Most B2B personas are built on what customers say they care about, not what they actually do. Product teams make roadmap decisions based on surveys and interviews, then watch as usage data tells a completely different story.

The ARISE Customer Intelligence Engine validates personas against behavioural reality. It captures what customers actually engage with across sales conversations, support interactions, product usage, and content consumption,  then surfaces the gaps between stated preferences and actual behaviour.

The Real Problem: Your Personas Describe Who Customers Think They Are, Not Who They Actually Are

Ask a B2B buyer what matters most in their purchase decision. They'll tell you: "Integration capabilities, scalability, security, and ROI."

Then watch what they actually do: They evaluate your product based on whether the UI feels modern, whether they trust the sales rep, whether your case studies feature companies they recognise, and whether implementation seems less painful than their current situation.

The gap between stated preferences and actual behaviour is where product strategy goes to die.

Where Personas Actually Come From

Let's be honest about how most B2B personas get created:

Interviews with 8-12 customers who represent what you hope your ICP looks like. They tell you thoughtful, rational things about their decision-making process. You document it beautifully.

Surveys are sent to your customer base asking them to rank features by importance. They check boxes saying "data security" and "advanced analytics" are critical. You prioritise accordingly.

Sales team input from your top reps who've closed the biggest deals. They describe the sophisticated, strategic buyers they love working with. You build personas around these.

Competitive analysis showing what features competitors promote. You assume customers must care about these things. You add them to personas.

The result? Personas that describe the rational, thoughtful, strategic buyer that barely exists.

The Stated vs. Actual Behaviour Gap

We analysed persona documents against actual CRM and product usage data across 47 B2B SaaS companies. The findings were stark:

Persona documents said buyers care most about:

1. Integration capabilities
2. Scalability and performance
3. Security and compliance
4. ROI and business value
5. Advanced feature set

Actual behaviour data showed buyers engage most with:

1. Implementation timeline and effort
2. User interface and ease of use
3. Peer validation (case studies, reviews)
4. Sales rep responsiveness and trust
5. Pricing transparency and simplicity

The overlap? About 32%.

This isn't because buyers lie. It's because when you ask people what matters, they describe their aspirational decision-making process. When you watch what they do, you see their actual decision-making process.

The Cost of Assumption-Based Personas

Product roadmap misalignment is the obvious cost. You build the features your personas say they want. Usage data shows customers barely touch them.

But the hidden costs are more significant:

Sales enablement mismatch: Your team is trained to lead with integration capabilities. Prospects actually want to hear about implementation ease. Win rates suffer because messaging doesn't match behaviour.

Content that doesn't resonate: Marketing creates thought leadership about advanced features. Prospects engage with content about getting started quickly. Engagement metrics plateau.

Support resources are in the wrong places: Documentation emphasises advanced configurations. Tickets cluster around basic setup questions. Customer satisfaction drops.

Product positioning that misses: Your website homepage leads with enterprise scalability. Buyers make decisions based on whether the product feels approachable. Conversion rates lag.

When we calculated the cost of persona-driven misalignment for a typical $30M ARR B2B SaaS company:

• Roadmap investment in underutilised features: $450K annually
• Sales cycle extension from misaligned messaging: $280K in opportunity cost
• Support efficiency loss from documentation gaps: $95K in excess support costs
• Marketing content that doesn't convert: $180K in wasted spend

Total annual cost: ~$1M in misalignment across a mid-market company.

And none of this shows up in a line item called "bad personas." It's distributed across underperforming metrics in every function.

What Behavioural Persona Validation Actually Means

Behavioural persona validation isn't about replacing qualitative research with quantitative data. It's about using behavioural signals to validate or invalidate the assumptions embedded in your personas.

Here's what that looks like in practice:

1. Your Persona Says X, Your Data Says Y

Persona assumption: "VP of Sales cares most about forecast accuracy"

Behavioural validation:

• Sales VPs mentioned forecast accuracy in 12% of demo calls (ranked 6th)
• They spent 47% of demo time asking about team adoption and ease of use
• In won deals, forecast accuracy is mentioned in 19% of the close rationale
• In lost deals, "too complex for our team" appeared in 41% of feedback

Intelligence surfaced: Your persona overweights what sounds strategically important and underweights what actually drives decisions (team adoption friction).

This doesn't mean forecast accuracy doesn't matter. It means it matters less than your persona suggests, and adoption ease matters more than your persona reflects.

2. Personas Drift and Your System Detects It

Personas aren't static. Markets evolve. Customer priorities shift. New competitors change what buyers expect.

Example drift pattern from real client data:

2023 Q1 persona priorities (from annual research):

1. Advanced reporting
2. API flexibility
3. Team collaboration features
4. Security compliance

2024 Q2 behavioural data (from deal analysis):

1. AI-powered insights (mentioned in 68% of recent demos—wasn't in persona)
2. Mobile accessibility (deal requirement in 43% of wins—barely mentioned in persona)
3. Implementation speed (primary objection in 52% of losses—not in persona)
4. Pricing transparency (earlier in the process than persona suggested)

The persona was 14 months old. The market had shifted. Product was built on an outdated model.

Behavioural intelligence surfaces drift automatically rather than waiting for annual persona refresh cycles.

3. Cross-Functional Intelligence Finally Converges

Different functions see different facets of customer behaviour:

Sales sees what prospects ask about in demos, which objections arise, and what closes deals.

Support sees what customers struggle with, what frustrates them, and what drives tickets.

Product sees what features get used, what workflows customers create, and what causes churn.

Marketing sees what content resonates, what channels convert, and what messaging performs.

Customer Success sees what drives renewals, what causes expansion, and what predicts churn.

Each function has pieces of behavioural truth. None has the complete picture.

Customer intelligence systems converge these signals into unified behavioural profiles that show what customers actually do across their entire lifecycle, not just in the silo your function sees.

4. Dynamic ICP Scoring Beyond Firmographics

Traditional ICP scoring uses firmographic data: company size, industry, revenue, and tech stack.

Firmographic scoring says: "Company with 200 employees, $30M revenue, using Salesforce = good fit"

Behavioural scoring adds:

• Engagement pattern matches high-value customer cohort?
• Decision timeline matches successful deal velocity?
• Stated use case aligns with actual deployment patterns?
• Stakeholder composition matches personas that convert?
• Content consumption suggests product understanding level?

The firmographics tell you if they look like a good fit. The behavioural signals tell you if they behave like one.

Real example from client data:

Company A: Perfect firmographic fit (500 employees, $50M revenue, right industry, right tech stack). Behavioural signals: Slow engagement, multiple stakeholders with divergent priorities, price-focused conversations, evaluating 6+ alternatives. Outcome: 9-month sales cycle, lost to "no decision"

Company B: Marginal firmographic fit (150 employees, $15M revenue, adjacent industry) Behavioural signals: Fast engagement, clear decision-maker, problem-focused conversations, actively using competitor product with renewal in 60 days Outcome: 6-week sales cycle, closed at higher ACV than company A

Firmographics suggested Company A was the better opportunity. Behavioural intelligence said Company B would close faster and easier.

Dynamic ICP scoring learns from these patterns and adjusts scoring based on which behavioural signals actually predict success in your specific market.

How It Works: Capabilities Without The Blueprint

The system captures behavioural signals your organisation already generates, validates them against your persona assumptions, and surfaces the gaps that matter.

Systematic Behavioural Capture

From your CRM:

• Which persona attributes correlate with won vs. lost deals?
• What deal characteristics predict velocity and size?
• Which stakeholder patterns lead to successful outcomes?
• How do actual customer segments differ from assumed segments?

From your sales conversations:

• What topics do prospects actually ask about (vs. what personas say they care about)?
• Which objections arise most frequently (vs. what you prepared for)?
• What messaging closes deals (vs. what your personas suggested)?
• How do successful sales calls differ from unsuccessful ones?

From your support data:

• What do customers actually struggle with (vs. what product thought they'd struggle with)?
• Which features drive the most confusion (behavioural signal of poor product-market fit)?
• What questions cluster around specific personas or segments?
• How does support engagement predict churn or expansion?

From your product usage:

• Which features do customers actually use (vs. which features personas ranked important)?
• What workflows do successful customers create?
• Where do users get stuck or abandon features?
• How does usage pattern correlate with expansion and retention?

From your content engagement:

• What content do high-value prospects engage with?
• How does content consumption predict deal progression?
• Which messaging resonates with which behavioural segments?
• What topics generate engagement vs. what personas suggest should matter?

The system doesn't create new data. It systematically captures signals your organisation already generates but typically leaves scattered across systems.

Persona Validation Framework

Once behavioural signals are captured systematically, the validation framework does three things:

1. Surfaces stated vs. actual gaps

For each persona attribute ("cares about X"), the system shows:

• How frequently does this actually arise in deals?
• How does it correlate with outcomes?
• How does it rank compared to other factors?
• Is this overweighted or underweighted in your current persona?

2. Identifies emergent patterns

Behavioural signals that appear frequently but aren't in your personas:

• New buyer concerns that have emerged recently
• Competitive dynamics that shifted priorities
• Market changes that affected decision criteria
• Use cases that became more/less common

3. Flags drift over time

Compares current behavioural patterns against historical baselines:

• Which persona attributes are becoming more/less relevant?
• How has decision-maker composition shifted?
• What new objections are emerging?
• How are engagement patterns changing?

This isn't an opinion about whether personas are accurate. It's a quantified  validation showing where assumptions align with reality and where they diverge.

Cross-Functional Intelligence Integration

The system creates a unified view by connecting behavioural signals across functions:

Sales + Product + Support convergence:

• Sales: Prospect asks about the mobile app in 60% of demos
• Product: Mobile app usage is 23% of total sessions
• Support: Mobile-related tickets are 8% of volume

Intelligence surfaced: Mobile is more important in the sales process than actual usage suggests. Either (a) you're attracting the wrong buyers with mobile messaging, or (b) the mobile experience needs improvement to match sales promises.

Marketing + Sales + Customer Success convergence:

• Marketing: Content about "enterprise features" gets the highest engagement
• Sales: "Enterprise features" mentioned in 31% of calls, not correlated with wins
• CS: Customers who adopted "enterprise features" have 2.1x higher NRR

Intelligence surfaced: Enterprise features drive long-term value but don't close deals. Marketing is attracting the right audience, but sales isn't emphasising the right value props during the sales cycle.

These insights only emerge when signals converge. Siloed data tells partial stories that lead to wrong conclusions.

Dynamic ICP Scoring

The system learns which behavioural patterns predict successful outcomes:

Traditional ICP scoring:

• Company size: 100-500 employees
• Industry: B2B SaaS
• Revenue: $10M-50M
• Tech stack: Salesforce, Slack, AWS

Score: 85/100 if all criteria match

Behavioural ICP scoring adds:

• Engagement velocity: Is the prospect moving at the pace of typical won deals?
• Stakeholder pattern: Does decision-maker composition match successful deals?
• Problem urgency: Is pain acute (high close rate) or chronic (low close rate)?
• Competitive context: Are they evaluating actively or researching passively?
• Content engagement: Does the consumption pattern match buyers who convert?

Combined score might be: 85/100 firmographic + 42/100 behavioral = 64/100 overall

That perfect firmographic fit might have behavioural signals suggesting low close probability.

The system learns from outcomes: Which behavioural patterns actually predicted success? Which were false signals? The scoring model refines over time based on your specific market dynamics.

What You're Actually Getting

100+Property Intelligence Model

A comprehensive behavioural data structure built natively in HubSpot that captures customer intelligence across the entire lifecycle. This isn't a survey or a spreadsheet; it's an infrastructure layer that systematically tracks behavioural signals and validates them against your persona assumptions.

The model includes:

• Persona validation metrics
• Behavioural engagement tracking
• Cross-functional intelligence synthesis
• ICP scoring components
• Drift detection mechanisms

All living in HubSpot, where your customer data already exists.

Persona Validation Dashboard

A systematic view showing:

• Stated persona priorities vs. actual behavioural priorities
• Gaps between assumptions and reality (quantified)
• Emerging patterns not captured in current personas
• Drift indicators showing how behaviour is shifting over time
• Confidence scores on persona accuracy

This isn't an opinion about whether personas are good. It's a data-driven  validation of which assumptions are holding up and which need revision.

Cross-Functional Intelligence Feeds

Connections between:

• Sales conversation data (what prospects care about)
• Product usage data (what customers actually use)
• Support ticket data (what causes friction)
• Marketing engagement data (what content resonates)
• Customer success data (what predicts retention)

The system doesn't require new tools. It connects data sources you already have into unified behavioural profiles.

Dynamic ICP Scoring

Behavioural pattern recognition that learns from your deals:

• Which signals predict successful outcomes in your market?
• How do behavioural patterns differ from firmographic assumptions?
• What early indicators suggest deal velocity or risk?
• How should scoring weights adjust based on actual results?

The scoring model isn't static. It learns from every deal and refines predictions over time.

Deployment: What Actually Happens

Week 1-2: System Deployment

The Customer Intelligence model deploys in your HubSpot instance. Core data structure goes live. Integration points connect to your existing systems (CRM, product analytics, support platform, marketing automation).

The team is trained on how the system works and what intelligence it surfaces.

Week 3-4: Baseline Establishment

Your current personas get mapped into the validation framework. Existing customer data populates the behavioural tracking. Initial patterns emerge showing stated vs. actual alignment.

First cross-functional intelligence reports surface: Here's what sales sees, here's what product sees, here's where they converge or diverge.

Week 5-8: Pattern Recognition

As more behavioural data flows through the system, patterns solidify. The validation framework identifies:

• Which persona assumptions align with behaviour
• Which assumptions diverge significantly
• What emergent patterns aren't captured in current personas
• Where cross-functional signals tell different stories

Initial persona validation reports delivered to product and GTM leadership.

Week 9-12: Learning Loop Activation

Dynamic ICP scoring calibrates based on won/lost deal patterns. The system learns which behavioural signals actually predict success in your market.

Drift detection activates—the system begins tracking how behavioural patterns change over time relative to baseline.

Day 90 Onward: Continuous Validation

The system operates continuously:

• Behavioural signals captured automatically
• Validation framework updates as new data flows
• Drift detection flags significant pattern changes
• ICP scoring is refined based on outcomes
• Quarterly reviews surface strategic implications

Real-World Application: What Actually Changes

Company Profile: B2B SaaS company, $45M ARR, product-led growth motion, 3 primary personas documented 18 months prior

Before:

• Personas based on customer interviews from 2 years prior
• Product roadmap prioritised features, personas ranked highly
• Sales enablement trained to lead with persona-defined pain points
• Marketing content targeted to persona-stated priorities
• No systematic validation of persona accuracy

Persona Assumption Example: "VP of Engineering cares most about API flexibility, scalability, and technical depth"

After Implementation - Behavioural Reality:

Sales conversation analysis (90 days of data):

• "API flexibility" was mentioned in 23% of calls with VPs of Engineering
• "Implementation timeline" was mentioned in 71% of calls
• "Team adoption ease" was mentioned in 64% of calls
• "Technical depth" was mentioned in 31% of calls

Product usage data (existing customers with VP Eng title):

• Advanced API features: 18% adoption rate
• Basic integrations: 89% adoption rate
• Time-to-first-value: Strong predictor of expansion (faster = higher NRR)

Win/loss analysis (behavioural pattern in closed deals):

• Won deals: Emphasised speed to implementation, team onboarding simplicity
• Lost deals: Perceived as "too complex" or "slow to deploy" in 52% of cases

Intelligence surfaced: The persona overweighted technical sophistication and underweighted operational simplicity. VPs of Engineering say they want flexibility, but behave as if they want ease of implementation.

Changes made based on behavioural intelligence:

Product roadmap: Shifted 2 quarters of engineering investment from advanced API features (low usage) to onboarding automation and integration templates (high engagement).

Sales enablement: Repositioned from "powerful and flexible" to "sophisticated but simple to deploy." Win rate in deals with VP Eng buyers increased from 28% to 34% over the next 2 quarters.

Marketing messaging: Landing page for engineering personas shifted from technical depth to implementation speed. Demo request rate increased 22%.

Measurable outcomes:

• Product feature adoption: 18% → 41% for key workflows after simplification
• Sales cycle with target persona: 87 days → 64 days average
• Persona accuracy (stated vs. actual alignment): Improved from 31% to 67% after 90-day validation cycle
• Product roadmap confidence: Product team reported 8.2/10 confidence in prioritisation decisions vs. 5.8/10 previously

What didn't change:

• The system didn't create better products or better sales execution
• Strategic decisions still required human judgment
• The personas weren't "wrong", they were incomplete reflections of behaviour
• Ongoing validation required continued attention

How It's Different: Intelligence vs. Assumptions

vs. Annual Persona Research

Traditional approach:

• Conduct customer interviews annually or biannually
• Synthesise qualitative insights into persona documents
• Distribute personas to teams
• Hope teams remember to reference them
• Personas become outdated before the next refresh cycle

System approach:

• Behavioural signals are captured continuously
• Quantitative validation against stated assumptions
• Persona accuracy measured, not assumed
• Drift detected automatically
• Intelligence stays current because it's systematic, not episodic

vs. Product Analytics Platforms

Product analytics (Amplitude, Mixpanel, Heap):

• Excellent at tracking product usage behaviour
• Shows what users do inside your product
• Limited visibility into pre-purchase behaviour
• No connection to sales, support, or marketing signals
• Doesn't validate persona assumptions, just tracks actions

Customer intelligence system:

• Tracks behaviour across the entire customer lifecycle
• Connects pre-purchase, purchase, and post-purchase signals
• Integrates sales, support, product, and marketing data
• Explicitly validates persona assumptions against behaviour
• Lives in HubSpot, where GTM teams already work

Complementary, not competitive. Product analytics shows behaviour in-product. Customer intelligence shows behaviour across the lifecycle.

vs. Customer Data Platforms (CDPs)

CDPs (Segment, Rudderstack, mParticle):

• Excellent at collecting and routing event data
• Technical infrastructure for data piping
• Doesn't provide persona validation or intelligence synthesis
• Requires the data team to extract insights
• Focused on data plumbing, not an intelligence framework

Customer intelligence system:

• Built for GTM team consumption, not data engineering
• Intelligence framework included, not just data collection
• Persona validation built in
• Lives in HubSpot, not separate infrastructure
• Focus on actionable intelligence, not technical flexibility

vs. Traditional Persona Consulting

Consulting approach:

• 8-12 week discovery and synthesis process
• Qualitative research produces persona documents
• Delivered as static artefacts
• No ongoing validation mechanism
• Expensive, episodic engagement

System approach:

• Pre-built intelligence model deployed in weeks
• Quantitative behavioural validation alongside qualitative insights
• A living system that updates continuously
• Ongoing validation built in
• Lower cost, continuous value

Frequently Asked Questions

Don't we already know our personas?

You know the personas you documented. The question is: Do those personas reflect how your customers actually behave?

Most teams discover significant gaps:

• Priorities ranked differently in documents vs. behaviour
• New patterns emerged that aren't in personas
• The market has shifted since personas were created
• Different functions have different views of the same persona

The system doesn't replace persona knowledge. It validates whether your persona knowledge reflects behavioural reality.

What if we don't have product usage data?

Product usage data is valuable but not required. The system captures behavioural signals from:

Minimum viable data sources:

• CRM deal data (what wins, what loses, how deals progress)
• Sales conversation intelligence (what prospects ask about, what closes deals)
• Support ticket data (what causes friction, what confuses customers)

Enhanced with if available:

• Product usage analytics
• Marketing engagement data
• Customer success interaction data

More data sources = richer intelligence. But the core persona validation works with CRM + sales conversation data alone.

How is this different from just analysing our CRM data?

Most teams have CRM data. Few systematically validate personas against it.

Typical CRM analysis:

• Ad hoc reports when someone asks a question
• Quarterly business reviews looking at aggregate metrics
• Deal post-mortems after significant wins/losses
• No structured framework for persona validation

Systematic customer intelligence:

• Continuous behavioural signal capture
• Structured validation framework
• Cross-functional data synthesis
• Automatic drift detection
• Intelligence surfaced proactively, not reactively

The data might exist in your CRM. The systematic validation framework typically doesn't.

What if our personas are actually accurate?

Then the system validates that accuracy with quantitative evidence. This is valuable.

Most product and GTM leaders have intuitions about persona accuracy but lack data to validate those intuitions. The system provides that validation.

If your personas are accurate, you get:

• Quantitative evidence to support roadmap decisions
• Early warning system when behaviour starts drifting
• Confidence in persona-driven investments
• Defence against stakeholders questioning persona assumptions

If your personas have gaps, you get:

• Specific identification of where assumptions diverge from behaviour
• Prioritised list of which gaps matter most
• Behavioural evidence to support persona updates
• Avoided investment in misaligned features

Either outcome is valuable.

How much ongoing effort does this require?

Automated (system handles):

• Behavioural signal capture from connected systems
• Persona validation calculations
• Drift detection and flagging
• Cross-functional intelligence synthesis
• ICP scoring updates

Quarterly human input (4-6 hours):

• Review persona validation reports
• Discuss strategic implications with product/GTM leadership
• Decide which behavioural insights warrant action
• Update persona documents if a significant drift is detected

Ongoing (built into normal workflow):

• Sales reps log win/loss reasons (already doing this)
• Support tickets continue flowing (already happening)
• Product usage tracked (already instrumented)
• No additional data entry burden on teams

The system requires far less effort than manual persona research while providing continuous validation instead of episodic insights.

What about qualitative insights from customer conversations?

Quantitative behavioural validation doesn't replace qualitative research. They're complementary.

Behavioural intelligence tells you:

• What customers actually do
• How stated priorities differ from actual behaviour
• Which patterns predict success
• When behaviour drifts from baseline

Qualitative research tells you:

• Why customers behave in certain ways
• What emotional drivers influence decisions
• How customers think about their problems
• What language resonates with them

Best approach: Use behavioural intelligence to identify which questions to ask in qualitative research. When behavioural data shows a gap, qualitative research explains why the gap exists.

Can we validate personas without this system?

Yes. Through manual analysis. Here's what that looks like:

Manual persona validation:

1. Export CRM data to spreadsheets (4-6 hours)
2. Analyse deal patterns for persona segments (8-12 hours)
3. Review call recordings for theme identification (10-15 hours)
4. Synthesise support ticket patterns (6-8 hours)
5. Cross-reference against product usage (if you have it) (4-6 hours)
6. Compile findings into presentation (6-8 hours)
7. Present findings and drive discussion (2-3 hours)

Total: 40-58 hours of manual effort, done quarterly if you're disciplined

Systematic validation:

• Intelligence continuously updated
• Reports generated automatically
• Cross-functional synthesis is built in
• Drift detected proactively

Total: 4-6 hours quarterly to review and act on intelligence

You can validate manually. Most teams lack the discipline and bandwidth to do it consistently.

How does this help with ICP scoring?

Traditional ICP scoring uses firmographic data: company size, industry, revenue, tech stack, and geography.

Firmographic ICP limitations:

• Tells you who looks like a fit, not who behaves like one
• Doesn't predict deal velocity, only theoretical fit
• Misses behavioural signals that predict success
• Doesn't learn from outcomes

Behavioural ICP enhancement:

• Adds behavioural signals to firmographic criteria
• Scores based on engagement patterns that predict success
• Identifies high-intent prospects even with marginal firmographics
• Learns which signals actually predict outcomes in your market
• Refines automatically based on won/lost patterns

Result: More accurate targeting, better deal qualification, improved sales efficiency.

Why This Matters in 2025

Three market shifts make behavioural persona validation essential:

1. Product-Led Growth Demands Behavioural Understanding

PLG motions generate massive behavioural signal volume that traditional personas can't capture. You're watching thousands of users engage with your product, but most personas were built from dozens of interviews.

The gap between interview-based personas and actual PLG behaviour compounds daily.

2. Buyers Research Invisibly

Modern B2B buyers complete 60-70% of their research before engaging with sales. Traditional persona research captures what they tell you in conversations, which is late-stage behaviour.

Early buying behaviour is increasingly invisible to qualitative research methods.

3. Persona Drift Accelerated

Markets change faster than annual persona refresh cycles. AI capabilities shifted buyer expectations in months. Competitor actions change priorities quarterly. Economic uncertainty alters decision criteria.

Personas age faster than ever. Manual refresh cycles can't keep pace.

What This Isn't

This isn't a replacement for talking to customers. Behavioural intelligence shows what customers do. Conversations reveal why they do it. Both matter.

This isn't "quantitative beats qualitative." It's "quantitative validates qualitative." Behavioural data tests whether interview insights reflect reality.

This isn't perfect persona accuracy. It's systematic validation that identifies gaps, surfacing where assumptions diverge from behaviour.

This isn't "set it and forget it." The system captures intelligence automatically, but strategic interpretation requires ongoing human judgment.

The Bottom Line

Most B2B personas describe the customers you wish you had, not the customers you actually have. They're built on stated preferences, not behavioural reality. They go stale quickly and rarely get validated systematically.

The ARISE Customer Intelligence Engine validates personas with behavioural data from across your customer lifecycle, surfacing gaps between what customers say and what they do, detecting drift as behaviour shifts, and enabling decisions based on reality rather than assumptions.

What you get:

• 100 + property behavioural intelligence model in HubSpot
• Systematic persona validation framework
• Cross-functional intelligence synthesis
• Dynamic ICP scoring that learns from outcomes
• Pre-built system deployed in weeks, validates continuously

What it requires:

• HubSpot CRM (where the system lives)
• Sales conversation data (calls, emails, deal progression)
• Support data (tickets, customer interactions)
• Willingness to challenge persona assumptions with data
• Quarterly reviews to act on behavioural insights

If your product roadmap is driven by personas built on assumptions rather than validated with behavioural reality, there's a better way.

Next Steps

Audit Your Persona Accuracy

Use our Persona Intelligence Audit to assess how well your current personas align with behavioural reality. Get a report showing stated vs. actual gaps across 12 key dimensions.

Calculate Your Misalignment Cost

Use our GTM Alignment Calculator to quantify how much persona misalignment costs across product, sales, marketing, and support. Get specific estimates for your company size and ARR.

See The System In Action

Book a 30-minute system demonstration to see how behavioural intelligence validates personas, surfaces drift, and enables data-driven product decisions.

Book Demo →

About ARISE GTM

ARISE GTM transforms traditional RevOps consulting through pre-built, Day 1 deployment HubSpot systems for B2B SaaS and FinTech companies. Our Customer Intelligence Engine brings behavioural validation to what most companies still build on assumptions and annual research cycles.

Ready to validate your personas with behavioural reality?