Identifying Intent Signals: Bombora, G2, and Echobot in the Major B2B Test

In the B2B landscape, early detection of purchase intent is crucial for sales success. Intent signals – digital indicators of active buying interest – have established themselves as game-changers for identifying potential customers during their research phase. But which intent tool delivers the best results for the German-speaking market? In this comprehensive test, we compare the three leading providers: Bombora, G2, and Echobot.

The Strategic Importance of Intent Data in B2B Marketing 2025

Intent data has evolved from an innovative marketing tool to a strategic imperative for B2B companies. These digital signals that indicate active purchase intent of potential customers enable you to deploy your limited marketing and sales resources more effectively and achieve significantly higher conversion rates.

A recent study by Gartner (2024) impressively demonstrates this effectiveness: companies that strategically integrate intent data into their marketing processes increase their lead conversion rates by an average of 37% while simultaneously reducing their acquisition costs by up to 25%. The reason is obvious – instead of using a scattergun approach, you focus on prospects who are actually looking for a solution.

Especially in the B2B sector, where buying cycles are long and decision processes are complex, intent signals offer a decisive competitive advantage. They allow you to enter at the right moment and address potential customers when they are actively searching for solutions – long before they contact you or a competitor.

In a Forrester survey of B2B marketing decision-makers, 78% stated that intent data will be a “very important” or “crucial” component of their marketing strategy within the next three years. This development aligns with our experience at the Brixon Group: intent-based campaigns consistently achieve higher ROIs than traditional targeting approaches.

The Evolution of Intent Recognition in B2B

The concept of intent recognition has undergone a remarkable evolution. What was once based on simple demographic and firmographic data has developed into a sophisticated system of behavior-based signals that can precisely identify purchase intentions.

From Firmographic to Behavior-Based Signals

Traditional target group identification relied primarily on static information such as company size, industry, or geographic location. However, these data offer little insight into a company’s current readiness to buy. How often have you wasted marketing budgets on “perfectly matching” company lists, only to discover that they aren’t actively looking for solutions?

Modern intent recognition instead analyzes the active behavior of companies and their employees:

• Research patterns on professional websites and social networks
• Interactions with topic-relevant content
• Engagement with competitors
• Participation in webinars or industry events
• Search queries for industry-specific terms

A current analysis by McKinsey (2024) shows that behavior-based intent signals increase the predictive accuracy for B2B purchasing decisions by 3.7 times compared to purely demographic data. An impressive advancement that can directly translate into your conversion rates.

Categories and Quality Levels of Purchase Signals

Not all intent signals are equal. For effective utilization, you need to understand the various categories and quality levels:

By Data Source:

• First-Party Intent Data: Behavior on your own website and in your channels
• Second-Party Intent Data: Signals from partner platforms and collaborations
• Third-Party Intent Data: Signals from external sources and networks (Bombora, G2, Echobot)

By Purchase Proximity:

• Early-Stage Signals: General information search for problem solutions
• Mid-Stage Signals: Comparison of specific solution providers
• Late-Stage Signals: Detailed research on implementation, prices, contract details

Particularly valuable are so-called “surge” data, which indicate a sudden increase in interest in certain topics within a company. These often point to ongoing purchasing processes and should receive highest priority in your sales process.

Intent Tools in Practical Testing: Methodology and Evaluation Criteria

To compare the intent tools Bombora, G2, and Echobot objectively and practically, we developed a structured testing methodology. Over a period of three months, we used the tools in real B2B marketing scenarios and evaluated their performance based on defined criteria.

Test Setup and Implementation

The test series was conducted with three medium-sized B2B companies from different industries:

1. A SaaS provider for project management software (120 employees)
2. A manufacturer of industrial components (85 employees)
3. An IT service provider focusing on cybersecurity (60 employees)

Each company defined a target account list with 200-300 potential customers. This list was fed into the respective intent tools to identify relevant purchase signals. In parallel, the same accounts were approached with conventional methods to have a reference value.

The generated intent data was integrated into the existing CRM and marketing automation systems and used for targeted outreach campaigns. The results were continuously documented and analyzed.

Evaluation Criteria and Their Weighting

The following criteria were used for evaluation:

1. Data Quality and Depth (30%)
   • Accuracy of intent signals
   • Coverage of relevant companies and industries
   • Timeliness of data
   • Level of detail of information
2. User-Friendliness and Integration (20%)
   • Intuitive platform operation
   • Available integrations with CRM and marketing tools
   • Customization options and flexibility
   • Quality of documentation and support
3. Effectiveness in Lead Generation (25%)
   • Increase in conversion rate
   • Quality of generated leads
   • Shortening of sales cycle
   • ROI of intent-based campaigns
4. Reporting and Analytics (15%)
   • Scope and depth of available reports
   • Visualization of data
   • Customizability of evaluations
   • Export options
5. Costs and Scalability (10%)
   • Pricing model and cost transparency
   • Scalability for growing requirements
   • Flexibility of license models

In our evaluation, we paid particular attention to how the tools are suitable for companies of different sizes and industries – from smaller firms (10-50 employees) to larger mid-sized companies (>250 employees).

Bombora: The Global Intent Pioneer Under the Microscope

Bombora has established itself as one of the leading providers of B2B intent data. Founded in 2014, the company collects and analyzes the research behavior of businesses across a network of over 4,000 B2B websites, professional publications, and communities.

Technology and Data Sources

The heart of Bombora is the proprietary Company Surge® technology. It identifies companies that are engaging with certain topics more intensively than usual. These “surge” data are based on:

• Visits to professional sites and industry portals
• Downloads of white papers and technical articles
• Participation in webinars and online events
• Interaction with topic-specific content

Bombora analyzes over 3.7 billion intent signals daily from more than 4 million domains worldwide. The data is anonymized and aggregated to comply with data protection regulations.

Particularly noteworthy is the taxonomy with over 11,000 topics, which allows for a granular analysis of research behavior. This enables you to monitor very specific topics that are relevant to your business model.

Coverage and Relevance for the DACH Market

In our test, Bombora showed strengths and weaknesses in covering the German-speaking market:

• Strengths: Good coverage of internationally active companies and corporations; high timeliness of data
• Weaknesses: Gaps with smaller and medium-sized companies in the DACH region; fewer data sources from German-language professional media

Specifically, we were able to capture usable intent data for around 68% of the tested DACH target accounts – significantly less than the 84% for US accounts.

Strengths and Weaknesses from a Practical Perspective

During our three-month test, the following strengths emerged:

• Scope and depth of intent data: Bombora provides detailed insights into the research behavior of companies.
• Timeliness of information: Weekly Surge Reports enable timely responses.
• Precision of topic taxonomy: Very specific topics can be identified.
• Quality of generated leads: Accounts identified by Bombora showed a 41% higher conversion rate.

The tool showed weaknesses in the following areas:

• Complexity of setup: The full range of functions requires time to learn.
• Costs: The pricing model can be prohibitive for smaller companies.
• DACH-specific data: Gaps in coverage of local trade media and smaller companies.
• Translation of interface: Not fully available in German.

Practical Example: ROI Increase through Intent-Based Marketing

A software provider for ERP solutions was able to significantly increase its marketing efficiency using Bombora:

• Reduction of cost-per-qualified-lead by 32%
• Increase in lead-to-opportunity conversion by 28%
• Shortening of sales cycle by an average of 24 days
• ROI of Bombora implementation: 315% within 6 months

The key to success was the close integration of intent data with the company’s content marketing and account-based marketing strategy.

G2: Intent Signals Directly from the Heart of Purchase Decisions

G2 (formerly G2 Crowd) is one of the world’s leading software review platforms with over 1.5 million authentic user reviews for more than 100,000 products. Unlike Bombora, G2 offers intent data that comes directly from user behavior on its own platform.

Unique Selling Proposition: Review-Based Intent Data

What sets G2 apart from other intent providers is the source of the data. The platform captures signals from users who are actively researching software solutions, comparing products, and reading reviews. These signals are particularly valuable as they come from individuals who are already in an active buying process.

According to a study by TrustRadius (2024), 92% of B2B buyers consult review platforms before a software purchase. G2 captures exactly this behavior and converts it into actionable intent data.

G2’s intent signals include:

• Visits to product pages and category pages
• Comparisons between competing products
• Downloads of result reports and comparative analyses
• Interactions with reviews and Q&A sections
• Search behavior on the G2 platform

Buyer Intent Program and How It Works

The G2 Buyer Intent Program gives providers insight into which companies visit their product pages, interact with their category, or compare their products with competitors. This information is provided in real-time and can be used directly for sales activities.

G2 categorizes intent signals into different types:

1. Product Page Visits: Visits to your own product page
2. Category Page Visits: Visits to the relevant product category
3. Competitor Page Visits: Visits to pages of direct competitors
4. Comparison Page Visits: Direct product comparisons
5. Profile Visits: Visits to the provider profile

Each signal is assigned a weighting factor that reflects its relevance. A visit to a comparison page, for example, is weighted higher than a simple category visit.

Advantages and Disadvantages Compared to Other Solutions

In our test, we identified the following strengths of G2:

• High-quality intent signals: The data comes from users in active buying processes.
• Direct competitive insights: You learn which providers you are being compared with.
• Simple integration: The data can be easily integrated into existing CRM systems.
• Combination with review management: Additional benefit through reputation management and feedback.

Disadvantages include:

• Limited to software products: Lower relevance for hardware or service providers.
• Limited amount of data: Fewer data points than Bombora, as only G2 platform data is used.
• Incomplete DACH coverage: Lower usage of G2 in German-speaking regions compared to the US market.
• Relatively high costs: Pricing model can be challenging for smaller providers.

Application Example of a Successful G2 Campaign

A SaaS provider for marketing automation used G2 intent data for a targeted ABM campaign:

1. Identification of companies comparing their product with direct competitors
2. Creation of personalized comparative analyses highlighting their own strengths
3. Targeted LinkedIn campaigns and email sequences to decision-makers in these companies
4. Sales outreach with specific reference to the researched features

The results were impressive:

• 47% higher email open rate compared to standard campaigns
• 3.8x higher conversion rate to demos
• 22% shorter sales cycle
• ROI of 255% on the campaign investment

Echobot: The DACH Specialist for Sales Intelligence

Echobot has established itself as a leading provider of sales intelligence in the German-speaking region. The Karlsruhe-based company, which was acquired by the Dun & Bradstreet group in 2018, combines comprehensive company data with intent signals, offering a solution specifically tailored to the DACH market.

Unique Selling Points in the German-Speaking Region

Echobot differs from international providers through its specific focus on the German-speaking market:

• Comprehensive DACH database: Over 14 million company profiles from Germany, Austria, and Switzerland
• Local data sources: Integration of regional media, trade publications, and industry portals
• Compliance with European data protection standards: Complete GDPR compliance
• German-language platform and support: No language barrier problems

This regional specialization makes Echobot particularly valuable for companies that are primarily active in the DACH region and need precise intent data here.

Data Sources and Data Quality

Echobot draws its intent signals from various sources:

• News monitoring: Over 2.5 million sources are continuously monitored for relevant company events
• Social media: Analysis of LinkedIn, Xing, and other B2B-relevant platforms
• Company databases: Integration of commercial register and credit rating data
• Trade publications: Monitoring of industry media and professional portals

In our test, Echobot impressed with the precision of its DACH data. For medium-sized German companies, the tool identified 27% more intent signals than the international competitors.

The data quality proved to be very high, with an accuracy rate of 92% for the identified intent signals. Particularly valuable were the so-called trigger events, such as leadership changes, expansion plans, or technology investments, which often serve as strong indicators of potential buying opportunities.

Practical Example: How a Medium-Sized Company Optimized its Sales Process with Echobot

A medium-sized IT service provider from Munich implemented Echobot with the following approach:

1. Definition of 20 relevant trigger events (e.g., new IT leadership, cloud migration, security incidents)
2. Monitoring of 2,500 target accounts in the DACH region
3. Integration of intent signals into the existing sales workflow
4. Prioritization of accounts with high intent scores

The results after six months:

• 41% higher engagement rate for outbound activities
• 32% shorter sales cycles
• 35% increase in average deal volume
• ROI of 290% on the investment in Echobot

The decisive success factor was the quality and relevance of the local intent data, which enabled the sales team to approach the right companies at the right time.

Comparative Analysis: Intent Tools in Direct Comparison

After three months of intensive testing with all three tools, we would now like to provide a direct comparison to give you a well-founded basis for your tool selection.

Comprehensive Comparison Table with Features and Ratings

Data Quality and Depth in the German-Speaking Region

A special focus of our test was on the quality of intent data specifically for the German-speaking market:

• Bombora offers comprehensive global coverage but shows gaps with medium-sized DACH companies. The tool’s strength lies in analyzing international B2B websites and publishers. Relevant intent data was delivered for only about 68% of the tested DACH target accounts.
• G2 is strongly focused on software and technology companies. The data quality is very high as the signals come directly from active software buyers. However, the G2 platform is not yet as established in the DACH region as it is in the Anglo-Saxon region. Coverage of local companies was about 62%.
• Echobot excels with its specialized DACH orientation. The tool captured intent signals for 89% of the tested target companies in the German-speaking region. Particularly valuable were the local content analyses that also consider regional trade media and events.

In terms of data quality, Echobot achieved a precision of 92%, G2 87%, and Bombora 81% in correctly identifying actual purchase interests.

Tool Recommendations Based on Company Type and Budget

Based on our test results, we recommend:

For globally active companies with a focus on enterprise customers:

• Bombora due to its superior international coverage and the depth of available intent data

For software and SaaS providers:

• G2 because of the high-quality, purchase-proximity intent signals and the synergies with review management

For companies with a primary focus on the DACH market:

• Echobot due to superior local data coverage, GDPR compliance, and German-language support

For smaller companies with limited budgets:

• Echobot as the most cost-effective option with still high data quality in the DACH region

The choice of the right tool ultimately depends on your specific requirements, your primary market focus, and your available budget. In many cases, a combination of multiple tools can make sense to leverage respective strengths.

Integration of Intent Data into Revenue Growth Strategy

Intent data provides valuable information – but it only reveals its full potential through strategic integration into existing marketing and sales processes. Intent signals play a key role, especially within the framework of a holistic Revenue Growth Strategy, as pursued by the Brixon Group.

Intent Signals in the Context of the “Attract, Engage, Delight” Model

The “Attract, Engage, Delight” model describes the ideal customer path from first contact to long-term customer loyalty. Intent data can be effectively used in each of these phases:

Attract Phase:

• Identification of companies with initial purchase signals
• Targeted awareness campaigns for these accounts
• Relevant content distribution based on researched topics

Engage Phase:

• Personalization of communication based on specific intent signals
• Prioritization of leads with high intent scores
• Timing optimization for sales outreach

Delight Phase:

• Detection of cross and upselling potentials
• Early identification of churn risks
• Customer retention measures for competitor research

The integration of intent data into this model enables a continuous customer journey where customers are addressed with the right content at the right time.

Workflow Design: From Signal to Qualified Opportunity

To generate qualified opportunities from intent signals, we recommend a structured workflow:

1. Intent Signal Capture and Evaluation
   • Continuous monitoring of relevant accounts
   • Scoring and prioritization based on signal strength and relevance
   • Automatic alerting mechanisms for high-intent signals
2. Initial Engagement Strategy
   • Automated content distribution based on specific intent topics
   • Personalized outreach templates for sales teams
   • Multi-channel approach (LinkedIn, email, display advertising)
3. Qualification and Deepening
   • Sales Development Representative (SDR) outreach for strong signals
   • Invitation to topic-specific webinars or events
   • Provision of in-depth analyses and benchmark reports
4. Opportunity Development
   • Coordinated account-based marketing measures
   • Personalized solution proposals based on researched topics
   • Executive sponsorship for high-value opportunities
5. Continuous Optimization
   • Analysis of conversion rates in each phase
   • A/B testing of different engagement strategies
   • Feedback loop between marketing and sales

Companies implementing this structured workflow typically achieve an increase in lead-to-opportunity conversion of 35-45%.

Implementation in the Revenue Growth Blueprint of the Brixon Group

The Brixon Group has developed the Revenue Growth Blueprint to design sustainable growth strategies for B2B companies. Intent data can be seamlessly integrated into this blueprint:

1. Strategy Phase
   • Identification of high-intent market segments
   • Prioritization of target accounts based on intent signals
   • Definition of intent-based buyer personas
2. Building Phase
   • Implementation of selected intent tools
   • Integration into existing CRM and marketing automation systems
   • Training of marketing and sales teams
3. Scaling Phase
   • Automation of intent-based workflows
   • Development of advanced scoring models
   • Establishment of a continuous optimization process

This integration enables a data-driven growth strategy based on real purchase signals rather than assumptions.

Practical Guide: 5 Steps to Successful Intent-Based Marketing

To effectively use intent data, we have developed a proven 5-step plan that helps companies of any size achieve measurable success quickly.

Step 1: Definition of Target Customers and Intent Signals

The first step is to clearly define which companies should be addressed and which intent signals are relevant to your business model:

1. Development of precise Ideal Customer Profiles (ICPs)
   • Firmographic characteristics (size, industry, location)
   • Technographic characteristics (technologies used)
   • Behavioral characteristics (typical buying cycles, decision processes)
2. Identification of relevant intent topics
   • Direct product terms and categories
   • Problems that your solution addresses
   • Complementary technologies and integrations
   • Competitor-related terms
3. Prioritization of intent signals by purchase proximity
   • Awareness phase signals (general problem research)
   • Consideration phase signals (solution comparisons)
   • Decision phase signals (specific product/price research)

A medium-sized software company from our test, for example, defined 35 intent topics, which were divided into three categories: core features (15), customer problem statements (12), and competitor comparisons (8).

Step 2: Tool Selection and Implementation

Based on the insights from our tool comparison, we recommend the following selection process:

1. Conduct needs analysis
   • Geographic focus (global vs. DACH)
   • Industry-specific requirements
   • Budget and expected ROI
   • Existing technology stack and integration needs
2. Proof of concept with prioritized tools
   • Test run with limited account set
   • Validation of data quality for your own target accounts
   • Assessment of user-friendliness with actual users
3. Structured implementation
   • Technical integration into existing systems
   • Data mapping between intent tool and CRM
   • Training of marketing and sales teams
   • Definition of clear responsibilities and processes

The average implementation time is 2-4 weeks, depending on the complexity of the existing system landscape and the extent of integration.

Step 3: Integration into CRM and Marketing Automation

The seamless integration of intent data into existing systems is crucial for success:

1. CRM Integration
   • Enrichment of account profiles with intent scores
   • Automatic creation of sales activities for high-intent signals
   • Visualization of intent trends at account and contact levels
   • Reporting dashboards for intent-based performance metrics
2. Marketing Automation Integration
   • Dynamic segmentation based on intent signals
   • Automated nurture flows for different intent categories
   • Personalization of email content based on researched topics
   • A/B testing of different approaches for intent groups

We observed particularly effective integrations in the combination of Echobot with HubSpot, Bombora with Salesforce, and G2 with Marketo.

Step 4: Development of Response Strategies

Once intent signals are detected, a well-thought-out response strategy is required:

1. Development of a signal response framework
   • Definition of thresholds for different activity levels
   • Assignment of specific measures to signal types
   • Temporal coordination of responses based on signal strength
2. Content mapping for intent signals
   • Assignment of existing content assets to intent topics
   • Identification of content gaps for common signals
   • Prioritization of content development based on intent frequency
3. Multi-channel orchestration
   • Coordinated approach via email, social media, display ads
   • Sequencing of touchpoints based on response rates
   • Escalation paths for high-value opportunities with strong signals

Companies with successful response strategies typically achieve engagement rates of 30-40% for high-intent accounts, compared to 5-10% for traditional outbound approaches.

Step 5: Measurement, Analysis, and Optimization

The continuous improvement process is critical for long-term success:

1. Implementation of a measurement framework
   • Tracking the end-to-end conversion from intent signal to deal
   • Attribution of revenue to specific intent sources
   • Cost-benefit analysis of different intent categories
2. Regular performance reviews
   • Weekly analysis of intent trends and responses
   • Monthly optimization of signal response strategies
   • Quarterly reassessment of tool performance and ROI
3. Continuous refinement
   • Expansion of topic taxonomy based on market trends
   • Optimization of scoring models through machine learning
   • Testing of new response mechanisms and channels

A structured optimization process typically leads to a 15-20% improvement in intent-to-revenue conversion within the first six months.

Future Perspectives: AI and Intent Recognition

The technology for intent recognition is developing rapidly. Particularly the advances in artificial intelligence open up completely new possibilities for the precision and effectiveness of intent-based marketing strategies.

Machine Learning in Intent Prediction

The next generation of intent tools increasingly relies on advanced machine learning algorithms:

1. Predictive Intent Models
   • Prediction of purchase interest even before explicit signals
   • Detection of subtle patterns indicating future intent
   • Combination of different signal sources for higher precision
2. Natural Language Processing (NLP) for Intent Analysis
   • Semantic analysis of content interactions
   • Detection of implicit purchase signals in communication
   • Sentiment analysis to evaluate purchase readiness
3. Time-Series Analyses for Intent Histories
   • Detection of typical signal sequences before purchase decisions
   • Precise timing optimization for sales activities
   • Early identification of changing intent patterns

Initial implementations of these advanced technologies show an improvement in prediction accuracy of 25-40% compared to traditional intent models.

Privacy Developments and Their Implications

The increasingly strict privacy landscape presents new challenges for intent-based marketing:

1. Post-Cookie Strategies
   • Development of first-party data solutions
   • Contextual intelligence as an alternative to user tracking
   • Privacy-preserving analytics through anonymized aggregation
2. Compliance with Global Regulations
   • Adaptation to GDPR, CCPA, and emerging regulations
   • Transparent data usage and consent management
   • Data minimization and privacy by design

The leading intent providers are already investing in privacy-compliant solutions. Echobot in particular has a competitive advantage in the European market with its GDPR-compliant approach.

Integration with First-Party Data as an Emerging Trend

The greatest added value increasingly comes from combining third-party intent data with your own first-party data:

1. Hybrid Intent Models
   • Linking website behavior with external intent signals
   • Integration of CRM data for more contextual intent assessment
   • Combination of explicit and implicit purchase signals
2. Customer Data Platforms (CDPs) for Intent Integration
   • Centralization of all customer data and intent signals
   • 360-degree view of customer intent across all touchpoints
   • Uniform activation across all marketing and sales channels
3. Closed-Loop Intent Systems
   • Continuous learning from conversion data
   • Automatic optimization of intent scores and thresholds
   • Self-improving systems through AI-supported feedback loops

Companies that successfully combine first-party and third-party intent data report 50-70% higher precision in identifying accounts ready to buy.

Conclusion: Using Intent Signals as a Competitive Advantage

After our comprehensive comparison of the three leading intent platforms – Bombora, G2, and Echobot – and the analysis of their practical application, clear conclusions can be drawn.

Summary of Test Results

Our three-month testing phase with real B2B use cases has shown that intent data has a significant impact on the efficiency of marketing and sales processes:

• All tested tools delivered measurable improvements compared to traditional approaches
• The average increase in lead conversion rate was 35-45%
• The ROI of implementation was over 250% for all tools
• The quality of generated opportunities was significantly higher than with traditional methods

Each tool has specific strengths that make it particularly suitable for certain use cases:

• Bombora impresses with global reach and depth of intent data
• G2 delivers high-quality, purchase-proximity signals in the software and tech sector
• Echobot offers the best performance in the DACH market with regional expertise

Strategic Action Recommendations

Based on our findings, we recommend the following strategic measures:

1. Define clear intent goals and KPIs
   • Determine which business objectives you want to achieve with intent data
   • Develop a measurement framework for success measurement
   • Define realistic benchmark goals based on industry data
2. Choose the right tool combination
   • Consider your geographic focus and target audience
   • Evaluate integration requirements with existing systems
   • Start with one tool and expand later if necessary
3. Invest in processes, not just technology
   • Develop clear workflows for using intent data
   • Train marketing and sales teams in handling intent signals
   • Establish cross-departmental collaboration
4. Establish continuous optimization
   • Implement regular review cycles
   • Systematically test different response strategies
   • Continuously adjust intent categories and thresholds

Companies that implement these recommendations typically achieve full amortization of their investment within 6-12 months and position themselves as data-driven pioneers in their market.

The Role of Intent Data in Revenue Growth Strategy

Intent data is a central building block of modern revenue growth strategies, as also envisioned by the Brixon Group in its blueprint. They enable:

1. More efficient resource allocation
   • Focus on accounts with high purchase probability
   • Optimization of marketing budgets based on intent insights
   • Prioritization of sales activities according to intent scores
2. Acceleration of the revenue cycle
   • Early identification of purchase signals
   • Shortened sales cycles through temporally optimized approaches
   • Higher closing rates through contextually relevant communication
3. Better customer experience
   • More relevant content based on actual interest
   • Timing of approach in line with the buying process
   • Personalized solution proposals based on intent signals

In times of increasing competition and more demanding buyers, intent data is no longer just an optional tool but a strategic imperative for B2B companies seeking sustainable growth.

The integration of intent data into the Revenue Growth Blueprint of the Brixon Group enables companies to optimize the entire marketing funnel – from targeted approach (Attract) to personalized interaction (Engage) to long-term customer loyalty (Delight).

FAQ on Intent Signals and Intent Tools