Product & Program Lead - Analytics & Insights

Job Purpose

What is the primary purpose of this job? Describe main objectives.

AkzoNobel is a global leader in paints and coatings, operating in over 150 countries, with a strong commitment to innovation and sustainability. Our Digital Transformation Team operates like a start‑up inside the enterprise, building next‑generation capabilities—including advanced analytics—to transition €10 billion in revenue through digital channels and accelerate profitable growth.

The Product & Program Lead - Digital Excellence Analytics sets the end‑to‑end strategy, portfolio, and operating model for analytics products that power Commercial, Marketing, Digital, and Customer Operations. This role converts business questions into insight products and decision tools, orchestrates multi‑year delivery programs, ensures adoption at scale, and routinely communicates data‑driven stories that move leaders and frontline teams to action.

Scope & Impact

• Own the Advanced Analytics product portfolio (marketing performance & ROI; commercial analytics, digital/lead funnel analytics; customer operations & service performance, SLA and OTIF/Backorders insights).
• Govern cross‑BU and cross‑region use cases, data standards, and reusability patterns to industrialize analytics into repeatable products.
• Lead the strategy, delivery, and adoption of analytics products that span the entire customer lifecycle—from acquisition and lead conversion, service, complaints etc. Drive actionable insights that improve revenue, retention, satisfaction, and operational efficiency across all customer touchpoints
• Define the holistic analytics vision, linking web, app, and omnichannel customer journeys for end-to-end visibility
• This role also collaborates with ISC – Deliver, Commercial Quality, I.T – Advance Analytics team  
• Ensure that every analytics release is valuable, usable, and adopted—with measurable outcomes (growth, margin, service, cycle time, and productivity).

Key Activities

Describe the responsibilities and accountabilities of the job. Which tasks is the job holder responsible for? What results are to be achieved?

Actionable Analytics – Strategy & Portfolio Ownership

• Define the vision, strategy, and north‑star metrics for Advanced Analytics; shape product roadmaps and value cases across Commercial, Marketing, Digital, and Customer Operations.
• Establish portfolio governance (stage gates, investment cases, benefits tracking) and prioritize initiatives using value, feasibility, and adoption potential.
• Define and own - Analytics Data products and its KPI framework

1. Customer Acquisition & Digital Sales Lead-to-opportunity conversion, Funnel performance (conversion rates, campaign ROI), Lead scoring and prioritization, Digital campaign effectiveness

2. Customer Onboarding & Service : SLA adherence, Service performance - OTIF (On-Time-In-Full),  Backorder

3. Complaints & After-Sales : Root cause analysis for complaints, Complaint cycle time

4. Customer Upsell/cross-sell propensity, Track impact on revenue and churn reduction

5. Web analytics using Google analytics, Adobe (Leveraging AI tools)

• Standardize product patterns (data model, features, UX, refresh cadence) to scale across BUs/regions.

Program Orchestration & Delivery

• Lead and orchestrate (with Advanced Analytics IT teams) multiple programs for analytics platform modernization and product rollout; run integrated program plans across data, model, visualization, and change workstreams.
• Align with IM/Digital/IT on platform evolution, integrations and non‑functional requirements (performance, security, scalability).
• Remove impediments, manage dependencies, and maintain executive‑level transparency on risks, timelines, and benefits.

Insight Excellence, Storytelling & Adoption

• Set standards for insight quality (accuracy, interpretability, explainability) and build storytelling rituals—executive readouts, frontline playbooks, and “what/so‑what/now‑what” narratives that drive decisions.
• Partner with Adoption/Change Leads to drive usage: communications, training, champions network, and role‑based enablement.
• Monitor adoption telemetry (active users, task completion, decision impact), run experiments (A/B), and iterate to improve product/market fit.

Cross‑Functional Leadership & Value Realization

• Engage senior stakeholders across Sales/Commercial, Marketing, Digital, and Customer Operations; co‑create value hypotheses and define decision moments where analytics must inform action.
• Translate insights into operational changes (process, policy, incentives) with BU/Regional leaders and shared services.
• Track and publish value realization—revenue uplift, margin improvement, service level gains, cycle‑time reduction.

Data, Platform & Architecture Alignment

• Partner with Data Science/Engineering to choose appropriate methods (predictive, prescriptive, causal) and productionize models reliably.
• Ensure robust data governance (definitions, lineage, quality rules) across the analytics portfolio and alignment to enterprise architecture.

Talent & Culture

• Build and mentor a high‑performing analytics organization — Product Owners, Product Managers in digital domains, GPO Customer Operations.
• Foster a culture of experimentation, evidence‑based decisions, and human‑centered analytics focused on end‑user outcomes

Experience

List the essential educational qualifications, relevant experience and required skills.

Education & Experience

• 10–15+ years in product management/program leadership for data & analytics, digital or commercial technology—within global, multi‑region organizations.
• Demonstrated success building and scaling analytics products (dashboards, decision tools, AI/ML models) from concept to adoption and measurable business impact.
• Deep exposure to Commercial, Marketing, Digital, and Customer Operations domains; comfortable engaging with senior business leaders.

Technical & Product Skills

• Strong understanding of various Systems , Datasets
• SAP ERP (SD): Sales Orders, Deliveries, Invoices (including credit/debit memos);
• SAP COPA for profitability analysis.
• Salesforce (CRM) or equivalent system : Leads, Opportunities, Accounts, Activities, Campaigns.
• Strong understanding of analytics platforms, modern data stacks, KPI frameworks, visualization tools (e.g., Power BI/Tableau/Qlik, Celonis), and AI/ML productization practices.
• Fluency in non‑functional requirements (performance, security, scalability), APIs/integrations, data governance, and lifecycle management.

Advanced Analytics & Data Science

• Strong understanding of descriptive, diagnostic, predictive and prescriptive analytics, along with forecasting, segmentation and optimization.
• Ability to translate business problems into analytical questions and guide data science on model design, validation, explainability and performance monitoring.
• Familiarity with ML Ops, model lifecycle management and productionization best practices.

Data Architecture & Engineering Alignment

• Solid understanding of modern data architectures (data lake/lakehouse), semantic layers and data pipelines.
• Ability to partner with Data Engineering on ingestion, lineage, data quality and dependencies.
• Knowledge of data governance, metadata, access controls and compliance considerations.

Visualization, UX & Decision Tools

• Strong command of visualization and dashboard best practices (Power BI/Tableau/Qlik).
• Skilled at shaping intuitive, action-oriented insights and decision tools rather than static reports.
• Ability to ensure analytics experiences are simple, relevant and aligned to real user journeys.

Leadership & Communication

• Exceptional business storytelling—able to turn data into compelling narratives and clear recommendations; confident presenter to senior leadership.
• Strong stakeholder management; able to influence across BUs/regions and align diverse teams without direct authority.
• Proven people leadership: hiring, coaching, career development, and building communities of practice.