The Revenue Growth Manager - AI-Enabled RGM for Personal Health will lead the commercial pricing and profitable growth agenda across Philips Personal Health products, with a focus on pocket price waterfall architecture, price-pack architecture, product pricing, bundling strategy, promotional effectiveness, trade spend optimization, and Philips sell-in price to retailers.
This is an AI-inclusive commercial role. The individual will use AI, GenAI, predictive analytics, scenario simulation, and decision intelligence to improve the speed, quality, consistency, and adoption of RGM decisions. The role is not expected to be a pure data scientist or ML engineer; however, the candidate must be able to frame AI use cases, define business logic, partner with data science and engineering teams, validate model outputs, and embed AI-enabled recommendations into business workflows.
Operating with a commercial strategist, analytics translator, and builder mindset, the individual will convert consumer, shopper, competitor, retailer, financial, and market data into clear decisions that protect margin, accelerate profitable growth, and strengthen retailer partnerships.
The role will build repeatable RGM frameworks, AI-assisted decision tools, dashboards, price guardrails, approval routines, and playbooks that move the organization from ad-hoc analysis to scalable, AI‑powered, insight‑led commercial decision making.
Key Responsibilities 1) RGM Strategy, Governance & AI-Enabled Operating Model- Own the RGM agenda across pricing, price-pack architecture, pocket price waterfall, mix, portfolio, bundling, promotions, trade spend, and retailer sell-in price.
- Define RGM decision rights, KPIs, governance routines, approval guardrails, and market/channel performance standards.
- Identify and prioritize AI-enabled RGM use cases that improve speed, consistency, granularity, and quality of commercial decisions.
- Create a human-in-the-loop operating model for AI-assisted recommendations, ensuring business accountability, explainability, and clear decision ownership.
- Partner with Sales, Marketing, Finance, E-commerce, Analytics, IT, and regional teams to embed RGM discipline into annual planning, customer planning, and business reviews.
- Own the pocket price waterfall architecture across list price, invoice price, discounts, rebates, trade terms, promo funding, value leakage, net price, pocket price, and contribution margin.
- Build transparent retailer- and channel-level views of gross-to-net movement and margin impact.
- Partner with Sales and Finance to define Philips sell-in price to retailers, customer terms, margin targets, and guardrails for non-standard discounts.
- Use AI-assisted anomaly detection and scenario analysis to identify price leakage, margin dilution, inconsistent trade terms, and improvement opportunities.
- Translate pocket price insights into clear commercial actions for retailer negotiations, joint business planning, and customer profitability reviews.
- Lead pricing recommendations for new launches, range refreshes, lifecycle pricing, retailer-specific offers, and price changes across Personal Health categories.
- Design price-pack architecture across good/better/best ladders, channel packs, promotion-ready configurations, bundles, accessories, refills, and replacement components.
- Analyze consumer value perception, competitor pricing, price gaps, willingness to pay, elasticity, retailer margin expectations, and channel price consistency.
- Use AI-enabled simulations and predictive analytics to evaluate pricing scenarios, portfolio trade-offs, demand impact, margin impact, and retailer economics before execution.
- Create pricing guardrails and decision playbooks by category, product tier, channel, and retailer to protect premium positioning and improve price realization.
- Develop bundling strategies for devices, accessories, refills, grooming kits, beauty devices, oral care products, and mother & child care offerings.
- Assess bundle economics, attachment rates, cannibalization, operational feasibility, customer margin, shopper relevance, and value communication.
- Use AI-assisted scenario planning to identify the best bundle constructs, promo bundles, retailer-exclusive packs, and premiumization opportunities.
- Shape portfolio mix and channel-specific assortments for modern trade, e-commerce, marketplaces, distributors, and strategic retailers.
- Support product and category teams with commercial inputs that improve launch success, margin quality, and range productivity.
- Measure promotion ROI, incrementality, uplift, cannibalization, stock-up effect, post-promo dip, margin impact, and retailer/customer profitability.
- Recommend optimal promotional mechanics, discount depth, timing, event participation, funding allocation, and guardrails by channel and retailer.
- Apply AI and advanced analytics to improve promo planning, simulate event outcomes, detect inefficient spend, and optimize trade investment decisions.
- Partner with Sales, Marketing, Finance, and E-commerce to embed promotion recommendations into annual operating plans, customer joint business plans, and campaign calendars.
- Create repeatable post-event learning loops so that promotion insights continuously improve future decisions.
- Act as the business product owner for AI-enabled RGM solutions such as a pocket price waterfall cockpit, price-pack architecture optimizer, promotion ROI advisor, bundle simulator, retailer negotiation copilot, and RGM self-serve insights assistant.
- Translate commercial questions into clear AI/analytics product requirements, success metrics, test cases, adoption criteria, and decision workflows.
- Enable conversational analytics and self-serve insights using GenAI, LLM-based tools, and internal enterprise platforms where appropriate.
- Work with data science, analytics, and engineering teams to validate model outputs, challenge assumptions, improve explainability, and ensure recommendations are commercially usable.
- Create reusable prompt patterns, decision playbooks, and AI-assisted workflows that reduce manual work while improving business judgment, speed, and consistency.
- Build RGM capability across commercial teams through training, toolkits, playbooks, KPI definitions, and governance forums.
- Train teams to use AI-enabled RGM tools responsibly, including when to trust, challenge, override, or elevate AI-generated recommendations.
- Embed responsible AI principles such as transparency, explainability, privacy, fairness, human oversight, traceability, and risk management.
- Automate manual RGM processes using data pipelines, workflows, BI tools, scripts, and AI-assisted methods where appropriate.
- Act as an evangelist for data-driven and AI-powered decision-making while keeping accountability with business owners.
- Deep understanding of Revenue Growth Management across pricing, price-pack architecture, promotions, trade spend, mix management, and gross-to-net / pocket price management.
- Strong experience with product pricing, retailer sell-in pricing, consumer price ladders, channel pricing, customer terms, and commercial margin management.
- Ability to design and manage pocket price waterfall models and convert insights into business actions.
- Experience creating pricing guardrails, promotion guardrails, trade investment frameworks, retailer/customer-level recommendations, and governance routines.
- Strong grasp of consumer goods, personal care, consumer health, consumer durables, or retail economics, including retailer margins, category dynamics, competitor pricing, and shopper behavior.
- Practical AI fluency: ability to identify high-value RGM use cases for AI, GenAI, automation, predictive analytics, simulation, and decision intelligence.
- Ability to work with data science and engineering teams to frame problems, define features and business logic, evaluate model outputs, identify bias or limitations, and translate analytics into decisions.
- Working knowledge of price elasticity, demand forecasting, promotional uplift, incrementality, optimization, scenario modeling, and AI-assisted decision support.
- Experience using GenAI or AI tools for insight generation, executive summarization, market signal synthesis, workflow automation, conversational analytics, or decision support.
- Strong ability to balance AI recommendations with commercial judgment, retailer realities, consumer value perception, and brand strategy.
- Strong analytical capability with advanced Excel and BI tools such as Power BI, Qlik, Tableau, or similar platforms.
- Strong data literacy and ability to work with large commercial datasets including sell-in, sell-out, POS, pricing, promotions, trade terms, customer margins, e-commerce pricing, and market share.
- SQL capability is strongly preferred; Python or R exposure is a plus for advanced analytics collaboration and rapid prototyping.
- Working understanding of enterprise data platforms, semantic layers, data governance, data quality, and reusable data products; Azure Data Lake / PHDOD and Databricks exposure is highly preferred.
- Ability to define data logic, assumptions, limitations, reconciliation checks, and governance requirements transparently to build trust in RGM outputs and AI-enabled tools.
- Commercially sharp, structured, and comfortable making recommendations with imperfect data.
- Strong stakeholder management and influencing skills across Sales, Marketing, Finance, Category, E-commerce, Analytics, IT, and regional leadership teams.
- Ability to simplify complex pricing, analytics, and AI model outputs into executive-ready decisions.
- Builder mindset with ability to create tools, frameworks, playbooks, AI-assisted workflows, and governance routines from scratch.
- High ownership, attention to detail, learning agility, and comfort operating in a matrixed global/regional environment.
- Bachelor degree in Business, Economics, Finance, Marketing, Engineering, Statistics, Data Analytics, Computer Science, or a related field.
- MBA or Master degree in Business Analytics, Economics, Finance, Marketing, Data Analytics, or a related discipline preferred.
- Continuous learning mindset expected, especially in RGM, pricing, AI, GenAI, analytics, data platforms, retail/e-commerce, and responsible AI adoption.
- 12+ years of experience in Revenue Growth Management, Pricing, Commercial Strategy, Trade Marketing, Sales Finance, Category Management, Commercial Analytics, or related commercial roles.
- Proven experience in pricing, price-pack architecture, pocket price waterfall / gross-to-net analysis, product pricing, promotional effectiveness, trade spend optimization, bundling, or retailer/customer profitability.
- Demonstrated experience adopting or building AI-enabled, analytics-led, or automated decision tools that improved commercial decision speed, accuracy, consistency, or business impact.
- Experience working with retailers, distributors, modern trade, e-commerce marketplaces, or key account teams, ideally including retailer sell-in price, customer terms, and joint business planning.
- Experience partnering with data science, analytics, data engineering, IT, or digital teams to define business requirements, validate outputs, and drive adoption of scalable tools.
- Demonstrated ability to convert analysis into measurable business impact, such as improved price realization, margin expansion, promotion ROI improvement, trade spend efficiency, or premium mix growth.
- Experience operating in cross-functional, matrixed environments with senior stakeholder exposure.
- Consumer goods, consumer health, beauty/personal care, small appliances, consumer durables, retail, or e-commerce experience strongly preferred.
- Experience in Personal Health, consumer healthcare, beauty, grooming, oral care, mother & child care, or adjacent categories.
- Experience with AI-enabled RGM tools, pricing engines, promotion optimization, scenario simulators, GenAI copilots, conversational analytics, or decision agents.
- Exposure to Azure Data Lake / PHDOD, Databricks, Lakehouse architecture, semantic models, common data models, or governed enterprise data products.
- Hands-on or working knowledge of SQL, Python, Power BI, Qlik, data visualization, automation scripts, or model evaluation workflows.
- Experience with syndicated market data, retailer POS, sell-out data, competitor pricing data, e-commerce price tracking, or customer P&L datasets.
- Familiarity with responsible AI, model governance, human-in-the-loop decisioning, data privacy, explainability, and adoption/change management for AI tools.
We believe that we are better together than apart. For our office-based teams, this means working in-person at least 3 days per week.
Onsite roles require full-time presence in the company’s facilities.
Field roles are most effectively done outside of the company’s main facilities, generally at the customers’ or suppliers’ locations.
This role is an office role.
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€80000 - €110000 monthly