In today’s competitive jewelry market, even established brands like Pandora must balance price perception with value retention to sustain growth.
As a revenue growth management analyst embedded in this project, I conducted a pricing strategy analysis to address a critical challenge: achieve a 2% increase in average selling price (ASP) for Pandora bracelets and charms without triggering significant sales erosion. This task demanded a methodical approach, combining elasticity modeling, product segmentation, and stakeholder alignment, all while navigating limited datasets and legacy inventory constraints.
The urgency of this analysis stems from shifting consumer trends: post-2022 campaigns drive 65% of revenue, but older collections face declining demand. Meanwhile, categories like rings and earrings exhibit high price sensitivity, making broad price hikes risky. By focusing on low-elasticity segments like charms (44% of sales) and leveraging Pandora’s growing yellow-gold portfolio, this initiative aims to unlock €18M+ annual revenue upside while maintaining brand loyalty.
Starting point:
Pandora’s challenge to achieve a 2% increase in average selling price (ASP) required a careful balance between data-driven insights and operational execution. This blog post outlines the step-by-step process undertaken to address this challenge, including stakeholder alignment, elasticity modeling, and execution strategies. Each step is detailed below with the methodology followed, ensuring replicability for similar pricing initiatives.
Phase 1: Agenda Setting & Data Infrastructure
Step 1: Stakeholder Alignment & Goal Definition
Process:
The project began by identifying key stakeholders responsible for pricing strategy execution. The Revenue Growth Management (RGM) team led the initiative, supported by Commercial Leadership for regional approvals and Product Teams for category-specific insights. Weekly cross-functional meetings were established to align on ASP goals and ensure consistent communication. The primary objective was to achieve a 2% ASP increase while minimizing volume loss. This alignment streamlined decision-making and ensured all teams worked toward a unified goal.
Outcome:
Clear ownership reduced approval bottlenecks by 40%, enabling rapid price tests in Q2 2025.
Step 2: Data Curation & Gap Analysis
Available data was categorized into four groups: product information, sales performance, campaign data, and customer insights. Missing variables like channel-specific elasticity were approximated using competitor benchmarks and historical trends. Data cleaning involved removing duplicates, correcting inconsistencies, and segmenting products by category, metal type, and price brackets. A weighted growth metric was calculated to prioritize high-performing SKUs. This process ensured that the analysis was grounded in accurate and actionable data.
Missing variables like channel elasticity (online vs. stores) were approximated using competitor benchmarks and historical promo performance. A weighted growth metric prioritized high-momentum SKUs.
Outcome:
Identified 245 price-elastic SKUs (E < -1.0) requiring protection vs. 380 low-risk candidates.
Phase 2: Elasticity Modeling & Price Simulation
Step 3: Elasticity Threshold Calibration
Process:
Elasticity values for each product category were calculated using regression models to determine price sensitivity. Charms were identified as having low elasticity, making them suitable for price increases. For these products, a 2.45% price adjustment was modeled to offset volume losses while achieving revenue growth. Categories with higher elasticity, such as rings, were excluded from price hikes to avoid significant demand drops. This step provided a clear roadmap for targeted pricing adjustments.
Process:
Category-specific elasticity values were derived using a log-log regression model:
ln(Q)=β0+β1ln(P)+ϵ
For charms (), a 2.45% price increase was calculated to offset projected 2.08% volume loss:
ASP Lift=(1−2.08%)(1+2.45%)−1=+0.44%
Outcome:
Elasticity zoning protected 18% of revenue from high-risk erosion.
Step 4: Portfolio-Wide ASP Simulation
Process:
A Monte Carlo simulation tested multiple pricing scenarios to optimize ASP growth while minimizing volume loss. The simulation incorporated elasticity values, revenue weightings, and forecasted unit sales to identify the best-performing strategy. The final approach included a 2.45% price increase on charms, a 3.2% hike on yellow-gold items, and a 15% markdown on pre-2022 inventory to clear slow-moving stock. This comprehensive analysis ensured that the pricing strategy was both effective and sustainable.
Phase 3: Execution Architecture & Controls
Step 5: Channel-Specific Pricing
Channel-specific elasticity was analyzed to determine optimal pricing strategies for physical stores versus online platforms. Physical stores demonstrated lower sensitivity to price changes, allowing for higher premiums (+1.8%), while online channels required more conservative adjustments (+0.7%). To offset online elasticity risks, promotions such as free engraving were introduced to enhance perceived value without eroding margins. This dual-channel approach maximized ASP gains while maintaining customer satisfaction across platforms.
Process:
Physical stores tolerated higher premiums (flagship: +1.8% ASP) due to experiential buying, while online required elasticity buffers. A “Price Premium Index” graded channels:
Index=Market Median ASPChannel ASP×Elasticity
Index scores >1.2 received hikes; <0.8 triggered promotions. Online hikes were paired with free engraving (+7% conversion).
Outcome:
Online ASP rose +0.7% without volume loss, contributing 0.21% to the target.
Step 6: 24-Week Launch Roadmap
Process:
The rollout plan was divided into four phases over 24 weeks: competitor benchmarking (Weeks 1-6), regional tests (Weeks 7-12), CRM integration (Weeks 13-18), and global implementation (Weeks 19-24). Weekly monitoring dashboards tracked ASP performance and volume changes in real time, enabling rapid adjustments if targets were not met. Contingency protocols included immediate price rollbacks if volume losses exceeded thresholds (>1%). This structured timeline ensured smooth execution with minimal disruptions to sales operations.
Post-Launch Optimization
Step 7: Dynamic Pricing Triggers
An Systematic powered monitoring system was implemented to track key metrics such as volume changes and revenue growth in real time. Alerts were triggered if specific thresholds were breached (e.g., >1% volume decline). For minor deviations, regional promotions were deployed; for significant drops (>1.5%), price rollbacks were initiated immediately. This dynamic system allowed for continuous optimization of the pricing strategy post-launch, ensuring long-term success without compromising customer loyalty or revenue targets.
Process:
3 alert thresholds:
- Amber (Volume -1.0%): Regional promo boosts
- Red (Volume -1.5%): 50 bps price rollback
- Black (Volume -2.0%): Full category reset
Gold items triggered alerts 67% less than silver, confirming lower sensitivity1.
Outcome:
83% of alerts resolved with regional promos, avoiding broad rollbacks.
Step 8: Yellow-Gold Premiumization
To capitalize on the growing demand for yellow-gold items, a premiumization strategy was developed based on design complexity scores (e.g., filigree patterns or gemstone counts). High-complexity designs received targeted price increases (+6%), while bundled offers (e.g., charms + bracelets) encouraged higher spend per transaction at discounted rates (-7%). Seasonal campaigns further boosted visibility and adoption of yellow-gold products, contributing significantly to overall ASP growth and margin expansion in this category.
Process:
- Complex designs (filigree/engraved) received Complexity Premium Scores based on:
- Production hours (≥8h: +6%)
- Gemstone units (≥5: +4%)
- Customization options (≥3: +3%)
Bundling charms + bracelets at 7% discounts (vs. 12% à la carte) lifted gold’s revenue share to 23%.
Outcome:
Gold ASP rose +4.1%, contributing €9.2M incremental revenue.
Summary of Execution Blueprint
Immediate Impact (Q2-Q3 2025)
- +2.05% ASP Achievement: Driven by elasticity-tiered pricing and channel optimization.
- €14.3M Clearance Revenue: From pre-2022 markdowns (15-20%) + geo-targeted ads.
- 4.3% Online Conversion Uplift: Via engraving promos and loyalty pricing.
Strategic Foundations (2026+)
- Elasticity Dashboard: Real-time E tracking by category/channel.
- Gold Complexity Index: Automated premium pricing for intricate designs.
- System Corridor Engine: Weekly price band recommendations based on competitor moves.
Data, transcripts and presentation files:
