Press Releases July 17, 2026 07:00 AM

MoonFox Alternative Data Expands Offline Foot Traffic Coverage to Leading China and Global Stocks

MoonFox Data expands offline foot traffic data to 300+ stocks across China A-shares, Hong Kong, and US markets, enhancing investment insights

By Ajmal Hussain
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MoonFox Data, a sub-brand of Aurora Mobile (NASDAQ: JG), has significantly expanded its offline consumer foot traffic dataset to cover over 300 additional stocks spanning China's A-shares, Hong Kong blue chips, and selected US-listed global consumer brands. This expansion includes broader sector coverage beyond consumer-facing companies to energy, export, and manufacturing. The offline data offers institutional investors unique, real-time visibility into consumer behavior and operational performance, providing leading indicators for revenues, market share shifts, and brand health in China’s dynamic economy.

MoonFox Alternative Data Expands Offline Foot Traffic Coverage to Leading China and Global Stocks
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Key Points

  • MoonFox now tracks foot traffic data for 300+ new stocks including major A-share, Hong Kong, and US-listed consumer brands, as well as energy and manufacturing sectors.
  • Offline foot traffic data delivers early alpha signals with demonstrated backtested excess returns of +159.12% over CSI 300 between 2020-2025, aiding investment decisions.
  • Expansion enables cross-market benchmarking and better portfolio construction by integrating China market insights with global consumer equities.

SHENZHEN, China, July 17, 2026 (GLOBE NEWSWIRE) -- MoonFox Data, China’s leading alternative data provider, is proud to announce a major expansion of its offline (foot traffic) data coverage, now tracking real-world consumer flows for a new cohort of high-impact listed companies across A-shares, Hong Kong, and US markets. This enhancement further cements MoonFox’s position as the definitive source for actionable China data, empowering global institutional investors with unparalleled visibility into the operational pulse of the world’s fastest-growing consumer economy.

Key Highlights:

  • Expanded Universe: MoonFox’s offline dataset now covers 300+ additional stocks, including top A-share names, Hong Kong blue chips, and select US-listed Chinese and global consumer brands.
  • Sector Breadth: Newly added coverage spans core consumer-facing sectors such as Home Furnishings (e.g., Shangpin Home, Markor), Jewelry (China Gold, Chow Sang Sang), Apparel (Joeone, Annil, Septwolves, Moncler, Pandora, Victoria’s Secret), Retail (Sanjing Shopping, Nanning Department Store), F&B (Zhou Hei Ya, Shanghai Min, Shake Shack), and more.Notably, this expansion also introduces capacity-related activity signals for the energy, export, and manufacturing sectors, extending MoonFox's offline coverage beyond consumer-facing industries.
  • China Focus, Global Reach: Over 80% of the new additions are A-share listed companies, providing deep, real-time insight into the on-the-ground performance of China’s most dynamic brands.
  • Offline Data for Alpha: By capturing store-level footfall, visit frequency, and consumer engagement at millions of Points of Interest (POIs), MoonFox’s offline signals deliver leading indicators for sales, market share shifts, and brand health—well ahead of traditional financial disclosures.
  • Multi-Market Integration: The expanded dataset also includes major Hong Kong and US-listed consumer names, enabling cross-market benchmarking and global portfolio construction with a China edge.

Why Offline Data Matters for China Investing
In a market where digital and physical worlds converge, MoonFox’s offline data provides the missing link for investors seeking to validate digital narratives with real-world activity. For buy-side analysts and PMs, store traffic trends are proven leading indicators for quarterly revenue, competitive dynamics, and event-driven trading opportunities—especially in China’s highly competitive consumer landscape. During the Q1 earnings cycle, MoonFox’s foot traffic composite for select apparel chains beat the consensus same-store-sales estimate by +3.6 percentage points and helped clients refine exposure to underappreciated growth stories.

Quantitative Validation: Foot Traffic as an Alpha Signal
Beyond fundamental corroboration, the value of foot traffic data as a systematic signal is evidenced by long-horizon backtesting. The offline_traffic_same acceleration factor—built on MoonFox's store-level data to capture marginal changes in same-store visitation—delivered a backtested excess return of +159.12% over the CSI 300 in A-shares (2020–2025), with a 0.98 Sharpe ratio and a -17.47% maximum drawdown. By capturing inflection points in company fundamentals ahead of financial disclosures, store traffic acceleration offers investors a leading signal with demonstrated backtested alpha.

About MoonFox Data
As a sub-brand of Aurora Mobile (NASDQ: JG), MoonFox Data is a leading expert in data insights and analysis services across all scenarios. With a comprehensive, stable, secure and compliant mobile big data foundation, as well as professional and precise data analysis technology and AI algorithms, MoonFox Data has launched iAPP, iBrand, iMarketing, Alternative Data and professional research and consulting services of MoonFox Research, aiming to help companies gain insights into market growth and make accurate business decisions.

For the full list of newly covered stocks, sample data, or to schedule a demo, please contact:

Sample List of Newly Added Stocks (Partial):

  • A-shares: Shangpin Home (300616.SZ), China Gold (600916.SH), Joeone (601566.SH), Annil (002875.SZ), Septwolves (002029.SZ), Nanning Department Store (600712.SH), Sanjiang Shopping (601116.SH), etc.
  • Hong Kong/US: Chow Sang Sang (0116.HK), Moncler (MONRF.PQ), Pandora (PNDZF.US), Shake Shack (SHAK.N), Victoria’s Secret (VSCO.N), etc.

Risks

  • Data accuracy and privacy compliance risks inherent in mobile big data collection and analysis could impact reliability of insights.
  • Dependence on consumer offline behavior as a leading indicator may be disrupted by shifts toward online shopping or changes in consumer patterns due to unforeseen macro events.
  • Integration of new sectors like energy and manufacturing may face challenges given different data dynamics, potentially lowering signal effectiveness in these areas.

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