The Undermonetized Asset in Every MNO
Mobile network operators generate geolocation data every second of every day. Every cell tower handoff, every network registration event, every data session — all of it produces a continuous stream of location intelligence covering millions of subscribers.
Yet most MNOs haven't figured out how to turn this data into meaningful revenue.
The reasons are familiar: regulatory uncertainty around GDPR, fragmented internal ownership (is this a network team problem or a commercial team problem?), and the lack of a clear product framework for how to package and sell location insights. Many operators have run pilot projects that never graduated to production. Others have outsourced the problem to ad-tech intermediaries, giving up both margin and control.
The opportunity is substantial. A mid-sized European MNO with 3-5 million subscribers sits on a geolocation dataset that, properly productized, can generate seven-figure annual revenue streams. The key is understanding that there are two distinct monetization models — and that you need both.
Model 1: First-Party Data Monetization
In the first-party model, the operator uses its own subscriber data to deliver targeted campaigns on behalf of advertising partners — but the data never leaves the operator's infrastructure.
How it works: A retail partner (say, an electronics chain) wants to reach potential customers near their stores. The operator identifies subscribers who have been in the vicinity of those stores in the past 7 days, filters by relevant demographics, and delivers a targeted SMS or push notification with a promotional offer. The retail partner pays the operator for this campaign.
Real-world example: An MNO in Central Europe runs geolocation-targeted campaigns for a national fuel station chain. Subscribers who regularly pass specific stations receive personalized offers — discounted fuel, loyalty points, car wash promotions. The campaign achieves 8-12% conversion rates, compared to 1-2% for untargeted mass SMS. The fuel chain pays per conversion, creating a performance-based revenue stream for the operator.
Why it works: The subscriber data stays within the operator's own systems. No personal data is shared with the advertising partner — they only receive aggregate campaign performance reports. This means the operator can rely on its existing terms of service and legitimate interest grounds under GDPR, making consent management relatively straightforward.
Revenue model: CPM (cost per message), CPC (cost per click), or CPA (cost per acquisition) — depending on the campaign type and the partner's preference.
Model 2: Third-Party Data Monetization
In the third-party model, the operator anonymizes and aggregates geolocation data, then sells the resulting insights as a standalone data product to external buyers.
How it works: Raw geolocation events are processed through anonymization and aggregation pipelines. Individual subscriber identities are stripped away. The output is statistical: how many people visited a location, where they came from, how long they stayed, what times of day see peak traffic. This aggregated data is then packaged into analytics products for different verticals.
Who buys it:
- Retailers want footfall analytics — how many people pass their stores, where their customers come from, and how their traffic compares to competitors nearby.
- Municipalities need population movement patterns for urban planning — transit route optimization, emergency evacuation planning, event crowd management.
- Real estate developers assess site potential by analyzing foot traffic density, demographic profiles, and movement patterns around prospective development locations.
- Tourism boards measure visitor flows, identify origin countries, and track length-of-stay patterns across regions and seasons.
Real-world example: A CEE operator provides anonymized footfall analytics to a major shopping mall operator across multiple cities. The mall operator uses this data to benchmark locations, optimize tenant mix, and negotiate lease renewals based on objective traffic data. The insights are delivered through a self-service dashboard with historical trends, heatmaps, and catchment area analysis.
Revenue model: SaaS subscription (monthly or annual access to the analytics platform), or per-query pricing for ad-hoc data requests.
Why You Need Both Models on One Platform
Most operators who attempt geodata monetization start with one model or the other. They build (or buy) a campaign platform for first-party targeting, or they partner with a data analytics vendor for third-party insights. Two separate solutions, two separate teams, two separate consent frameworks.
This is inefficient and, frankly, unnecessary.
MarketLocator was built from the ground up to handle both models on a single platform. The same geolocation data ingestion pipeline feeds both the campaign targeting engine and the anonymized analytics engine. The same consent management layer handles both first-party and third-party opt-ins. The same operator team can manage both revenue streams from a single interface.
This unified approach has been running in production for over 10 years with Slovak Telekom and Orange in Central Europe. It is not a prototype or a pilot — it is a battle-tested, revenue-generating platform processing data for millions of subscribers daily.
GDPR Compliance: Two Consent Models
The regulatory framework for first-party and third-party data monetization is fundamentally different, and your platform needs to handle both.
First-party consent is simpler. The operator is using its own subscriber data to deliver communications to its own subscribers. In many EU jurisdictions, this can be handled under the operator's existing terms of service or legitimate interest basis, particularly when the communication provides value to the subscriber (relevant offers, promotions). The key requirement is transparency — subscribers must know their data is being used for targeting, and they must have a clear opt-out mechanism.
Third-party consent requires explicit opt-in. When subscriber data (even anonymized and aggregated) is being used to generate products for external parties, regulators expect a higher standard of consent. This typically means a dedicated opt-in flow — either during onboarding, through a consent management screen in the operator's app, or via a dedicated communication. The subscriber must understand what data is being used, for what purpose, and by whom.
MarketLocator manages both consent types within the platform. Consent status is checked in real-time before any data processing occurs. Opt-out requests are honored immediately. Audit trails are maintained for regulatory reporting. This is not a bolt-on compliance layer — it is integral to the platform architecture.
The GenAI Evolution: From DemandGen to RevenueGen
The first generation of geodata monetization was about DemandGen — push campaigns based on location and basic demographics. "You are near Store X, here is an offer." This works, and it generates revenue. But it leaves significant value on the table.
The next generation — what we call RevenueGen — uses AI-optimized targeting that goes beyond simple location triggers. Instead of asking "who is near the store right now?", the system predicts "which subscribers are most likely to convert, and when?"
This means combining geolocation patterns with behavioral signals: purchase history, app usage, browsing patterns (all within the operator's own data ecosystem). Machine learning models identify high-propensity segments and optimize send timing, message content, and channel selection for maximum conversion.
Early results from our pilot deployments show 2-3x improvement in campaign ROI compared to rule-based targeting. The system learns from every campaign, continuously improving its predictions.
For the third-party model, GenAI enables natural-language querying of location analytics. Instead of navigating complex dashboards, a retail analyst can ask: "Show me the top 5 locations in Bratislava with the highest weekend foot traffic from the 25-34 age segment" and get an instant, visualized answer.
This is the frontier — and operators who invest in AI-enhanced geodata platforms now will have a significant competitive advantage in the next 3-5 years.
Getting Started
If you are a telco product manager or CDO evaluating geodata monetization, here is the pragmatic starting path:
- Audit your geolocation data assets — what data do you already have, at what granularity, and what is your current consent baseline?
- Start with first-party campaigns — lower regulatory friction, faster time-to-revenue, and immediate proof points for internal stakeholders.
- Layer in third-party analytics — once the data pipeline is proven, extend to anonymized insights for external buyers.
- Invest in a unified platform — avoid building two separate solutions that will inevitably diverge and double your maintenance costs.
The operators who are generating meaningful revenue from geodata today are the ones who committed to a platform approach early. The ones still running pilots are the ones who tried to solve it with point solutions.
The data is already flowing through your network. The question is whether you are going to monetize it — or leave the revenue on the table.
instarea is a software product factory based in Bratislava, Slovakia. We build enterprise-grade B2B SaaS for telco, banking, and scale-ups across CEE and beyond. Get in touch →