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Unveiling the "Gold Mine": Our Unique Data Generation Engine

Background Story

One of our objectives during the research and development phase was to bring in more local customers to physical stores. To achieve this, we leveraged the best practices we've gained over 25 years of experience with Local SEO. A unique Local SEO methodology we call HOSP - Hyper-local Optimization Submission and Promotion. We then aimed to verify the effectiveness of our unique Local SEO methodology, which required us to determine the visitor's location. Once we accomplished that, we discovered that by aggregating all the trackable and monitorable data, and by connecting the dots, we could gain valuable insights into the offline consumer's wants and needs. We believe that the possibilities are endless, and we're just getting started.


Mission: Understanding Offline Consumers

Our goal is to decode the trends, needs, and behaviors of offline consumers, bridging the gap between online searches and in-store experiences.


Purpose: Empowering Local Stores

We aim to empower local stores to serve their communities better, optimize marketing strategies, and make data-driven decisions to match supply with local demand.


The Process: How We Do It

Data Generation Flow

  1. Seamless Online Storefront Integration: Each local store branch effortlessly establishes its digital presence to reflect its physical store's offerings.

  2. Exposure through Local SEO: Physical store products are being exposed using the AI-powered Local SEO engine, targeting local searches.

  3. Local Search Discovery: Consumers find these products through local search engine queries, such as "nearby", "near me", "local", and |small business" filters on Shopping and Search listings.

  4. Visitor Tracking: When consumers visit these online storefronts' product pages, we pinpoint their location.

  5. Unique Identification: Each visitor is assigned a Unique Identifier, capturing their entire journey on the storefront.

  6. Data Aggregation: Combining visitor location with data from Google Search Console, Google Analytics, and Mixpanel.

  7. AI-Powered Analysis: AI models can learn from this data, identifying consumer behavior patterns based on location or any other query that will be specified.

The Fire Triangle: Our Data Formula

  • Oxygen: Data flow from local organic product searches and clicks. In the US alone, there are 12.5 billion monthly searches for local products.

  • Heat: The initial local purchase intent is evident in Google Search. The local supply and demand become clear when customers intend to shop locally. The first touchpoint in the customer journey that can be tracked is Google Search.

  • Fuel: Multiple data sources, including visitor location tracking data, Google Search Console search queries, and user interaction data from Google Analytics and Mixpanel, are added to the formula.


Why It's a Game-Changer

Our approach relies on three fundamental elements:


1. Flawless Local SEO: Captures nearby customer searches effectively.


Shopper's platform excels in Google's Local SEO ranking with a perfect score of 100, outshining major players like Walmart, Etsy, and Amazon in SEO, Best Practices, and Performance metrics.
Shopper's platform excels in Google's Local SEO ranking with a perfect score of 100, outshining major players like Walmart, Etsy, and Amazon in SEO, Best Practices, and Performance metrics.

2. Precise Location Tracking: Accurately locates visitors within a 5-10 meter radius.


Visual heat map showcasing Shopper's precise location tracking capability, pinpointing visitor positions within a 5-10 meter radius for accurate consumer behavior analysis.
Visual heat map showcasing Shopper's precise location tracking capability, pinpointing visitor positions within a 5-10 meter radius for accurate consumer behavior analysis.

3. Pre-visit Search Data Integration: Merges visitor location data with user interaction data for comprehensive insights.


When you have a website for each of your physical stores and you attract local customers who are searching for local products, and you collect the accurate location and interests of the customer, you can collect valuable information about what offline customers want and need.


Together, these three elements create a powerful system for understanding consumer needs and trends. Just like in a fire, removing any one of these elements breaks the system.


The Challenge of Replication: Why Others Can't Replicate Our Results

In the world of eCommerce, hybrid shops usually operate with a single website that offers various shipping options for multiple store locations. However, this structure falls short of sticking to Local SEO best practices, which is where our unique advantage comes into play.


The Local SEO Dilemma

For optimal Local SEO, every product page must link to a specific store location selling that product. Imagine a brand selling its product in 100 different branches. In the case of traditional eCommerce websites, you would typically find a single eCommerce website for the brand, where on every product page, you would find a filtration option "Pickup" or "Available at" (See attached screenshot below).


Screenshot illustrating Nike's online-to-offline integration, where customers can choose store pickup options, highlighting the complex challenge brands face in managing individual websites for each store location.
Screenshot illustrating Nike's online-to-offline integration, where customers can choose store pickup options, highlighting the complex challenge brands face in managing individual websites for each store location.

However, to comply with the best practices of Local SEO, all the product pages need to be showcased on each store's site. This means that they would require 100 different websites, one for each store. Yet, creating 100 different websites for each store is a bulky, costly, and impractical task.


Unique Site Architecture for Precise Data

Our platform architecture is designed to align perfectly with Local SEO requirements. The platform allows effortless onboarding of online storefronts for each store location within minutes so that each product page is unique and tied to a specific store location. This specificity is crucial for three reasons:

  1. Aligning Visitor Location with Purchase Intent: By having unique product pages for each store location, we can accurately align a visitor's location with their intent to purchase. This alignment is key to understanding customers' local wants and needs per location and for monitoring them throughout the customer journey.

  2. Differentiating Local from Global Shopping: The distinction between local and online shopping is significant, as it becomes challenging to verify a customer's purchasing preference when products are mixed up without clear differentiation between pure eCom and physical store experience. This clarity is essential for understanding consumer behavior and preferences.

  3. Higher Quality Data: The combination of location and purchase intent dataset per store location enriches the data pool, enhancing its quality. High-quality data is crucial for training AI models to predict cross-category trends, customer behavior, and preferences accurately per location.

Our technical approach provides a competitive advantage in attracting nearby local customers to each store location, while also enabling us to capture and analyze their behavior and preferences precisely. This level of insight is not possible with traditional eCommerce platforms. Our technology is specifically tailored to enhance local store performance, making it a unique offering in the market.



Ways to Utilize the Exclusive "Gold Mine" Data Insights

For Physical Store Owners

  • Targeted Local Marketing: Leverage precise local consumer behavior data to create highly effective, localized marketing campaigns.

  • Demand-Driven Inventory Management: Use insights into local trends to stock products that meet specific local demands, reducing excess inventory and increasing sales.

  • Strategic Expansion Decisions: Make informed expansion decisions based on local demand data for profitable growth.

For Mall Groups

  • Optimal Store Mix & Placement: Analyze consumer preferences to determine the best tenant mix and store placement, enhancing visitor experience and foot traffic.

  • Enhanced Marketing Strategies: Utilize detailed consumer trend data to tailor marketing efforts for the entire mall, attracting more visitors and improving tenant performance.

  • Maximized Marketing ROI: Allocate marketing budgets more effectively with detailed insights into consumer local preferences, locations, and behaviors.

  • Strategic Expansion Decisions: Make informed decisions about expansion or scaling based on comprehensive local demand data, ensuring profitable growth.

For Commercial Real Estate Owners

  • Data-Driven Property Development: Use unique local market insights for strategic development and property enhancements to attract specific tenants.

  • Informed Leasing Decisions: Leverage consumer trends to make informed leasing decisions, maximizing the value of commercial spaces to match supply with local demand.


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