Role Overview
The Head of Pricing Analytics is responsible for developing the data, analytical capabilities, and decision-support tools that underpin the organisation’s pricing strategy. The role manages pricing-related data sources, builds analytical models, and provides insights that enable the business to optimise pricing decisions across products, channels, and customers.
Key Responsibilities
Pricing Data Management
- Manage and maintain the data infrastructure supporting pricing analysis, ensuring accuracy, consistency, and accessibility of pricing-related data.
- Develop and maintain datasets covering sales transactions, pricing levels, customer behaviour, cost structures, and competitive pricing.
- Utilise tools such as SQL and data management platforms to extract, transform, and structure data for analysis.
- Work with IT and data teams to ensure pricing data is integrated effectively across systems.
Pricing Analytics & Insights
- Develop analytical frameworks to assess price performance, margin delivery, and pricing effectiveness across products, channels, and customer segments.
- Provide regular insights on price realisation, discounting behaviour, customer profitability, and margin drivers.
- Support pricing teams with data-driven analysis that informs pricing decisions, negotiations, and strategic initiatives.
- Identify trends and opportunities within pricing data to improve commercial outcomes.
Pricing Models & Decision Tools
- Design and build analytical models and decision-support tools that enable the pricing team to evaluate pricing scenarios and strategies.
- Develop models that assess price elasticity, margin impact, and customer response to pricing changes and ultimately drive pricing recommendations.
- Create scalable tools that allow pricing teams and commercial stakeholders to make informed pricing decisions quickly and consistently.
- Support the development of pricing simulations to evaluate potential pricing adjustments and strategic initiatives.
Price Optimisation & Advanced Analytics
- Explore and implement price optimisation approaches using advanced analytical methods and technologies.
- Leverage latest analytical tools, machine learning techniques, and data science methodologies to enhance pricing decision-making.
- Identify opportunities to improve pricing outcomes through data-driven experimentation and optimisation.
- Stay up to date with developments in pricing analytics, artificial intelligence, and optimisation technologies.
Reporting & Performance Monitoring
- Develop and maintain pricing dashboards and reporting frameworks to provide visibility on pricing performance.
- Deliver clear reporting on key metrics including margin performance, price realisation, discount trends, and promotional effectiveness.
- Provide analytical support for commercial reviews, pricing strategy discussions, and business planning.
Collaboration with Pricing Teams
- Partner with the Category Pricing Business Partner to support category-level pricing analysis and cost pass-through evaluation.
- Work with the Channel Pricing Manager to analyse pricing performance across routes to market.
- Support the Network Pricing Business Partner with insights on customer pricing behaviour, discounting patterns, and profitability.
- Translate analytical findings into practical recommendations that support pricing teams in their decision-making.
About You
Skills & Experience
- Experience in pricing analytics, commercial analytics, data science, or business intelligence roles.
- Strong technical capability in data analysis and modelling, including experience with SQL and analytical tools.
- Experience building data models, dashboards, and analytical frameworks.
- Understanding of pricing strategies, commercial performance metrics, and margin analysis.
- Ability to translate complex analytical insights into clear business recommendations.
- Experience with tools such as Python, R, Power BI, Tableau, Alteryx or similar analytics platforms is advantageous.
Key Competencies
- Strong analytical and problem-solving capability
- Data-driven decision making
- Commercial awareness
- Technical and data management skills
- Communication and storytelling with data
- Cross-functional collaboration
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