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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