In an age where the fusion of digital and physical retail environments is paramount, Footprints AI stands out with its next-gen recommendation engine, designed to hyper-personalize in-store experiences and drive premiumization & high profit margins for retail media revenues.

This innovative Footprints AI technology adapts for the physical store environments the very effective sponsored product recommendation ads we know from the likes of Amazon.com and Walmart.com.

Powered by advanced AI technologies, indoor positioning and real-time behavioral analytics, the in-store recommendation engine from Footprints AI offers retailers and advertisers unprecedented capabilities to target and engage customers more effectively.

Advanced Proprietary AI

Footprints AI utilizes a sophisticated AI model that integrates real-time, in-store behavioral data with advanced profiling techniques. This system goes beyond traditional analytics by leveraging machine learning to understand and predict customer behaviors and preferences. It dynamically adjusts product recommendations and advertisements based on a customer's real-time actions within the store.

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This level of personalization not only enhances the customer's shopping experience but also maximizes the impact of retail media by placing the right products in front of the right customers at the right time.

Real-Time Data Integration and Predictive Modeling

The core of Footprints AI’s recommendation engine is its ability to process and analyze data from a variety of sources instantly. This includes data from mobile apps, digital screens, and sensor-equipped store environments. By synthesizing this information, Footprints AI can predict customer movements and likely purchase intentions. Its predictive models also enable proactive engagement, sending personalized recommendations and promotions to customers' mobile devices before they even enter the store, based on their predicted likelihood of visiting.

In-Store Multitouch Contextual Retail Media

Footprints AI transforms physical stores into interactive retail media platforms. Through digital touchpoints integrated throughout the retail environment—such as interactive kiosks, mobile apps, and smart digital signage—advertisements and product suggestions are seamlessly incorporated into the customer journey. This creates a cohesive and engaging shopping experience that not only drives sales but also offers advertisers premium spaces to display targeted content that resonates with shoppers and drives 5-10X bigger Return on Ad Spend for their retail media budgets.

Technology Approach & Capabilities Overview

Objective: Enhance next best product/offer scenarios to boost conversion rates and increase basket size.

Features: Recommendations include a curated collection of items, suitable for 1:1 communication channels (like web, email, mobile apps) or one-to-few (such as digital screens).

Timing and Relevance: Recommendations can be issued in real-time during shopping or pre-visit, tailored for both in-store and omnichannel shopping behaviors, overcoming limitations of online-only engines.

Triggers: Activated by user actions, presence detection, in-store "hot points," contextual factors (time, day, week, etc.), and a predictive "propensity to visit" model.

Cold Start: Capable of engaging first-time users by utilizing their initial interactions and contextual data.

Shelves Distance Consideration: Ensures recommended products are within a reasonable distance to avoid confusing or deterring shoppers.

Hyperlocal AI Model Training

• Customization: Each business location's model is uniquely trained, incorporating catchment area-specific data to account for local shopping patterns.
• Data: Involves categorizing shopping missions to diversify the training dataset, which includes a variety of attributes (affinity scores, socio-economic factors, etc.).
• Methodology: Utilizes a combination of models, learning shopper preferences based on physical interactions within the store, and adjusts for context (weather, time of day, day of the week, week of the month and year, holidays, celebrations, local events, country of origin for incoming flights etc.).
• Retraining: Conducted monthly to adapt to changes in seasonality, store layout, product offerings, and shopper feedback.
• Model Composition: Integrates collaborative and content-based filtering, with options to prioritize paid recommendations while maintaining relevance.

Footprints AI is pioneering a new era of retail media, where the value of in-store advertising is maximized through precise targeting and personalization.

Premiumization is critical for the future of retail media and its profitability index. Just like with the evolution of the digital advertising, having a banner ad on a digital screen to be seen by all and any customer at any time does not do the trick any longer. Brands & media agencies expectations are to have the same AI-driven capabilities in in-store as in online when it comes to their advertising budgets and their expected return for their spending.

This is why Footprints AI is not just enhancing the in-store experience; it's redefining what is possible in retail media.

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