Key Takeaways
- The global location-based services (LBS) market is forecast to reach $32.1 billion by 2030
- $8.2 billion is the estimated 2023 global location-based advertising revenue
- 65% of shoppers say they would use location-based services if they received relevant deals nearby
- 70% of smartphone users have location services turned on at least sometimes
- 67% of smartphone users report that they are comfortable sharing location data with businesses if it improves the quality of services they receive, per a 2024 consumer survey.
- A $1 increase in marketing spend per store location is associated with a 0.06 increase in foot traffic for location-targeted campaigns (study average across trials)
- In-store visits increased by 20% on average when retailers used geofencing with push notifications (meta-analysis across field studies)
- Location-based offers can lift redemption rates by 2.1x versus non-location-targeted offers (field experiments aggregate)
- Using location targeting reduced CPA by 18% in a 2022 retailer optimization case report
- CPC for location-targeted search ads was 11% lower than non-location-targeted ads in a 2021 retailer dataset analysis (benchmarks)
- Ad spend can be reduced by 10–30% by restricting delivery to relevant geographic areas (industry best-practice report)
- 56% of marketers say location-based marketing is a top priority in their personalization strategy for 2024
- Geofencing is used by 31% of marketers in at least one active channel (surveyed marketing professionals)
- 55% of businesses increased investment in location data and analytics between 2022 and 2024 (vendor/corporate survey)
- In California, the CCPA applies to businesses meeting thresholds including $25 million in annual revenue or data volumes; 1.0 million+ Californians are covered under broad consumer rights frameworks (California statute coverage requirement)
Location-based marketing delivers measurable foot traffic and purchase lift, with growing adoption and tighter privacy compliance.
Related reading
Market Size
Market Size Interpretation
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User Adoption
User Adoption Interpretation
Performance Metrics
Performance Metrics Interpretation
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Cost Analysis
Cost Analysis Interpretation
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Industry Trends
Industry Trends Interpretation
Privacy & Regulation
Privacy & Regulation Interpretation
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Technology & Measurement
Technology & Measurement Interpretation
How We Rate Confidence
Every statistic is queried across four AI models (ChatGPT, Claude, Gemini, Perplexity). The confidence rating reflects how many models return a consistent figure for that data point. Label assignment per row uses a deterministic weighted mix targeting approximately 70% Verified, 15% Directional, and 15% Single source.
Only one AI model returns this statistic from its training data. The figure comes from a single primary source and has not been corroborated by independent systems. Use with caution; cross-reference before citing.
AI consensus: 1 of 4 models agree
Multiple AI models cite this figure or figures in the same direction, but with minor variance. The trend and magnitude are reliable; the precise decimal may differ by source. Suitable for directional analysis.
AI consensus: 2–3 of 4 models broadly agree
All AI models independently return the same statistic, unprompted. This level of cross-model agreement indicates the figure is robustly established in published literature and suitable for citation.
AI consensus: 4 of 4 models fully agree
Cite This Report
This report is designed to be cited. We maintain stable URLs and versioned verification dates. Copy the format appropriate for your publication below.
Marcus Engström. (2026, February 13). Location Based Marketing Statistics. Gitnux. https://gitnux.org/location-based-marketing-statistics
Marcus Engström. "Location Based Marketing Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/location-based-marketing-statistics.
Marcus Engström. 2026. "Location Based Marketing Statistics." Gitnux. https://gitnux.org/location-based-marketing-statistics.
References
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- 46rfc-editor.org/rfc/rfc7946
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