Analytics Head

Key Responsibilities

Analytics Strategy

• Define and execute the organization’s analytics strategy aligned with business, marketing, sales, and customer objectives.
• Establish a roadmap for data integration, reporting, dashboards, attribution, forecasting, and advanced analytics.
• Define how performance should be measured across campaigns, channels, customer segments, accounts, services, and markets.
• Create an analytics operating model covering data ownership, governance, reporting standards, and stakeholder access.
• Identify high-impact use cases where analytics can improve growth, productivity, and decision-making.

Measurement Framework and KPI Governance

• Define key performance indicators for brand, content, social media, lead generation, CRM, sales, and customer engagement.
• Establish consistent business definitions, calculation methods, targets, and reporting frequencies.
• Create a centralized metric dictionary and reporting governance framework.
• Ensure teams use consistent and trusted data in reviews and decision-making.
• Review existing metrics and eliminate conflicting or low-value reporting.

Marketing and Campaign Analytics

• Measure campaign reach, engagement, conversion, lead quality, pipeline contribution, revenue influence, and return on investment.
• Analyse performance across paid media, search, social media, email, events, webinars, content, and account-based programs.
• Develop multi-touch and source-based attribution approaches appropriate for enterprise sales cycles.
• Identify the audiences, channels, messages, and assets that drive the strongest outcomes.
• Recommend campaign and budget optimizations based on performance data.

Sales and Revenue Analytics

• Build analytics around pipeline, conversion, sales velocity, win rates, deal size, forecast accuracy, and revenue performance.
• Analyse performance by region, industry, service line, account segment, source, and sales stage.
• Identify pipeline bottlenecks and revenue risks.
• Partner with sales leaders to improve forecasting, resource allocation, and account prioritization.
• Develop models that support opportunity scoring and pipeline planning.

Customer and Account Analytics

• Build account-level views combining engagement, campaign, sales, opportunity, and customer data.
• Identify high-potential accounts, buying signals, engagement trends, cross-sell opportunities, and churn indicators.
• Support account-based marketing and strategic account programs with actionable insights.
• Develop segmentation models based on industry, company size, behavior, needs, and commercial value.
• Analyse customer journeys and lifecycle performance.

Digital and Content Analytics

• Analyse website traffic, search visibility, user behavior, content engagement, conversions, and digital journeys.
• Measure the performance of blogs, reports, case studies, videos, landing pages, and thought-leadership assets.
• Identify content gaps, high-performing themes, audience interests, and conversion opportunities.
• Work with content, social media, and digital teams to improve engagement and organic growth.
• Develop dashboards for website and content performance.

Data Integration and Quality

• Partner with CRM, IT, marketing operations, and business teams to integrate data from relevant systems.
• Define data requirements, source-of-truth rules, validation standards, and refresh schedules.
• Monitor data accuracy, completeness, consistency, and reliability.
• Resolve discrepancies across CRM, advertising, marketing automation, web analytics, finance, and other platforms.
• Build documented and auditable data pipelines and reporting processes.

Dashboards and Business Reporting

• Develop executive, operational, and campaign-level dashboards.
• Automate recurring reports wherever possible.
• Present insights through clear narratives, visualizations, trends, risks, and recommended actions.
• Tailor reporting for leadership, marketing, sales, and operational audiences.
• Support monthly, quarterly, and annual business reviews.

Forecasting and Advanced Analytics

• Build forecasting models for leads, pipeline, conversions, revenue, and campaign outcomes.
• Develop lead-scoring, account-prioritization, propensity, and segmentation models.
• Apply statistical techniques to identify patterns, correlations, and performance drivers.
• Explore responsible use of artificial intelligence and machine learning for prediction and insight generation.
• Continuously test and improve model accuracy.

Team and Stakeholder Leadership

• Build and manage a team of business analysts, marketing analysts, data analysts, and reporting specialists.
• Partner with leadership, finance, sales, marketing, CRM, and technology teams.
• Translate stakeholder questions into analytical requirements and decision-ready outputs.
• Build analytical capability among business teams through training and self-service reporting.
• Manage analytics vendors, data providers, consultants, and technology partners.