, the Advanced tier, typically start in the $25,000 to $30,000 range per year as of 2026. [19, 20] These investment levels reflect the strategic value that timely, accurate intent signals provide for enterprise revenue teams aiming to improve pipeline efficiency and win rates.
Measuring Intent Data ROI: A Framework for RevOps
A critical first step in measuring intent data ROI is tagging all intent-influenced accounts, opportunities, and contacts within the CRM to enable accurate attribution reporting. [1, 20] This foundational process ensures that every marketing and sales activity driven by intent signals can be tracked from its origin to its revenue outcome. The primary goal is to rigorously measure the impact on pipeline value, sales cycle velocity, and win rates for these intent-influenced accounts versus a control group of non-intent accounts. [1, 6] Without a disciplined approach to CRM tagging, typically managed within platforms like Salesforce or HubSpot, any subsequent ROI analysis becomes unreliable. [20] For example, RevOps teams must establish clear rules for how platforms like Bombora's Company Surge® Q3 2024 update account records, ensuring that a spike in research activity on a specific topic triggers a status change or task creation. [13, 14] This systematic tracking allows leadership to compare performance directly, answering the core question: are intent-prioritized accounts outperforming their non-prioritized counterparts in meaningful ways, such as converting to closed opportunities at a higher rate? A 2024 B2B buying study found intent-prioritized accounts converted at 21.3% versus just 8.4% for the control group, a differential that is impossible to prove without clean CRM data hygiene from the start. [3]
RevOps leaders must focus on a specific set of key metrics to quantify the business impact of an intent data program, moving beyond vanity metrics like clicks and impressions. Core pipeline metrics include the intent-to-engagement rate, which measures how many flagged accounts are successfully engaged by sales or marketing, and the opportunity creation rate from those intent-qualified accounts. [1, 2] These leading indicators provide an early signal of whether the intent data is being operationalized effectively. Further down the funnel, the most crucial metrics are the total revenue influenced or sourced by intent-driven campaigns and the comparative win rates for intent-influenced deals versus the baseline. [2, 8] For example, a Forrester analysis of Bombora's Company Surge® found that users could see an ROI of 342% and an increase in sales velocity of up to 30%, outcomes that are tracked by measuring the time from the first intent signal to a closed-won deal. [15] An effective measurement framework, as outlined in a 2026 guide from Prospeo, separates these KPIs into leading indicators (activation rate, meeting rate) and lagging indicators (pipeline created, revenue influenced), which together tell the complete performance story. [6]
Setting realistic expectations for the timeline to see a return on an intent data investment is crucial for maintaining executive buy-in and program momentum. The median time from a contract signature on an intent data platform to the first qualified pipeline contribution was 94 days, according to a 2024 audit of 47 separate ABM programs conducted by The Starr Conspiracy. [3] This benchmark highlights that intent data is not an instant pipeline generator; it requires a strategic ramp-up period for teams to integrate the data, refine their activation plays, and for sales to adopt the new workflows. The initial 90 days are often focused on operationalizing the data, such as syncing tools for seamless activation and training sales development representatives to customize outreach based on specific intent signals. [10] According to a 2026 report, organizations that successfully implement intent data typically report seeing a 2-4x ROI within the first year, driven by benefits like 25-35% higher conversion rates and 30-40% shorter sales cycles, underscoring that the initial setup period gives way to significant, measurable returns. [8]
Related reading
- see our anatomy of a buying signal analysis
- see our apollo vs zoominfo vs hunter vs snov analysis
- see our b2b buyer intent signal statistics analysis
- see our b2b contact data decay rate analysis
Frequently Asked Questions
What is the difference between buyer intent data and engagement data?
The primary difference is the source of the information; engagement data is first-party information you collect from your own properties, while buyer intent data is third-party information aggregated from across the web. [17, 35] Engagement data includes actions like website visits and email clicks, showing how an account interacts directly with your brand. [16] In contrast, third-party intent data reveals an account's broader research on specific topics and keywords, signaling interest in a solution category before they even know your company exists. [22, 36] Combining both data types provides a complete view of the buyer's journey.
How much does Bombora Company Surge® cost in 2024?
Bombora does not publish its pricing, operating on a custom quote-based model for its Company Surge® data. [8] However, third-party data and user-reported contracts from 2024-2026 consistently place the starting cost for an annual subscription at approximately $25,000 to $30,000. [1, 5] This price can increase significantly, with mid-market plans often ranging from $50,000 to $100,000 per year depending on the number of topics monitored, data volume, and integration needs. [3] These costs cover the intent data feed only and do not include the separate tools required to act on the signals. [20]
Which is better, Bombora or 6sense?
Choosing between Bombora and 6sense depends on whether you need a specialized intent data feed or a comprehensive ABM platform. [6] Bombora is a dedicated data provider, offering high-quality Company Surge® intent signals to enrich an existing tech stack like a CRM or marketing automation platform. [11] In contrast, 6sense is an all-in-one platform that includes intent data (partially sourced from Bombora), predictive analytics, and campaign orchestration tools to manage the entire account-based experience. [2, 9] Therefore, Bombora is better for teams that want to pipe focused intent signals into their own systems, while 6sense is a better fit for those seeking a single platform to both identify and engage in-market accounts. [10]
What is a good conversion rate for B2B intent-based marketing?
A strong conversion rate for accounts prioritized with intent data is around 21.3% from initial identification to a closed opportunity, which is a significant improvement over the 8.4% rate for non-prioritized accounts, according to a 2024 B2B buying study. [15] General B2B website conversion rates for lead generation typically fall between 2% and 5%. [32, 40] The performance lift from intent data comes from focusing sales and marketing efforts on accounts that are already actively researching solutions. For example, blending third-party intent data with first-party engagement signals has been shown to increase MQL-to-SQL conversion by up to 34%. [15]
How do you measure the ROI of an intent data investment?
The ROI of an intent data investment is calculated by comparing the revenue or pipeline value generated from intent-driven activities against the total cost of the investment. [12] This total cost should include the data subscription fee plus any associated expenses for tools and team capacity needed to activate the data. [12, 30] A common measurement framework involves tracking a cohort of accounts targeted with intent data against a control group, comparing metrics like account-to-opportunity conversion rate, sales cycle length, and win rate. [4] By attributing the difference in revenue between the two groups to the program, you can use a standard formula: (Revenue from Intent - Investment Cost) / Investment Cost. [18]
Last updated: July 2026