Joint Head of Pricing, Promotions & Analytics

BP•Louisville, KY

4d•Hybrid

About The Position

The Head of Pricing & Data Analytics – Merchandising is responsible for building and leading the pricing and analytics capabilities that support the Merchandising organization across BP and TA brands. This role will use data, analytics, competitive intelligence, customer insights, and advanced pricing technology to improve sales, margin, inventory productivity, and the overall customer value proposition. The position will play a key leadership role in helping develop, implement, and expand the use of BP's enterprise pricing tool/system across its brands. This includes partnering with Merchandising, Pricing, Operations, Loyalty, and other key stakeholders to determine how the platform can best support the business and ensuring its capabilities translate into practical, actionable pricing recommendations. This leader will manage a team of data scientists and analytics professionals responsible for turning complex data into clear, actionable recommendations. The team will provide insights and recommendations across all merchandise categories, including base pricing, promotional pricing, markdowns, competitive positioning, assortment, inventory, and other sales and margin opportunities. The role will also own and manage the Store Archetype Strategy, ensuring merchandising decisions reflect the different customers, missions, sales opportunities, and operating environments across the store network. Additionally, this leader will ensure that pricing, promotional, customer, and store archetype insights effectively support EarnifyPro and other loyalty programs as part of the overall Merchandising strategy. The role will partner with Loyalty and other key stakeholders to help develop more relevant offers, increase customer engagement, and ensure loyalty strategies complement the broader pricing and promotional strategy. The overarching objective is to use data, analytics, pricing technology, and customer insights to help put the right products, in the right stores, at the right price, in the right quantities, with the right offers for the right customers.

Requirements

Significant leadership experience in retail pricing, merchandising analytics, data science, category analytics, or a related discipline.
Demonstrated experience building or leading high-performing analytics or data science teams.
Strong understanding of retail pricing concepts, including base pricing, promotional pricing, markdown optimization, competitive pricing, and price elasticity.
Experience using advanced analytics, AI, or predictive modeling to support retail pricing and merchandising decisions.
Understanding of retail loyalty programs, customer segmentation, personalization, and targeted promotional strategies.
Strong financial and commercial understanding with the ability to connect analytical recommendations to sales, margin, inventory, customer behavior, and profitability.
Proven ability to turn complex analytical findings into practical business recommendations.
Strong communication and influencing skills with the ability to work effectively across multiple brands and with senior leadership, Category Managers, Operations, Loyalty, and other cross-functional partners.

Nice To Haves

Experience implementing, developing, or optimizing enterprise retail pricing and analytics platforms strongly preferred.
Experience with store segmentation, clustering, archetypes, assortment analytics, or localized merchandising strongly preferred.

Responsibilities

Help develop, implement, and continuously improve the use of the enterprise pricing tool/system across BP and TA brands.
Work across the broader BP organization to identify opportunities to leverage the pricing platform's advanced analytics and AI capabilities.
Partner with Category Management and Merchandising leadership to establish an effective and consistent pricing framework while recognizing differences in customers, markets, brands, and store formats.
Help translate the capabilities of the pricing system into practical business processes and actionable recommendations for Category Managers and leadership.
Drive adoption and effective utilization of the pricing system within Merchandising.
Develop and provide data-driven recommendations for: Base and everyday pricing, Promotional pricing, Markdown and clearance strategies, Competitive price positioning, Price zones and geographic pricing, Price elasticity and customer response, Margin and sales optimization.
Establish processes for regularly reviewing pricing performance and identifying opportunities for improvement.
Ensure pricing recommendations balance customer value, competitive position, sales growth, margin, and long-term category health.
Develop clear reporting that allows leadership and Category Managers to understand the expected and actual impact of pricing decisions.
Measure results and continuously refine pricing strategies based on performance.
Lead, develop, and mentor a team of data scientists and analytics professionals supporting Merchandising.
Establish priorities for the team based on the most significant sales, margin, inventory, pricing, and customer opportunities.
Translate large and complex data sets into practical recommendations that Category Managers and business leaders can act upon.
Develop analytics around customer behavior, product performance, pricing, promotions, loyalty, inventory, assortment, and store segmentation.
Identify trends and opportunities that may not be visible through traditional merchandising reporting.
Leverage advanced analytics and AI capabilities to improve the quality, speed, and predictive nature of merchandising decisions.
Build analytical capabilities that move the organization beyond simply reporting what happened toward understanding why it happened, what is likely to happen next, and what actions should be taken.
Create scorecards and measurement tools that clearly demonstrate the financial and operational impact of merchandising decisions.
Own and continuously develop the Store Archetype Strategy for Merchandising.
Use customer, sales, demographic, geographic, competitive, operational, loyalty, and store-level data to define meaningful store groups and customer missions.
Partner with Category Management and Space Planning to ensure assortments, space allocation, pricing, promotions, and inventory strategies reflect the needs of each archetype.
Help determine which products should be carried by store type and where assortment should expand, contract, or vary.
Use archetype analytics to help determine appropriate inventory levels and quantities by store.
Incorporate customer and loyalty behavior into archetype development to better understand who is shopping each type of location and what drives their purchasing decisions.
Continuously evaluate store performance and refine archetypes as customer behavior and business conditions change.
Ensure the archetype strategy becomes an actionable merchandising tool rather than simply a store classification exercise.
Partner with the EarnifyPro and broader loyalty teams to ensure merchandising pricing, promotional, customer, and archetype insights support the overall loyalty strategy.
Use customer and transaction data to identify opportunities for targeted and personalized offers based on customer behavior, store archetype, geography, category, and shopping mission.
Help determine when the business should use base price, broad-based promotions, loyalty-specific offers, or targeted customer offers to produce the best overall result.
Evaluate loyalty offers based on incremental sales, margin, trips, basket size, customer engagement, and return on investment.
Ensure loyalty promotions work in coordination with the overall Merchandising promotional calendar rather than operating as a separate strategy.
Use the enterprise pricing system and other analytical capabilities, where appropriate, to identify pricing and promotional opportunities that can be activated through EarnifyPro or other loyalty platforms.
Provide Merchandising leadership and Category Managers with insights into how loyalty customers respond differently to products, pricing, promotions, and categories.
Partner with Loyalty leadership to identify opportunities to increase frequency, retention, cross-category purchasing, and overall customer value.
Help ensure the organization understands the interaction between pricing, promotions, loyalty, and customer behavior rather than evaluating each independently.
Serve as a strategic analytics partner to Category Managers and Merchandising leadership.
Provide Category Managers with fact-based recommendations while ensuring their category expertise, vendor knowledge, and market understanding remain an important part of the decision-making process.
Identify opportunities to improve sales, margin, turns, inventory productivity, and customer value.
Support assortment rationalization and SKU productivity analysis.
Evaluate promotional effectiveness and recommend changes based on incremental sales, margin, customer response, and overall return.
Develop markdown recommendations designed to efficiently exit discontinued, aged, seasonal, and underperforming inventory.
Identify opportunities where changes in assortment, pricing, promotion, loyalty offers, or inventory levels could improve category performance.
Help leadership prioritize opportunities based on potential financial impact and ease of execution.
Build a highly capable, commercially focused pricing and analytics organization.
Develop team members who understand both data science and the practical realities of retail merchandising.
Create a culture where analytics leads to action rather than simply producing reports.
Communicate complex analytical findings in clear business terms that can be understood and acted upon at all levels of the organization.
Work collaboratively across BP, TA, Merchandising, Operations, Loyalty, and other key functions, while being willing to challenge existing practices when the data supports a different approach.
Help create consistency and share learnings across BP brands while recognizing that different brands, customers, and store formats may require different strategies.
Ensure analytical recommendations consider the impact on the customer and the ability of stores to execute.
Maintain a strong focus on practical, scalable solutions that produce measurable business results.