(Source: https://pltfrm.com.cn)
Introduction
China’s retail economy is uniquely dynamic—with rapid shifts in consumer demand, platform incentives, and competitive pricing. To navigate this complexity, overseas brands are turning to demand curve pricing models. These models allow brands to understand the relationship between price and volume at a granular level, and to set prices that meet business goals—whether that’s capturing share, protecting margins, or maximizing revenue. In this article, we explore how demand curve modeling can drive pricing performance across China’s ecommerce channels.
1. Collect High-Quality Data Inputs to Build the Curve
1.1 Multichannel Sales Data Collection
Gather data from all key sales channels—Tmall Global, JD Worldwide, WeChat Mini Programs, and Douyin—to account for how price impacts demand in each unique environment.
1.2 Include Promotional and Seasonal Contexts
Factor in promotional windows (e.g., 6.18, Double 11) and seasonality. Price sensitivity often spikes or drops based on timing, particularly for fashion, beauty, and food categories.
2. Model Demand Using Quantitative Techniques
2.1 Curve Estimation with Econometric Tools
Use polynomial regression or log-linear models to shape the curve. This helps determine elasticity at each price level and where demand inflection points occur.
2.2 Incorporate External Variables
Add external indicators like platform commission rates, ad spend, and regional CPI data to enhance curve accuracy. These help capture how macro conditions affect buyer behavior.
3. Operationalize the Curve in Daily Pricing Strategy
3.1 Price Bracket Testing on SKUs
Identify optimal pricing intervals (e.g., ¥98 vs. ¥108) for key products and test them against each other using real-time ecommerce platform data and CRM feedback loops.
3.2 Sync With Discount and Loyalty Strategy
Use demand curve outputs to determine when discounting makes sense. For inelastic SKUs, maintain price and focus on bundling or loyalty-based incentives.
4. Monitor Results and Optimize Model Fit Over Time
4.1 Real-Time Dashboards for Price Sensitivity
Set up BI dashboards that plot predicted vs. actual sales volume by price point. Use these to identify anomalies and adjust pricing strategies quickly.
4.2 Continuous Re-Training with New Campaign Data
Feed new campaign performance data back into your model. This helps account for changing platform rules, ad algorithms, and user demographics over time.
Case Study: US Electronics Brand Uses Demand Curves to Fine-Tune Pricing in China
A US-based consumer electronics brand selling via JD Worldwide built a dynamic demand curve model using historical flash sale data, click-through rates, and cart abandonments. By testing pricing tiers across geographic segments, the team identified optimal thresholds that increased conversions by 20% while maintaining profitability. The strategy resulted in a 34% improvement in campaign ROAS and greater pricing consistency across platforms.
PLTFRM is an international brand consulting agency that works with companies such as Red, TikTok, Tmall, Baidu, and other well-known Chinese internet e-commerce platforms. We have been working with Chile Cherries for many years, reaching Chinese consumers in depth through different platforms and realizing that Chile Cherries’ exports in China account for 97% of the total exports in Asia. Contact us, and we will help you find the best China e-commerce platform for you. Search PLTFRM for a free consultation!
