A GEO-TARGETING SYSTEM FOR DELIVERING REAL-TIME LOCATION-BASED PERSONALIZED OFFERS TO CUSTOMERS

Abstract

7. ABSTRACT The present invention relates to a geo-targeting system for delivering real-time, location-based personalized offers to customers. The system comprises a mobile application (102) that tracks customer location and triggers personalized promotions upon entering predefined geographic zones. The geo-targeting engine (104) dynamically adjusts offers based on factors such as foot traffic, time of day, and inventory levels. The system includes a business-facing dashboard (106) for monitoring customer interactions and optimizing promotional campaigns. A customer preference module (108) uses machine learning to refine future offers based on customer behavior. The inventory management interface (110) integrates with real-time stock updates, while the real-time market condition tracker (112) adapts offers based on external factors like weather and local events. A loyalty integration system (114) tailors promotions for repeat customers, and a security module (116) ensures compliance with data protection regulations. The figure associated with abstract is Fig. 1.

Specification

Description:4. DESCRIPTION
Technical Field of the Invention

The present invention relates to geo-targeting and location-based marketing technologies. More particularly, it involves a sophisticated system for delivering personalized, real-time offers to customers based on their geographical location, behavioural data, and individual preferences.

Background of the Invention

In today's rapidly evolving digital world, businesses face increasing challenges in engaging customers effectively. Traditional marketing methods such as television commercials, print ads, and billboards are becoming less effective, as consumers seek more personalized, real-time interactions. Customers today expect immediate, tailored experiences, whether they are shopping online or visiting physical stores. The widespread use of smartphones, GPS, and online data analytics has fueled the rise of personalized marketing, where offers are targeted based on location, preferences, and behavior. However, despite technological advancements, businesses still struggle to deliver relevant, timely, and personalized offers, especially when it comes to location-based promotions.

The core issue lies in the fact that traditional geo-targeting systems primarily focus on static location-based offers without integrating customer behavior, market conditions, and inventory levels into the process. These existing systems only deliver offers when customers enter predefined geographic zones, but they fail to adjust dynamically based on changing real-time factors such as foot traffic, sales trends, external events, or inventory status. As a result, offers may not be timely or relevant, leading to low customer engagement and missed opportunities for businesses.

Consumers today expect personalized experiences not only based on their location but also on their purchase history, preferences, and behaviors. However, traditional geo-targeting systems tend to rely on a one-size-fits-all approach, failing to account for the nuances of individual customer behavior. This limits the effectiveness of marketing campaigns and fails to nurture long-term customer relationships. Businesses also struggle to integrate real-time data such as weather conditions, events, or foot traffic patterns, which are crucial for ensuring that the offers remain relevant and contextually aligned with customers' immediate needs.

Furthermore, many existing systems are not equipped to handle the increasing data privacy concerns surrounding the use of location and personal data. With strict regulations such as GDPR and CCPA in place, businesses must ensure that their marketing systems comply with legal requirements regarding data protection, consent management, and secure data transmission. Many systems fall short in offering sufficient data protection, putting customer trust at risk.

Over the years, several systems have been developed to address the growing demand for geo-targeted marketing, primarily leveraging geo-fencing technology. Geo-fencing creates virtual boundaries around physical locations and triggers promotional messages when a customer enters or exits these boundaries. These systems typically use GPS to track the location of mobile devices and deliver basic promotions such as discounts or special offers when a customer is near a business. However, while geo-fencing offers significant potential, the technology has some notable limitations.

Traditional geo-fencing-based systems send customers generic promotional messages based solely on their location. These messages often fail to take into account individual customer behaviors, preferences, or purchase history, leading to irrelevant or untimely offers. Additionally, these systems do not adapt based on other crucial factors such as market conditions, inventory availability, or real-time events, making the offers less effective and reducing customer satisfaction.

In the realm of location-based advertising (LBA), some systems use GPS data to deliver promotional messages to mobile devices based on a customer's location. These systems can push advertisements to a broad audience, but they also suffer from the same weaknesses as geo-fencing systems: lack of personalization, static promotions, and inadequate integration with customer behavior data. In most cases, the offers delivered are generic and do not account for the specific needs or preferences of individual customers.

Similarly, customer relationship management (CRM) systems have been used to track customer behavior and segment customers for targeted marketing. However, CRM systems are often disconnected from geo-targeting and location data, limiting their ability to offer real-time, personalized promotions based on a customer's location. Many businesses still manually align CRM data with geo-targeting efforts, which can result in inefficiencies and missed opportunities for real-time engagement.

In recent years, some advanced systems have attempted to integrate market condition tracking by considering local events, weather conditions, and other environmental factors when delivering promotions. These systems help businesses to adapt to changes in external conditions but still fall short in providing real-time, personalized offers based on customer preferences or inventory status. While these systems offer useful data insights, they often fail to create fully integrated, dynamic promotional campaigns that deliver value to both businesses and customers.

Despite the advancements in geo-targeting and location-based marketing, several disadvantages persist in existing systems.

First, most geo-targeting systems lack personalization. They deliver offers based solely on location and proximity, which can lead to irrelevant or untimely promotions. Without integrating customer behavior, purchase history, and loyalty status, these offers are disconnected from the customer's true needs and preferences. This results in poor customer engagement and low conversion rates.

Second, existing systems typically rely on static promotional offers that do not adapt to real-time conditions. The inability to dynamically adjust offers based on changing factors such as foot traffic, market conditions, inventory levels, and customer interactions limits their effectiveness. Customers are more likely to engage with offers that feel timely, relevant, and personalized based on their current situation.

Third, many systems operate in silos, meaning that geo-targeting, inventory management, customer behavior data, and market condition tracking are not fully integrated. As a result, businesses lack a cohesive approach to delivering location-based promotions, and often miss out on opportunities to align offers with customer needs, inventory levels, and real-time conditions.

Fourth, data privacy concerns and regulatory compliance issues present a significant challenge for existing geo-targeting systems. Many of these systems do not have sufficient data protection measures in place to ensure compliance with privacy regulations such as GDPR and CCPA. This can lead to loss of customer trust and potential legal consequences.

Lastly, traditional systems tend to be reactive rather than proactive, only sending offers when a customer enters a geo-fenced area. This limits the ability of businesses to anticipate and engage customers based on more complex patterns of behavior, such as shopping habits, seasonal trends, or previous interactions with the brand.

Given the drawbacks of current geo-targeting systems, there is an urgent need for a more comprehensive and dynamic solution that can integrate location data, customer behavior, market conditions, and inventory management in real-time. Businesses require a system that can automatically adjust promotions based on factors such as foot traffic, sales trends, customer preferences, and available inventory. The system should allow businesses to create highly personalized offers that reflect the needs of individual customers, driving higher engagement and sales conversion rates.

Moreover, the need for real-time adjustments cannot be overstated. As customers are becoming more tech-savvy and accustomed to instant gratification, businesses must be able to provide relevant offers at the right time and in the right context. The system should integrate real-time market conditions, such as local events, weather, or even time of day, to ensure promotions are aligned with customers' immediate circumstances and preferences.

Another critical need is the integration of data privacy and security into geo-targeting systems. With growing concerns over data breaches and privacy violations, businesses must adopt solutions that safeguard customer data while complying with global data protection regulations. A system that ensures secure data handling, user consent management, and encryption is vital for gaining customer trust and ensuring regulatory compliance.

Lastly, businesses need an intelligent system that can dynamically optimize promotional strategies and maximize ROI. The solution should provide actionable insights into customer behavior, sales trends, foot traffic, and inventory status so that businesses can make data-driven decisions in real time. This level of adaptability and intelligence is essential for businesses to stay competitive in a rapidly changing market landscape.

Brief Summary of the Invention

The primary object of the present invention is to provide a geo-targeting system that delivers real-time, location-based personalized offers to customers, enhancing customer engagement and business performance. The system utilizes advanced technologies such as geo-fencing, real-time market condition tracking, machine learning, and inventory management integration to offer businesses the ability to dynamically adjust offers based on customer location, market conditions, and inventory status. This integration allows businesses to provide highly personalized, relevant promotions to customers, thereby increasing customer satisfaction and driving sales growth.

Another key object of the invention is to enable businesses to target specific customer segments based on their location and behavioral patterns. By tracking customers in real time through their mobile applications, the system can deliver personalized offers based on customer preferences, purchase history, and loyalty status. This system moves beyond simple geo-fencing and location-based offers by continuously refining and optimizing promotional strategies through advanced analytics and machine learning algorithms.

An additional object of the invention is to help businesses optimize inventory management by integrating real-time inventory data with the promotional offers. This integration ensures that businesses can prioritize promotions for overstocked items, soon-to-expire products, or high-margin goods. By aligning promotional strategies with inventory levels, businesses can reduce waste, manage product lifecycles effectively, and maximize sales, contributing to better operational efficiency.

Furthermore, the invention aims to enhance customer loyalty by offering personalized rewards based on customer engagement and loyalty status. The system integrates a loyalty module that allows businesses to tailor offers for repeat customers, encouraging customer retention through exclusive rewards and incentives. This feature addresses the increasing demand for customer-centric marketing and ensures that businesses can nurture long-term relationships with their clientele.

Another key objective is to provide a business-facing dashboard that enables businesses to track campaign performance, manage multiple geo-fencing zones, and adjust promotional offers in real time. This interface offers businesses actionable insights by providing data-driven reports on foot traffic, conversion rates, and customer interactions, making it easier for businesses to monitor and optimize their marketing strategies.

Lastly, a significant object of the invention is to ensure data security and regulatory compliance. With growing concerns over data privacy, the invention integrates a security module that uses encryption protocols, user consent management, and multi-factor authentication to ensure that customer location data and personal information are protected. This guarantees that businesses comply with data protection regulations such as GDPR and CCPA, ensuring customer trust and protecting sensitive information.

The invention is a geo-targeting system that integrates various modules to provide businesses with the ability to deliver location-based, personalized offers to customers in real time. The system includes a mobile application that tracks the location of customers using GPS data from their mobile devices. When a customer enters or exits a predefined geographic zone, the application triggers personalized push notifications, offering relevant promotions based on the customer's preferences, purchase history, and loyalty status.

At the heart of the system is the geo-targeting engine, which utilizes geo-fencing technology to define virtual geographic zones. The engine processes customer location data in conjunction with behavioral information (such as past purchases, interactions with promotions, and loyalty status) to create personalized offers that resonate with each customer. Unlike traditional systems that rely on static offers, this system can dynamically adjust offers in real time based on a variety of factors such as foot traffic, time of day, sales trends, market conditions, and inventory levels. This ensures that the offers remain relevant and timely.

To enable businesses to manage these promotions effectively, the system includes a business-facing dashboard. This interface allows businesses to define multiple geo-fencing zones, create promotional campaigns, and monitor real-time data on customer interactions. It provides actionable insights, such as conversion rates, foot traffic patterns, and the performance of specific promotional campaigns, giving businesses the tools they need to optimize their marketing strategies.

The customer preference module plays a crucial role in refining future promotions. By continuously updating customer profiles based on interaction history, purchase behavior, and location-based engagement, the system uses machine learning algorithms to enhance the accuracy of future offers. This allows businesses to deliver highly personalized promotions, which increase the likelihood of customer engagement and improve conversion rates.

The inventory management interface integrates with the business's point-of-sale system to provide real-time stock updates. This integration enables the system to automatically adjust promotions based on inventory levels, helping businesses prioritize promotions for overstocked items or soon-to-expire products, thus optimizing inventory turnover and reducing waste.

Additionally, the real-time market condition tracker allows the system to modify promotions based on external factors such as local events, weather, and public holidays. This ensures that the promotions are contextually relevant, adapting to external changes that could influence customer behavior, making the offers more timely and engaging.

The loyalty integration system adds another layer of personalization by tailoring offers based on customer loyalty status. This system rewards repeat customers with exclusive offers, creating a strong incentive for customer retention and fostering long-term relationships. By integrating loyalty programs with real-time location-based offers, the system builds customer loyalty through targeted promotions and incentives.

Finally, the security module ensures that customer data and location information are protected. Using encryption protocols and multi-factor authentication, the system guarantees that personal information is securely stored and processed in compliance with data protection regulations, such as GDPR and CCPA. This not only enhances customer trust but also ensures businesses comply with increasingly stringent privacy laws.

The geo-targeting system described in this invention offers several advantages over traditional marketing systems.

First, the system ensures real-time personalization, dynamically adjusting promotional offers based on factors such as foot traffic, sales trends, and market conditions. Unlike conventional geo-targeting systems, which deliver static offers, this system provides a continuously evolving customer experience, maximizing the chances of engagement and conversion.

Second, the system utilizes machine learning algorithms to personalize promotions based on customer behavior and preferences, significantly improving customer engagement and increasing marketing effectiveness.

Third, the system optimizes inventory management by integrating real-time inventory data with promotional offers. This allows businesses to prioritize promotions for overstocked or soon-to-expire items, improving inventory turnover and reducing waste.

Fourth, the business-facing dashboard provides actionable insights into campaign performance, allowing businesses to monitor foot traffic, conversion rates, and the effectiveness of specific promotions in real time. These insights empower businesses to make data-driven decisions to enhance their marketing strategies.

Fifth, the integration of a real-time market condition tracker ensures that promotions remain contextually relevant by adjusting based on external factors like weather, local events, and public holidays. This increases the timeliness and engagement of offers, driving higher conversion rates.

Sixth, the loyalty integration system incentivizes repeat customers with personalized offers, fostering long-term customer loyalty and increasing customer lifetime value.

Lastly, the security module ensures that customer data is protected through encryption and compliance with data protection regulations, such as GDPR and CCPA, enhancing customer trust and ensuring compliance with privacy laws.

This geo-targeting system has numerous applications across various industries, benefiting from personalized, real-time location-based promotions.

In retail, the system can be used to send personalized discounts, promotions, and offers to customers when they are nearby or enter a store. The integration with inventory management allows retailers to prioritize promotions for overstocked items, optimizing sales and reducing waste.

In the hospitality industry, hotels and restaurants can leverage the system to offer real-time promotions to customers based on their location and loyalty status. Customers could receive exclusive room rates or lunch specials when near the property, incentivizing immediate bookings or visits.

For entertainment and events, the system can be used for concerts, festivals, and sporting events, sending location-based offers such as discounts on tickets, merchandise, or food, driving attendance and increasing sales during events.

In the travel and tourism industry, businesses can use the system to send personalized offers for local attractions, discounted tours, or hotel stays based on a customer's current location or past travel behavior.

For real estate, agents can use the system to notify potential buyers about new property listings, open houses, or special offers in specific geographic areas, increasing visibility and sales opportunities.

Lastly, the system can be used for loyalty programs across various industries, offering personalized rewards and incentives to loyal customers, fostering long-term customer engagement.

In summary, this geo-targeting system revolutionizes the way businesses deliver location-based promotions. By integrating personalized offers, real-time data, inventory management, and customer loyalty, the system provides a comprehensive marketing solution that enhances customer engagement, drives sales, and optimizes business operations across various industries.

Brief Summary of the Drawings

The invention will be further understood from the following detailed description of a preferred embodiment taken in conjunction with an appended drawing, in which:

Fig. 1 illustrates the comprehensive architecture of the geo-targeting system.

Fig. 2 illustrates the the user-friendly interface of the mobile application, designed to enhance customer engagement and satisfaction.

Fig. 3 illustrates the workflow within the geo-targeting engine.

Fig. 4 illustrates business-facing dashboard, a crucial tool that empowers businesses to manage their geo-targeting campaigns effectively.

Fig. 5 illustrates the the customer preference module to personalizing the user experience by collecting and analyzing individual customer datas.

Fig. 6 illustrates the inventory management interface of system.

Fig. 7 depicts the real-time market condition tracker, which analyzes various external factors that can influence customer behavior and purchasing decisions.

Fig. 8 illustrates the architecture of the security module, which is essential for protecting customer location data and personal information, in accordance with the exemplary embodiment of the present invention.

Detailed Description of the Invention

The present invention relates to a geo-targeting system designed to deliver real-time, location-based personalized offers to customers. The system employs several advanced technologies and components that allow businesses to track customer locations, analyze customer behaviors, and deliver offers tailored to their needs, preferences, and real-time situations. The invention aims to provide a comprehensive solution for geo-targeted promotions by integrating location tracking, customer data analysis, real-time market conditions, inventory management, and loyalty programs, while ensuring data privacy and regulatory compliance.

The invention is directed at overcoming the challenges faced by existing geo-targeting systems, which tend to deliver generic offers based solely on location, without considering other important factors such as customer behavior, market conditions, or inventory levels. Through the integration of various components, the present invention provides a solution that is dynamic, adaptive, and contextually relevant, ensuring that the offers are not only personalized to each customer but are also timed perfectly and tailored to the business's operational needs.

The invention includes a mobile application designed to track customers' real-time locations using GPS data from their mobile devices. When a customer enters or exits a predefined geographic zone, the application triggers personalized push notifications offering promotions that are relevant to that particular customer. These promotions are based on a variety of factors, including customer preferences, purchase history, and loyalty status.

A central component of the system is the geo-targeting engine, which defines geographic zones using geo-fencing technology. Geo-fencing is used to create virtual boundaries around physical locations, such as retail stores, shopping malls, or restaurants. Once a customer enters or exits these zones, the system processes the customer's location data and behavioral information, such as past interactions with offers, purchases made, and preferences indicated. This data is used to generate personalized offers, targeting specific customer segments with relevant promotions. The geo-targeting engine also dynamically adjusts these offers in real-time based on foot traffic, time of day, sales trends, inventory levels, and market conditions, ensuring that the offers remain timely and impactful.

To manage and optimize these geo-targeted promotions, the system includes a business-facing dashboard. This dashboard allows businesses to define and manage multiple geo-fencing zones and create promotional campaigns for each zone. Businesses can also track and monitor real-time data on customer interactions with these offers. This feature provides valuable insights into customer behavior, campaign performance, and conversion rates, allowing businesses to fine-tune their marketing strategies and optimize the delivery of geo-targeted offers. The dashboard enables businesses to adjust the timing, type, and frequency of offers based on customer engagement and sales data.

The customer preference module is another critical component of the system. This module continuously updates customer profiles by analyzing their purchase behavior, interaction history, and location-based engagement. By employing machine learning algorithms, the customer preference module allows the system to refine future offers and ensure that they are increasingly relevant and personalized. For example, if a customer frequently purchases a particular brand or type of product, the system will prioritize offers related to that product, enhancing the customer experience and increasing the likelihood of conversion.

The invention also integrates an inventory management interface, which works in conjunction with the business's point-of-sale (POS) system to provide real-time stock updates. This interface allows businesses to dynamically adjust promotional offers based on real-time inventory levels. For instance, if a product is overstocked or nearing its expiration date, the system can increase the promotion frequency for that product to ensure it sells before inventory expires. This integration not only improves the efficiency of promotions but also helps businesses manage inventory more effectively, reducing waste and optimizing stock turnover.

Additionally, the system includes a real-time market condition tracker, which modifies offers based on external factors such as local events, weather conditions, and public holidays. This feature ensures that promotions are not only location-based but also contextually relevant. For instance, if a customer is near a shopping mall on a rainy day, the system might offer discounts on indoor activities or promotions for food and beverage stores inside the mall. Similarly, if a large event, such as a concert or a festival, is taking place nearby, the system can adjust promotions to align with the increased foot traffic and consumer behavior associated with the event.

The system also incorporates a loyalty integration system that tailors offers based on customer loyalty status. Customers who engage more frequently with the business, make repeat purchases, or achieve certain loyalty milestones are rewarded with exclusive offers or additional discounts. This feature strengthens customer retention by offering personalized incentives that encourage customers to return, thus building long-term relationships between the business and the customer.

Finally, to ensure data security and regulatory compliance, the system integrates a security module. This module uses encryption and multi-factor authentication to protect customer data and ensures that the system complies with data protection regulations such as GDPR and CCPA. By securing sensitive location data and personal information, the system helps businesses maintain customer trust while adhering to privacy laws. The system also incorporates user consent management, allowing customers to control the sharing of their location data and opt in or out of promotional communications.

The invention's exemplary embodiment brings together all these components to create an integrated, dynamic, and personalized geo-targeting system that adapts in real time to customer needs and business objectives. By combining location-based offers, real-time inventory updates, market conditions, and personalized promotions, the system offers businesses a comprehensive solution for driving sales, improving customer engagement, and optimizing operational efficiency.

In the exemplary embodiment of the present invention, the geo-targeting system comprises several components working together to provide a personalized and dynamic marketing solution.

Figure 1 illustrates a block diagram of the system architecture, with key components such as the mobile application (102), geo-targeting engine (104), business-facing dashboard (106), customer preference module (108), inventory management interface (110), real-time market condition tracker (112), loyalty integration system (114), and security module (116). Each of these components interacts to provide businesses with the tools needed to deliver personalized, real-time offers to customers while ensuring security and compliance.

Figure 2 shows a diagram of the system's operation, from the moment a customer enters a predefined geo-fence (103) to the point where the business delivers a personalized offer. The geo-targeting engine (104) is responsible for defining the geo-fences and processing customer location data. Once a customer enters a geo-fence, the engine processes data from the customer preference module (108) and generates a personalized offer based on customer behavior and historical data. The offer is then delivered through the mobile application (102), which triggers a push notification to the customer's device, prompting them to take action.

Figure 3 demonstrates how the system dynamically adjusts promotions in real time based on various factors, such as foot traffic (104a), time of day (104b), and inventory levels (110a). The business-facing dashboard (106) allows businesses to monitor these adjustments and make changes to promotional campaigns as necessary. The dashboard provides businesses with real-time insights into the performance of geo-targeted offers, including metrics such as customer engagement, conversion rates, and sales trends.

Figure 4 shows how the real-time market condition tracker (112) works in conjunction with the geo-targeting engine to modify offers based on external factors. For example, if a local event (112a) is taking place, the system might adjust promotions to align with the increased foot traffic. Similarly, if the weather is unfavorable, the system might offer promotions for indoor activities or products. These changes ensure that promotions remain relevant and aligned with the customer's current environment and needs.

Figure 5 illustrates how the loyalty integration system (114) works to deliver exclusive offers based on customer loyalty. The system tracks customer behavior, purchases, and engagement with previous offers, rewarding repeat customers with personalized promotions that encourage them to return. These loyalty-driven promotions are integrated with the geo-targeting system, ensuring that the offers are timely, relevant, and aligned with customer preferences.

The inventory management interface (110) is shown in Figure 6, where the system dynamically adjusts offers based on real-time stock availability. For instance, if a product is overstocked (110b) or nearing its expiration date (110c), the system will automatically prioritize offers for that product to encourage quick sales. This feature ensures that businesses can efficiently manage inventory while maximizing sales and minimizing waste.

Figure 7 provides a security and compliance overview of the system. The security module (116) ensures that all customer data, including location information and personal details, is securely encrypted. The module also provides features for user consent management, allowing customers to opt-in or opt-out of location-based offers. Additionally, the system complies with data protection regulations such as GDPR and CCPA, ensuring that customer data is handled in accordance with legal requirements.

Through the integration of these components, the geo-targeting system described in the exemplary embodiment ensures that businesses can deliver highly relevant, timely, and personalized promotions to their customers. By continuously adapting to customer preferences, market conditions, and inventory needs, the system maximizes customer engagement, sales conversion, and operational efficiency, all while maintaining data security and regulatory compliance.

Let us consider an example of a retail business, Fashion Hub, which operates multiple stores in a metropolitan city. In the exemplary embodiment of the present invention, Fashion Hub, a retail business with several locations, uses this geo-targeting system to deliver dynamic and personalized promotions based on a combination of factors, including customer preferences, location, foot traffic, and inventory levels.

Fashion Hub deploys a mobile application (102) that allows the system to track real-time customer locations using GPS data from the customers' smartphones. As soon as a customer enters a geo-fence (103) defined around one of Fashion Hub's physical store locations, the system triggers personalized push notifications offering promotions tailored to that customer's behavior and preferences. These promotions are generated based on data from the geo-targeting engine (104), which processes the customer's location data along with their historical purchase data and customer profile information to create highly relevant offers.

The geo-targeting engine (104) is responsible for defining the geo-fences and determining the relevant promotional offers based on the customer's proximity to the store. Once a customer enters the defined geographic boundary, the system checks their customer profile for relevant purchasing history, preferred product categories, or loyalty status, and sends a personalized promotion based on this information. For example, a customer who frequently purchases summer dresses might receive a 20% discount on the latest dress collection, while a customer interested in men's casual wear may receive a 15% discount on newly released shirts. This process is automated and real-time, ensuring that offers are as relevant and timely as possible.

In addition to location and customer data, the geo-targeting engine (104) also adjusts promotions dynamically based on real-time conditions such as foot traffic (104a), time of day (104b), and sales trends (104c). For example, during a busy shopping period, such as a weekend afternoon, the system might offer special promotions or flash sales to increase foot traffic even further. Conversely, during off-peak hours, the system may offer additional discounts to drive traffic to the store. This ability to adjust promotions based on real-time conditions ensures that the offers remain relevant, engaging, and timely.

The system also includes a business-facing dashboard (106), which allows businesses to track the effectiveness of their geo-targeted campaigns in real time. The dashboard provides detailed insights into customer interactions, conversion rates, and sales data for each geo-fencing zone. For example, Fashion Hub can view how many customers entered the store after receiving a push notification, what products were purchased, and how effective different promotional campaigns were at converting foot traffic into sales. This data-driven approach enables businesses to make informed decisions and adjust their strategies to improve future campaigns.

The customer preference module (108) is another key feature of the system, continuously updating customer profiles based on purchase history, interaction history, and location-based engagement. This module utilizes machine learning algorithms to personalize future offers by identifying patterns in the customer's behavior. For example, if a customer frequently purchases summer clothing, the system can prioritize offers related to summer apparel, ensuring the customer receives promotions that match their preferences. Over time, as more customer data is collected, the system becomes more accurate and efficient at delivering personalized promotions, improving the customer experience and increasing conversion rates.

Fashion Hub also integrates inventory management into the system with an inventory management interface (110), which connects with the business's point-of-sale system to provide real-time stock updates. This allows the system to automatically adjust promotions based on inventory availability. For example, if a certain product is overstocked or nearing its expiration date, the system might increase the promotion on that product to ensure it sells before the stock is lost. This feature helps businesses avoid overstocking issues and ensures that inventory management aligns with promotional efforts, reducing waste and improving efficiency.

The system also incorporates a real-time market condition tracker (112), which adjusts promotions based on external factors such as local events (112a), weather conditions (112b), and public holidays (112c). For example, if a major local event, such as a music festival, is taking place nearby, the system might increase promotions for related products, such as casual wear or accessories, to capture the attention of event-goers. Similarly, if the weather forecast predicts rain, the system might offer promotions for indoor products, such as jackets or umbrellas, to respond to changing conditions. By adapting the promotions to market dynamics, the system ensures that customers receive contextually relevant offers that are aligned with their current needs.

In addition, the loyalty integration system (114) enhances the effectiveness of the geo-targeting system by offering personalized rewards to repeat customers. Customers who frequently engage with the business or accumulate loyalty points are rewarded with exclusive offers and discounts based on their loyalty status. For instance, a loyal customer who frequently purchases summer dresses might receive an additional 10% off during the summer sale or exclusive access to early product launches. This system strengthens customer retention, providing an incentive for customers to return to the store and engage with the brand over the long term.

The security module (116) ensures that customer data, including location information and personal details, is securely handled in compliance with data privacy regulations such as GDPR and CCPA. The system uses encryption and multi-factor authentication to protect sensitive customer data and ensures that businesses maintain customer trust. Additionally, the system features user consent management, which allows customers to opt in or out of location-based promotions and control their data-sharing preferences. This focus on data security and regulatory compliance ensures that the system meets the legal requirements for data protection while fostering consumer confidence.

The system's overall architecture enables businesses like Fashion Hub to deliver personalized and dynamic geo-targeted promotions that increase sales, customer loyalty, and operational efficiency. The system not only optimizes promotions based on customer behavior and location but also integrates real-time market conditions and inventory management to create timely and relevant offers. By providing businesses with the tools to track and analyze customer interactions, the system offers valuable insights that help optimize marketing strategies and ensure that businesses stay competitive in a rapidly evolving retail landscape.

In total, the geo-targeting system described in this invention integrates location data, customer preferences, market conditions, inventory levels, and real-time data to deliver highly relevant and timely promotions to customers. By personalizing offers, dynamically adjusting based on real-time factors, and ensuring data security and compliance, the system provides a comprehensive solution for businesses seeking to enhance customer engagement, optimize sales, and foster long-term loyalty. The innovative combination of these features ensures that the geo-targeting system provides a robust, scalable, and adaptive solution that can meet the needs of businesses in today's fast-paced digital and retail environments.
, Claims:5. CLAIMS
I/We Claim:
1. A geo-targeting system (100) for delivering real-time, location-based personalized offers to customers, comprising:
a mobile application (102) configured to track the real-time location of customers using GPS data from their mobile devices, wherein the mobile application triggers personalized push notifications when the customer enters or exits a predefined geographic zone;
a geo-targeting engine (104) configured to:
define geographic zones using geo-fencing technology and process customer location data and behavior information to generate personalized offers based on real-time customer interactions, purchase history, and preferences;
dynamically adjust the offers in real-time based on factors such as foot traffic, time of day, sales trends, market conditions, and inventory levels to ensure that the offers remain relevant and timely;
a business-facing dashboard (106) that enables businesses to define and manage geo-fencing zones, create and manage promotional campaigns, and monitor real-time data on customer interactions to optimize the delivery of geo-targeted offers;
a customer preference module (108) that continuously updates customer profiles and refines future promotions based on past interactions, allowing for highly personalized offer delivery;
an inventory management interface (110) that integrates with a business's point-of-sale system to provide real-time stock updates and dynamically adjust promotions to prioritize overstocked or soon-to-expire items;
a real-time market condition tracker (112) that modifies promotions based on external factors such as local events, weather, and public holidays to ensure the offers are contextually relevant;
a loyalty integration system (114) that personalizes offers based on customer loyalty status, providing exclusive rewards or incentives for repeat customers;
a security module (116) that ensures the protection and privacy of customer location data and personal information, employing encryption and consent management to ensure data security and regulatory compliance;
wherein,
the geo-targeting system (100) comprises the integration of real-time dynamic adjustments to promotions based on a variety of factors such as foot traffic, time of day, sales trends, inventory levels, external events, market conditions, and customer preferences;
the geo-targeting system (100) ensures continuous refinement of personalized promotions through the customer preference module (108), which uses machine learning algorithms to analyze and update customer profiles based on interaction history, purchase behavior, and location-based engagement, thereby improving the relevance and effectiveness of future offers;
the geo-targeting system (100) results in the real-time synchronization of promotions with market conditions through the real-time market condition tracker (112), which enables the system to modify offers dynamically in response to external factors such as local events, weather, and public holidays, ensuring that promotions remain contextually relevant;
the geo-targeting system (100) ensures seamless integration with inventory management systems, allowing the system to adjust offers based on real-time stock availability via the inventory management interface (110), which helps businesses prioritize promotions for overstocked or near-expiration items, driving sales and reducing waste;
the geo-targeting system (100) with the inclusion of a business-facing dashboard (106) provides businesses with the ability to define multiple geo-fencing zones, create custom promotional campaigns, and monitor performance in real-time, offering businesses actionable insights for optimizing their marketing strategies and making data-driven decisions;
the geo-targeting system with the inclusion of the customer loyalty integration system (114) tailors promotions based on customer loyalty status, incentivizing repeat customers with exclusive offers, and fostering long-term relationships.

2. The geo-targeting system of claim 1, wherein the mobile application (102) further includes a user interface allowing customers to customize their preferences for the types of offers they wish to receive, including opting for specific product categories, discounts, or time-sensitive promotions.

3. The geo-targeting system of claim 1, wherein the geo-targeting engine (104) utilizes real-time analytics to predict customer behavior, adjusting offers dynamically by analyzing customer foot traffic patterns, historical purchase data, and engagement with previous offers.

4. The geo-targeting system of claim 1, wherein the customer preference module (108) incorporates machine learning algorithms that adaptively refine customer profiles by analyzing the frequency, timing, and categories of promotions a customer interacts with, enabling better-targeted future promotions.

5. The geo-targeting system of claim 1, wherein the business-facing dashboard (106) provides customizable reports and data visualization tools for businesses to track the effectiveness of promotional campaigns, including click-through rates, conversion rates, sales uplift, and foot traffic patterns.

6. The geo-targeting system of claim 1, wherein the real-time market condition tracker (112) integrates weather data and local event data from third-party APIs to modify promotions in real-time based on changes in environmental factors or community events, ensuring timely and contextually relevant offers.

7. The geo-targeting system of claim 1, wherein the inventory management interface (110) integrates with a business's point-of-sale system and uses real-time stock levels to automatically prioritize promotions for high-margin or overstocked items, enhancing the business's inventory turnover rate.

8. The geo-targeting system of claim 1, wherein the geo-targeting engine (104) is configured to define multiple geo-fencing zones around a business location, with the ability to assign different promotion rules or offer types based on proximity to the customer, allowing businesses to target specific areas or types of customers within a zone.

9. The geo-targeting system of claim 1, wherein the loyalty integration system (114) tracks customer loyalty points and offers personalized rewards or exclusive offers based on the frequency of purchases, amount spent, and engagement with previous promotions, fostering long-term customer loyalty.

10. The geo-targeting system of claim 1, wherein the security module (116) employs multi-factor authentication and end-to-end encryption to safeguard customer data and ensure compliance with regional data protection regulations, including GDPR and CCPA, while providing customers with the ability to manage consent preferences for location tracking and promotional notifications.

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