Question 1

What is a Fractional Chief Growth Officer (CGO)?

Accepted Answer

A Fractional Chief Growth Officer (CGO) is a senior executive who provides strategic growth leadership on a part-time or project basis, typically working 6-10 days per month with multiple companies. Unlike a full-time CGO (€120K-€180K+ annually), fractional CGOs offer C-suite expertise at a fraction of the cost (€20K-€50K annually), making them ideal for mid-sized companies (€2M-€30M ARR) and funded scale-ups that need strategic guidance without the commitment or expense of a full-time hire. Fractional CGOs architect revenue systems, align cross-functional teams (Marketing, Sales, Customer Success, Product), and implement AI-powered growth infrastructure while building internal capability rather than creating ongoing dependency.

Question 2

What is RevOps (Revenue Operations)?

Accepted Answer

RevOps (Revenue Operations) is the strategic alignment and integration of Marketing, Sales, and Customer Success teams, processes, and technology to optimize the entire revenue generation lifecycle. Rather than operating in silos, RevOps creates unified systems for lead management, pipeline visibility, customer lifecycle tracking, and revenue forecasting. Core components include: unified CRM infrastructure (single source of truth for customer data), standardized processes across revenue teams, integrated technology stack with API-first architecture, shared KPIs and attribution models, and data governance ensuring GDPR compliance and audit trails. Companies with mature RevOps typically see 15-20% higher revenue growth and 10-15% improvements in customer retention compared to siloed operations.

Question 3

What is MarTech?

Accepted Answer

MarTech (Marketing Technology) refers to the software, platforms, and tools used to plan, execute, measure, and optimize marketing activities. The typical B2B MarTech stack includes: CRM platforms (Salesforce, HubSpot, Pipedrive), Marketing Automation (Pardot, Marketo, ActiveCampaign), Customer Data Platforms (CDPs) for unified data management, Analytics & Attribution tools (Google Analytics 4, Amplitude), Email & Communication platforms, AI-powered tools for personalization, content generation, and lead scoring, and Compliance & Governance tools (GDPR, consent management). For mid-sized companies, MarTech stack complexity is a common challenge—the average B2B company uses 15-25 different tools, often disconnected. Strategic MarTech architecture focuses on integration, data flow, and avoiding vendor sprawl while maintaining compliance in regulated industries like Financial Services.

Question 4

What is GTM (Go-to-Market)?

Accepted Answer

GTM (Go-to-Market) is the strategic plan for how a company brings a product or service to market, acquires customers, and achieves competitive advantage. A comprehensive GTM strategy includes: Target market definition (ICP - Ideal Customer Profile, buyer personas, market segmentation), Value proposition and positioning (differentiation vs. competitors, messaging framework), Sales & marketing strategy (inbound vs. outbound, channel selection, pricing model), Customer acquisition playbooks (lead generation, qualification criteria, sales enablement), Revenue model and unit economics (CAC, LTV, payback period), and Launch execution plan (timeline, resource allocation, success metrics). For spin-offs and new ventures (common in Florian's FinTech background), GTM strategy is particularly critical as it determines product-market fit and early traction velocity. Effective GTM reduces time-to-revenue and increases capital efficiency—especially important for funded scale-ups with 12-18 month runways.

Question 5

What is Marketing Automation?

Accepted Answer

Marketing Automation is the use of software platforms to automate repetitive marketing tasks, personalize customer communications at scale, and track engagement throughout the buyer journey. Key capabilities include: Email campaign automation (drip sequences, behavioral triggers, A/B testing), Lead scoring and grading (behavioral scoring, demographic fit, engagement tracking), Multi-channel orchestration (email, LinkedIn, retargeting, SMS), Personalization engines (dynamic content, AI-powered recommendations), Analytics and attribution (campaign ROI, multi-touch attribution, pipeline impact), and CRM integration (bidirectional data sync, lead routing, sales notifications). Common platforms include Pardot (Salesforce), Marketo (Adobe), HubSpot, ActiveCampaign, and Brevo (formerly Sendinblue). For B2B companies, marketing automation typically delivers 15-20% increase in qualified leads and 10-15% improvement in conversion rates when properly implemented. Critical success factors: clean data governance (GDPR compliance), progressive profiling strategies, and sales-marketing alignment on lead definitions.

Question 6

What are MQL and SQL?

Accepted Answer

MQL (Marketing Qualified Lead) is a prospect who has shown interest through marketing engagement and meets basic fit criteria, but has not yet been vetted by sales. Qualification typically includes: behavioral signals (content downloads, webinar attendance, demo requests, email engagement), demographic fit (company size, industry, job title), and engagement score threshold (varies by company, often 50-100 points). SQL (Sales Qualified Lead) is an MQL that has been vetted by sales, confirmed to have genuine buying intent, budget, authority, need, and timeline (BANT framework). The MQL→SQL conversion rate is a critical RevOps metric, typically ranging from 15-30% in healthy B2B funnels. Low conversion rates indicate misalignment between marketing and sales on lead definition, poor lead quality, or inadequate follow-up processes. Companies with strong RevOps alignment typically see 20-25% higher MQL→SQL conversion rates due to clearer definitions, faster routing, and better handoff processes.

Question 7

What are AI Revenue Systems?

Accepted Answer

AI Revenue Systems are integrated platforms and workflows that apply artificial intelligence and machine learning to revenue generation processes, automating repetitive tasks, improving decision-making, and accelerating pipeline velocity. Key applications include: Predictive Lead Scoring (ML models analyzing historical conversion patterns to prioritize high-intent leads), Competitive & Market Intelligence (automated monitoring of competitor pricing, product launches, regulatory changes), Personalization Engines (AI-powered content recommendations, dynamic website experiences, email customization), Sales Forecasting (predictive models for pipeline close rates, deal velocity, revenue projections), Customer Churn Prediction (ML algorithms identifying at-risk accounts for proactive retention), and Process Automation (AI-powered customer support, document generation, data enrichment). Unlike generic AI hype, Applied AI focuses on practical, compliance-aware implementations using tools like ChatGPT, Claude (Anthropic), Perplexity, and industry-specific platforms. Critical for regulated industries: AI Act compliance, GDPR data governance, explainable AI for audit trails, and human-in-the-loop approval workflows.

Question 8

What is Data Governance in B2B Marketing?

Accepted Answer

Data Governance in B2B Marketing is the framework of policies, processes, and controls ensuring data quality, security, compliance, and ethical use across marketing systems. Essential components include: GDPR Compliance (consent management, right to erasure, data portability, privacy-by-design), Data Quality Standards (deduplication rules, enrichment protocols, standardized field formats), Access Controls (role-based permissions, data classification, audit logging), Integration Governance (API security, data flow mapping, master data management), Retention & Deletion Policies (automated purging, archive strategies, litigation holds), and Documentation & Training (data dictionaries, process documentation, team onboarding). For Financial Services companies (banks, fintechs, rating agencies), data governance is non-negotiable due to regulatory requirements (ESMA for rating agencies, Basel III for banks, PSD2 for payment platforms). Companies with strong data governance see 25-30% improvements in marketing campaign performance due to higher data quality and 50-60% faster compliance audits. Common pitfalls: treating governance as one-time project vs. ongoing practice, lack of executive sponsorship, insufficient training budgets.

Question 9

What is Lead Scoring?

Accepted Answer

Lead Scoring is a methodology for ranking prospects based on their perceived value and likelihood to convert, using a points-based system that combines behavioral engagement (explicit scoring) and demographic/firmographic fit (implicit scoring). Behavioral Scoring includes: content engagement (whitepaper downloads +15 pts, webinar attendance +20 pts, pricing page visits +25 pts), email engagement (email opens +5 pts, link clicks +10 pts), website activity (multiple visits +10 pts, demo requests +50 pts), and social engagement (LinkedIn profile views +5 pts, post comments +10 pts). Firmographic Scoring includes: company size (target range +20 pts, too small/large -10 pts), industry (ideal vertical +25 pts, out-of-scope -20 pts), job title (decision-maker +30 pts, IC +5 pts), and geography (target market +15 pts, excluded region -15 pts). Advanced implementations use AI-powered Predictive Lead Scoring, where machine learning models analyze thousands of historical conversions to identify patterns invisible to rule-based systems, typically improving conversion predictions by 20-30%. MQL threshold is typically set at 75-100 points, though this varies by sales cycle length and average deal size. Best practice: review and recalibrate scoring models quarterly based on actual conversion data.

Question 10

What is Pipeline Velocity?

Accepted Answer

Pipeline Velocity measures how quickly deals move through the sales pipeline and generate revenue, calculated as: (Number of Opportunities × Average Deal Size × Win Rate) ÷ Sales Cycle Length. This metric is critical for revenue forecasting and identifying bottlenecks in the sales process. Key drivers include: Sales Cycle Length (days from MQL to closed-won—shorter is better, typical B2B range: 30-180 days), Win Rate (percentage of opportunities that close—industry average 15-25%, top performers 30-40%), Average Deal Size (ARR per customer—impacted by pricing, upsells, multi-year contracts), and Opportunity Volume (number of qualified opportunities entering pipeline monthly). To improve pipeline velocity: Reduce sales cycle length (better qualification, sales enablement, decision-maker access), Increase win rate (competitive positioning, proof points, pricing optimization), Expand deal size (upsell opportunities, multi-product bundles, enterprise tier), and Accelerate opportunity creation (marketing efficiency, outbound effectiveness, partnership channels). RevOps teams typically focus on pipeline velocity as a North Star metric because it synthesizes multiple revenue drivers into a single, actionable number. Companies improving pipeline velocity by 20% see corresponding 15-20% revenue growth within 6-12 months.

Question 11

What is CAC (Customer Acquisition Cost)?

Accepted Answer

CAC (Customer Acquisition Cost) is the total expense required to acquire a new customer, calculated as: (Total Sales & Marketing Expenses) ÷ (Number of New Customers Acquired) over a specific period (typically monthly or quarterly). Comprehensive CAC includes: Marketing expenses (paid advertising, content production, events, tools/software, agency fees), Sales expenses (salaries, commissions, enablement, travel, CRM costs), and Overhead allocated to revenue teams (office space, technology infrastructure, management). Blended CAC (all customers) vs. Segmented CAC (by channel, segment, or product) provides deeper insights—often inbound CAC is 30-50% lower than outbound CAC. Critical metrics: CAC Payback Period (months to recover acquisition cost—healthy B2B SaaS: 12-18 months), LTV:CAC Ratio (lifetime value to acquisition cost—target 3:1 or higher, <2:1 is unsustainable), CAC by Channel (identifies most efficient acquisition sources). To optimize CAC: Improve conversion rates (better targeting, messaging, sales enablement), Reduce sales cycle (faster qualification, streamlined processes), Optimize channel mix (shift budget to highest-ROI channels), and Implement attribution models (understand true contribution of each touchpoint). For funded scale-ups, high CAC is acceptable early if LTV supports it, but CAC must decrease as efficiency improves (Rule of 40: Growth Rate + Profit Margin ≥ 40%).

Question 12

What is LTV (Lifetime Value)?

Accepted Answer

LTV (Lifetime Value or Customer Lifetime Value) is the predicted total revenue a company will earn from a customer relationship over its entire duration. For subscription businesses (SaaS, FinTech platforms), LTV is calculated as: (Average Revenue Per Account × Gross Margin %) ÷ Churn Rate. Example: ARPA €500/month, 80% gross margin, 5% monthly churn = LTV of €8,000. Key components include: Revenue Expansion (upsells, cross-sells, usage-based growth—often 20-40% of LTV), Retention Duration (longer retention = higher LTV, enterprise customers typically 3-5x longer than SMB), Gross Margin (SaaS typically 70-85%, services 40-60%), and Discount Rate (for multi-year projections, apply time-value-of-money adjustments). Strategic implications: High LTV enables higher CAC (justify expensive acquisition channels), Cohort Analysis reveals LTV trends over time (improving = product-market fit strengthening), Segmentation by ICP shows which customer profiles deliver highest LTV (informs targeting), and Product Development prioritizes features that improve retention and expansion (directly increasing LTV). Healthy SaaS metrics: LTV:CAC ratio ≥3:1, CAC payback <18 months, Net Dollar Retention (NDR) ≥110%. For Financial Services (banks, rating agencies), calculating LTV requires adjusting for regulatory compliance costs, churn drivers (M&A activity, regulatory changes), and multi-product relationships (cross-selling multiplier effect).

Question 13

What is Attribution Modeling?

Accepted Answer

Attribution Modeling is the process of determining which marketing touchpoints receive credit for conversions and revenue, enabling data-driven budget allocation and channel optimization. Common models include: First-Touch Attribution (100% credit to first touchpoint—useful for top-of-funnel awareness), Last-Touch Attribution (100% credit to final touchpoint—useful for conversion optimization), Linear Attribution (equal credit to all touchpoints—gives full journey visibility), Time-Decay Attribution (more credit to recent touchpoints—balances awareness and conversion), Position-Based (U-Shaped) Attribution (40% first, 40% last, 20% distributed—emphasizes discovery and conversion), and Machine Learning Attribution (data-driven models using algorithms to assign credit based on historical patterns—most accurate for complex B2B journeys). For B2B companies with long sales cycles (90-180+ days) and multiple touchpoints (15-25 interactions typical), multi-touch attribution is critical because single-touch models severely distort reality. Implementation challenges: Cross-device tracking (mobile, desktop, email), Offline touchpoint integration (events, sales calls, direct mail), Dark social attribution (private messaging, direct traffic), and Data quality (UTM parameters, consistent tracking, CRM integration). Advanced attribution requires: Unified data platform (CDP or data warehouse), Clean taxonomy (campaign naming, UTM standards, channel definitions), Statistical rigor (sufficient conversion volume for ML models), and Executive buy-in (shifting budgets based on attribution insights). Companies with mature attribution see 15-25% improvements in marketing ROI through better budget allocation.

Question 14

What is a CDP (Customer Data Platform)?

Accepted Answer

A CDP (Customer Data Platform) is a centralized system that collects, unifies, and activates customer data from multiple sources to create a single, persistent customer view accessible to marketing, sales, and service teams. Unlike CRMs (which manage relationships and workflows) or DMPs (which handle anonymous audience data), CDPs specialize in identity resolution and real-time data orchestration. Core capabilities include: Data Ingestion (APIs, webhooks, batch imports from websites, CRM, email, mobile apps, customer service, offline sources), Identity Resolution (matching disparate identifiers—emails, device IDs, account IDs—to unified profiles using probabilistic and deterministic matching), Segmentation & Audience Building (real-time segments based on behavioral, demographic, and predictive criteria), Activation (pushing unified data to execution platforms—email, ads, personalization engines, analytics tools), and Governance & Compliance (consent management, data retention, GDPR controls, audit trails). Popular CDPs include Segment, mParticle, Tealium, Adobe Real-Time CDP, and Salesforce CDP (formerly Salesforce DMP). For B2B companies, CDPs solve the 'data silo problem'—marketing automation knows email behavior, CRM knows sales interactions, product analytics knows usage, but none share a complete view. ROI typically comes from: Improved personalization (15-25% lift in engagement rates), Reduced data integration costs (centralized pipelines vs. point-to-point connections), Faster time-to-insight (unified reporting vs. manual data assembly), and Better compliance posture (centralized consent and deletion workflows). Critical for Financial Services due to GDPR/data residency requirements and audit trail needs.

Question 15

What is Technical CEO Advisory?

Accepted Answer

Technical CEO Advisory is strategic guidance for non-technical CEOs navigating complex MarTech decisions, vendor evaluations, and AI marketing integration. This service helps CEOs without a technical marketing background make informed decisions about: MarTech Stack Selection (evaluating CRM platforms like Salesforce vs. HubSpot, choosing marketing automation tools, assessing integration complexity and total cost of ownership), Vendor Due Diligence (translating vendor technical specifications into business implications, identifying overpromises or lock-in risks, ensuring compliance with GDPR, AI Act, and industry regulations), AI Implementation Strategy (separating AI hype from practical applications, designing compliant AI workflows for lead scoring, personalization, and automation, building internal AI capability vs. vendor dependency), Build vs. Buy Decisions (when to use off-the-shelf platforms vs. custom development, evaluating technical feasibility and resource requirements, balancing speed-to-market with long-term flexibility), and Data Infrastructure Planning (architecting data flows between systems, ensuring GDPR compliance and audit trails, planning for scale without over-engineering). Common triggers for Technical CEO Advisory: 'Our CMO departed and I need to make MarTech decisions,' 'We're choosing between 3 CRM platforms and can't evaluate technical trade-offs,' 'Vendors promise AI miracles but I don't know what's realistic,' 'I need independent validation of our agency's technical recommendations,' 'How do we implement marketing automation without violating GDPR?' This service often serves as an entry point for deeper engagements (Fractional CGO, RevOps Architecture) once trust and strategic alignment are established. Typical engagement: 2-4 focused advisory sessions over 4-8 weeks, deliverables include decision frameworks, vendor comparison scorecards, implementation roadmaps, and risk assessments.

B2B Growth & RevOps Glossary

What is a Fractional Chief Growth Officer (CGO)?

What is RevOps (Revenue Operations)?

What is MarTech (Marketing Technology)?

What is Technical CEO Advisory?

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